value in time

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Value inTime

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Founded in 1807, John Wiley & Sons is the oldest independent publishingcompany in the United States. With offices in North America, Europe, Aus-tralia, and Asia, Wiley is globally committed to developing and marketingprint and electronic products and services for our customers’ professionaland personal knowledge and understanding.

The Wiley Trading series features books by traders who have survivedthe market’s ever changing temperament and have prospered—some byreinventing systems, others by getting back to basics. Whether a novicetrader, professional, or somewhere in-between, these books will providethe advice and strategies needed to prosper today and well into the future.

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Value inTime

Better Trading through

Effective Volume

PASCAL WILLAIN

John Wiley & Sons, Inc.

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Copyright C© 2008 by Pascal Willain. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.Published simultaneously in Canada.

No part of this publication may be reproduced, stored in a retrieval system, or transmitted inany form or by any means, electronic, mechanical, photocopying, recording, scanning, orotherwise, except as permitted under Section 107 or 108 of the 1976 United States CopyrightAct, without either the prior written permission of the Publisher, or authorization throughpayment of the appropriate per-copy fee to the Copyright Clearance Center, Inc.,222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600, or on the web atwww.copyright.com. Requests to the Publisher for permission should be addressed to thePermissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030,(201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permissions.

Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their bestefforts in preparing this book, they make no representations or warranties with respect to theaccuracy or completeness of the contents of this book and specifically disclaim any impliedwarranties of merchantability or fitness for a particular purpose. No warranty may be createdor extended by sales representatives or written sales materials. The advice and strategiescontained herein may not be suitable for your situation. You should consult with aprofessional where appropriate. Neither the publisher nor author shall be liable for any loss ofprofit or any other commercial damages, including but not limited to special, incidental,consequential, or other damages.

For general information on our other products and services or for technical support, pleasecontact our Customer Care Department within the United States at (800) 762-2974, outside theUnited States at (317) 572-3993 or fax (317) 572-4002.

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Library of Congress Cataloging-in-Publication Data:

Willain, Pascal, 1959–Value in time : better trading through effective volume / Pascal Willain.

p. cm. – (Wiley trading series)Includes bibliographical references and index.ISBN 978-0-470-11873-3 (cloth)

1. Investment analysis. 2. Stocks–Charts, diagrams, etc. I. Title.HG4529.W539 2008332.63′2042–dc22 2007049358

Printed in the United States of America.

10 9 8 7 6 5 4 3 2 1

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www.wiley.com


The world’s ever-growing population increasingly affects our daily

lives. Like never before, this demographic challenge is forcing us to

address critical issues: boosting the production of basic foods,

finding new sources of energy, recycling base metals,

and tackling environmental issues, to name a few.

In my view, the stock market provides people with a simple way

to take part in these high-growth sectors by investing in

companies that offer solutions to these problems.

However, because many people are too busy just trying to survive,

they will not be able to adapt to or even recognize

the coming changes.

Simply dedicating this book to these people does not help much, but

perhaps monetary donations will. I have decided to offer free of

charge the Effective Volume tool described in Chapter 1. I welcome,

however, donations to the Nello and Patrasche Foundation,

a foundation that my wife and I created some years ago

for the benefit of handicapped orphans.

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vi


Contents

Foreword xi

Acknowledgments xiii

Introduction: Revolution Is at Your Doorstep 1

PART ONE The Set of Tools That Will ChangeTechnical Analysis 9

CHAPTER 1 Effective Volume: An Open Windowinto the Market 11

Traders Get a Secret New Tool: A Brief Introduction to the

Trading Mechanisms and the Market Players 14

Volume That Moves the Markets 20

Effective Volume 30

Practical Examples of Effective Volume Calculations 40

Technical Section: How to Calculate the Separation Volume 51

Improve Your Trading: Decide on the Big Picture 57

A Comparison with Traditional Tools 59

What We Learned Regarding Effective Volume 66

CHAPTER 2 Price and Value: The ActiveBoundaries Indicator 67

Buy Low 67

Traditional Measure of “Cheap" 69

Why Do Trends Exist? 81

Grandmothers Are Always Right! 101

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viii CONTENTS

For Math Lovers: How to Calculate the Active Boundaries 109

What We Learned Regarding Active Boundaries 110

CHAPTER 3 When Volume Diverges from Price 113

Effective Volume: Two Arrows from One Bow 114

Price and Effective Volume Trends 118

Price-Volume Divergence Analysis 134

Examples of Divergence Analysis 144

How to Set the Optimal Analysis Window 176

Empty Trading Minutes 181

What We Learned Regarding Divergence Analysis 183

CHAPTER 4 Supply and Demand: The Key to Trading 185

Supply/Demand Equilibrium 186

Funds’ Strategies 205

Funds and Market Manipulation 211

What We Learned Regarding the Supply Analysis 218

PART TWO Trading Strategies 219

CHAPTER 5 Performance: The Risk/Return Balance 221

The Trading Strategy 223

Optimizing Profits 226

Minimizing Risks 245

Measures of Risk-Adjusted Performance: The Sharpe and

Burke Ratios 260

What We Learned in This Chapter 262

CHAPTER 6 Automated Trading Systems 265

Production of Trading Signals 268

Trading Strategies 274



Contents ix

PART THREE The Bonus Section 319

CHAPTER 7 The Market Is a Two-Way Street:Shorting Strategies 321

The Short Sale “Tick Test” Rule 321

How to Use This Book’s Tools for Short Trading 322


CHAPTER 8 Market and Sector Analysis 341

When Is the Market Becoming Expensive? 342

Sector Analysis 350


Conclusion 363

Data Providers 371

Sources 373

About the Author 375

Index 377


x


Foreword

Y ou have opened a revolutionary book that explodes the envelope ofstandard technical analysis. It introduces several new tools that canhelp you recognize when a trend is likely to reverse. It reveals new

ways to profit from trends and their reversals.Pascal Willain used inexpensive off-the-shelf software to slice each

trading day of a stock into one-minute segments, like cutting a long stick ofsalami into thin slices. He measures each slice to see whether prices rise orfall during that minute and discards the minutes with no changes. He findsthe average one-minute volume for the day and separates the minutes withprice changes into those with above-average or below-average volume. Ineach group, he adds up the volume of minutes with rising prices and sub-tracts the volume of minutes with falling prices. This gives him two cumu-lative volume lines: one for the minutes with above-average volume and theother minutes with for below-average volume. He named them Large andSmall Effective Volume.

Pascal explains that the minutes with above-average volume reflect theimpact of the big money. He discovered that Large Effective Volume oftenhas predictive value. When you find a condition in which the big moneystarts pushing up a stock while the small money remains negative or neu-tral, an upside reversal is in the cards. When the big money starts pushingthe stock down while the small money is flat or buying, a downside reversalis more likely.

Pascal compares his method to dropping down to the cell level andpredicting the movement of the entire organism from the behavior of in-dividual cells. He first described his concept of Effective Volume in theinterview for my book Entries & Exits, but he goes much further in hisnew book.

The author introduces another key concept, which he calls ActiveBoundaries. His research shows that the group of professional traders inany given stock is relatively stable and they shoot for relatively steadygains. When the returns from a stock over a period of time reach their Up-per Active Boundary, the expectations for a further rise diminish and a

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xii FOREWORD

downside reversal is more likely. When a stock declines and hits its LowerActive Boundary, bullish expectations become high and the stock has agreater probability of an upside reversal. Numerous charts show how tocatch reversals using these concepts. In addition to Effective Volume andActive Boundaries, Pascal describes several other concepts. He even pro-vides what he calls “more complex examples for a second reading.”

Pascal has a very rare ability to stand apart from the crowd, to questionaccepted concepts, and to come up with new ideas. For example, whileacknowledging his debt to my Force Index, he stands the original concepton its head by asking why not have a Weakness Index, and even suggestsits formula.

This book abounds with examples of Pascal’s unorthodox approach.For example, he addresses a commonly heard rule, “You must buy wheneveryone else is selling,” and writes:

For me, this is a sure recipe for financial disaster. There are only

two clear times when you should buy:

1. You buy when everybody else is buying, but you do it early in the

trend.

2. You buy when everybody else has stopped selling. In other words,

you buy when the supply of shares has dried up, when only a few

shares are available for sale.

You have to invest time and energy in reading this book. Pascal, likemany original thinkers, follows his own train of thought, sometimes leav-ing less prepared readers behind. During the past year I have been receiv-ing the analytic e-mails in which Pascal shares his research into currentmarkets. It took me a little while to catch on to these concepts, and I hopethat e-mails from readers will prompt Pascal to offer both his software andhis analyses to the wider public. Publishing a book is like giving birth to ababy. This baby will require a bit of nurturing to grow and become strongenough to stand on its own.

The reader must keep in mind that technical analysis alone is notenough to enable one to become a successful trader. Money managementis essential for controlling risks, and you need good record keeping to learnfrom your profits and losses.

I expect the concepts of Effective Volume, Active Boundaries, and oth-ers in this book to become accepted by many serious traders. As always,the early adopters will reap the greatest rewards.

—DR. ALEXANDER ELDER

New York City

December 2007


Acknowledgments

I f the Japanese government had not offered me a scholarship to studyapplied mathematics in the 1980s, I never would have been able to eventhink about creating new tools for the stock market.If my friend Bob had not advised me some years ago to study technical

analysis and buy Dr. Alexander Elder’s book, I would certainly by now beleading another life.

If Dr. Elder had not recognized the novelty of my approach, and hadnot written about it; if he had not first introduced me to his own agent,Ted Bonanno, who helped negotiate the publishing contract, and then toMatthew Kushinka, my copy editor, who helped me find the proper flowof words in the English language; if Dr. Elder had not advised me on thebook structure, the style, the title—even the look and feel of the cover—this book, frankly, would not exist. I am truly grateful for Dr. Elder’s help,especially considering how busy he is.

I also want to thank the four early readers of this book: Bob Grush,from the United States; Barry Silberman, also from the United States;Frederic Snoy, from Belgium; and Thanassis Stathopoulos, from Greece.These four readers are independent traders who continuously look at im-proving their trading. Their comments and suggestions were of immensehelp to me, and I will always be grateful to them for their support.

The last word is for my loving wife and life partner Michiko. Thank youfor your continuous love and support.

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intro JWBK129-Willain March 15, 2008 15:38 Char Count=

I N T R O D U C T I O N

Revolution Is atYour Doorstep

I n the eighteenth century, a Japanese rice trader named MunehisaHomma noticed that it was possible to predict the evolution of prices bystudying certain patterns of past prices. He invented what is now called

candlestick analysis, still one of the most widely used technical analysistools. He assumed at that time that current prices represent all known in-formation about the markets. This hypothesis is still shared by many pro-fessional traders, although we will see how limited it can be.

In the twentieth century, many improvements in technical analysisappeared as new ideas emerged. These include Fibonacci retracements,Elliott wave analysis, moving average convergence/divergence (MACD)lines, and stochastics, to name a few.

In 2001, lightning struck, but it went largely unnoticed. Why? As hasoften been the case throughout history, this revolution was the natural re-sult of a change that had different causes. Everybody noticed the change,but very few noticed the revolution. It was similar to Louis Pasteur’s dis-covery of microbes. That was a revolution, but the real change that madethat revolution possible was the invention of the microscope.

The change that would bring about a revolution to the technical analy-sis of stock trading was decimalization. It happened on April 9, 2001, whentraders began to measure stock prices to the penny instead of in sixteenthsof a dollar (or 6.25 cents). The objective was to make the stock price fluctu-ations easier for the general public to understand (thereby attracting moreretail investors), as well as to reduce the spread cost. On the contrary, aswe will see later, this change had a large impact on the way institutionalinvestors play the market. Decimalization killed market visibility and, as

1


2 VALUE IN TIME

some believe, may have encouraged price manipulation. At the same time,decimalization allowed the precise detection of traders’ movements.

What you will find in this book is not just one tool, but a complete set ofrevolutionary tools. These tools are based on how market players act—noton their behavior or on their potential reactions, but on their real, tacticalmoves. These tools are so powerful that I believe they will eventually beprogrammed into your favorite stock-trading platform.

You probably know that kids learn new languages much more quicklythan grown-ups do. An adult’s brain is already formed (the synapsesare already connected), so changing the brain takes more than just in-stalling some new wiring. Similarly, it will be easier for beginning tradersto read and understand this book than for confirmed traders. Most con-firmed traders have years of experience that have crystallized their habits;they have automatisms that follow given patterns and chart formations.Bring them a new idea, and doubt will set in, endangering their wholetrading system.

However, if you are not already set in your trading ways, if you arenot a monk who comes out of his cell to pray to the gods of trading atspecific hours, then you will greatly enjoy this book. I will lead you downunexpected paths to a complete new vision of stock trading.

Since the time I started doing technical analysis, I have been awestruckby the specific art of charting and chart interpretation. There are somany superb books on the market explaining how to interpret chart pat-terns. When I try to interpret chart patterns, I feel like an amateur musi-cian who reads an unfinished sheet of music and tries to figure out howit could develop into a full concerto. Technical analysis has become atrue art form, and I am thrilled when I meet artists who have masteredtheir art.

Unfortunately, these great artists are on the verge of extinction. Withthe advent of computer trading and with the appearance of new tools suchas those presented in this book, it is clear that traditional chartists willsoon be forced to adapt in a new technical environment. In fact, everybodywill have to adapt. The markets will be very harsh to those who do not.

At the outset of this book, I would like to thank one of the great artistsin chart reading: Dr. Alexander Elder. I came to technical analysis after Iread two of Dr. Elder’s books: Come into My Trading Room and Trad-

ing for a Living. I applied his methods, but even if I turned a good profit,I was not totally satisfied. I first tried to improve upon Dr. Elder’s meth-ods, but soon I realized that I had to start from scratch. I came up with aset of concepts and tools and developed them into a full trading method.I sent Dr. Elder a technical paper that explained my tools, and we met inAmsterdam. I was a bit nervous when we met: Would Dr. Elder listen tome? Would he like the ideas? To interest Dr. Elder in my work, I even toldhim that my method was a continuation of his work, when the truth was


Revolution Is at Your Doorstep 3

that even if I had learned the basics of technical analysis through his in-credibly eye-opening books, I was coming up with totally new concepts.For 30 minutes I presented my tools to Dr. Elder, while he asked for moreinformation: Show me this and that. How do you calculate this or that?

How do you get your data? I replied, “Just as you wrote in your book,” or“It is like the method you explain in your book.” But Dr. Elder looked at meand said in a deep baritone, “Noooooo . . . this method stands on its ownfeet.” I was indeed lucky that Dr. Elder had looked at my tools the way adoctor diagnoses a patient—by concerning himself with the facts.

Later, when Dr. Elder visited me in Belgium, he told me that I shouldnot be so humble about my method. Instead of introducing it in a sort oftechnical manual, I should call it what it is—a revolutionary method—andexplain it in terms that are as simple as possible. He then went throughthe structure of this book, simplifying and reorganizing it to make thingseasier to understand, and advising me on the types of figures I should useand how to present them. When I told him that he should become coauthorof the book, he then replied, “Noooooo . . . it is not my method. Just write‘Thank you, Alex’ somewhere in the book. That’ll do it.”

Well, here you are, Alex: Thank you!

WHAT IS THIS REVOLUTION ABOUT?

First, I need to say that I am a dumb engineer, of sorts: I keep asking dumbquestions, and I will continue to ask those questions until satisfactory an-swers are found that are also experimentally proven to be correct.

Here are a few questions for which I could not find satisfactory an-swers in the literature:

� How can you find out what institutional players are doing? Are insidersbuying or selling?

� How can you see that news is coming?� When is a stock cheap? When is it expensive?� Why do trends exist? Where do they come from?� What is the supply/demand equilibrium?

This book is therefore about finding out what insiders are doing, whatlarge funds are doing, what traders’ expectations are, and how the equilib-rium between supply and demand evolves. It is also about understandingwhen large funds are moving in and will eventually establish a new pricetrend, as well as knowing what buying power is necessary to support atrend, what will break trends, and when trends will be broken. In short, itis about reading the markets instead of guessing about them.


4 VALUE IN TIME

Traditional charts look very complex, because they are based mainlyon prices. It is always difficult to make a decision based on one singlepiece of information (price), even if a price chart is supposed to includeall the information about the market. The complexity lies in the guess-work, something at which traders need to become skilled if they want to begood traders.

Many books talk about large funds as the “smart money.” Smart money

is a term I have a hard time accepting, because it implies that the individ-ual trader is not smart. I prefer to say that information leaks and price ma-nipulations are routine occurrences in the markets. The objective of theseleaks and manipulations is to take advantage of others. I would not call thatsmart. The tools that I developed will not allow you to become smart in theway that most traders define it. But they will allow you to see through ma-nipulation. You will become smarter, then, if you learn to see the marketsmore clearly and if this transforms you into a better trader.

I disagree with people who reduce the market to a competition be-tween large players and individual traders, or who believe that marketmakers are behind the price moves. The market is much more complexthan that, with an increasing number of connected, online traders scatteredaround the world, with 50 percent of the trades being computer-generated,and with large players often moving in opposite directions.

You will, however, have to keep the following point in mind: The newtools for reading the market that I show you will not enable you to read themarket exactly, all the time, and forever. Markets evolve, and I believe thatall tools for analyzing the markets must evolve, too, including mine.

HOW THE BOOK IS ORGANIZED

This book is divided in two regular parts, followed by a bonus section. Thefirst part describes in detail the four new tools that I developed in orderfor each of them to find a solution to a specific problem. The second partintegrates the various tools into trading strategies, and I show what worksand what does not for either retail players or fund managers. Part Three,the bonus section, shows how to adapt the tools to sector analysis.

Part One: The Set of Tools That Will ChangeTechnical Analysis

I developed the sets of tools presented in this section because I neededthem for my own trading. Each tool addresses a specific issue with onlyone goal in mind: to better understand the market. I am convinced that



these new methods of measuring the market have the power to changetechnical analysis as we know it today. The industry will be slow to adapt,but in the end, I believe that the tools that provide a better measure of themarket forces will prevail.

Chapter 1: Effective Volume: An Open Window into theMarket In Chapter 1, I show that the monitoring of the volume involvedin small price changes from one trading minute to the next, which I defineas the Effective Volume, is a very good tool to detect tactical moves by in-siders and large players. The Effective Volume tool is excellent for detect-ing trendsetters and often allows the detection of coming price changes. Ialso review in comparison how traditional tools use volume data.

Chapter 2: Price and Value: The Active Boundaries IndicatorChapter 2 deals with the monitoring of price trends. It is based on the hy-pothesis that the group of active traders who follow a stock is relativelystable and that their automatic trading tools use buy/sell strategies that donot evolve in time. The Active Boundaries indicator takes advantage of thisstability to capture trends between boundaries of expectation: The price ofa stock has a great probability to reverse up when it hits the Lower Bound-ary (where expectation is the highest) and to reverse down when it hits theUpper Boundary (where expectation is the lowest).

Chapter 3: When Volume Diverges from Price In Chapter 3 I showthat the historical comparison of price trends to Effective Volume trends al-lows detecting, for a specific stock, thresholds that define levels of high ac-cumulation or distribution. This Divergence Analysis, after being adjustedfor volatility discrepancies between price and volume, produces buy andsell signals that prove to be very effective. I then show how the combi-nation of Active Boundaries and Divergence Analysis can lead to a set oftrading rules that could be combined to form a trading strategy.

Chapter 4: Supply and Demand: The Key to Trading Chapter 4takes a hard look at the supply/demand equilibrium as the major marketforce. This study leads to the presentation of the Supply Analysis tool.The Supply Analysis tool is based on the calculation of the probabilitythat a share will be offered for sale, depending on the price at which itwas bought, the time elapsed since it was bought, and the price evolutionsince then. I then show in practical examples how the Supply Analysis tool,combined with the Effective Volume tool, can very effectively measure thesupply/demand equilibrium and therefore lead to winning trades.

This chapter then moves on to study how funds play in an illiquid envi-ronment. It shows that funds have great difficulty making money, primarily


6 VALUE IN TIME

because of the large size of the positions they must take. Finally, we dis-cover that markets are very efficient and that therefore price manipulationby funds is not likely to occur. This leads us to the conclusion that tradi-tional funds will not be able to beat the market.

Part Two: Trading Strategies

After developing in the previous section a set of new tools that can beused independently, this section shows how these tools can be combinedinto various trading strategies. These trading strategies are tested against abuy/hold trading method not only in terms of risk/return balance, but alsoin terms of the total efforts that a trader must invest in order to sort out thebest trading opportunities.

Chapter 5: Performance: The Risk/Return Balance Chapter 5shows that at the level of the trading strategy the risk/return balance isbest measured using:

� For the risk: the expected monthly loss transferred (MLT) by the trad-ing strategy to the portfolio.

� For the return: the yearly expected return (YER) of the trading strat-egy. A performing trading strategy must produce a YER higher thanthat produced by a standard buy-and-hold strategy.

We will also see in Chapter 5 how the profit target, the stop loss, andthe time limit parameters can be used to manage an opened trade.

Chapter 6: Automated Trading Systems Chapter 6 first reviews thealert and production screens, two information displays used to alert thetrader to the evolution of a set of stocks. These two screens summarizethe material covered in Chapters 1 through 4.

Chapter 6 then combines the tools introduced in Chapters 1 through4 into various trading strategies. We gradually discover the characteris-tics of the three pillars of good trading strategies: the discovery of value,the selection of the right buying trigger, and the management of the tradeevolution.

Part Three: The Bonus Section

This part is called a bonus section because it opens the door to the under-standing of different facets of trading using the trading tools introduced inChapters 1 to 4.



Chapter 7: The Market Is a Two-Way Street: Shorting StrategiesAfter explaining the “tick test” rule, Chapter 7 studies how the various toolscan be used for shorting strategies.

Chapter 8: Market and Sector Analysis Chapter 8 shows how theapplication of the Active Boundaries tool to the general market trend canhelp in determining when the market is overpriced. It then studies how amodified version of the Effective Volume tool can be used to study sectormovements.

READY FOR THE REVOLUTION

The revolution of decimalization is here to stay. The only way that investorsand traders can avoid becoming victims of insiders and manipulators is touse techniques that detect their moves. This is why I believe that tools suchas those I present in this book will be widely used in the coming years.

You will see that the different concepts introduced in this book arevery simple in nature. The mathematics may look complex at first, but infact it is mostly addition, subtraction, multiplication, and division. It is im-portant that you understand what the math represents, what it measures,and how you can take advantage of what it tells you.

But it is not the math that is doing the trading; it is the trader.

c01 JWBK129-Willain March 25, 2008 7:0 Char Count=

P A R T O N E

The Set of ToolsThat Will Change

Technical Analysis


C H A P T E R 1

Effective VolumeAn Open Window into the Market

W hen you are considering a stock to trade, you have to view yourselfas a doctor treating a patient. You have three points of view to helpyou in your diagnosis:

1. The patient’s general condition: age, gender, any preexisting condi-tions, regular exercise or not, smoking or heavy drinking, and so on.

2. The patient’s symptoms: pain, fever, swelling, and the like.

3. The patient’s internal examination: a blood test, a scan, an X-ray, andso on.

When analyzing a stock, you may think that the general condition is thefundamental analysis: earnings, profit growth, and so on. It may disappointyou to learn that these are only external measures of value. Value itselfis useless if not compared to how it is priced. How value is priced is alsovirtually useless if you do not know what the expectation of shareholdersis. Indeed, being a shareholder means possessing equity (value) for whichthe shareholder expects a return.

You will understand in Chapter 2 that the general condition of a stockis partly represented by its price trend. You will often see a price mov-ing above and then below its price trend, indicating the evolving per-ception of value. Good trading requires you to catch this perception ofvalue. I translate it into the measure of the expectation of active traders.You need to position your trades in harmony with this expectation: buy

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12 VALUE IN TIME

when expectation is high, and sell when it is low. Chapter 2 explains thisexpectation concept and how to measure it. It is my first pillar for success-ful trading.

The second thing to look for when diagnosing a stock is its symptoms.Today’s technical analysis is still performed at the level of the symptoms:Traders like to catch trends and their reversals, they will look for over-bought and oversold situations, they will search for crowd movements,they will examine the demand/supply equilibrium, and so on. These tradersare like doctors who look at a fever and know that after a few days the fevershould dissipate. These traditional analysis tools are very useful if you mas-ter the art of interpreting them. Traders, like doctors, need a fair amountof experience to become truly skilled. Only then will they be able to see inthe charts where a market or an individual stock is heading.

These traditional tools require skills, training, and thinking. The greatmajority of traders use these tools with end-of-day data and react in unisonduring the following trading session. The “doctors” will see similar symp-toms and will prescribe similar treatments (though this is not always thecase). I also use these traditional technical analysis tools, because it is crit-ical to see what others see to know where the technical analysis will pushthe crowd of traders.

A doctor who has a doubt about a patient’s diagnosis will order a bloodsample to be analyzed; the doctor can then diagnose the disease and pre-scribe the necessary medicine.

Now, suppose that it were possible for a doctor to insert a tiny mi-croscope inside the patient’s body, and that this microscope had a wire-less communication with the doctor’s health monitoring station. The doc-tor could then monitor the fever not only after it appears (when thepatient has already become sick), but before the fever appears, by monitor-ing any conditional change occurring at the microscopic level. The doctorcould sort those changes and take into consideration only those that mightcause a fever. Of course, this capability doesn’t yet exist in medicine. Sim-ilarly, what is lacking in today’s technical analysis is a way to detect microchanges that are strong enough to propagate over time into a full-blownsickness.

A very useful tool that I present in this book therefore allows traders toreach what might be called the cell level. Going down to the cell level doesnot necessarily mean analyzing each transaction, looking over the tradingbook size, or studying all the coming orders. You need to look at the mi-crobes through your microscope, but remember that you are more inter-ested in seeing their propagation than their mere existence. When Pasteurdiscovered microorganisms such as viruses and bacteria, it was not findingout they existed that revolutionized medicine but rather the interpretation


Effective Volume 13

of how these organisms work. This interpretation led the way to vaccinesthat changed our everyday life.

I love the work of Russian Nobel Prize winner Dr. Ilya Prigogine and histheories of dissipative structures. I have to confess that it was when I waslooking for a way to discover a new tool that I remembered my readingsof Prigogine during my student days. Although these theories would notapply to understanding how the stock market works, I found the principlesstrikingly close to how I believe the stock market functions. The work ofDr. Prigogine states that the dissipation of matter or energy is usuallylinked to the ideas of efficiency loss and to the evolution toward a largerdisorder. However, far from the equilibrium of a structure, the dissipationcould be at the origin of new states of matter. In short, life was createdby dissipation that brought a system far from the equilibrium and forced itinto a new state of order. Prigogine states:

Far from the equilibrium, a state of operation can look like an orga-

nization because it results from the amplification of a microscopic

deviation that at “the right timing” has privileged a reactive behav-

ior as opposed to other reactive behaviors that were also possible. The

individual behaviors can therefore in certain circumstances have a

decisive role.

—Translated by the author from La nouvelle Alliance,

by Ilya Prigogine and Isabelle Stengers

(Paris: Gallimard, 1979), page 237.

As you may now understand it, the market may be moving en masse,and this pattern has been greatly amplified by the advent of the Inter-net and fast communications. However, I will show you that many mar-ket movements are started at a much lower level and that the broadprice trend changes are often triggered by only a fraction of the volumeexchanged.

Figure 1.1 shows the analogy between the stock market evolutions andthe evolutions of an organism. An organism that is in a state of equilibriumfirst needs to be put out of equilibrium by an external trigger. This externaltrigger is strong enough to generate a micro change. If this trigger repeatsitself for a period of time, it can propagate the change to the whole organ-ism, which will then enter into a new state of equilibrium.

I am not saying that we have to forget traditional technical analysis,but rather that traditional technical analysis is less and less adapted to fast-moving markets where information and manipulations are the basis of themarket movements.


14 VALUE IN TIME

LivingOrganisms

StockMarket

Strong trigger

Stable Organism

Instability

Organism with adifferent stablility

Micro change

Spread with time

Strong volume

Price change

Spread with time

FIGURE 1.1 Change in market equilibrium. The price of a stock goes from onestate of equilibrium to another. This change has triggered an abnormal increase involume of transactions, one that is strong enough to trigger micro price changeswhose spread will force a change in equilibrium.

TRADERS GET A SECRET NEW TOOL:A BRIEF INTRODUCTION TO THE TRADINGMECHANISMS AND THE MARKET PLAYERS

Before explaining how things changed in 2001, I would like to point outthree basic rules that govern the largest stock markets (NASDAQ, NewYork Stock Exchange, etc.):

1. The price precedence rule says that if you offer to sell a stock at thelowest price, your offer will be executed first. (If simultaneously Johnoffers to sell his shares at $10, Jim at $10.01, and Martin at $9.99,Martin’s order will be executed first.) This guarantees that buyers alsoget the best price for the stock they purchase. Buy orders that offer thehighest buying prices are also executed first.

2. The time precedence rule says that buy or sell orders that have thesame price are ranked in their order of submission: The first to arrive isexecuted first. (If John offers to sell his shares at $10, and five secondslater Martin also offers his shares at $10, John’s order will be executedfirst, followed by Martin’s.)

3. A lesser-known rule is called the public order precedence rule. Thisrule states that members of a public exchange cannot execute theirown orders ahead of orders from the general public that are standingat the same price. This rule was created to increase investor confidencethat members of the exchange will not use their superior informationto their advantage by trading ahead of the public.


Effective Volume 15

These three rules are applicable for orders only when they reach themarket. However, before reaching the market, some orders go through abroker. The broker can just forward the order, or can take advantage of itand trade for himself ahead of the client’s order. This is seldom the case,but dishonest brokers do exist, and the bad behavior of a few is finallypushing human beings out of the loop in favor of electronic order-routingsystems.

As brokerage houses get a commission on each transaction, some havefound out that it is more profitable to trade their clients’ accounts of-ten, despite the fact that eventually they bankrupt their own clients andlose them.

This reminds me of a friend who once told me that he had an e-mailexchange with a trading company in the United States; this company wasready to trade his account and, besides the traditional commissions oneach transaction, take only 10 percent of his profits as commission. Myfriend thought that it was a good deal. Indeed, since he did not have to payany management fee, he believed that this trading company would try tomaximize its own profit, which was linked to my friend’s profit. My friendeven asked me if I wanted to invest with him. I had to refuse, because I donot let other people manage my money. After only a few months, the first$25,000 he tested in that account had been reduced to almost nothing. Hetold me at that time that he was not very happy, but that he was calling thetrading company on a regular basis; apparently they were always willing togive him an explanation on why they lost his money. Still later, when I metmy friend again, he explained to me that after losing his first $25,000, hereally wanted to get his investment back. He decided to get “really tough”with the trading company and gave them one last chance—he put in an-other $12,500. I remember telling him at the time that if the manager ofhis account was truly looking for a 10 percent profit, he never would havelet the account go bust and he would certainly never have accepted myfriend’s second investment. Why? The manager would have had to make a$25,000 profit to compensate for the first loss even before being paid onecent out of any profit from the additional $12,500. I advised my friend totake out whatever he could, because he was probably the victim of accountchurning, which is the term for when trading companies generate as manycommissions as possible with useless trades. My friend did not follow myadvice and thus learned an expensive lesson, losing his subsequent $12,500investment.

How Decimalization Changed the Markets

Before 2001, prices were quoted in sixteenths of a dollar. Suppose youwanted to buy 1,000 shares of a stock whose bid price was $10.1875 and


16 VALUE IN TIME

whose ask price was $10.25. If there were liquidity at $10.25, you couldeither (1) get your shares at $10.25 (for a total amount of $10,250) or (2)place a bid at $10.1875 (for a total amount of $10,187.50) and wait for yourbid to get filled, hoping that nobody would bid higher than you, buy all theavailable shares at the ask, and consequently push the price up. The spreadcost—the difference between the ask (the best price offered by sellers) andthe bid (the best price offered by buyers)—was rather high at $0.0625; for1,000 shares, the difference between placing the order at the ask and plac-ing it at the bid was $62.50. This high cost would have pushed buyers toplace their orders at the bid and sellers to place their orders at the ask.Because of the time precedence rule that prioritizes the execution of or-ders, traders would place their orders early enough to be executed first. Asa consequence, you could have market visibility and guess what large play-ers wanted to do. Indeed, Table 1.1 shows the order size for the bid and theask before decimalization.

Another consequence was that the price did not change much, sinceit took quite a large volume to move the price up or down by one tick(the smallest level of price change between the bid and the ask). Beforedecimalization, a tick was one-sixteenth of a dollar, or 6.25 cents.

We see in Table 1.2 a similarly sized order book after decimalization. Itshows 20,000 shares on the bid, but distributed between $10.19 and $10.13.It also shows 22,000 shares on the ask, distributed between $10.20 and$10.25.

As a trader, suppose that you want to order shares at the bid. In Table1.1, you are competing against 20,000 shares. If you wait longer, the bidcould increase, and your chances to get shares at $10.1875 could diminish.Therefore, you will be inclined to rush your order in.

However, if you want to buy shares at the bid in Table 1.2, you arecompeting against only 500 shares. You now have less motivation to placeyour order at the bid, since competition is not showing up. You prefer tokeep your hand closed like a good poker player. If you are lucky enough,someone will sell 600 shares at market price and the bid will be loweredone cent. This may allow you to get your shares at a cheaper price.

TABLE 1.1 Book of Orders(before Decimalization)

Buyers Sellers

Bid Volume Bid Ask Ask Volume

20,000 $10.1875 $10.25 22,000


Effective Volume 17

TABLE 1.2 Book of Orders (after Decimalization)

Buyers Sellers

Bid Volume Bid Ask Ask Volume

500 $10.19 $10.20 10,0003,000 $10.18 $10.21 3,0005,500 $10.17 $10.22 7,0001,000 $10.16 $10.23 3005,000 $10.15 $10.24 7004,000 $10.14 $10.25 1,0001,000 $10.13

The book of orders lists the prices at which buyers and sellers areready to trade as well as what volume they want to trade. Thespread is the difference between the best bid price (here, $10.19)and the best ask price (here, $10.20). In this example, the spreadis $0.01. A buyer wanting to buy 100 shares may buy them at thebid for $10.19 per share and wait in line until the existing bidorder of 500 shares is first executed, or may pay one cent moreand have the order executed instantaneously at the ask for $10.20.

Furthermore, if you need to buy 12,000 shares in Table 1.2 and youplace an order at the ask, the price will move up one tick to $10.21. Thismay be undesirable, especially since you would still like to buy another100,000 shares at a good price. If you put large buy orders at the bid, youwill show your hand to the market and attract other buyers.

The cheapest course of action would be to buy 9,500 shares at the ask,then sell 600 shares at the bid. The ask price would stay unchanged, but thebid price would fall one cent to $10.18. This would eventually cause sellersto lower the ask to $10.19, allowing you to buy your next set of shares at alower price. It is a legitimate price manipulation that funds need to use inorder to accumulate or distribute shares during sideways trading ranges.In Chapter 4, we will see if this manipulation is common practice.

In addition to this, program trading would automatically use these tac-tics to dispose of or purchase large blocks during sideways trading ranges,making sure that the price stays in the trading range until the strategicmove is finalized.

In conclusion, we have seen that decimalization killed market visibilitywhile favoring price manipulations. Fortunately, the Effective Volume toolthat I present in Chapter 1 provides a way to see through tactical movesby large players. It allows all other traders to analyze the repetitive markettactics and show the underlying strategic decisions of large players.

The Effective Volume tool does not imply that these strategic de-cisions are correct and that you need to trade in the same direction.


18 VALUE IN TIME

However, knowing what large players are doing is key to helping yourtrading decisions, whether you are a retail player or a competing institu-tional player—especially if you have a correct measure of value. The ActiveBoundaries tool that I present in Chapter 2 will allow you to obtain a veryaccurate measure of value.

Since I began using the Effective Volume tool, one of my trading prin-ciples has been not to trade against large players. This is not to say thatI always trade with the large players, but I am not fool enough to tradeagainst them.

How Large Funds Adapted to Decimalization

A large fund has the dual advantage of size and power, but it also has limi-tations. Funds provide liquidity to the markets, and the system is designedto allow them some flexibility. The decimalization rule served them thisflexibility on a silver platter. This rule was initially conceived as a way toattract private investors and lower the spread costs, but it was in fact an im-plicit authorization for large funds to manipulate markets. Indeed, beforedecimalization, if a fund wanted to lower the price of a stock, it had to sellat the bid enough shares to take out all the outstanding buy orders. Sincethe spread cost was high, all players entered their order in advance (firstcome, first served), and it was easy to see what large players wanted to do.Market manipulation at that time was quite costly. (For example, you hadto sell perhaps 10,000 shares at a spread cost of $0.0625. This meant that ifin fact you wanted to lower the price in order to buy a larger quantity at alower price, you would have to purchase these 10,000 shares back $0.0625higher. The manipulation would have cost you $625.)

Decimalization, though, lowered the spread cost, and therefore freedlarge players from disclosing their orders. This greatly reduced the size ofthe order book, allowing anybody with just a few hundred shares to in-crease or decrease the stock price. Typically, these days, if you have 1,000shares, you can easily push the price down by one cent, at a cost of 1,000 ×$0.01 = $10, which is 60 times less than before decimalization. I have noproof that markets are constantly manipulated. However, if a service sud-denly costs 60 times less than it did the day before, you can be sure thatthis service will be used more often.

New Tools Are Necessary

Further on in this chapter I make detailed comparisons of the differenttools that are used to study the price/volume relationship, but what I wantto stress here is that a tool is an instrument that you are using to take a


Effective Volume 19

measurement. That measurement gives you some clue about the underly-ing reality.

I like to compare trading a stock through technical analysis to the ac-tions of an engineer who is in charge of a petroleum extraction rig. This en-gineer is responsible for digging a deep hole and eventually hitting a target.When drilling, the crew will encounter different types of ground texture; re-sistance and friction will increase. They will also encounter changing heatand pressure conditions. The engineer knows by experience that they willneed more than one instrument to understand what is happening to the rigdeep down in the hole. Similarly, traders need to use different tools whenanalyzing a stock.

The market is very complex. It is, of course, different from what it was100 years ago, but it is also more complex than even 15 years ago. Just lookat three key changes that have happened since then:

1. Communication speed has resulted in very quick price adjustmentsto news. Markets are becoming more efficient, but also crowded withmany retail investors enjoying online communication.

2. Decimalization has changed the tactics of large players.

3. Hedge funds are bringing liquidity but also volatility (very large swingsof price and volume).

A trader needs tools that can handle these changes. Such tools there-fore need the following characteristics:

� The tools need to catch the strategic moves using an analysis of theaccumulation/distribution tactics. Usually, institutional investors frag-ment one large order into many small orders that can then be sentundetected—this is called order fragmentation. Each small order willthen be executed in one or more market transactions. Although datarelated to each transaction and each fragmented order is available, thetools need to “reconstruct” the fragmented orders, using minute dataso that traders can have a better understanding of what institutionalplayers are up to.

� The tools must be able to filter the noise out of the important signal.(We will see later in the chapter that only 25 percent of the total ex-changed volume is responsible for 75 percent of the price changes.You’d better know the direction of the 25 percent and not move againstthe direction of these changes.)

� The tools must tell you if the moves of the large players are significantenough to induce a price change or to make or break a trend.

� The tools must allow you to make volatility adjustments between priceand volume, which carry very different levels of volatility.


20 VALUE IN TIME

� The tools must show you what the position and expectation of otheractive traders are, because you will need to buy cheap—and a cheapprice will be harder to find if everybody else expects the share price todecrease and is ready to sell. A cheap price is found when the last sellerhas finished selling and new buyers come in with a high expectation forthe price to increase.

Finally, wouldn’t it be nice if your computer scanned hundreds ofstocks, applying all these new tools and giving you buy and sell signals?

VOLUME THAT MOVES THE MARKETS

When I started this work, I was almost completely convinced that largeplayers were mainly responsible for stock price movements, because ofthe large size of their trades. Therefore, monitoring the movements of largeplayers seemed to be the best way to monitor the whole market. My con-cern was to find out when institutional investors were moving in or out ofstocks.

The analogy with Dr. Ilya Prigogine’s work was telling me that I neededto do three things:

1. Measure the impact of volume changes to price changes at a level thatwas as close as possible to the transactional level.

2. Separate large from small volume.

3. See the evolution of such volume.

Therefore, I needed to be able to compare this evolution between fixedperiods of time.

I was looking for a tool that could do these things. Because I am lazy, Itried to find an already existing tool, one that I could use right away.

I found two categories of tools: the “tick volume” tools and the “endof day” tools. As we will see later in this chapter, both types of tools havetheir own limitations and therefore neither could meet my needs.

Still wondering why nobody had found an answer to an obvious ques-tion (“What are large players doing?”), I started to develop my own tools.

In trying to answer that question, I realized that there are not that manydifferent types of data to work with: on the time interval basis (1-minute,5-minute, 10-minute), you can play with the open, high, low, and close ofthe price data, and add to it the volume data. On the transactional level,you have the order size, the execution time, the execution size, the price ofexecution, and some other minor information.


Effective Volume 21

TABLE 1.3 One-Minute Data

Open High Low Close Volume

9/25/06 14:27 $11.07 $11.07 $11.06 $11.06 5,8899/25/06 14:26 $11.06 $11.06 $11.06 $11.06 2009/25/06 14:25 $11.06 $11.07 $11.06 $11.06 28,3359/25/06 14:24 $11.05 $11.06 $11.05 $11.06 18,1319/25/06 14:23 $11.04 $11.06 $11.03 $11.05 33,1889/25/06 14:22 $11.03 $11.04 $11.03 $11.04 3,2989/25/06 14:21 $11.02 $11.04 $11.02 $11.04 29,6589/25/06 14:20 $11.02 $11.02 $11.02 $11.02 17,8259/25/06 14:19 $11.01 $11.02 $11.01 $11.02 11,3519/25/06 14:18 $11.02 $11.02 $11.02 $11.02 40,8899/25/06 14:17 $11.04 $11.04 $11.01 $11.02 14,0159/25/06 14:16 $11.05 $11.06 $11.04 $11.04 13,8029/25/06 14:15 $11.06 $11.06 $11.05 $11.06 32,5369/25/06 14:14 $11.07 $11.08 $11.06 $11.06 16,3999/25/06 14:13 $11.07 $11.08 $11.07 $11.07 20,041

Typical one-minute data format, including the minute opening price (open), thehigh of the minute (high), the low price of the minute (low), and the closing priceof the minute (close). The last column represents the volume exchanged duringthat trading minute.

I started with the raw one-minute data such as that displayed in Table1.3. Because the trading day is 6.5 hours long, there is a maximum of 390trading minutes. Table 1.3 shows a typical data set, each line representingone minute. More recent data are shown at the top of the table. As shownin Table 1.3, each trading minute has an opening price, a high price, a lowprice, and a closing price. This means that during a trading minute, tradershave been buying and selling shares. The price variations between the lowand the high indicate that such activity existed. These price variations areusually done tick by tick. Conventionally, upticks and downticks are tinyprice movements that move the price up or down by one tick (usually onecent). Within one trading time interval of one minute, there can be severalupticks and downticks. From time to time, a volume spike takes the priceup or down several ticks at a time.

I call price inflections the small price changes that occur between onetrading minute and the next. Let’s call the volume that is responsible for aprice inflection the Effective Volume. We will see later how to calculate it.Please note that for me a price inflection of one tick has the same weightas a price inflection of two or more ticks, at least to measure the up anddown buying and selling movements.


22 VALUE IN TIME

A price inflection indicates that the equilibrium between the bid andthe ask was broken because of underlying market activity (traders pushingthe price down instead of traders pushing the price up or vice versa). Dur-ing one trading minute, the equilibrium can be broken on one side and thensuddenly reverse to the other side. This usually happens when buyers andsellers trade similar volume sizes with a similar determination.

In the course of trading, suppose that an institution intends to placea large buy order. Either this large buy order will go as a block directlybetween institutions or that institution will have to buy from the market.Large orders placed at market usually push the price up. In order to gounnoticed, a large order has to be fractioned into tiny orders that will bebrought to the market on a systematic basis. This must be done withouttriggering a new uptrend before the whole lot has been bought. This re-quires a careful tactical execution that involves a mix of order sizes andtiming variations. Institutions either use special order-placing algorithmsor obtain the assistance of a market maker.

How to Detect Such Movements

Only a fraction of the orders that reach the market are executed. Executedorders create transactions between a buyer and a seller. The buyer’s andthe seller’s respective buy and sell orders are mutually filled. If you onlystudy the transactions, it is very difficult to see the direction of these trans-actions. Because for each transaction there is a buyer and a seller, it isimpossible to tell if sellers are stronger than buyers.

The direction of the trade is indicated by the small price change that oc-curs on the transaction: If the price increased on the transaction, the buyerwas stronger (pushed the price up). Otherwise, the seller was stronger. Be-cause institutions split up large orders into numerous small orders, study-ing the transaction size does not help in figuring out whether the transac-tion was generated by an institution. What we therefore need to do is studyall the aggregated transactions within regular time intervals of one minute.The idea is to reconstruct the size of the original order by adding up all thetransactions that occurred within one minute and to compare that numberto the price variation.

Let’s study this idea in one example: a large buyer. Let us suppose thatan institution wants to buy 100,000 shares on the market of a stock thatis trading 500,000 shares per day. The institution will probably have to useone of the following four tactics:

1. Place a large buy order at the bid.

2. Place regular small buy orders at the bid.


Effective Volume 23

3. Place regular small buy orders at the ask.

4. Place a large buy order at the ask.

Let’s study the consequences of such moves.

1. Place a large buy order at the bid. This is a passive strategy, since theinstitution has to wait for sellers to come to it. But, since regular buyersare still active, these players would need to bid the price up to get theirshares. As a result, the institution will also have to raise its large bid,with the risk of starting a new uptrend. This method is ineffective foraccumulating shares. It is easily detectable, since the large bid signalsto the market that a large player is accumulating.

2. Place regular small buy orders at the bid. This is also a passive strat-egy, but the institution will not be easily detected. As we will see inChapter 4, this strategy does not allow large players to take a signifi-cant position, and is therefore probably not often used.

3. Place regular small buy orders at the ask. This strategy is more active,because the institution is actively buying shares. This method requiresthat the institution have patience in its accumulation, to avoid pricespikes that could trigger a new uptrend. However, because the buy-ing is regular during a short period of time, the supply of shares willmomentarily dry up and the price will momentarily increase. The insti-tution needs to monitor these small price increases. If the small priceincreases trigger a change of key technical patterns, they could attractmore buyers while the institution has not met its targeted number ofshares. On small price increases, the institution must therefore either(1) wait for new sellers to come and push the price back down or (2)push the price back down by itself with a small sell order. Becauseof the statistical significance of the repetitive buying pattern, even dis-tanced buy orders placed at the ask form a pattern using the EffectiveVolume, which I explain in the next section. The visible pattern is thatlarge volume will be more often linked to price increases than to pricedecreases.

4. Place a large buy order at the ask. This very active strategy is usedonly when several institutions are competing to get shares, or when aninstitution wants to trigger a price increase by signaling to the marketthat it is buying shares. This is easily detectable through the monitoringof the price trend.

It should now be clear to the reader that what we need to analyze is notthe situation at one point in time, but the regularity of the pattern during aset of consecutive, identical time intervals.


24 VALUE IN TIME

Traditional Way to CalculateShares Accumulation

Larry Williams created a now widely used formula to calculate the accu-mulation/distribution (A/D) balance on daily charts. Figure 1.2 shows sucha calculation. The principle is to weight the total volume exchanged duringthe day by the price gain/loss, divided by the price spread during that day.

� Share accumulation means buying.� Share distribution means selling.

The simple idea behind this is to say that if shares are exchanged dur-ing the day and the closing price is higher than the opening price, for exam-ple, the total result is considered positive: Buyers are stronger than sellers.This means that on average, there is share accumulation during the day.

However, if the price spread during the day is very large compared tothe gain, it means that traders have been fighting during the day. Therefore,the strength in accumulation of shares should be proportional to the extentof the fight.

In Figure 1.2:

� The gain = the outcome of the fight = $10.4 − $10.2 = $0.2.� The spread = the extent of the fight = $10.5 − $9.8 = $0.7.

High $10.5

Gain/Loss

Spread

Close $10.4

Open $10.2

Low $9.8

FIGURE 1.2 Larry Williams accumulation/distribution example #1.

Accumulation = GainSpread

× Volume

$10.4 − $10.2$10.5 − $9.8

× 100,000 shares = 28,571 shares


Effective Volume 25

� The number of shares shown by the accumulation calculation istherefore:

100,000 × $0.2$0.7

= 28,571 shares

However, if the opening price had been $10.4 and the closing price$10.2, there would have been a loss of −$0.2, and we would have seen thesame number of shares (28,571 shares), but on the distribution side.

We can note two potential problems with this formula:The first problem is shown in Figure 1.3. Because the spread and the

gain shown in Figure 1.3 are identical to the spread and the gain shown inFigure 1.2, the result of the accumulation/distribution calculation is identi-cal: 28,571 shares in both cases.

However, some traders will tell you that the close of Figure 1.2 isstronger than the close of Figure 1.3, because the price in Figure 1.2 closedhigher. Therefore, the share accumulation shown in Figure 1.2 is maybemore important than the share accumulation shown in Figure 1.3. This iswhy traders who calculate the accumulation/distribution of shares on the

FIGURE 1.3 Larry Williams accumulation/distribution example #2.

Accumulation = GainSpread

× Volume

$10.1 − $9.9$10.5 − $9.8

× 100,000 shares = 28.571 shares

Accumulation as defined by Larry Williams is independent of the relative position ofthe gain within the high-low range.


26 VALUE IN TIME

basis of price spread during a day will also look at where the close endedcompared to the price spread. If the close ended near the high, they wouldconclude that the buying side had been stronger than the selling side.

The second potential problem is a lesser-known one: manipulation ofthe opening price sometimes exists. A strong opening price may attractbuyers, while a strong closing price may attract sellers. A fund that wantsto sell a large number of shares could therefore try to set a positive tone byforcing a strong opening.

In case of a different opening price, the Larry Williams accumula-tion/distribution formula yields quite a different result. Figure 1.4 showsthat if the opening price had been $10.5 instead of the open of $10.2 shownin Figure 1.2, the Larry Williams formula would have resulted in a distri-bution of 14,286 shares instead of the accumulation of 28,571 shares calcu-lated in Figure 1.2.

My message here is simply that in some cases, the opening price mightbe less valid as a parameter than the closing price. In general, methods thatuse end-of-day data could be more vulnerable to price manipulations, sincethey rely on fewer data points. The comments relative to Figures 1.2–1.4have not been backed by any research data. The interested reader shouldrefer to Larry Williams’ book Long-Term Secrets to Short-Term Trading.

FIGURE 1.4 Larry Williams accumulation/distribution example #3. This methodis very sensitive to opening price manipulations. In this case, an opening price at$10.5 instead of $10.2 results in a distribution of 14,286 shares instead of an accu-mulation of 28,571 shares.

Distribution = LossSpread

× Volume

$10.2 − $10.5$10.5 − $9.8

× 100,000 shares = −14.286 shares


Effective Volume 27

Do Not Trade Like My Grandmother

The classic technical tools that use the daily price changes and the dailyvolume are based on two assumptions regarding volume:

The first assumption is the price repartition of volume. These tools sup-pose that the volume is regularly distributed at every tick between the lowand the high prices of the day.

Let us take the example of the company Tellabs on September 20, 2006.On that day, during trading hours, about 11 million shares changed hands.The Larry Williams accumulation formula gives:

Opening price: $10.35

High price: $10.41

Low price: $10.05

Closing price: $10.29

Distribution = $10.29 − $10.35$10.41 − $10.05

× 11,000,000

Distribution = −1,833,333 shares

Based on this example, this means that at every tick between $10.05and $10.41, 297,000 shares were exchanged (see Figure 1.5a) (11,000,000divided by the difference between $10.05 and $10.41 plus 1 tick, since thesubtraction eliminates one of the two ticks at the extremities—the tick of

FIGURE 1.5a The linear volume repartition by price level. Linear volume reparti-tion by price level is the first simplification implied by technical tools that use dailyprice variations and volume.


28 VALUE IN TIME

FIGURE 1.5b The real volume repartition by price level. The real volume reparti-tion by price level is very different from the linear repartition.

the minute low price or the tick of the minute high price; between $10.05and $10.41, there are therefore 37 ticks and not 36 as a normal subtractionwould show).

In reality, the volume exchanged forms an irregular pattern, as shownin Figure 1.5b.

The second assumption is the time repartition of volume. The tradi-tional tools suppose that the volume is regularly distributed every minutebetween the open and the close of the trading day. In our example, thismeans that at every minute between 9:30 A.M. and 4:00 P.M., 11,000,000 ÷390 = 28,205 shares have been exchanged (see Figure 1.6a). In reality, thedaily buying and selling pattern clearly shows that volume came in spikes,and that a large proportion of the transactions occurred at the end of thetrading day (see Figure 1.6b).

The two assumptions made by traditional tools that use end-of-daydata to calculate the accumulation/distribution of shares are so drastic thatas a trader, I have little confidence in using these tools, although sometraders may find them reliable.

Indeed, one big characteristic of volume is that it comes in spikes. Typ-ically, you would see many transactions of 100 shares and then suddenly asingle transaction for 10,000 shares, or a set of transactions that would fillmany small orders. In short, volume has a very high volatility on a minute-by-minute level.

On the day-by-day level, too, volume could jump 100 percent from oneday to the next. This volatility is well known to traders whose mantra is“A price increase on a strong volume day is more valid than a price in-crease on a weak volume day.” This is experience talking, similar to my


Effective Volume 29

Tellabs: Linear Volume Historam by Time, 09/20/2006

Nu

mb

er o

f S

har

es

FIGURE 1.6a The linear volume repartition. The linear volume repartition by timelevel is the second simplification implied by traditional tools that use daily pricevariations and volume.

grandmother’s advice when making jam: “If you close the pot when thejam is hot rather than when it’s cold, the jam will keep longer.” She wastalking about what she knew from experience; she didn’t have to be knowl-edgeable about the microbiological phenomenon.

Most of today’s traders still act in the markets like my grandmotherdid in the kitchen. They understand little about the trading mechanism,and few really are aware of what their trading tools are calculating or whattheir limitations are.

Tellabs: Real Volume Historam by Time, 09/20/2006

Nu

mb

er o

f S

har

es

FIGURE 1.6b The real volume repartition by time level. The real volume reparti-tion by time level is very different from the linear volume repartition.


30 VALUE IN TIME

Do not trade like my grandmother made jam. You need to understandwhat is going on in the market that you trade. There are two ways to gainknowledge:

1. Invest time to study how the market works. I advise you to participatein one of the seminars that Dr. Alexander Elder gives, or even one of hisweeklong trading camps. Not only do these courses give you a workingstructure, but they also help you to feel how the market is moving. Youwill gain knowledge and confidence.

2. Use modern tools that will tell you what is happening.

EFFECTIVE VOLUME

To define the Effective Volume tool, I applied three modifications to LarryWilliams’ method. Let’s look at Figure 1.7, where we see the evolution ofthe price during one trading minute. We can see that the price evolvedamong five ticks: $10.00, $10.01, $10.02, $10.03, and $10.04. If we supposethat 5,000 shares were traded during that trading minute, Larry Williams’formula would tell us that the share accumulation is:

Accumulation = $10.03 − $10.01$10.04 − $10.00

× 5,000 = 2,500 shares

1. The first modification is to replace the open of the actual tradingminute with the close of the previous trading minute. This modifica-tion looks at the volume that has a real impact on the price from onetrading minute to the next. If the price increased, the Effective Volumewill be positive. Otherwise it will be negative.

FIGURE 1.7 Larry Williams accumulation/distribution.


Effective Volume 31

2. If the close from the previous minute is lower than the low of the actualminute, the second modification is to replace the low of the currentminute with the close of the previous minute. Similarly, if the closefrom the previous minute is higher than the high of the actual minute,then the Effective Volume method requires replacing the high of thecurrent minute with the close of the previous minute. In our example,if we suppose that the previous close was at $9.99, we then would needto use that previous close in our calculation instead of the open of theminute (see Figure 1.8). This modification would give us the followingnumber of shares being accumulated:

Accumulation = $10.03 − $9.99$10.04 − $9.99

× 5,000 = 4,000 shares

3. A last small adjustment still needs to be done: When applying the mod-ified Larry Williams formula on small time intervals, it is necessary toadd 0.01 to the top and to the bottom of the formula. The reason forthis is that when shares are distributed between, for example, a lowof $9.99 and a high of $10.04, it means that the shares traded at $9.99,$10.00, $10.01, $10.02, $10.03, and $10.04—six ticks instead of five (aswould have been the case with the Larry Williams formula that sim-ply subtracts $9.99 from $10.04, which would equal $0.05 or only fiveticks).

Applying the three small modifications to our example, the EffectiveVolume calculation gives the following results:

Accumulation = $10.03 − $9.99 + $0.01$10.04 − $9.99 + $0.01

× 5,000 = 4,167 shares

FIGURE 1.8 Modified Larry Williams accumulation/distribution.


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Effective Volume Formula

The Effective Volume is calculated by using the following formula, whichis a modified version of the Larry Williams accumulation/distribution (A/D)formula:

(Closei−1 − Closei) + PIHighi − Lowi + PI

× Volumei

where Closei−1 = Closing price corresponding to time interval(i − 1): TIi−1

Closei = Closing price corresponding to time interval i: TIi

Highi = Max (Highi, Closei−1)Lowi = Min (Lowi, Closei−1)

PI = Price interval (usually US $0.01)

As you can see, the Larry Williams formula was changed in three ways:

1. I replaced the open of the time interval with the close of the previoustime interval.

2. I adapted the high and the low of the current time interval to the valueof the close of the previous time interval.

3. I added the PI number, usually 0.01, to use the exact number of ticksbetween Closei−1 – Closei and between Highi – Lowi, and not just themathematical difference between these values.

The last column of Table 1.4 shows the calculated values for EffectiveVolume using the preceding definition. A simpler definition of the EffectiveVolume, as presented in the Foreword written by Dr. Elder, consists of con-sidering as “selling volume” the volume that pushed the price down fromone minute to the next and as “buying volume” the volume that pushed theprice up. Both definitions would produce similar results, because the maininfluential element of both definitions is to consider only the volume thatis linked to a price change from one minute to the next, while the pricevariations within one trading minute carry a relatively small importance.

The rightmost column shows the Effective Volume figures calculatedfor every trading minute. It is their cumulative value that gives the EffectiveVolume flow shown in Figure 1.9.

What Is the Effective Volume Flow?

The Effective Volume flow is the total value of cumulated Effective Vol-ume values from one minute to the next. It is interpreted similarly to any


Effective Volume 33

TABLE 1.4 Effective Volume Example

Open High Low Close Volume Effective Volume

9/25/06 14:27 $11.07 $11.07 $11.06 $11.06 5,889 09/25/06 14:26 $11.06 $11.06 $11.06 $11.06 200 09/25/06 14:25 $11.06 $11.07 $11.06 $11.06 28,335 09/25/06 14:24 $11.05 $11.06 $11.05 $11.06 18,131 18,1319/25/06 14:23 $11.04 $11.06 $11.03 $11.05 33,188 16,5949/25/06 14:22 $11.03 $11.04 $11.03 $11.04 3,298 09/25/06 14:21 $11.02 $11.04 $11.02 $11.04 29,658 29,6589/25/06 14:20 $11.02 $11.02 $11.02 $11.02 17,825 09/25/06 14:19 $11.01 $11.02 $11.01 $11.02 11,351 09/25/06 14:18 $11.02 $11.02 $11.02 $11.02 40,889 09/25/06 14:17 $11.04 $11.04 $11.01 $11.02 14,015 −10,5119/25/06 14:16 $11.05 $11.06 $11.04 $11.04 13,802 −13,8029/25/06 14:15 $11.06 $11.06 $11.05 $11.06 32,536 09/25/06 14:14 $11.07 $11.08 $11.06 $11.06 16,399 −10,9339/25/06 14:13 $11.07 $11.08 $11.07 $11.07 20,041 0

other indicator that gives a general view of the accumulation or distribu-tion of shares (see Figure 1.9). Its interpretation is straightforward: Effec-tive Volume that is trending up means accumulation (buying); if it is trend-ing down, that means distribution (selling). Effective Volume sometimesprecedes price and sometimes follows price. We will study later how tointerpret its movements compared to the price movements.

Why Does the Effective Volume Not Use the FirstMinute of Trading?

The Effective Volume method requires ignoring the first minute of a trad-ing day, because the volume exchanged during the first minute of tradingrelates to overnight news and overnight orders. Traders who place theirtrades before the opening are usually not professional traders. Large play-ers such as institutions do not react on news, but instead carefully plantheir entries and exits. I therefore believe that since the main objective ofEffective Volume is to monitor large players, it is better to avoid the firstminute of trading.

Why Take the Close of the Previous Minute?

It is also important to take the close of the previous minute instead ofthe open of the actual minute. The reason is that if the open is different,it means that the last transaction that ended the previous trading minute


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Tellabs: Total Effective Volume Flow, 09/20/2006 (in ,000 shares)

Tellabs Share Price, 09/20/2006

FIGURE 1.9 The Effective Volume flow and the price pattern. The Effective Vol-ume flow shows the accumulation/distribution trend. It is computed by cumulatingthe Effective Volume calculated at every trading minute. We can see in this examplethat the Effective Volume trend is consistent with the price trend during the tradingday of September 20, 2006.

exhausted either the bid or the ask, forcing the next transaction to openthe next trading minute at a different price. This indicates the movementof the supply/demand balance. Indeed, if we start a new trading minute ata higher price, we know that the previous transaction took out the ask, andthat the next transaction is a buying transaction that could not meet a sellerat the previous ask, and therefore forced the ask up.

How Can We Monitor the Movementsof Large Players?

In order to follow the tactical moves of large players, we need to sepa-rate the Effective Volume into two groups: the large players and the smallplayers. A separation volume separates these groups. This means that forevery trading minute we will examine the size of the Effective Volume thathas been exchanged during that time interval. If the size of the exchanged


Effective Volume 35

Effective Volume is higher than the separation volume, then we put thisEffective Volume into the group of the Large Effective Volume. Otherwise,we put it into the group of the Small Effective Volume (I explain later inthe chapter how to define the separation volume). Obviously, the total Ef-fective Volume is the sum of the Large Effective Volume and the SmallEffective Volume.

The Large Effective Volume separation is easier to understand withgraphs. Figure 1.10 shows the price evolution during the last trading hourfor the company Tellabs on September 20, 2006. Figure 1.11 shows the cor-responding volume during the last trading hour.

The first step is to calculate the Effective Volume. Figure 1.12 showsthe total volume from which only the volume that corresponds to priceinflections was kept. Figure 1.13 shows the total Effective Volume. You canalready visually notice that the Effective Volume represents only about halfof the total volume exchanged.

The second step was to then separate the Large Effective Volume(Figure 1.14a) from the Small Effective Volume (Figure 1.14b). Now, lookclosely at these two figures. If you count the number of bars in Figure 1.14a,you will notice that there are only 17 bars. Figure 1.14b, by contrast, showsa total of 26 vertical bars, but these 26 bars are shorter than the bars inFigure 1.14a. The bars in Figure 1.14b represent the volume exchangedby small players, while the bars in Figure 1.14a represent the volume ex-changed by large players.

Now, if you take a pair of scissors and cut out the vertical bars of Fig-ure 1.14a, adding them end to end, you will create a very high vertical bar.

Tellabs: Price of Last Trading Hour, 09/20/2006

FIGURE 1.10 Tellabs closing price of last hour of trading on September 20, 2006.The chart shows an example of the minute-by-minute closing price bars for the lasttrading hour.


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Tellabs: Total Volume of Last Trading Hour, 09/20/2006

FIGURE 1.11 Tellabs total volume of last hour of trading. The chart shows an ex-ample of the minute-by-minute volume bars for the last trading hour for the companyTellabs on September 20, 2006.

The height of this vertical bar represents the sum of all the volume ex-changed by large players during the last hour of trading, and that is respon-sible for a price inflection.

If you do the same with the small players, you will create a secondvertical bar by adding up all the bars of Figure 1.14b. The height of this sec-ond vertical bar represents the sum of all the volume exchanged by small

Tellabs: Total Volume Corresponding to Price Inflectionsof Last Trading Hour, 09/20/2006

FIGURE 1.12 Total volume corresponding to price inflections. The first step isto consider only those trading minutes for which there was a price change betweenthe previous minute and the actual minute.


Effective Volume 37

Tellabs: Total Effective Volume of Last Trading Hour, 09/20/2006

FIGURE 1.13 Total Effective Volume.

Tellabs: Large Effective Volume of Last Trading Hour, 09/20/2006

FIGURE 1.14a Large Effective Volume.

Tellabs: Small Efffective Volume of Last Trading Hour, 09/20/2006

FIGURE 1.14b Small Effective Volume.


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players. You will then notice that this second vertical bar has the sameheight as the first one.

The separation volume between small and large players during a fixedanalysis period (one trading day, for example) is defined as the one-minutevolume that divides all the players into two groups (large and small) insuch a way that the volume of shares exchanged during that fixed periodbecomes equal (or as equal as possible) between the two groups.

Since the Effective Volume method builds two groups of players withthe same number of shares, we can say that each group has the same pur-chasing or selling power as the other. The difference between the twogroups is the determination or the overall trend that they are able to forceon the price.

Let’s have a look at Figures 1.15a and 1.15b. Which is easier to see: thetrend of arrows in Figure 1.15a or in 1.15b? The difference between the twofigures is that in Figure 1.15b I have erased all the smaller arrows. Withoutthe noise generated by smaller arrows, the general trend is easier to graspat a glance.

Playground Analogy

To better understand the Effective Volume concept and its analyticalpower, let’s imagine schoolchildren aged six through 15 playing on a

FIGURE 1.15a Large and small arrows. It is difficult to see the overall trend be-cause of the noise generated by the smaller arrows.


Effective Volume 39

FIGURE 1.15b Large arrows only. The overall trend direction is clearer after thesmaller arrows are eliminated.

playground in winter. These kids are instructed to throw snowballs at twoChinese gongs that are placed in the center of the playground. The firstgong is called “Long” and the second is called “Short.” The goal of the kidsis to make as much noise as possible. The two gongs make distinct sounds,and the stronger the throw, the louder the sound.

Now, the goal of the teachers is to find out which gong produces thelouder sound. To complicate the game, the school principal has two bags ofmagic powder that he throws every quarter of an hour on the playground.These two types of powder are called “positive earning surprise” and “neg-ative earning surprise.” When the “positive earning surprise” powder isthrown, the “Long” gong gets bigger and the “Short” gong gets smaller. Theopposite happens when the “negative earning surprise” powder is thrown.

Obviously, the children aiming at the bigger gong will hit it more often.Therefore, it pays to study the size modification of the gong, and to studyhow many balls are hitting the gong. This is called the standard technical

analysis.Some professors prefer to study what the principal is doing, to see if

he takes from one bag more often than the other, to see if he brought moreof this or that powder, and so on. This is called fundamental analysis.

Obviously, if no child throws a snowball, no sound will be heard. There-fore, it is fair to say that the sounds originate from the children’s activity,just as price moves are a consequence of buying and selling activity. On theplayground, a six-year-old makes fewer snowballs, strikes with less power,and misses more often than a 15-year-old. It pays to look at the older kids’


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FIGURE 1.16 How volume is distributed.

behavior to guess the strength of the coming sound. Because we cannotwatch all the children (there could be 2,000 or 20,000), what we end up do-ing is looking at all of the coming snowballs and listening to the collectionof small and loud sounds made by each snowball. What we need is record-ing equipment that can both separate the coming balls by strength (size andvelocity) and analyze only the sounds of the stronger snowballs.

My studies have shown that on a regular basis, half of the volume ex-changed in one day has no impact on price movements (price inflections).These are the snowballs that miss the gong. The other half, which doeshave an impact on price changes, is called the Effective Volume. The Ef-fective Volume represents the balls that hit the gong.

My studies have also revealed that if you separate the Effective Volumefurther in two groups of identical size in terms of total number of shares,the group with larger volume will be responsible for most of the pricemovements (see Figure 1.16). As a rule of thumb, I can say that 25 percentof the volume involved in stock trading is responsible for 75 percent of theprice movements. I call this the Large Effective Volume, which is roughly25 percent of the total volume. Knowing whether the players responsiblefor this 25 percent are buying or selling is critical for successful trading.

PRACTICAL EXAMPLES OF EFFECTIVEVOLUME CALCULATIONS

The Effective Volume method can be used in three instances:

1. A flat or sideways trading range will probably break in the direction ofthe Large Effective Volume flow (a trading range is formed when theprice stays for some time at about the same value).


Effective Volume 41

2. A price uptrend with a negative Large Effective Volume flow indicatesthat problems may lie ahead.

3. A price downtrend with a positive Large Effective Volume flow indi-cates that the downtrend might not be sustainable.

Follow the Large Players Accumulating in aTrading Range

The most effective way of using the Effective Volume tool is to look foraccumulation by large players during a trading range. When I see suchan accumulation taking place during a few consecutive days, I purchaseshares. I then place a stop below that trading range and wait for the stockprice to rise. If large players stop buying before the price rises, I review thesituation.

Unfortunately, large players are not always right. They do not alwaysact on privileged information or after running sophisticated analysis. How-ever, if you have to buy a stock, you are certainly better off buying a stockthat is experiencing strong accumulation by large players, especially if yourtraditional tools indicate that the time is right to buy.

Here is a good example with the company Federated Investors Inc.(FII). The weekly traditional analysis chart shows in Figure 1.17 that atpoint A the stock price is back to a one-year-old support level. Since it ishitting the line of support, the probability for a reversal is high. The RelativeStrength Index (RSI) shows an oversold signal, indicating a possible cheapvalue compared to past prices.

The daily graph (Figure 1.18) shows that at point A we are in a tradingrange, but it is difficult to evaluate the best timing to purchase the stock: Isit better to buy at point A or at point B? Please note that during a tradingrange, both RSI and moving average convergence/divergence (MACD) areof little use.

The Effective Volume analysis clearly shows that during the tradingrange, one or more large players have been heavily accumulating (see Fig-ure 1.19). You can see that the difference between the buying and the sell-ing pressure by large players was greater than 1,000,000 shares during thelast 20 trading days leading to point B, or 50,000 shares per day. Know-ing that 800,000 shares on average are exchanged every day, the imbalancebetween buyers and sellers was 50,000/800,000 = 6.25%, which is by expe-rience very important.

A good question would be: How do we know that we need to buy atpoint B instead of buying at point A?

The answer is: We do not know! There is no way to know when thelarge players will be satisfied with the accumulated shares, and when (if


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FIGURE 1.17 Federated Investors weekly price graph.Source: Chart courtesy of StockCharts.com.

ever) the price will move up (we may suppose that the price will movein the direction of the Effective Volume accumulation, which is often thecase). My own rule of thumb is to buy during a trading range when theaccumulation by large players is constantly above 5 percent of the dailyvolume for a minimum of three consecutive trading days.

Figure 1.20 shows an increase in price from point B, which was nottriggered by any news. We may speculate that the large players decidedthat they had bought enough shares and that it was now time to push theprice up, attracting new buyers. (Please note the dating convention usedin the graphs: 07/10/06 means July 10, 2006. The same convention is usedthroughout the book.)

Another question that you may ask is “When do I need to sell?” Thisis a good question, but Effective Volume alone will not give a satisfactoryanswer. Indeed, finding a good selling point is much more difficult thanfinding a good entry.

You could sell for a few different reasons:

� Your target has been reached.� Large players have stopped buying.


Effective Volume 43

FIGURE 1.18 Federated Investors daily price graph.Source: Chart courtesy of StockCharts.com.

� The price has not moved for some time.� There has been bad news.� The price has become expensive compared to the underlying value of

the equity.

We will analyze the selling decision process in Chapters 5 and 6.

Follow the Insiders

Insiders have many reasons to sell, but only one reason to buy: to make aprofit.

We are going to see how to try to catch insiders’ moves. I do not con-sider company officers here, but rather indirect insiders, the ones whoby chance got access to restricted information (although they are not re-stricted from buying and selling shares).

What characterizes the difference between an insider and a largeplayer is the time span they use to buy or sell. A fund will need quite a


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FII: Effective Volume by Size20 days (in ,000 shares)

FII Share Price, 20 days

1,200

FIGURE 1.19 Federated Investors Effective Volume analysis.

long time to accumulate or distribute shares, while a typical insider may besatisfied with only a one-time purchase of a few thousand shares. Also, aninsider plays like an option investor: The option will expire at some time.Similarly, the insider’s advantage will expire at the publication of the news.The closer we are to the release date of the news, the smaller the insider’sadvantage is, because of the higher probability that other insiders will getthe information and will move ahead of him.

There are several types of news:

� News that is linked to the day-to-day business: the discovery of oil fora petroleum company, a new patent for a high-tech company, an ap-proval from the Food and Drug Administration for a pharmaceuticalcompany, a very large contract, and the like.


Effective Volume 45

FIGURE 1.20 Federated Investors Effective Volume analysis.

� Exceptional news: a Securities and Exchange Commission (SEC) in-quiry for any listed company, the purchase of another company, andso forth.

� The regular news: the good/bad quarterly earnings releases.

Day-to-Day Business Insider Let’s have a look at an example ofan insider move for PetroQuest Energy, Inc., a natural gas explorationcompany.

Looking at Figure 1.21, we can see two uptrends in Large Effective Vol-ume: uptrend A and uptrend B. Uptrend A is normal; large players buy thestock, pushing the share price up. However, uptrend B is more difficult toexplain, since the price is decreasing during the same period. Also, uptrendB in Large Effective Volume is stronger than uptrend A; during uptrend A,there was a net difference of 120,000 shares being bought. This difference


46 VALUE IN TIME

PetroQuest: Effective Volume bySize 10 days (in ,000 shares)

PetroQuest Share Price, 10 days

FIGURE 1.21 PetroQuest: Effective Volume analysis.

pushed the price up from $9.3 to $9.9, or 6.5 percent. How can we explainthat during uptrend B the price declined by 3 percent while the net buyingby large players was about 170,000 shares? The price should have propor-tionally increased by another 9 percent.

The B arrow of Figure 1.21 shows that some lucky investor bought170,000 shares at an average price of $9.9, to see the price increase to over$12 in a matter of two days, as shown in Figure 1.22. This is a no-riskprofit of more than $357,000. This is not a hedge fund or an institutionalinvestor—just a standard information leak.

The PetroQuest 8-K filing on January 27, 2006, stated:

On January 27, 2006, PetroQuest Energy, Inc. (the ‘Company’)

issued a press release announcing production and estimated

proven reserves results for the year ended December 31, 2005. In


Effective Volume 47

PetroQuest Share Price, 10 days

FIGURE 1.22 PetroQuest: price spike.

addition, the Company provided 2006 production guidance, an up-

date of hedge transactions, and an overview of recent acquisition

and drilling activities.

The main reason I developed the Effective Volume analysis was that Iwas fed up with being the last to know when some news was coming intothe market.

When I returned home after a good working day, it often was too lateto profit from good news or to avoid losses if the news had been bad. I lostmoney more often than necessary on bad news that was already known bya few. I also sold too quickly before good news hit the market.

Today, I still miss some big moves, but with the Effective Volume tool,I gain more of an insider view. Most important, I now have some time toact before the news hits the market rather than just react to the news.

Earnings Leaks Let’s have a look at an earnings-related insider movefor Ariba, Inc. (ARBA). Figure 1.23 shows normal behavior by large playerswho are pushing the price up during the A uptrend. By contrast, we canadmire the share accumulation that took place during the B price down-trend, just before the earnings release that triggered the price jump shownin Figure 1.24.

Of course I cannot say with 100 percent certainty that this is an ex-ample of insider trading, but those 100,000 shares that were purchased be-tween $7.5 and $8 (between January 19 and January 23) saw their valuejump to $9.5 overnight—a gain of about $175,000. Another lucky investor!

A Mistaken Signal Signals before earnings can be dangerous, how-ever, especially signals on the downside. Let’s have a look at the company


48 VALUE IN TIME

FIGURE 1.23 Ariba: Effective Volume analysis.

FIGURE 1.24 Ariba: price spike.


Effective Volume 49

Cognizant Technology Solutions. Cognizant is an interesting case study. Ilost money on it by following the large volumes. You can see from Figure1.25 that large players had been selling before May 2, 2006, while the priceduring the last trading day seemed to hold fast. At that time, I was lookingfor a short play, and thought I had found one. (A short play means bettingthat the price of the stock will go down, by borrowing shares and sellingthem on the market. The profit is made by repurchasing the stock later onfrom the market at a lower price and pocketing the difference.) I placedmy short order on May 2, just before the market closed. I have rarely lostmoney so quickly for not doing my homework (see Figure 1.26).

The reason for the misinterpretation was that we were very close to theday on which earnings would be announced. At such a time, some fundswould prefer to be out of the stock instead of taking a possible hit becauseof a bad earnings report. Very few large funds would increase their risk just

FIGURE 1.25 Cognizant: Effective Volume analysis.


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FIGURE 1.26 Cognizant: price spike.

one day before earnings are announced. In this case, the decrease of LargeEffective Volume just prior to the earnings release had risk reduction asits motive, and I should not have interpreted it as selling due to superiorinformation.

Had we seen large players increasing their positions just before theearnings release, it would have been a positive sign for the stock. Why?Because funds do not increase risk without reason, especially just before amajor earnings release; therefore, we could have concluded that the fundshad superior information indicating positive earnings.

Since that time, I have avoided placing shorts ahead of an earningsrelease.

Standard Technical Tools

This box explains a few standard technical tools, which are sometimes re-ferred to in the later chapters of this book. The experienced trader may justwant to skip to the next section.

All these are very well explained in Trading for a Living and Comeinto My Trading Room, both by Dr. Alexander Elder. If you do not knowthese indicators, I strongly advise you to study them. I will give some briefcomments about these indicators.

Moving Averages: A moving average is a measure of the average pricethat people have been ready to pay for a stock during a period of time. Itis the consensus value of the stock over a short-term or longer-term period(depending on whether you are averaging on 20, 50, or 200 days). Theprice may stay above or below the average for a considerable time, drawingan uptrend or a downtrend. If you are in an uptrend, it pays to buy whenthe price is back down to its value line. If you are in a downtrend, it pays


Effective Volume 51

to sell when the price is back up to its value line. This means that tradingagainst the price trend is very difficult, because you have to fight against theconsensus opinion of the herd. Unless you are superhuman, it is frankly nota good idea to try to change the direction of a moving boat all by yourself. Amoving average will give you an indication of value compared to past priceswithin a trend. (See Come into My Trading Room, by Dr. Alexander Elder.)

Relative Strength Index (RSI): J. Welles Wilder developed this momentumoscillator. This indicator compares the recent price gains to recent pricelosses and converts the result into a number between 0 and 100. A numberbelow 30 indicates that the stock is oversold. Typical long downtrends cankeep an RSI signal below 30 for many days. The buy signal comes whenthe RSI moves back up over the 30 line, indicating that the stock is proba-bly changing its momentum to a new buying trend. Another buy signal isgenerated when the stock reaches a new bottom; a stronger RSI, however,indicates that the new push-down in price did not increase the average loss,and the RSI indicates a higher number for the gain/loss comparison. My ex-perience shows that the RSI is widely followed. I suspect that since it is easyto program, many automatic trading methods use this signal to enter orexit stocks. At the start of a new trend, the RSI will give you an indication ofvalue compared to past prices. (See Trading for a Living, by Dr. AlexanderElder.)

Moving Average Convergence/Divergence Histogram (MACDH):This momentum oscillator was developed by Gerald Appel. It comparesa fast and a slow moving average in order to detect whether the pricechange is quicker or slower than before. It compares the acceleration (rateof change) of the fast and the slow moving averages. If the acceleration ofthe fast moving average is higher than the acceleration of the slow movingaverage, this indicates a positive momentum in the price. (See TechnicalAnalysis: Power Tools for Active Investors, by Gerald Appel.)

Support/Resistance Lines: These lines are important. They indicate pricecongestions, or the price levels where many buy/sell decisions are taken.(See Trading for a Living, by Dr. Alexander Elder.)

TECHNICAL SECTION: HOW TO CALCULATETHE SEPARATION VOLUME

This section is for readers who want to know more about some of the tech-nical details of the Effective Volume method.


52 VALUE IN TIME

Fixed Separation Method

There are many ways to separate Large from Small Effective Volume. Youcould, for example, pick a number and categorize every volume that isabove this number as large volume. This separation method does not workwell, though. Indeed, since volatility is one of the main characteristics ofvolume, if you fix the separation volume at a specific number, on sometrading days you will end up having mainly Large Effective Volume, and onother trading days, mainly Small Effective Volume.

The reason for this is found in the behavior of large players. The buy-ing by large players is usually executed during trading ranges, when no-body else is really paying attention. During that time, it is difficult to buymore shares than the supply side can support without raising the price.This means that large players will adapt their buying and selling order sizeto what the market can give or take from them. This situation changes ev-ery day. Therefore, on some days, a large player who wants to accumulatewill buy more shares than on some other days. The consequence of thisfact is that we need to recalculate our separation volume every day.

Average Separation Method

The most obvious way to separate Large from Small Effective Volume isto calculate the per-minute average Effective Volume exchanged for all theminutes of the day where a price inflection was found. The volume abovethat average is called Large Effective Volume and the volume below it iscalled Small Effective Volume. Such a separation is represented in Figure1.27; I have used the Effective Volume of one trading day only (September20, 2006) for the company Tellabs.

For Figure 1.27, the separation between the Large and Small Effec-tive Volume is calculated as 25,951 shares, which is the average Effec-tive Volume. As a reference, in Figure 1.27 there are 260 vertical bars.(Each bar represents a trading minute with valid Effective Volume, re-gardless of whether negative or positive. Please note that since I am inter-ested only in the size of each bar, I turned positive all the negative Effec-tive Volume in Figure 1.27.) The total number of Effective Volume shareswas about 6,750,000 shares, compared to the total number of exchangedshares—about 11,000,000. We can see that the total Effective Volume rep-resents only about 50 percent of the total number of shares traded.

If we plot the Large and Small Effective Volume flow using the averageEffective Volume as separation volume, then we can see that the price pat-tern is following the large players’ pattern (see Figure 1.28). In general, wemay say that large players are the ones moving the price.


Effective Volume 53

Nu

mb

er o

f S

har

es

FIGURE 1.27 Tellabs one-day Effective Volume. On a sequential view of the ver-tical bars that correspond to the Effective Volume, it is difficult to see if the averageEffective Volume is a good separation between the Large and the Small EffectiveVolume.

Figure 1.28 represents the Large and Small Effective Volume flow forSeptember 20, 2006, using the average Effective Volume as separationvolume.

Let’s analyze this separation volume in more detail. If we come backto Figure 1.27 and sort the 260 vertical bars from the highest to the lowest,we obtain the very interesting results shown in Figure 1.29. What standsout immediately is that the total surface covered by the Large EffectiveVolume bars looks much bigger than the total surface covered by the SmallEffective Volume bars.

An analogy will help us to understand the importance of that discovery.Suppose that the Department of Transportation wants to assess whetherthe new speed limit regulation is well respected on a particular road. Onejunior engineer places a very precise radar system on the side of the road.After one week of measurements, he notices that the radar registers anaverage speed of 14 mph, compared to the 40 mph limit. He concludes thatthe speed limit is well respected. However, looking at the data, we noticethat among the vehicles that passed on that road, there were 90 bicyclesand 10 cars. The bicycles’ average speed was 10 mph, but the cars’ averagespeed was 50 mph. Now we see that the problem lies within the data: Thecars’ speed is much higher than the bicycles’ speed, and there are manymore bicycles than cars.

It is quite similar to the stock market: We need to cope with vol-ume volatility. The Internet is bringing more retail players to the market(more bicycles), and a growing number of funds are taking sizable posi-tions (faster cars). In real life, it would make sense to put the cars on thehighway and the bicycles on a cycling path. In the stock market, however,


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FIGURE 1.28 One-day Effective Volume separated by average size. The separa-tion of Effective Volume into Large and Small Volume, when done using the averagevolume by vertical bars, produces a pattern where the Large Effective Volume isclosely following the price pattern.

bicycles and cars compete for the same shares. This is where volatility wasborn. If you add to this volatility the fact that the recent decimalizationkilled market visibility, it is no wonder that the general public now believesthat markets are manipulated. Later in the book I will provide more detailson volatility (Chapter 3) and possible market manipulation (Chapter 4).

Equi-Power Separation Method

Remember that I define the Effective Volume as the volume responsiblefor a price change from one minute to the next. This means that it makesno difference whether the price increased by $0.01 or $0.02. In both cases,I consider the Effective Volume as buying volume. Therefore, every sharethat enters into the definition of Effective Volume has the same ability to


Effective Volume 55

FIGURE 1.29 Repartition of Effective Volume by size. When rearranging all thebars of Figure 1.27 by size, we notice that the Effective Volume separation betweenthe Large and the Small Effective Volume using the average separation method leavestoo many shares within the pool of the Large Effective Volume. In this example, 70percent of the shares belong to the large players group.

move the price up or down as any other share. If we separate all the Ef-fective Volume shares into two groups that include the same number ofshares, we have in theory two groups with the same intrinsic power tomove the price.

In our example in Figure 1.29, we know that the total number of Ef-fective Volume shares was 6,750,000 shares. Let’s count the shares fromthe left of Figure 1.29 to the right until we reach 50 percent of 6,750,000shares (i.e., 3,375,000). The calculation shows that we reach the 50 percentmidpoint with a separation number of 42,500 shares. This means that allthe Effective Volume sizes that are larger than 42,500 shares are labeledas large, and the rest are labeled as small. Figure 1.30 shows this newseparation.

Please also notice in Figure 1.30 that I have labeled as Large Volumethe volume corresponding to only 48 one-minute time intervals. This is outof the 260 one-minute time intervals that constitute the total number oftime intervals for which we had price inflections. In other words, these 48one-minute time intervals theoretically have the same power to move themarket as the remaining 212 one-minute time intervals, because these 48minutes include the same number of shares as the remaining 212 minutes.


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FIGURE 1.30 Equi-Power Effective Volume separation method. The Equi-PowerEffective Volume separation method labels an equivalent number of shares as Largeand Small Effective Volume. It allows for a more balanced separation where, in theory,each group of shares has the same power to move the price.

However, the difference between the two groups is that small playersare scattered, and therefore they deliver their power in a very diffuse way;it will have only a limited influence on the price. In contrast, the volumecorresponding to the large players will carry the colluding will of a fewlarge holders, which will have much more influence on the price pattern.Large holders will have a general tendency to deliver their purchasing orselling power in a dedicated way that will determine the price direction.

If we now come back to Figure 1.28 and adapt it using our new def-inition of the separation volume, we can see in Figure 1.31 quite a differ-ent pattern of behavior. In Figure 1.28, which uses an average separationmethod, we see that large players have more influence than small players.This is normal, since the large players group includes 70 percent of the Ef-fective Volume shares. However, in Figure 1.31, which uses the Equi-Powerseparation method (each group has 50 percent of the number of EffectiveVolume shares), we notice a much more balanced influence between largeand small players.

The interpretation of the graph issued using the Equi-Power separationmethod is as follows: If both large and small players show a well-balancedpattern, it means that no institution was active during the period of analy-sis. (The large players group is therefore the group of large retail players,


Effective Volume 57

FIGURE 1.31 One-day Effective Volume separated by midpoint size. Comparedto Figure 1.28, Figure 1.31 shows that the separation of Effective Volume into Largeand Small Effective Volume using the Equi-Power method produces a more balancedpattern for the repartition of influence of each group on the price evolution.

while the small players group is the group of small retail players.) How-ever, if the large players’ pattern is very different from the small players’pattern, this shows how institutions have been moving.

IMPROVE YOUR TRADING: DECIDE ON THEBIG PICTURE

The stock market is getting quite complex, with many different players us-ing a large variety of analytical tools and trading instruments. As a trader(retail or professional), you need to know what other traders are doing andwhen they are doing it.


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The great majority of traders are momentum players or trend follow-ers. This means that they are moving in and out of the market by followingothers’ decisions. We may call it herd behavior. Herd analysis is a conceptthat is easy to understand and easy to model through price-based technicalindicators (RSI, MACDH, trend indicators, etc.). However, indicators thatare based only on price usually give you information after the fact.

Tools that are based on the price/volume relationship tend to catch thebuy/sell decisions somewhat earlier, when these decisions are being spreadto the herd. These tools are more powerful than tools based only on price,because they combine unrelated data (volume and price are believed tobe unrelated) to strengthen the analysis. In the following section, I brieflystudy the tools that use a price/volume spread analysis, as well as the tickvolume analysis tools. I find these tools useful, but fuzzy in the sense thatyou can’t be sure whether the indicator tells what really happens in themarket. These indicators may be generally correct; otherwise, why wouldthey be used extensively?

The objective of the new tools that I present in this book (see Fig-ure 1.32) is to study the decisions of other traders before these decisionsare spread to the herd level. To move a herd of traders, two things musthappen:

1. The herd needs to be ready to be moved. It is impossible to move aherd that is not in a position to move. You therefore need to studythe position of the herd. This is the purpose of the Active Boundariesindicator presented in the next chapter.

2. You need trendsetters. These are a few key people who provokechange. You cannot see their move easily, except if you go down tothe tactical level. This is what the Effective Volume method and itsassociated Effective Ratio and divergence analysis methods are for.

FIGURE 1.32 The evolution of the technical tools.


Effective Volume 59

A COMPARISON WITH TRADITIONAL TOOLS

This section is very theoretical and reviews other well-known volume-based tools. You can skip it without compromising your understanding ofthe rest of the book.

Price-Based Indicators

When I started trading, I tried all the technical indicators that were avail-able on the market, with different time frames and settings. I discoveredthat it was quite easy to find an indicator that would justify any possibletrading decision. I also quickly found indicators that worked well for me:moving averages, RSI, MACDH, and support/resistance lines. These havebeen mentioned previously in this chapter.

Price/Volume-Based Indicators

The importance of a price/volume combination analysis has been under-stood for many years, so I will cite here only the work of a great pioneer ofthat analysis. Richard Wyckoff worked extensively on the price/volume re-lationship more than 80 years ago. A trader from the 1920s, Wyckoff wroteseveral books on the market, and eventually set up the Stock Market In-stitute in Phoenix, Arizona. At its core, Wyckoff’s work is based on theanalysis of trading ranges, determining when stocks are in basing, mark-down, distribution, or markup phases. Incorporated into these phases arethe ongoing shifts between “weak hands” (public ownership) and “compos-ite operators,” now commonly known as smart money.

There are several ways to use the combination of volume and priceend-of-day data:

� Weight the volume to the price spread for the day.� Weight the volume to the price change from the previous day.� Compare the day price/volume relationship to the previous day’s

price/volume relationship.� Use volume to weight other price-based indicators such as the RSI or

the MACDH.

These different variations follow a similar purpose: to determine the

behavior of traders at the decisional level. They are used to assess whethermore traders decided to buy or to sell that day, and to give an indication forthe next trading day. These tools help you to figure how the demand/supplybalance is working out at the global level. The idea is that all the actions andall the opinions of traders are in the volume, while the price will indicatethe direction of the movement.


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What You Really Need to Know

I often read in the literature that this or that indicator measures the sup-ply/demand equilibrium or the buy/sell equilibrium. Some will even usea still less precise term called bear/bull equilibrium. These terms are nothelpful, because they are difficult to define and therefore to measure.

Let’s look at a few general statements:

� When bulls are present or are getting stronger, the price should in-crease.

� When bears are getting stronger, the price should decrease.� When bulls are getting weaker, the price should decrease.� When bears are getting weaker, the price should increase.� When the price trends up and if you recognize that the supply/demand

equilibrium is moving in the direction of the bears, it means that thesmart money is moving out.

� The key point is to find where the herd is moving by studying thestrength of the buyers when the price reaches a top or the weakness ofsellers when the price reaches a bottom.

I think that most of these statements are meaningless and thus useless.You can find them all over many trading books because they make thingseasy to understand, but the underlying concepts are so imprecise that theonly way to explain them is by using a general market psychology type ofexplanation or supply/demand equilibrium.

Measuring the supply/demand equilibrium is an objective that is pub-licized by the majority of technical analysis books and tools. They wouldtypically say that this or that divergence between the signal and the priceindicates that bears or bulls are getting weak or strong.

The great majority of these authors transmit their trading experiencein terms of patterns (price, price/volume, etc.). They will explain the pat-tern in terms of equilibrium or in terms of force or weakness. Very fewcome out with a mathematical formula to model this or that pattern or tocatch the buyer/seller equilibrium. The reason is that formulas are imper-fect; on many occasions they do not work well. Even general patterns arenot sure bets (the head-and-shoulders pattern, the cup-and-handle pattern,etc.). Everybody agrees that markets are not perfect; this is the reason youneed a risk-management policy such as stop loss.

The problem of unpredictability is not in the markets, but it is in howwe interpret and measure the markets. As a trader, you need to better un-derstand how the market works and have tools to better measure marketmovements. Then your trading will progress. You will gain confidence in


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what you are doing and will place stops only when you are physically awayfrom the markets.

Do not trade like a grandmother, who follows only the well-knownpatterns. Grandmothers are, of course, usually right, but you will greatlyincrease your confidence after you understand and can measure the forcesthat are behind the formation of patterns.

Let’s revisit some more specific tools.

Force Index Force Index, which was invented by Dr. Alexander Elder,is best defined by taking Dr. Elder’s own words in his book Come into My

Trading Room:

Force Index helps identify turning points in any market by tying

together three essential pieces of information—the direction of price

movement, its extent, and volume. Price represents the consensus of

value among market participants. Volume reflects their level of com-

mitment, financial as well as emotional. Price reflects what people

think, and volume what they feel. Force Index links mass opinion

with mass emotion by asking three questions: Is the price going up

or down? How big is the change? How much volume did it take to

move the price?

It is very useful to measure the force of a move because strong

moves are more likely to continue than weak ones. Divergences be-

tween peaks and bottoms of prices of Force Index help nail impor-

tant turning points. Spikes of Force Index identify zones of mass

hysteria, where trends become exhausted. Here is the Force Index

formula:

Force Index = (Close today − Close yesterday) × Volume today

Then Dr. Elder continues with an explanation of how to use Force In-dex with price divergence:

Trend reversals do not have to come as a surprise; divergences be-

tween Force Index and price usually precede them. If the market is

trying to rally, but the peaks in Force Index are becoming lower, it is

a sign of weakness among the bulls. If a stock or a future is trying to

decline, but the bottoms in Force Index are becoming more shallow,

it is a sign of weakness among the bears.

As I do not take anything for granted, I was wondering how correctlythe multiplication of two different variables (price and volume) could leadto a meaningful result. Usually in physics, you would compare variables by


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dividing them, in order to see the influence of a change in the first variableon a change in the second variable. For example, if you drive 100 milesfor two hours, your speed would be the distance divided by the time, or 50miles per hour. Then why can we not adjust the Force Index formula andcall it the Weakness Index? (We know that the objective of both the ForceIndex and the Weakness Index is to measure the bulls/bears equilibrium.)

Weakness Index = Volume todayClose today − Close yesterday

Let’s take an example. If on day one, the price change is +10 cents ona volume of 100,000 shares, which is twice the average volume, but on thefollowing day, the price change is only +5 cents on a volume of 400,000shares, what could we conclude?

The Force Index formula would conclude that the market is going tomove higher, because the Force Index is increasing. In fact, the strength isdoubling: more buyers came in, and as a consequence the price continuedto move up.

Day one Force Index = +10 cents × 100,000 = 1,000,000

Day two Force Index = +5 cents × 400,000 = 2,000,000

The Weakness Index formula would conclude that the market is goingto move lower, because the Weakness Index is increasing. In fact, it saysthat to move the price up by one cent on day two, we needed eight timesmore shares than on day one, indicating an increasing weakness. Betweenday one and day two, it looks like the supply of shares is increasing and thatthe market will soon reverse down. (This calculation is commonly knownas the measure of price elasticity to volume.)

Day one Weakness Index = 100,000/10 cents = 10,000 shares/cent

Day two Weakness Index = 400,000/5 cents = 80,000 shares/cent

Both analyses are correct, because neither the Force Index nor theWeakness Index indicates if the price moved on the strength of demandor on the weakness of supply, which are very different. However, we sawbefore in this chapter that the buying/selling strengths are stronger thanthe strength of the demand/supply, because of the will difference. There-fore, I am inclined to say that the Force Index is a better indicator than theWeakness Index.


Effective Volume 63

Another difficulty in interpreting the Force Index lies in the mere mul-tiplication of price by volume. Indeed, because of its name, we may assumethat the Force Index measures the buyers’ strength in an increasing pricetrend and the sellers’ strength in a decreasing price trend. However, it alsoincludes the influence of the sellers’ strength in an increasing price trendand the buyers’ strength in a decreasing price trend. The problem is thatsince it multiplies price by volume, we have no certainty as to which ele-ment of the buy/sell balance is stronger. We may, however, suspect that itis the price direction that dictates the side to which the buy/sell balancewill tilt.

Volume Weighted by the Price Spread Other methods try to mea-sure the buy/sell balance during the day by weighting the volume withthe balance between (Close price − Open price) and (High price − Openprice). The idea is that this balance in prices is a good representation ofthe balance between buyers and sellers (volume balance). Unfortunately,this is a very incorrect assumption because of the large intraday volumevolatility—we all know how strong the volume is at the beginning and atthe end of the day.

Let’s have a look at professional players at the close of the trading day.Since many technical tools follow the closing price, professional playerswill put a lot of energy (volume) into a close that will favor their positions.End-of-day indicators are therefore easily manipulated by large players,and may be giving an incorrect view on the buyers/sellers equilibrium.

Let’s take one example that will show you the limitations of this typeof indicator. Suppose that a share price moves down 50 cents on 400,000shares that were exchanged during the trading day up until 30 minutes be-fore the close. During the last 30 minutes of trading, however, a large fundappears and pushes the price up 70 cents on only 100,000 shares. The pushis sudden and the supply of shares dries up, which results in a sudden priceincrease. It happens so quickly that sellers do not have time to offer newshares for sale, and as a result, the share price ends the day strongly at+20 cents.

All the end-of-day methods will consider that the price moved up 20cents on volume of 500,000 shares, while in fact the price was mostly downduring the day. A strong close would indeed normally attract buyers on thenext trading day, which is probably a requirement for the large fund to sellits shares at a good price.

In this case, we see that a tactical move by a large fund, because it wastaken at a key period of time (at the closing of the day), may induce othertraders to make strategic decisions that will not be to their advantage. Younow understand why there is a need for a tool that allows you to see tacticalmoves as well as the strategic ones.


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I believe that these methods that combine price and volume workfine when used with end-of-day data, but that they lose efficacy when youshorten the analysis period to 1 hour, 30 minutes, 15 minutes, 5 minutes,and finally 1 minute. These methods were indeed developed before deci-malization. Before decimalization, if you looked at intervals of one minute,you would hardly see any significant change in price. You would have tolook at longer time intervals such as 10 or 15 minutes to try to analyze thevolume/price relationship. Typically, you would use on the 10-minute anal-ysis period one of the methods used for the end-of-day analysis, such asthe on-balance volume method or the volume spread analysis method. I be-lieve that a 10-minute analysis period is about the shortest time frame forwhich you can use these methods.

On very small time intervals of one minute, these methods do not work,because they have been designed to model accumulation/distribution andsupply/demand at the decisional level of all the players, including largefunds. The one-minute time range is more suited for analyzing tacticalmoves than decisional moves.

When a large fund is accumulating, this accumulation takes place dur-ing many days. Therefore, a repeating pattern of accumulation using thesetraditional methods on end-of-day data has a good chance of catching thestrategic buying decisions of a large fund. This is especially true knowingthat accumulation is better executed by active buying (placing buy ordersat the market price) than by passive buying (placing limit buy orders, whichwait for sellers to come in). Indeed, large limit orders are more visible tothe market than market buy orders, and will invariably push other buyersto buy higher and sellers to retrieve their orders, waiting for better prices.As a position trader, you do not need to know more to make a successfultrading decision.

However, if you go down very close to the transactional level, you willquickly notice that these traditional methods no longer work well. Themain reason is that close to the transactions, you are facing tactical de-cisions and not strategic decisions. Tools that are used to model strategicdecisions perform very poorly for tactical decisions. A typical tactical de-cision would be, for example, to send small or midsize buy orders on aregular basis, as long as the price stays within a given range, and then senda quick sell order to attract more sellers and bring the price back into therequired range. You would continue this tactic until the supply of sharesdries up.

Traditional tools are very poor at capturing such tactical behaviors,because they encounter two important mathematical issues:

1. On very small time intervals of one minute, the price spread is also verysmall. It is so tiny that interpretation becomes extremely hazardous.


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Any type of volume can easily move the price up or down by one tick.At the level of the transaction, the market looks very erratic. Volumespikes would come on the bid or on the ask at what looks like stochas-tic, unpredictable moments.

2. At the level of the minute bar, price and volume exhibit a very differ-ent behavior: For example, you can imagine on the first bar that 5,000shares have been traded, pushing the price up two cents. Then, on thefollowing bar, imagine that 100,000 shares pushed the price down onecent, and then one minute bar later, that 200 shares pushed the priceup again two cents. The problem that must be tackled is the incredibledifference in volatility between price and volume. Price is fundamen-tally nonvolatile at the one-minute bar level, while volume is extremelyvolatile. Therefore, multiplying price by volume at the end-of-day levelcould still have some meaning, whereas multiplying price by volume atthe level of the trading minute is mathematical nonsense.

However, even if you can solve these issues, there is still the danger offocusing on the tactical movements instead of trying to grab the strategicmoves. What you then need to do is view these tactical moves in a dailyor weekly flow and then compare the flow to past flows to understand thestrategic moves.

It is like assembling a big puzzle with pieces coming from differentbags. You first need to sort the pieces before assembling them to get thecomplete image.

Tick Volume Analysis It is now clear that going down to the transac-tional level gives us a better chance of catching tactical moves by largeplayers. To use transactional data, a first tool was developed by DonWorden but later on extensively used and publicized by Laszlo Biriny. Thistool, which Biriny called the “Money Flow Index,” compares the amount oftrading on small price upticks with the amount on small downticks, withthe hypothesis that large-volume single trades are more important thansmall-volume trades.

This was true in the past, but less so today. According to research fromthe consulting firm Aite Group, at the end of 2006 the share of automatedcomputer trading following predefined algorithms approached one-third oftotal U.S. equities trading volume; this number is expected to rise to 53percent by the end of 2010.

These trades use algorithms that slice orders to intentionally hide theorder size, drip releasing orders to the exchange, and as a consequence in-crease order fragmentation. Therefore, the hypothesis on which this MoneyFlow Index tool is based is quickly challenged by market reality.


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A second set of tools counts orders executed at the ask as buying or-ders and orders executed at the bid as selling orders. The balance indicatesthe equilibrium between buyers and sellers. These tools are used in thesame way that the end-of-day accumulation/distribution methods are used:They give a broad understanding of the market direction (decisional level).

When working with transaction data, however, the problem is thattransactions occur randomly. Therefore, the only way to start measuringand comparing them is to link them to a specific time interval: You need toadd transactions into cumulative minute data. You then arrive at the minuteanalysis and the Effective Volume method.

WHAT WE LEARNED REGARDINGEFFECTIVE VOLUME

We started this chapter with a simple question: What are large players do-ing? We saw that the monitoring of the volume involved in small pricechanges from one trading minute to the next, which I defined as the Ef-fective Volume, is a very good tool for detecting tactical moves by insiders,institutional investors, and other large players. Effective Volume often al-lows us to detect future price changes. The Effective Volume tool is excel-lent for detecting trendsetters, but we will see in the next chapter that inorder to really monitor trends, the Effective Volume tool must be used inconjunction with the Active Boundaries tool.


C H A P T E R 2

Price and ValueThe Active Boundaries Indicator

T his chapter is about trends: why trends exist, how they are created,and how we can monitor them. Before studying the general themeof trends, it is first necessary to examine a central question: Why are

shares bought or sold?

BUY LOW

Every investor knows that it is easier to make money when you buy at thebottom than when you buy at or near the peak. As a consequence, in orderfor you to know if a stock is cheap, you need to determine what the valueof that stock is.

There are as many ways to define the value of a stock as there areinvestment strategies:

� One very common way for selecting stocks with good value is tochoose stocks that show a low price-earnings (P/E) ratio. Since in-vestors look at the future, you may elect to look at a forecast of nextyear’s P/E ratio. Typically, some industries maintain a P/E ratio of 10,while high-growth industries command a higher P/E ratio.

� A second way to find stocks with good value is to read the future earn-ings estimates issued by analysts.

� Still another way is to study the earnings surprise. This means that youcompare the real earnings published by the company to the analysts’

67


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FIGURE 2.1 Traditional methods to detect value: RSI, trend line, moving average,and support line.Source: Chart courtesy of StockCharts.com.

average estimate. In case of positive surprise, you may decide to buythe stock, since you could also expect positive surprises in followingquarters.

Before I came up with my own tools, I used to follow four types oftechnical signals that indicate when a stock is becoming cheap (see Fig-ure 2.1):

1. Support line. The support level is indicated by a horizontal line thatconnects price bottoms. It is said that if the price goes below its sup-port, it has the potential to go much lower, but if support holds, pricewill bounce up. This is why buying at support is a strategy followed bymany traders, who place a stop-loss limit order just below the supportline.

2. Trend line. A price trend line is generally a line that is drawn by con-necting tops or bottoms on charts. The trend line is the traders’ con-sensus value. If the price hovers above its trend line, we say that priceis above value; when it is hovering below, we say that it is below value.

3. Relative Strength Index (RSI). This indicator compares the recentprice gains to recent price losses and converts the result to a num-ber between 0 and 100. A value lower than 30 indicates an oversold


Price and Value 69

position, while a value higher than 70 indicates an overbought posi-tion. In fact, you can also say that the price has increased a lot and thestock is becoming overbought or the price has decreased a lot and itis becoming oversold. Traders would simply buy when the price is inoversold territory (I suspect that quite a few automatic trading systemsuse this indicator).

4. Moving average. A moving average (MA) of a price is used to smoothout the daily price variations in order to focus on the trend itself. Forexample, the 50-day simple moving average shown in Figure 2.1 is themean of the previous 50 days’ closing prices. At each new day, the mostrecent price is added to calculate the mean, while the price data of theoldest of the previous 50 days is dropped out. Linear or exponentialweighted moving averages use a mean of price data that have beenweighted to give more importance to more recent data. The interpreta-tion of the moving average is similar to the interpretation of the trendline. However, many traders use a combination of moving averages ondifferent time scales to assess the potential future value of a stock.For example, some short-term traders would buy stocks whose 20 MAcrosses above the 50 MA, while long-term investors would look at the50 MA crossing above the 200 MA.

TRADITIONAL MEASURE OF “CHEAP”

For an investor with a long-term view, value is best measured in terms ofgrowth, earnings, market strength, and the like. However, a trader who typ-ically has a shorter-term view will prefer to follow the price-based technicalindicators such as those discussed earlier. The striking difference betweenthe two approaches is that long-term investors look at the future earningsgrowth, while traders look at past price levels.

Who is right? My gut feeling is that future earnings growth expressesreal value. However, past price levels express the market’s interpretationat that time of the future earnings growth. Past price levels only representa speculation of what future earnings will be. Therefore, for traders to lookat past price levels in order to predict future prices is highly speculative.The trader who does this is just saying, “Yesterday, the stock was moreexpensive. Therefore, it is now comparatively cheap.” Obviously, this mea-sure of value does not give a clue as to whether tomorrow’s price will beeven cheaper.

We are not here to judge who is correct and who is not. An individualis sometimes an investor, sometimes a trader, and sometimes a speculator.But what an active trader, a speculator, and an investor have in common is


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that they all want to make a profit. They expect to make a profit. But is thisexpectation shared by others?

What most people tend to forget is that buying and selling a stockmainly means dealing with other people. When you buy a stock, it meansthat you consider it cheap. It also means that someone else (a seller) con-siders it expensive. This means that the concepts of “cheap” and “expen-sive” had better be measured against the group of traders who are activelyfollowing that specific stock, instead of against some other measure. Why?It’s simple: In the stock market we are not trading reality, but rather theperception of the reality. Therefore, instead of talking about “cheap” or“expensive,” we should talk about “expectation.”

The Expectation Concept

Indeed, we don’t really buy a stock because it is cheap or because it isa good stock. These reasons are rationalizations of the buying act. Some-times a trader buys a stock to cover an existing position. Even in this case,the trader would not buy the stock unless the share price is expected toincrease. Later on the trader expects to sell the position at a profit.

The stock market is not like a supermarket. In a supermarket you buybread because you want to eat that bread, not because you want to resellit to someone else at a higher price. If the price of the bread increases, foryour next purchase you will most likely turn to some other food or somecheaper store. In the stock market, you buy a stock because you want toresell it at a higher price. Your feeling about the expensiveness of the stock(its value) is intimately linked to your expectation to sell the stock higher.To meet that expectation, the price will need to increase, which itselfdepends on other traders’ expectations (you will need to find a buyer).

What is this expectation concept, then, and how can we measure it?Let’s consider an example. Suppose that the market for stock XYZ is com-posed of 500 shares, divided equally among five investors: Mary, John,Stewart, Ely, and Kris. These five investors have purchased their 100 sharesat different prices (see Table 2.1). Now, suppose that two other investors,Lilly and Edward, also want to buy 100 shares each. If the market price forthe XYZ share is at $10.5, Lilly and Edward simply have to find someonewho is willing to sell their 100 shares at that price.

Let’s suppose that both Lilly and Edward know the purchase price ofthe actual shareholders. With whom would they start bargaining? My guessis that Mary and John have no specific reasons to sell at the present price,since neither of them is showing a significant profit. However, any of theother three are good candidates, since they might be happy to take theirprofit.

Now, let’s look at Table 2.2, which shows a different situation inwhich Ely and Kris purchased their stocks at different prices from


Price and Value 71

TABLE 2.1 Shares Distribution Resulting in a High Average Profit

Shares Buy Price Profit

Mary 100 $10.8 −2.78%John 100 $10.0 5.00%Ely 100 $7.0 50.00%Stewart 100 $8.5 23.53%Kris 100 $9.0 16.67%Average profit 18.48%

If you take a group of shareholders, it is probable that those who arerunning the highest gains will sell their shares first (we suppose here amarket price of $10.5). Here, Ely, Stewart, and Kris will be more likely to sellthan Mary and John.

Table 2.1. I kept the same name for each investor, but we have to viewthis as a completely new situation. We see that Ely is in real trouble. Hehas incurred such a large paper loss that most likely he will not be willingto part with his shares and realize that loss. We say that such an investor islocked in.

The other change concerns Kris, who is now turning an 8.7 percentloss, maybe close to his stop-loss limit. Kris is not locked in, because hisloss is relatively small. Everyone knows that it is easier to take a smallloss than a large one. Kris is probably in the right state of mind to be aseller now.

Neither Mary nor John is ready to sell, because their profit or loss isstill small. Stewart might be willing to sell to take his profit.

The conclusion is that there are now only two candidates (Kris andStewart) who are ready to sell their shares to the two buyers.

Suppose now that Stewart did not buy at $8.5, but instead at $18. If youlook at Table 2.3, it is clear that Stewart has also become locked in due to

TABLE 2.2 Shares Distribution Resulting in a Small Average Loss


Mary 100 $10.8 −2.78%John 100 $10.0 5.00%Ely 100 $15.0 −30.00%Stewart 100 $8.5 23.53%Kris 100 $11.5 −8.70%Average profit −2.59%

We can see here that Ely became locked in because of his large paper loss,and Kris is ready to sell in order to avoid Ely’s fate and be locked in by alarger loss.


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TABLE 2.3 Shares Distribution Resulting in a High Average Loss


Mary 100 $10.8 −2.78%John 100 $10.0 5.00%Ely 100 $15.0 −30.00%Stewart 100 $18.0 −41.87%Kris 100 $11.5 −8.70%Average profit −15.63%

Only Kris is now ready to sell his shares. The others are either locked in ortoo close to their purchase price.

a large paper loss. The problem is that now the only possible selling candi-date is Kris, but we have two parties willing to buy: Lilly and Edward. Oneof the two parties will have to raise the offered price in order to increasethe paper gain of the other shareholders. Table 2.3 shows a supply problem:There are not enough shares potentially for sale at the $10.5 price.

There are two ways to attract sellers:

1. One of the investors could increase the offering price until, for exam-ple, John has a high enough profit to decide to sell.

2. Someone pushes the price down (by shorting and/or by selling smallquantities of stocks at the right time) and therefore pushes Mary to herstop-loss limit, forcing her to sell.

When reading this example, you might say that the market does notwork like this, because there are always some people ready to sell theirshares. If I have to raise the price by one cent, who cares? I will still getmy shares. This is true if you are a retail player, but funds think differently.If you are a billion-dollar fund (a midsize fund) and you want to invest amaximum of 1 percent of your portfolio in a single stock, this would meaninvesting up to $10 million in a single stock. If you want to buy $10 millionworth of shares of a $500 million capitalized company, you would end upowning 2 percent of the total issued shares of the company. For funds,shares availability is a critical issue.

What did we learn from this example? Three things:

1. If you know the exact profit situation of all the shareholders, you arein a much better position to properly time your trading activities (buyor sell your shares).

2. If you compare the average profits of Tables 2.1, 2.2, and 2.3, it seemsthat the higher the average profits (Table 2.1), the easier it will be


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to find a large supply of shares to buy. The lower the average profits(Table 2.3), the more difficult it will be to find cheap shares.

3. Shareholders who have just bought shares still have a high expectationof generating a profit; therefore, they will not sell until their expecta-tion is met or until they reach their maximum endurable loss undertheir risk management policy—if they have one.

We will come back in Chapter 4 to point 3, which tends to give us someinsight on how to measure the supply of shares.

Let’s examine points 1 and 2 again. It seems from these points that ifwe monitor the average profits of all the shareholders, we will have morechances to improve our timing for our trading activities, even if we do notknow the exact profit position of each shareholder.

Let’s come back to our XYZ company. This time, suppose that 20traders are involved. Suppose now that none of them is at present in-vested in the XYZ company, whose stock price is sitting at $10. If XYZ isa good company with good prospects, we may believe that the great ma-jority of these 20 traders will decide to buy the stock. We may rightfullybelieve that these traders will have positive expectations of the stock priceappreciating.

Now, suppose that one of the traders already bought XYZ at $5 onemonth ago. Do you believe that, today, this trader’s expectation regard-ing future price appreciation is the same as the expectations of the other19 traders? Probably not! That trader has been invested for a month. It isclear that, because he is sitting on a 100 percent profit, his expectationshave already been met. His expectations of a further price gain would besignificantly lower than the expectations of the other 19 traders. In otherwords, if you look at the average expectation of the 20 traders, that trader’sexpectation brings the average down.

Now, suppose that this group of 20 traders is the only active tradergroup that is authorized to trade the XYZ shares, but that, as a specialist,you are also authorized to trade XYZ, and you have access to all the transac-tions that the group of 20 traders performs. Couldn’t you make an excellentprofit on the stock by just tracking the average expectation of that tradergroup, buying when expectation is high and selling when it is low?

Return on Investment as a Measureof Expectation

This means that it pays to track expectation. We saw that the early trader’sexpectation was lower than that of the other traders. Just how low? We donot know, but certainly lower.


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We can say that the higher the profits traders are enjoying, the lowertheir expectations will be of getting further profits from their shares. Thepropensity to sell their shares will increase as their profits increase.

In mathematical terms, we say that a trader’s expectation at time t of afurther price increase is inversely proportional to the profit that this traderis enjoying at time t.

This means that, for example, if you already have a 50 percent profit,your expectation for a further profit increase is lower now than at the timeyou bought the stock, when you had 0 percent profit. Please note that fromnow on, I will use the term return on investment (ROI) to mean the profitor loss (expressed as a percentage) compared to the initial purchasing cost.

Float ROI Forgetting our 20 traders for the time being, suppose thattrading of XYZ is opened to the public. However, let’s now suppose thateach trader is authorized to own only a single share of our XYZ company.Let’s also suppose that XYZ has 100 million shares outstanding and readyto trade. This means that 100 million traders play that XYZ stock with theironly share. They will buy their single share, resell it later, and so on. Let’stake the hypothesis that ROI is reflecting expectations quite well!

We can then easily calculate at time t the average ROI of all 100 millionshares that were traded in the past. To do this, we calculate the ROI of eachshare, take the sum of those ROIs, and divide the sum by the total numberof shares (100 million). If you repeat the calculation at time t + 1, t + 2,and so on, you will end up with a visual picture of the evolution over timeof the ROI of the 100 million shares (see Figure 2.2a and b). I call this con-cept the Float ROI. It is the average ROI of all the shares that are availablefor trading.

FIGURE 2.2a Google share price.


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FIGURE 2.2b Google: Active Boundaries for 219 million shares. The UpperBoundary sets the limit at which price tends to reverse downward. The Lower Bound-ary sets the limit at which price tends to reverse upward.

Let’s analyze Figure 2.2a and b. Figure 2.2a shows the share price ofthe company Google during the nine-month period prior to September 1,2006. Notice the swings in the share price. These swings are rather wellcaptured in Figure 2.2b, which shows, for the total of the 219 million is-sued shares, the average ROI calculated at each trading minute. (You canimagine the number of calculations involved: It took 872 million mathemat-ical operations to build Figure 2.2b. Using mathematical optimizations, thistype of calculation can now be executed in a few seconds, but this was veryburdensome to calculate with the slow computers that existed just a fewyears ago.)

If you look at Figure 2.2b, you will see that I drew two horizontal lines.The upper line, which I call the Upper Boundary, joins as much as possibleconsecutive maxima in average ROI. The lower line, which I call the LowerBoundary, joins as much as possible minima in average ROI.

What Do These Lines Say? The Upper Boundary says that at this highlevel of collective ROI, the average expectation for a further price increaseis very low, and we may expect more profit taking to occur.

The Lower Boundary says that at this low level of collective ROI, theaverage expectation for a further price decrease is very low, and we mayexpect more bargain seekers.

I selected Google because of the high turnover of its total float: The219 million shares exchange hands every 34 trading days on average. Thisis very fast, compared to the standard turnover of around 90 to 180 trad-ing days. Google is a very actively traded company. This is the reason theaverage ROI on the float captures the price movements so well. On less


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actively traded companies, the average ROI on the float would not givesuch good results, because the number of shares belonging to activetraders is much lower than the total number of issued shares. Let’s examinewhat I mean by active traders.

Look for the Actively Traded Shares The most common measureof a company’s profitability is its earnings per share (EPS). The EPS is thencompared to the stock price using the price-earnings (P/E) ratio. To calcu-late the profit per share, it is best to use the fully diluted number of shares,including the shares that must be issued in the future when employee andmanagement options are exercised, or shares that are part of a convertibledebenture.

However, to calculate the liquidity, people use the available float,which is the number of shares that are issued and available for trading.Since insiders are restricted from selling their shares, and since institu-tional investors move rather slowly, the available float is smaller (some-times much smaller) than the number of issued shares. When large buy-ing/selling volume shows up, the price of very illiquid shares tends to movemore quickly than the price of very liquid shares.

In reality, I have noticed that the number of shares that are available fortrading at a certain point is much lower than what can be calculated in the-ory. Many shares are locked in by long-term investors or by funds. Locked-in shares will be sold either (1) on exceptional circumstances (such asnews that completely changes the long-term outlook for the company) or(2) slowly over time, at a pace that is much slower than the price swings.

This is why I use still another calculation, which is the Active Float.The Active Float is defined as the number of shares that are actively traded.The Active Float is smaller than the available float. This number of ac-tively traded shares is not a publicly available figure, and it must be read-justed on a yearly basis. My method for approximating the Active Float isdescribed next.

How Can We Evaluate the Active Float of a Company? First, wehave to recognize that there are many types of shareholders, all workingin different time spans. Let us separate them into two groups: investorsand traders. Usually, investors have a much longer investment time spanthan traders, who come in and out of stocks rather quickly. Now, there isno clear distinction between the two groups, but separating them in ourminds allows us to focus only on the traders.

Let us take a few hypotheses:

� The first hypothesis is that for most companies, the pool of active

traders is very stable over time. After studying a company, active


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traders will trade it on multiple occasions. Indeed, since it took a lotof time for them to read the company’s financial reports, to study com-petitors, and to analyze the market and growth perspectives, they willnaturally prefer to stay with a company that they feel they know verywell. In general, active traders follow the stock of the same companyfor years, while the price trend itself changes in shorter periods of timesuch as weeks or months. At a minimum, we can say that the pool ofactive traders for a given stock changes much more slowly than theprice cycle of the stock.

� The second hypothesis is that each single trader does not change his

trading strategy overnight. This means that all things being equal,traders will perform the same analysis and will react the same wayas they did before. Also, their endurance for loss or their joy whenrunning profits reflect their own personalities, which will not changeovernight. This means that the Active Float ROI will show very stablereversal points in time, because it is an inverted image of the averageexpectation of the active shareholders.

� The third hypothesis is that computers automatically generate a largepart of the trading that occurs in the stock market. Program trading

requires a long development time, with very slow adjustment cycles

compared to market swings. This means that for the same stock, analgorithm will make its consecutive automatic trading decision by fol-lowing the same programmed logic. The Active Float ROI will thusnicely catch this repetitive pattern generated by program trading.

Basing my reasoning on the last two hypotheses, I define the ActiveFloat as the number of shares that are necessary to give to the Active FloatROI pattern the most regular set of reversal points. This means that if thetotal float of a stock is 100 million shares, I first plot the Float ROI graphbased on these 100 million shares. I then plot another graph of a Float ROIbased on 90 million shares, another one on 80 million shares, and so ondown to 10 million shares. I then compare the graphs and take the one thatgives the most regular reversal pattern. What the hypotheses say is thatthis pattern will continue in the future as long as no major event hits thecompany. If a major event occurs that completely changes shareholders’expectations regarding the company’s future earnings, this pattern will nolonger be valid. Therefore, a new pattern will develop that will reflect thenew expectation of the active pool of shareholders.

Because the term ROI could be confused with the concept of returnon investment that is most known for a company investing in assets, andbecause the Active Float terminology could be confused with the availablefloat, from now on I will use the term Active Boundaries instead of theActive Float ROI terminology.


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FIGURE 2.3 Becton, Dickinson: share price evolution.

Figure 2.3 shows medical technology company Becton, Dickinson andCompany’s share price evolution in the 12 months ending September 1,2006. We see the stock price evolving in five phases:

1. Phase A: three months; slow price slide resulting in a 10 percentdecrease.

2. Phase B: 4.5 months; uptrend for a 30 percent gain.

3. Phase C: 2.5 months; 8 percent retracement.

4. Phase D: two months; trading range.

5. Phase E: three months; uptrend for a 20 percent gain.

As you can see, Figure 2.4 captures the stock price movements rathernicely. Let’s look at this in detail (I will show later how to calculate theActive Boundaries):

� Notice that the general Active Boundaries pattern moves between a−5 percent Lower Boundary and a 10 percent Upper Boundary. Be-cause the 10 percent average profit is twice as high in amplitude asthe −5 percent average loss, this indicates that the stock price is in along-term uptrend.

� The phase A downtrend is well captured between the Lower Boundaryand an intermediate Upper Boundary (Int. UB) that is located at a levelof 0 percent. Please note that if the Upper Boundary is close to or lowerthan 0 percent, this indicates a downtrend, since below 0 percent theaverage profit is negative. Also note that at the beginning of November2005, at point 1, there was a large price gap upward due to a good earn-ings release. This news changed the way that shareholders regarded


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FIGURE 2.4 Becton, Dickinson: Active Boundaries for an Active Float of 80 millionshares. The 80 million shares that constitute the Active Float for Becton, Dickinsoncapture the price reversals very well, allowing for a good evaluation of the value ofthe stock.

the company’s potential future profits. Phase A was broken at point 1,when the signal crossed over the intermediate Upper Boundary.

� Phase B forms a strong uptrend. The price gap upward is followed by asmall retracement until point 2. This retracement, which leaves us wellinside the positive average profit zone, indicates that a new uptrendis probably emerging. We can draw an intermediate Lower Boundary(Int. LB) starting at point 2, which will monitor the uptrend.

� Phase B was broken at point 3, where the new downtrend of phase Cstarts. This C downtrend continues until, at the start of phase D, weagain reach the long-term Lower Boundary.

� At the right of Figure 2.4, the last phase (phase E) has now reachedthe Upper Boundary, where active traders have historically judged thestock price as expensive. We may expect the price to reverse downfrom that point.

Figure 2.5 shows that taking 40 million shares to calculate the ActiveBoundaries does not allow us to nicely catch the different phases the sameway as was possible in Figure 2.4. The whole process of selecting the rightnumber of shares takes a few trials and requires past data for at least asix-month period.

At key turning points, such as when the Active Boundaries signal hitsthe Upper or the Lower Boundary, the Effective Volume analysis, which westudied in Chapter 1, will often show the direction of the next move.

For example, in the case of Figure 2.6, the end of downtrend A is sig-naled by the accumulation in shares preceding October 31, 2005.


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FIGURE 2.5 Becton, Dickinson: Active Boundaries for an Active Float of 40 millionshares.

FIGURE 2.6 Becton, Dickinson: Effective Volume analysis up to October 31, 2005.The combination of the Active Boundaries signal and the Effective Volume analysisoften shows the future direction of the price. In this case, the divergence of the largeplayers’ shares accumulation during the downtrend indicates that the downtrend iscoming to an end.


Price and Value 81

WHY DO TRENDS EXIST?

Trends exist because members of the group of active shareholders haveconcurrent levels of long-term expectation regarding the stocks’ futureprice movements, but divergent short-term expectations. It is the aver-age of their long-term expectation that helps to form a price trend, andit is the average of their short-term expectations that moves the stockprice within the trend’s boundaries. Active Boundaries, since they aremeasuring a range of expectations, are an excellent tool for monitoringa trend.

Let’s take the example of Tellabs (Figure 2.7a and b). Tellabs is a largetelecommunications equipment provider. Between July 2005 and May 2006,the share price gained 100 percent (Figure 2.7a). It is interesting to followit step by step, from point 1 to point 14.

In Figure 2.7b, points 1 and 2 define the Active Boundaries. Point 3confirms the Upper Boundary. You will notice that the price at point 3is higher than the price at point 1, even if they both command the samelevel of Active Boundaries. In both cases, active traders consider that theTellabs stock price is high and that in the short term they do not expect itto increase further.

Active Boundaries to Monitor Uptrends

Let’s examine points 6 and 7. These are continuous hits of the UpperBoundary without pulling back to the Lower Boundary. This is typical ofa strong uptrend. At point 6, the Active Boundaries indicator does not tell

FIGURE 2.7a Tellabs: price evolution.


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FIGURE 2.7b Tellabs: Active Boundaries. Active Boundaries monitor activetraders’ expectations. Using Active Boundaries is an excellent way to monitor trends,because trends are created by a combination of the long- and short-term expecta-tions of active traders.

whether the price will reverse down or will continue to move up. It just saysthat it is hitting the expensive Upper Boundary limit, a limit where tradersusually reverse their positions. Since they did not reverse position, we mayconclude that the trend is strong. At this stage, it is better to observe theLarge Effective Volume in order to assess the behavior of large players. Ifthe Large Effective Volume indicates profit taking, or even a lack of buyingby large players, we can conclude that the continuous price increase willsoon slow, stop, and possibly reverse down.

Points 9 to 11 also reflect the same strong uptrend character-ized by consecutive hits of the Active Boundaries signal to the UpperBoundary limit.

As you can see, the Active Boundaries indicator does not give buy andsell signals. It simply indicates when the expectation of active traders ishigh or low. When the average profit of the active traders is high, this meansthat their expectation for a further price increase is low. This indicates thatthe probability for the price uptrend to end is high. However, this does notguarantee that the price will decrease. In a strong uptrend such as the onebetween points 9 and 11, my strategy is to sell only when the large playersare no longer buying. Indeed, if large players are not buying (since the priceuptrend will attract profit takers), it means that retail players are the onlyones fueling the uptrend.

In Figure 2.8a and b, I represent the Effective Volume analysis, theprice evolution, and the Active Boundaries of Tellabs over the 80 trading


Price and Value 83

FIGURE 2.8a Tellabs: Effective Volume and price evolution.

FIGURE 2.8b Tellabs: Active Boundaries.


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days ending March 6, 2006. This allows a much better interpretation of whatis going on for the stock:

� From point 6, even though we are at the Upper Boundary, the LargeEffective Volume flow shows a strong accumulation, indicating thatselling is not warranted.

� From point 7, although the price has started to decline, the healthy ac-cumulation is continuing. In such a case, you can either sell to protectyour profits or decide to go on since large players are continuing theiraccumulation. If you sell, the probability that you will buy back later atpoint 8 is very limited, because at point 8, the signal slightly and quicklytouches the Lower Boundary before surging sharply due to a price gap.The rapidity of the move makes it unlikely that you will be able to catchthe opportunity. (Disclosure: I sold at point 7 and took my profit, buthad decided to buy at point 8 after the market closed. Unfortunately,the price gapped up the next day and continued up without me.)

� From point 9, the accumulation is increasing.� From point 10, even if the price continues its uptrend, the accumula-

tion by large players has stopped. You don’t have to be a rocket sci-entist to know that without fuel, the rocket will stop its ascent andeventually fall.

I have to clearly state that I do not believe that large players are wiserthan retail investors. I even find that many retail investors perform deeperresearch on a company before and after they invest than large players do.They do that because they are risking their hard-earned money and becausethey know how strong the competition is. The only advantage they can getover the competition is by doing research and using good risk managementtechniques.

Let’s come back to large players for a moment. Even if they are notnecessarily wiser than retail investors, large players must be followed veryclosely, because they are the ones providing market liquidity. We alreadysaw in the previous chapter that the Large Effective Volume flow detectsthe movements of large players, who often are responsible for startingtrends. It should also be clear that uptrends couldn’t be sustained if largeplayers were not involved.

In an uptrend, a pullback can be monitored by following the behaviorof large players. If the Active Boundaries signal hits the Upper Boundarywhile continuous accumulation by large players is taking place (as shownby the Large Effective Volume pattern), it indicates that the uptrend willcontinue.

As you see, it is the combination of the Active Boundaries and theEffective Volume that allows us to take advantage of pullbacks during


Price and Value 85

uptrends. The Active Boundaries indicator allows you to see the positionsof the active players. It is a static view of the market. The Effective Vol-ume gives you a view of the tactical decisions of large players. It offers adynamic view. What we will see in the next chapter is how to detect strate-gic decisions.

Is It Better to Trade Bounces and Pullbacksor Long-Term Trends?

When a stock is trading at $8.5 in September 2005 and at $17 in May 2006, itis evident in hindsight that it would have been wise to buy at $8.5 and sellat the top (a 100 percent gain in about nine months). The problem is thatat point 2 of Figure 2.7a and b, it is impossible to know that the stock has a100 percent gain potential.

The Active Boundaries do not help in any way to give you a clue aboutthe breadth of the trend. Active Boundaries do, however, help in two ways:

1. They enable you to detect value inside of a trend, especially in combi-nation with the Effective Volume.

2. They enable you to detect when a long-lasting trend is broken.

In Chapter 6, I revisit in detail the different trading strategies that couldbe planned using the various indicators presented throughout the book.However, if we try to play the bounces and pullbacks in Figure 2.7a and bwe could have the following scenario:

� We decide to invest at around $9.5, at point 4. Indeed, the previouspoints 1, 2, and 3 are utilized to set the Active Boundaries for the actualtrend.

� At point 5, there is a first unexpected pullback. Suppose that we arelucky enough for our stop loss not to be hit. The pullback to $8.8 couldhave very well hit a stop loss, since it was a decrease of 7.4 percentfrom our buying price.

� At point 6, we hit the Upper Boundary and sell at around $10.8 for a13.7 percent profit.

� We watch with great sorrow (since we just sold off) as the stock pricerises to point 7, but then it dips to the Lower Boundary of point 8. Thisis a very critical point, because if we miss the point 8 buy opportunity,we miss the trend from point 8 to point 11. Point 8 is very difficult tocatch, because the Active Boundaries signal only touches the LowerBoundary once and then jumps out.

� Suppose that we catch the buy signal at point 8. Without the EffectiveVolume signal to tell us to keep the stock for the uptrend from point 9


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to point 11, we would have sold at point 9 and missed the subsequentuptrend that would have given us a 17.6 percent gain: from $12.5 to$14.7. That is correct if we could have exactly guessed the point 11 sell,which is impossible to guess if we rely only on the Active Boundariesindicator. As a matter of fact, point 11 is really detectable only throughthe divergence analysis tool that is explained in Chapter 3, even if theEffective Volume analysis is already showing that we are reaching theend of the uptrend at point 11.

A Comment on Shorting

You may have already noticed that I do not talk about shorting. If thetool is so good as to detect when a stock is turning expensive, why notshort it? We could! Trading is a two-way street, and shorting is a respectablestrategy. At this point in the book, I believe that it is too early to introduceshorting through modern volume analysis. Chapter 7 will show shortingexamples, but I first need to walk you through other subjects, such as theprice/volume divergence analysis and the supply/demand equilibrium.

If we add to the previous gain the last 21 percent profit that was gen-erated by the uptrend starting at point 12 up to point 13, the whole pro-cess of timely buying and selling would have produced a 77.7 percent profit(without reinvesting the gains from the previous trades). Compared to the100 percent profit of the long-term holding, it is clear that long-term valueinvestors would have handsomely beat active traders, if they had guessedvalue and initial timing correctly.

What Are the Advantages and Disadvantagesof Swing Trading?

A swing trader typically catches short-term price trends that range from afew days to a few weeks. There are several advantages and disadvantagesto swing trading. Some of the disadvantages are:

� Swing trading involves more buy/sell decisions, which implies the pos-sibility of more mistakes, and also the increased costs of frequentmovements (commission and slippage). It is unrealistic to believe thateven with the best tools, we will be able to obtain the 77.7 percentprofit stated earlier. I consider it a good job when I grab 30 percent ofa long-term trend, and an exceptional job when I get 50 percent.

� Swing trading involves a lot more work and stress than long-term in-vesting does.


Price and Value 87

Some of the advantages of swing trading are:

� When you swing trade, you are invested in a specific stock only duringthe time of the swing. Therefore, if you calculate the profit you gener-ate for each day during which your money is invested, you will see thatmost often the return will be greater for swing trading than for long-term investment. In our example, we were invested only one-third ofthe time with swing trading compared to the length of time of a long-term investment strategy, but we produced more than three-quartersof the profit generated by that long-term strategy. If during the idle pe-riods we can invest our money in other positive swing trades, we couldbe better off than with long-term investing in a single stock.

� The second, lesser-known benefit lies in the lower risk implied byswing trading: Swing trading allows using risk management tech-niques that give good results, because the risk is fractioned intomultiple trades that are well controlled during the course of their life-times. Long-term investors typically allow for much larger intermedi-ate losses, because they know very well that they have bought valueand they are confident that, in the long term, value always prevails.These investors typically hold losing positions for years, and someeven die with their losing positions. I believe that I will one day inheritfrom my father real paper stock certificates of now-unknown Africanmining companies that he bought in the 1960s, companies that havelong since been out of business.

Active Boundaries may be used for any investment style. What is im-portant to understand is that you need to use the Active Boundaries tooldifferently depending on your investment style.

Investing is a very stressful game, because you are risking your hard-earned money to get future profits. When investors enter the market, theyknow that they may lose money. However, it is only when they actuallystart losing that things turn bad: “What could I have done with the moneythat I lost? How many hours, days, or weeks will I have to work to gain thatmoney back? Maybe I should risk more money to make up the loss.” Thesame types of emotions arise when you start making money: “Maybe I’ll beable to move into a nicer place. Maybe I’ll get the new car I wanted to buy.”Needless to say, this is not how you make rational investment decisions.The market does not care about your new car or even about the tradingposition that you just took. To be more specific, all the other players aretrying their best to grab your money, and believe me, they want to use theirprofit for a new car, too.

One of the main advantages of the Active Boundaries indicator is thatit will help you dissociate your personal objectives from what the market


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is telling you. This will allow you to make decisions based only on marketsignals. However, you must use it in a way that matches your investmentstyle.

Active Boundaries and Price Gaps

The gap down to point 12 in Figure 2.7a and b is interesting to point out,because it covers almost exactly the distance between the Upper and theLower Boundaries. It is comparable to the gap up from point 8 that alsocovers the distance between the Upper and Lower Boundaries. When willa gap in price push or not push the Active Boundaries signal beyond theUpper and Lower Boundaries? The answer relies on the definition of theUpper and Lower Boundaries and what they represent: They representthe limits of the value of the company in the eyes of the active traders.The stock price will tend to evolve between these limits.

A price gap is the result of news that came after the market closedfor the day. If the news is within the normal business development of thecompany (such as an earnings surprise), the active traders’ expectationwill not change, and the Active Boundaries signal will continue to fluctu-ate within the actual Upper and Lower Boundaries. However, if the newsis so strong (SEC inquiry for options back-dating, earnings restatement,etc.) that it changes the company’s future itself, then the expectation ofthe active traders will widely change, and the Upper and Lower Boundariesthemselves are likely to evolve.

Let’s take a look at a real-life example: IMAX Corporation (Figure 2.9aand b). IMAX is well known for its trademark large-screen movie theaters.

Figure 2.9a shows how the stock price reacted to negative news thatwas published by the company on August 8, 2006. IMAX had been putting

FIGURE 2.9a IMAX: price evolution.


Price and Value 89

FIGURE 2.9b IMAX: Active Boundaries. A significant price gap may result fromovernight news. If the news is important enough, active traders could change theiropinion about the company, which will result in a new set of Active Boundaries. Thisnew set will correspond to the new expectation of the active traders.

itself up for sale and just announced that it could not find any buyer at itstarget price, which was probably higher than $11 per share. The companyconcurrently announced an informal SEC inquiry related to the way rev-enue had been reported in the past. The speculative high prices were cutby 40 percent, and the Active Boundaries signal broke its Lower Bound-ary. Shareholders’ expectation of a potential share price appreciation hadchanged so strongly overnight as to form a new set of Active Boundaries.In other words, it is as if there had been an old IMAX and a new IMAX.

This change of expectation was due more to a very rapid rotationof shareholders on the bad news than to a change in the expectation ofthe previous shareholders. As we can see in Figure 2.10, about 20 millionshares changed hands during the three days following the August 8 news.This volume is close to the number of active traders as computed by the Ac-tive Float method. This means that the total pool of active traders changedwithin three days.

In conclusion, we can say that if a price gap pushes the Active Bound-aries signal far beyond the Upper Boundary or the Lower Boundary, thecompany is likely going through fundamental changes that force us to re-consider its real value.

Active Boundaries and Trend Reversals

Active Boundaries do not help much in detecting price trend reversals, be-cause we can detect reversals only when these reversals are strong enoughto pierce through the Lower Boundary. As can be seen in Figure 2.11a,


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FIGURE 2.10 IMAX: volume surge on price gap downward. Very heavy volumehappening in conjunction with a dramatic price change usually indicates that thecompany is going through a fundamental change.Source: Chart courtesy of StockCharts.com.

Tellabs reversed its trend from point 14, but it is only at point B that it wasnoticed, when the signal pierced through the Lower Boundary (see Figure2.11b). At point B, the strength of the downward movement was too strongfor it to be considered a mere correction. Do not forget that in the up-trend, we would already have sold at point 13, as explained with Figure 2.7aand b.

FIGURE 2.11a Tellabs: price evolution.


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FIGURE 2.11b Tellabs: Active Boundaries. The Active Boundaries signal is late atpointing out trend reversals. These reversals are shown when one of the boundariesis broken, usually on heavy volume.

Point B was followed by two consecutive lower highs (H1 and H2) thatshowed how weak buyers had become. On the Active Boundaries signal,if the signal reverses upward when it hits the Lower Boundary but after-ward reverses downward again before reaching the Upper Boundary, thisindicates a strong selling pressure.

Point C is interesting to analyze: We experience a steep drop in pricefrom $11 to $9, followed by a small trading range around $9 and then aslow climb back to $10. However, the Active Boundaries signal shows asteep drop from −10 percent to −25 percent followed by a quick reversalduring the trading range, and then a quick rise to +10 percent (the UpperBoundary).

This is very peculiar to the Active Boundaries indicator: It will mainlyhave short-term moves due to price variations and long-term moves due tothe much slower volume changes.

Price and Volume Changes Impact the Active Boundaries

The Active Boundaries movements are influenced by price and volumechanges:

� Price changes are responsible for the short-term Active Boundaries sig-nal variations.

� Volume changes are responsible for the long-term Active Boundariessignal variations.


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In the case of Tellabs, however, from the C gap downward and dur-ing the following five days, more than 70 million shares were exchanged(Figure 2.12), which is about half of the Active Float. (The Active Float isthe number of shares that are used to calculate the Active Boundaries in-dicator. For Tellabs, the Active Float was 150 million shares.) In this case,since the exchanged volume was very important, the Active Boundariessignal showed a steep change due to the combination of steep price andvolume changes.

Active Boundaries in Downtrends

Using the Active Boundaries indicator, downtrends can be monitored thesame way as we monitor uptrends. Meridian Resource Corporation (TMR)is a natural gas exploration company that in 2006 was experiencing a set ofdry holes and hence declining reserves. Figure 2.13a shows the stock pricedowntrend, while in Figure 2.13b both the Upper and the Lower Boundariesindicate the most probable reversal points in the downtrend. Once again,these reversal points are not pure buy or sell signals. They still need tobe combined with the Effective Volume signal, which indicates what largeplayers are doing at these critical points.

An easy rule of thumb that you can use to determine if the stock isin a long-term downtrend or in a long-term uptrend is to add the value of

FIGURE 2.12 Tellabs: volume surges on price gaps downward.Source: Chart courtesy of StockCharts.com.


Price and Value 93

FIGURE 2.13a Meridian Resource: price evolution.

FIGURE 2.13b Meridian Resource: Active Boundaries. Active Boundaries are veryuseful for the monitoring of downtrends. They must be used in conjunction with theEffective Volume tool in order to detect reversals within a trend.

the Upper Boundary to the value of the Lower Boundary. A positive re-sult means that, on average, active traders have positive returns, which istypical of uptrends. If the result is negative, the opposite is true.

In Figure 2.13b, the Upper Boundary is set at 10 percent, and the LowerBoundary is set at −22 percent. The sum is −12 percent, indicating a down-trend. The midpoint is −6 percent (midpoint = 10% − [(10% + 22%) ÷ 2]).

This midpoint figure is important, because it tells you the force you areup against. Indeed, if you decide to go long at the Lower Boundary level(points 1, 3, 7, and 12 in Figure 2.13b), you know that you have to fight a−6 percent downtrend that is working against you. Needless to say, you


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must be very confident about what you are doing. I would never purchasea stock that is in a downtrend, even at the Lower Boundary, except if largeplayers are signaling a coming trend change.

Trading on Active Boundaries

The price trend is up when (Upper Boundary + Lower Boundary) > 0.

The price trend is down when (Upper Boundary + Lower Boundary) < 0.

From this we can deduct two simple trading rules:

1. Go long when:� The long-term price trend is up,� The Active Boundaries signal is reaching the Lower Boundary, and� Large players are buying.

2. Sell when:� The Active Boundaries signal is reaching the Upper Boundary, and� Large players are selling.

How to Measure Your Profit Potential UsingActive Boundaries

Active Boundaries catch the upswings and downswings and give good in-dications about the potential trading profit that you can make by buyingat the Lower Boundary and selling at the Upper Boundary. This profit canbe roughly evaluated by looking at the spread between the Upper and theLower Boundaries. For example, Figure 2.13b shows that the spread is32 percent. You might therefore think that when buying at the LowerBoundary, if the signal later reaches the Upper Boundary, you will makea 32 percent profit.

This is unfortunately not always the case, but in reality it is still agood approximation. The profit could be higher or lower than 32 percent.Table 2.4 lists the trading profits that could have been made by buyingat the Lower Boundary and selling at the Upper Boundary of the ActiveBoundaries indicator for Meridian Resource, as shown in Figure 2.13b. Youcan see that buying at point 1 and selling at point 2 generated only 11 per-cent profit. The reason for this difference is that between point 1 and 2, theprice was generally trending down, even if at point 2 the price is slightlyhigher than what it was at point 1. In general it is easier to make a profit if


Price and Value 95

TABLE 2.4 Meridian Resource: Trading Profits from Long Trades

Buying Point Selling Point Buying Price Selling Price Real Profit

1 2 $5.7 $6.3 11%3 4 $3.9 $5.4 38%7 8 $3.5 $4.5 29%

you are long in an uptrend rather than short, because you do not have tofight against the trend.

Table 2.5 shows the profits made by shorting at the Upper Boundaryand covering at the Lower Boundary.

The Influence of the Active Float on the Spreadbetween the Upper and the Lower Boundaries

It is interesting to note that the size of the Active Float has a direct impacton the spread between the Upper and the Lower Boundaries. Indeed, thesmaller the Active Float, the smaller the spread between the Upper andthe Lower Boundaries. However, the smaller the Active Float, the morereversals you will have on the Active Boundaries signal, pushing you intoovertrading.

This is illustrated in Figure 2.14, which shows the Active Boundariessignal for an Active Float that is 6.5 times smaller than the Active Floatused in Figure 2.13b. We can see that the spread between Upper and LowerBoundaries fell to 20 percent from the 32 percent seen in Figure 2.13b. Wewill also notice that there are many more reversals in Figure 2.14. How-ever, what I find the most striking is that when we select a small num-ber of shares for the Active Float (compared to the total number of issuedshares), the Active Boundaries signal is then influenced only by short-termprice changes and completely ignores the long-term trend.

TABLE 2.5 Meridian Resource: Trading Profits from Short Trades

Buying Point Selling Point Buying Price Selling Price Real Profit

2 3 $6.3 $4.0 37%4 7 $5.4 $3.5 35%8 12 $4.5 $3.3 28%

Source: It is easier to make profit if you are long in an uptrend or short in adowntrend than the opposite. In the case of Meridian Resource, Table 2.5 shows aslightly higher profit than Table 2.4.


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FIGURE 2.14 Meridian Active Boundaries for an Active Float of 10 Million Shares.Source: Trend direction = Upper Boundary + Lower Boundary = 10% − 10% = 0%.

This can clearly be seen in both Figures 2.14 and 2.15, where the verysmall number of shares used for the Active Float gives a general trend of0 percent (the midpoint between the Upper Boundary and the LowerBoundary), compared to a real long-term downtrend of −6 percent shownin Figure 2.13b.

If we use a small number of shares for the Active Float, the ActiveBoundaries signal will miss the long-term trend direction.

Table 2.6 summarizes for the company Meridian the influence of thesize of the Active Float on the other parameters such as the profit potentialand the average number of days between trend reversals.

FIGURE 2.15 Meridian Active Boundaries for an Active Float of 5 Million Shares.Source: Trend direction = Upper Boundary + Lower Boundary = 6% − 6% = 0%.


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TABLE 2.6 Summary of the Influence of the Active Float Size on the ProfitPotential and the Trend Reversal Cycle

Active Float Active Float Turnover Profit Potential Trend Reversal

65 Million shares 99 days 32% 60 days30 Million shares 45 days 28% 56 days10 Million shares 15 days 20% 20 days5 Million shares 8 days 12% 12 days

For example, if we take the last line of Table 2.6, we can see that an Ac-tive Float of five million shares will turn over in eight days. This means thatin eight days, five million shares of Meridian Resource are exchanged. Inother words, after eight days, new active shareholders have replaced previ-ous active shareholders. The average profit of these new active sharehold-ers who own the last five million exchanged shares will therefore rapidlyfluctuate around 0 percent. Indeed, these new active shareholders will haveonly eight days to make a profit or a loss, and within eight days the pricetrend has no time to fully develop. Therefore, as shown in Table 2.6, thelower the Active Float, the shorter the trend reversals and the smaller yourprofit potential will be—although this lower profit can be repeated on moretrades. However, do not forget that in the case of Meridian, buying at theLower Boundary and selling at the Upper Boundary forces you to fightagainst the −6 percent downtrend. Table 2.6 shows that an Active Float offive million shares allows you a maximum profit of only 12 percent. Onceagain, it is much safer to trade in the direction of the price trend!

How to Set a Profit Target UsingActive Boundaries

Let’s examine how to set a profit target.Openwave Systems Inc. is a software provider for wireless communi-

cations. I traded the company on several occasions before its incredibledrop that started in April 2006, and Active Boundaries were very helpful inindicating the profit potential and setting price targets for my trades.

First, on the general price movement, we can clearly see in Figure2.16a that during the analysis period, the stock evolved within three dif-ferent trends, which were captured by three sets of Active Boundaries(Figure 2.16b):

1. The first trend (A) was a slow 2.75 percent uptrend that lasted overa long period of about 10 months. The 2.75 percent is the midpoint


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FIGURE 2.16a Openwave Systems: price evolution.

between the Upper Boundary value (12 percent) and the Lower Bound-ary value (−6.5 percent).

2. The downtrend (B) was very strong (−7.5 percent) and lasted onlythree months.

3. The new uptrend (C) that can be seen at the right of the graph is a6 percent uptrend.

Let’s look at Figure 2.16b. At point 1, on December 5, 2005, if you de-cide to invest, you have to give yourself a profit target within the range ofthe Upper Boundary value. At that time, the Lower Boundary (A) was setat −6.5 percent. The stock price for point 1 corresponding to this Lower

FIGURE 2.16b Openwave Systems: Active Boundaries. Active Boundaries allowyou to evaluate a realistic price target when investing in a swing trade.


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TABLE 2.7 Openwave Systems Target onDecember 5, 2005

Target to Upper Boundary $19.11Percentage increase to Upper Boundary 19.8%

Boundary was $15.95. This means that, on average, at point 1 the activeshareholders were losing 6.5 percent on their investment, and that the av-erage buying price of the active shareholders was 6.5 percent higher thanthe current price of $15.95:

Average buying price = $15.95 ÷ (1 − 0.065) = $17.06

At point 1, for the average profit to reach the Upper Boundary, we willhave to increase the average buying price by 12 percent:

Target price for the average price to reach the Upper Boundary

= $17.06 × (1 + 0.12) = $19.11 = Target when buying at $15.95

As shown in Table 2.7, if you can enter a trade at $15.95, this $19.11target price, if achieved, would give you a 19.8 percent gain. Of course, thereal price when we reach the Upper Boundary depends on the number ofshares exchanged between point 1 and point 2, but this target calculationgives you a good evaluation of your potential trading profit.

This potential profit must be compared to the potential maximum trad-ing loss. For example, if you fix a stop loss at −8 percent, the potential gainis 2.4 times the potential loss:

19.8% ÷ 8% = 2.4

At point 2, on January 9, 2006, if you decide to short the stock, youhave to give yourself a profit target within the range of the Lower Bound-ary value. At that time, the Upper Boundary (A) was set at 12 percent. Thestock price for point 2 corresponding to this Upper Boundary was $19.73.This means that, on average, at point 2 the active shareholders were earn-ing 12 percent on their investment, and that the average buying price of theactive shareholders was 12 percent lower than the current price of $19.73:

Average buying price = $19.73 × (1 − 0.12) = $17.36

At point 2, for the average profit to reach the Lower Boundary, we willhave to decrease the average buying price by 6.5 percent:

Target price for the average price to reach the Lower Boundary

= $17.36 × (1 − 6.5%) = $16.23 = Target when shorting at $19.73


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TABLE 2.8 Openwave Systems Target on January12, 2006

Target to Lower Boundary $16.23Percentage decrease to Lower Boundary −16.7%

As shown in Table 2.8, if you can short at $19.73, this $16.23 target pricewill give you a 16.7 percent gain.

This potential profit must be compared to the potential maximum trad-ing loss. For example, if you fix a stop loss at 8 percent, the potential gainis about twice the potential loss:

16.23% ÷ 8% = 2.03

You probably have noticed in this small example that investing in thedirection of the trend usually gives you a higher profit potential than in-vesting against the trend. In our example, going long in the uptrend offeredus a 19.8 percent profit potential, while shorting the reversal on the sameuptrend offered us only a 16.7 percent profit potential, because of the 2.75percent uptrend (A) that we needed to fight against when shorting.

How to Set the Active Boundaries

The Active Boundaries indicator is an oscillator whose natural tendencyis to return to zero. (An oscillator is a type of indicator that will oscillatearound a specific value, which is usually zero.)

Two forces trigger the movements of the Active Boundaries indica-tor: price and volume. Price is responsible for the quick movements: If theprice increases one cent, it affects the profit/loss linked to all the sharesthat were previously bought. Therefore, the average profit/loss will moveaccordingly. However, it is volume that produces the slow-moving reversalpattern.

Therefore, if the volume exchanged every day is high compared to theActive Float, then the volume change will have an important influence onthe Active Boundaries signal reversals. This is why Figures 2.14 and 2.15show a very jagged Active Boundaries signal: The Active Float used in Fig-ure 2.15 was five million shares, which is only about eight times the dailynumber of traded shares.

It is obvious that the float number used for the calculation is impor-tant, especially compared to the daily turnover (the number of shares ex-changed per day). For example, if the Active Float is set at 50 millionshares but the turnover is only 100,000 shares per day, it will take 500trading days to trade a number of shares that is identical to the Active


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Float. As a rule of thumb, the Active Float should take more than 30 trad-ing days but less than 90 trading days to turn over. These limits are set byexperience.

The 90-day limit implies that you will need stocks that trade a goodpart of their Active Float each day. Since 1 ÷ 90 = 1.1%, it means thatthe stock you select must see 1.1 percent of its Active Float exchangedevery day. For the company Openwave Systems, you therefore need a min-imum of 444,444 shares exchanged every day: 40 million ÷ 90 = 444,444shares.

The 30-day limit implies that you need an Active Float that is largeenough to incur stock movement cycles at least every 30 trading days. Byexperience, the more quickly the Active Float turns over, the more difficultit will be to use this Active Float signal for trading. The reason is that thepossible trend change must be confirmed by the Large Effective Volumeflow. However, in very actively traded stocks, the activity of large playersis partly masked by the traders’ activity, which makes the signals moredifficult to read.

When shares are trading very actively so that the turnover is very fast,this means one of two things:

1. The number of issued shares is low compared to the demand or the

supply. This means that the fast equilibrium moves between demandand supply will easily push the price up or down. A low number ofissued shares is often the cause of high price volatility.

2. The stock is in the hands of day traders. For example, Finisar Cor-poration, a maker of fiber-optic systems for telecommunications net-works, is trading its entire 308,000,000-share float in about 20 tradingdays, probably indicating a very active group of day traders.

Once an Active Float is fixed, it must stay fixed for a very long periodof time. It is only after one year, for example, that we adapt the Active Floatto reflect, for instance, stock splits, stock dilution, and the like.

GRANDMOTHERS ARE ALWAYS RIGHT!

Many expressions that traders use come from their long experience inthe market. I call this the “grandmother experience,” because grandmoth-ers have been right for centuries without always analyzing why. I will gothrough a few of these traditional grandmotherly sayings and try to explainthem through the lens of the Active Boundaries indicator.


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Stocks That Need to Rest

Stock message boards are full of stocks that “need to rest”:

� “After a climb, a stock needs to rest before climbing again.”� “After a downslide, a stock needs to rest before continuing the

decline.”

These statements can easily be explained by the Active Boundariesbehavior:

� After a climb, we reach the Upper Boundary, and the stock cannotmove up again in price. The upward movement will continue if theActive Boundaries indicator comes down, which naturally occurs dur-ing a price trading range, since the indicator tends to move back to 0.During this phase, new buyers come in with higher expectations thanshareholders who sold off. These new buyers expect the share priceto continue to rise. As soon as the stock price rises again out of thetrading range, this attracts still a new set of buyers who want to ridethe trend until it again reaches the Upper Boundary.

� After a down movement, we reach the Lower Boundary, and the stockcannot continue its down movement. The downward movement willcontinue if the Active Boundaries indicator comes up, which naturallyoccurs during a price trading range, since the indicator tends to moveback to 0. During this phase, new buyers come in with higher expec-tations than shareholders who sold off. The Active Boundaries indica-tor increases until a new wave of selling starts. This new wave forcesmany recent new buyers to cut their losses before they are locked inwith deeper losses. These new sellers fuel the price downtrend untilwe again reach the Lower Boundary.

Let’s take the example of Chico’s FAS, Inc. Chico’s FAS operates 799retail stores specializing in women’s clothing and accessories. In Septem-ber 2006, the company had a market capitalization of $3.5 billion with netannual profits around $200 million. The company enjoyed healthy growthfrom late 2004 until early 2006, increasing the number of its stores. Inlate February 2006, the company announced that its gross margin wouldslightly decline over the year due to marketing expenses. In August 2006,the company reported its first negative same-store sales in more than adecade.

This explains the price movements shown in Figure 2.17 and Figure2.18a. Notice the very long uptrend (A) and the long downtrend (B).


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FIGURE 2.17 Chico’s FAS: volume surge on price gap downward.Source: Chart courtesy of StockCharts.com.

You can see in Figure 2.18b that these two trends have been capturedby a set of Active Boundaries: set A for the uptrend A and set B for thedowntrend B.

The strength of uptrend A equals 6.5 percent, which is the midpointbetween the A Upper Boundary (23 percent) and the A Lower Boundary(−10 percent).

FIGURE 2.18a Chico’s FAS: price evolution. Trading ranges allow the pool ofactive shareholders to change. New shareholders come in with a higher expectationthan departing shareholders with regard to the future share price increase.


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FIGURE 2.18b Chico’s FAS: Active Boundaries. During a trading range, the ActiveBoundaries signal will trend back to the 0 percent equilibrium, making space for thenext move to the Upper or Lower Boundary.

The strength of downtrend B equals −11 percent, which is the midpointbetween the B Upper Boundary (−2 percent) and the B Lower Boundary(−20 percent). Note that downtrend B is much steeper than uptrend A,and is unsustainable. Hence, the Active Boundaries will soon need to beadapted to reflect a coming sideways trading or a price reversal.

The purpose of this example is to study in Figure 2.18a and b two trad-ing ranges, or periods of sideways trading: trading range A (TR-A), a three-and-a-half-month trading range from late November 2005 to early February2006, and a two-month trading range B (TR-B) during May and June 2006.

Trading range A (TR-A) happened after a very strong price surge thatsaw the price increase 50 percent, from $30 to $45. This very strong movebrought the Active Boundaries signal close to the Upper Boundary, indicat-ing that the traders’ expectation for a further price increase was at its low-est. At this position, you would expect a reversal to happen, with traderstaking their profits. However, the duration of the trading range had as aconsequence a turnover in the pool of active traders. Indeed, during thatperiod, more than 100 million shares changed hands. Since the Active Floatfor Chico’s is set at 120 million shares, we may assume that the majority ofthe active shares changed hands and went into the hands of traders with ahigher expectation for the price to continue to increase.

We can see in Figure 2.18b the consequence of the trading range:the new traders brought the average profit down close to 0 percent (seepoint 1), the point from which the expectation is neutral and from which anew “leg-up” can start.

A similar move appeared during trading range B (TR-B), in May andJune 2006. After a gap down of about 20 percent, the price stabilized for


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an entire month. During that period, new shareholders were looking for abargain entry price into a stock that seemed much cheaper than before.These new shareholders slowly replaced old shareholders. This sidewaysmovement alone brought the Active Boundaries signal close to the UpperBoundary, where expectation was very low, considering the fight againstthe downtrend. At the next negative news, the share price resumed itsdescent.

Panic Selling (the Falling Knife)

I often read the following types of sayings on message boards of stocksthat have experienced a very steep price decline: “This is panic selling; thestock will come back,” “It is the specialist getting cheap shares; do not fallinto the trap,” and “I will not give my shares away.” These are emotionalcomments from traders who are usually locked in and are trying to justifytheir positions.

When can we say that heavy selling is panic selling or is legitimateselling?

Let’s have a look again at the three large selling movements on Fig-ures 2.17 and 2.18a and b. These three movements (2, 4, and 6) showquick selling moves that stop at the Lower Boundary. The Active Bound-aries signal does not extend past the Lower Boundary, meaning that pro-fessional traders are looking for value. This is legitimate selling, becausethese professionals interrupt the selling trend once they recognize value atthe Lower Boundary.

Reliant Energy (Figure 2.19a and b) shows us a good example of panicselling (sometimes called capitulation) that was quickly followed by along new price uptrend. As you can see, the stock was experiencing a

FIGURE 2.19a Reliant Energy: price evolution in a panic selling move.


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FIGURE 2.19b Reliant Energy: Active Boundaries in a panic selling move.

healthy uptrend (A) until the price reversed down to the Lower Bound-ary, which was already low at −14 percent. From that point, the stockgapped down to −30 percent, which is unsustainable, except if investorsbelieve that the company will soon be bankrupt. The professional investorsquickly recognized the price bargain and pushed the stock into a newuptrend (B).

Some people have asked me if it is worth it to buy stocks whenthere is heavy selling. Grandmothers tell you “not to catch a falling knife.”The decision to buy into heavy selling depends on two factors: who istrading the stock and what the level of the value zone you are enter-ing into is. Of course, if the company’s existence is in doubt, do notstep in.

You may consider buying stocks that fall heavily through their LowerBoundary and reach average profit levels that are historically at their low-est, but do so only if the following three conditions are met:

1. The ratio of institutional investors to the total number of sharehold-ers should be greater than 1:2, because institutional investors have agood perception of the intrinsic value of the company and will heavilymove into the stock once they notice that the price is not reflecting thecompany value.

2. The drop must have locked in so many traders that the supply of shareswill have gone down to less than 10 percent of the total float (we willsee in Chapter 4 how to measure the change of supply of shares).

3. Large Effective Volume must clearly indicate that large players aremoving in.


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The Dead Cat Bounce

Have you ever heard of the dead cat bounce? I will not explain the originof this saying, but in trading terms, it means that if a long downtrend is be-ing reversed, some traders will announce that “it is just a dead cat bounce.”What they mean is that the future for the company is bleak, and the reversalin price will be short-lived. Bargain hunters are the ones who triggered thebounce, but the selling pressure on the price will continue and the down-trend will regain its momentum.

If you look again at Figures 2.17 and 2.18a and b, you can see that thereare three gaps down in price (2, 4, and 6) that are preceded by three smallbounces (1, 3, and 5). The first price bounce (1) does not qualify as a deadcat bounce since the downtrend is not yet established. At that point, wemay think that the market is going into a new up-leg.

The second price bounce (3) sends us to the newly redefined UpperBoundary, where the price could reverse down. This bounce can be nameda dead cat bounce since it is a false upside reversal that will fail and priceswill continue to fall further down to the Lower Boundary (4).

The third price bounce (5) also copies the pattern of a dead cat bouncesince it is followed by a lower low in price. You may note here that thelast gap down (6) is so strong both in price and in volume (see the veryhigh volume bar in the lower part of Figure 2.17) that it almost completelyexhausted the supply of shares to less than 1 percent of the total float.

Exuberance

It is normal for a group of traders to change their collective expectationon a stock. For example, after stellar earnings and a few upgrades, thecollective expectation will usually change. The stock price will gap up andnew Upper and Lower Boundaries will form.

However, a sudden price increase could happen as a result of grade-B news, an article in a popular magazine, or simply pure rumor. Duringextreme price increases, retail traders are overwhelmed by joy and con-gratulate each other on stock message boards. By experience I know thatexuberance attracts correction and that correction on exuberance is usu-ally violent. I therefore always find it more useful to measure the priceincrease using the Active Boundaries signal than by comparing the price topast price levels. Active Boundaries are very good for discovering points oftraders’ exuberance.

Stocks that are heavily followed by retail players could escape com-mon sense. An example of such a move is shown in Figure 2.20a and b. En-voy Communications Group, a Canadian company listed on the NASDAQ,specializes in retail branding in Europe and North America. Much could be


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FIGURE 2.20a Envoy Communications Group: price evolution duringexuberance.

written about this mismanaged company, whose share price lost 80 percentof its value between February 2004 and December 2005 (Figure 2.20a).

Let’s concentrate on the technical side. We can see in Figure 2.20b thatthe Active Boundaries captured the downtrend very well. The spread be-tween the Upper and the Lower Boundaries was about 40 percent, givinggood trading opportunities. The downtrend was very strong (−8 percent).

You can notice two points of exuberance, where the stock price in-creased so much that the Active Boundaries signal passed over the Up-per Boundary, indicating that traders did not expect the price to go muchhigher. You will notice that the second exuberance run (point 8) was

FIGURE 2.20b Envoy Communications Group: Active Boundaries during exuber-ance. Exuberance is mainly found in stocks that are traded by retail traders. Individ-uals indeed have no good reference regarding stock value, and can find themselvesquickly transfixed in their hopes for even higher gains.


Price and Value 109

extraordinarily strong, pushing to an average profit of 50 percent. This isimpossible to sustain; hence the following price decrease. This is typicalof stocks that are mainly traded by retail players who do not have a goodgrasp of how to ascertain value.

FOR MATH LOVERS: HOW TO CALCULATETHE ACTIVE BOUNDARIES

Let us consider that the total Active Float of a given stock is set (for ex-ample) at 80 million shares. The Active Boundaries indicator is simplythe average profit/loss of the last 80 million shares that have been traded.Since the minute-by-minute data stream tallies all the stock transactionsthat took place during one minute, for each of these transactions we canevaluate the profit/loss at any given time.

Indeed, we can calculate at every trading minute the average profit/losson the last fixed number of shares that were traded. If there is a 1 percentprice jump from one minute to the next, for example, the average profit ofall shares that are part of the Active Float will be largely affected—if theaverage profit was 5 percent, it will jump to 6 percent. However, if duringthat new trading minute only 1 percent of the Active Float was traded, thisamount of shares will have very little influence on the average profit/lossof the total number of active shareholders.

If the share price is stable within a trading range, this indicator willtrend back to 0 percent. For example, suppose that today the price is $4with an average profit of −10 percent. This means that on average theshares traded at $4.44 ($4.44 − $0.44 = $4.00). Now, suppose that thereis a news story that moves the price to $6 overnight. That will bring theaverage profit to +35 percent for the 80 million shares:

($6 − $4.44) ÷ $4.44 = 35%

However, if you have 10 million shares that change hands at $6, theaverage profit for the last 80 million shares will come down as indicated bythe following formula:

New profit (80 million shares) = 35% × 7/8 + 0% × 1/8 = 30.6%

The last purchased 10 million shares have a profit of 0 percent, whichmeans that the average profit will decrease from 35 percent to 30.6 percent,even if the price stays at $6.


110 VALUE IN TIME

Average Profit on a Company’s Shares Bought atDifferent Prices

If an investor buys shares of the same company X over two different peri-ods of time, that investor makes buying/selling decisions about X based onthe average profit on X. The procedure for average profit calculation is tofirst take the average purchasing price and then calculate the profit.

For example, if an investor bought 100 shares at $3 and 1,000 shares at$4, and if the current share price is $5, the average profit is calculated as:

Step 1: Average buying price = [(100 × $3) + (1,000 × $4)] ÷ 1,100 = $3.909

Step 2: Average profit = ($5 − $3.909) ÷ $3.909 = 27.9%

However, traders make their buy/sell decisions independently of othertraders. Therefore, there is no meaning in calculating an average price ofa portfolio of all the traders together. What we need to calculate is theprofit for each share and then calculate the average profit per share of thecompany X. In this case, that gives:

Step 1: Profit on 100 shares bought at $3: ($5 − $3) ÷ $3 = 66.7%

Step 2: Profit on 1,000 shares bought at $4: ($5 − $4) ÷ $4 = 25%

Step 3: Average profit: (66.7% × 100/1,100) + (25% × 1,000/1,100) = 28.8%

This average profit reflects the sum of all the individual decisions basedon each shareholder’s own profit, weighted by the number of shares ownedby these shareholders. In this case, we suppose that each shareholderbought shares at one specific time, and did not average up or down. Be-cause this is not always the case, the correct average profit is situated be-tween the two methods of calculation shown above. Both these methodsgive similar results in terms of Active Boundaries definition.

WHAT WE LEARNED REGARDINGACTIVE BOUNDARIES

Three hypotheses were made to define the Active Boundaries indicator:

1. The pool of active traders is stable.

2. Each single trader does not change his trading strategy or personalityovernight.


Price and Value 111

3. Program trading requires a long development time, with very slow ad-justment cycles compared to market swings.

The Active Boundaries tool indicates when a stock is cheap relative totraders’ expectations.

� It is a slow-moving oscillator on volume change.� It shows fast-moving variations due to price fluctuations.

It is easy to say that we should “buy low and sell high,” and it is tempt-ing to use the Active Boundaries as buy and sell indicators. However, wesaw that, when following the Active Boundaries indicator,

� Position traders will only expect profits that are not larger than the dis-tance between the Upper Boundary and the Lower Boundary, missingout on long-term trends.

� Long-term investors will have to go through pullbacks in order to getthe most out of the uptrend. The Active Boundaries indicator will nottell the long-term investor if the pullback is temporary or the start of anew downtrend.

The Active Boundaries indicator will simply indicate whether, for allthe traders, the share price is getting cheaper or more expensive.

Here are some more remarks on position trading using only the ActiveBoundaries indicator:

� Active Boundaries produce cheap and expensive signals that are notas intuitive as the price level can be. We will sometimes have to buy astock at a higher price than our previous selling price.

� When we sell at the first expensive signal, we could end up either miss-ing the next run or rushing to buy the stock again in the next run, be-cause we have the feeling that we are missing something. The ActiveBoundaries indicator will not help the emotionally weak trader. Sinceany trader is at some point emotionally weak, it is unwise to trade onthe basis of the Active Boundaries indicator only.

� It is rather easy for me to say that we should buy at the Lower Bound-ary. However, what tells us that the price will not go lower than theLower Boundary? Indeed, all the long uptrends have to come to an end.Likewise, long downtrends also have to end. This means that at somepoint, we will break through the Lower or the Upper Boundary. Thereis no way, using the Active Boundaries signal, to know whether wewill rebound or go through. Different signals are necessary: EffectiveVolume, the Effective Ratio (see Chapter 3), and divergence analysis.


112 VALUE IN TIME

� It is more difficult to earn money by betting against the price trend.� Price targets are set by the calculation of the distance to the Upper

Boundary or Lower Boundary, depending on whether you are long orshort.

Active Boundaries capture trends very well; to my mind, they providethe only rational method that explains why trends exist. The Active Bound-aries tool is excellent for monitoring trends.


C H A P T E R 3

When VolumeDiverges

from Price

I n Chapter 1, “Effective Volume: An Open Window into the Market,” welearned how to detect when trendsetters and large players are cominginto or getting out of a stock. Effective Volume is indeed very useful for

detecting if a price trading range is going to break to the upside or to thedownside.

In Chapter 2, “Price and Value: The Active Boundaries Indicator,” wesaw how to detect the market value of a stock. We also saw how to captureand monitor trends. We finally understood that both methods are comple-mentary and that each contributes in its own way to helping you makesound trading decisions.

In this chapter, we are going to measure the balance between the buy-ing and the selling forces. This will enable us to point out levels abovewhich the change of equilibrium will have a high probability of impact-ing the price. We will therefore be able to make decisions before pricechanges occur.

This is what this whole chapter is about: the introduction of a new toolfor monitoring trends that is complementary to the Active Boundaries tool.Indeed, long-term trends are often interrupted by small pullbacks beforeresuming and going on to new highs. In a pullback, you may be temptedto sell in order to protect your profits or to buy the stock because youbelieve that it will regain its upward momentum. By performing an analysisof relative strength between Effective Volume and price, you can get a verygood image of the strength that underlies the price movements. Most ofthe time, this analysis will tell you what you need to do. Since I developedthe divergence analysis tool, I have greatly relied on it to separate true

113


114 VALUE IN TIME

from false Effective Volume signals. The math is a little more complicatedthan what I used in previous chapters, but you will see that the results areamazing.

In this chapter, you will learn:

� The importance of measuring the demand/supply equilibrium in or-der to assess future price movements, and how to measure thisequilibrium.

� How to separate false from correct buy/sell signals.� The dynamic of a market that involves both large funds and retail

players.� The importance of volatility adjustments to produce the right signals.

If you are a fund manager, one ancillary benefit to this chapter is thatthe Effective Ratio tool presented will allow you to determine the num-ber of shares that you can possibly accumulate/distribute per day withouthaving an impact on the price.

EFFECTIVE VOLUME: TWO ARROWS FROMONE BOW

Before starting with the Divergence Analysis, we need to come back tothe Effective Volume concept. Remember that the Effective Volume is de-fined as the volume that is responsible for a small price change from onetrading minute to the next. In Chapter 1, I called this price change a priceinflection. I also showed in Chapter 1 that we can separate the Large Effec-tive Volume from the Small Effective Volume, which is helpful in detectingtrendsetters. The Effective Volume analysis therefore allows us to shoottwo arrows with the same bow: We can look at the general long-term trendof the total Effective Volume, or we can look at only the evolution of theLarge Effective Volume.

Let’s first examine how to use the general trend of the total EffectiveVolume such as that seen in Figure 3.1, which represents the Effective Vol-ume flow trend and the price trend for Meridian Resource Corporation. InFigure 3.1, I have identified four segments:

1. Segment A: The price is in a downtrend. This downtrend is confirmedby the downtrend in total Effective Volume flow.

2. Segment B: The price is in a trading range. This range is confirmed bythe flat total Effective Volume flow.


When Volume Diverges from Price 115

FIGURE 3.1 Meridian Resource, Effective Volume and price evolution.

3. Segment C: The price is in an uptrend. That uptrend is confirmed bythe uptrend in total Effective Volume flow. However, the total EffectiveVolume flow uptrend is much steeper than the price uptrend.

4. Segment D: The price uptrend continues, but the total Effective Vol-ume flow is in a downtrend.

What can we conclude from this example?

� In trend C: If detected early, the difference in steepness between theLarge Effective Volume flow trend and the price trend can give us agood warning of what is happening. Conclusion: We need to compare

trend strength.� In trend D: A pure divergence between the Large Effective Volume flow

trend and the price trend is important enough to get our attention. Con-clusion: We need to compare trend direction.


116 VALUE IN TIME

What is not shown in Figure 3.1 is that we also have to know if thedifference in trend strength between the Effective Volume flow trend andthe price trend is strong enough by historical standards. If you remember,one of the hypotheses of the Active Boundaries theory is that the sametraders and the same funds will play the same stock over and over again.The Active Boundaries pattern is formed because, all things being equalfrom one situation to the next, traders make the same decisions.

I believe that this is also true when analyzing the price/volume bal-ance. Indeed, the price will move up or down depending on how the de-mand/supply of shares is evolving. The role of the Effective Volume methodis precisely to measure the level of accumulation and distribution of shares.If we look at past Effective Volume accumulation/distribution patterns andcompare them to price patterns, we may eventually be able to say:

� The present accumulation/distribution trend is much stronger thanpast accumulation/distribution trends.

� In the past, when the accumulation/distribution reached such strength,the price started to increase/decrease.

� Therefore, we may conclude that the probability that the same pricemovements will occur in the near future is high.

The importance of measuring the strength of a trend clearly appearsin Figures 3.2 and 3.3, which represent the Effective Volume analysis forthe natural gas producer Chesapeake Energy (CHK) during 10 days and40 days, respectively. In Figure 3.2, we can see that the price trend is flat,but that the Large Effective Volume is trending up, indicating that the trad-ing range will probably break to the upside. Figure 3.3, however, shows adifferent picture: The last 10 days of trading (trading range B) are showingsomething that looks like a pause in the uptrend A that occurred prior tothe trading range. This means that we actually do not know at this point iftrading range B will break to the upside. The Large Effective Volume up-trend B, since it is much weaker than uptrend A, does not seem to be strongenough to move the price up.

If you look more closely at Figure 3.2, you will see that the accumula-tion on Effective Volume is showing that 800,000 shares were accumulatedduring the last 10 trading days. Is this significant or not? During the sameperiod, a total of 68 million shares have been exchanged. The Effective Vol-ume accumulation is just about 1.2 percent of that total. If you now com-pare the accumulation that took place during uptrend A in Figure 3.3, youcan see that four million shares were accumulated over 40 days, when a to-tal of 150 million shares were traded. This is approximately 2.7 percent. Is2.7 percent significant? It is more than 1.2 percent, but does this representsignificant accumulation?



FIGURE 3.2 Chesapeake Energy: 10-day Effective Volume analysis.

I would like to remind you of something important when you look atthe upper panel of Figure 3.3. What you see is not just a graph that trends upand then goes flat. It really represents the buying and selling by large play-ers. Do you notice that in the upper panel of Figure 3.3, the large players’uptrend A starts a few days before the price starts its move up? What hap-pened was that the fund simply started to accumulate stocks and changedthe demand/supply balance that was characteristic of the trading rangepreceding uptrend A. In order to attract more sellers, the buyers had toincrease the stock price. This is not insider trading. It is just funds buyinga cheap stock.

To see if this type of accumulation is abnormally significant, we needto compare it to past accumulations that occurred during past price trends.The historical analysis of the past behavior of large funds will give us ameasure of what is a normal accumulation and what is not.

We now understand that we need a tool that compares the EffectiveVolume trend strength to the price trend strength.


118 VALUE IN TIME

FIGURE 3.3 Chesapeake Energy: 40-day Effective Volume analysis. The EffectiveVolume analysis will often give different results depending on the time frame used. Itis preferable to use longer time frames. This allows us to see if the short-term trendis significant compared to the long-term one.

PRICE AND EFFECTIVE VOLUME TRENDS

Before starting with the comparative analysis of price and Effective Vol-ume trends, we first need to better understand how each of them movesindividually.

Price Trend

Let’s work on the concrete case of Darden Restaurants Inc. (DRI). Capital-ized at $9 billion, Darden Restaurants is a restaurant operator in the UnitedStates and Canada. The company operates restaurants under the namesRed Lobster, Olive Garden, Bahama Breeze, and others.



Figure 3.4 shows both the company share price (upper panel), as wellas the price change rate for the past 60 days (lower panel). Please notethat from now on I will call the price change rate the “price rate of change”since the terminology “rate of change” (ROC) is well known in traditionaltechnical analysis.

To understand if a trend is increasing, you need to take two points inthe trend and calculate their difference in terms of percentage. For exam-ple, if we take point A (which shows a price of $34.98) and point B (whichshows a price of $35.58), the difference between them in terms of percent-age is 1.7 percent. This is represented on the lower panel by point C. Thentake two other points, A′ and B′, whose distance is identical to the distanceseparating A and B, and calculate their difference as a percentage. Theresult is 9 percent and is shown as point C′ on the lower panel ofFigure 3.4. Obviously, 9 percent is higher than 1.7 percent. You can there-fore deduce that at point C′, the trend slope is more significant than at

FIGURE 3.4 Darden Restaurants: price compared to the price rate of change.


120 VALUE IN TIME

point C. Indeed, you can see that the A′B′ slope is much steeper than theAB slope.

The distance between point A and point B is called the analysis win-dow. In this case, I took 2,000 trading minutes as the analysis window,which is about 5.3 trading days. If you move that analysis window fromleft to right and calculate for each trading minute the difference in pricebetween the beginning of the analysis window and the end of the window,you get the graph represented in the lower panel of Figure 3.4.

As you may have noticed, there is a price gap between points A′ andB′. When we calculate the price rate of change, this price gap will influencethe calculations twice: when it enters into the analysis window (when B′

reaches it) and when it exits the analysis window (when A′ reaches it).This is clearly shown by the double gaps that appear on the lower panel ofFigure 3.4, which I have labeled “entry point of price gap into the analysiswindow” and “exit point of price gap from the analysis window.” We willsee later how to handle such gaps.

One advantage of the price rate of change is that it usually changesits trend before the price signal itself. Measuring the rate of change of theprice of a stock is similar to measuring the change of altitude of a climbingrocket. Assume that the rocket climbs from 1,000 feet to 2,000 feet in x

seconds, and then from 2,000 to 2,500 feet during the next x seconds. Youcan conclude that since its climbing altitude is reduced within the sameperiod of time (called the climbing speed), the rocket is in trouble and mayeventually come back to earth. The rate of change measures this change inclimbing speed. For the rocket, this rate of change is therefore decliningbefore the rocket itself starts falling down.

In Figure 3.5, I have depicted an exponential average function that Iapplied on the lower panel of Figure 3.4 in order to smooth it. The expo-nential average is a standard formula that smoothes curves. Compared toa straight average, the exponential average gives more significance to re-cent data than to older data. For the exponential average I usually use thesame number of days that have been used to define the analysis window.As we will see later, the length of the analysis window itself is adjusted tothe difference in volatility between volume and price.

The advantage of working with a smoothed curve is that with it wecan more easily detect trend changes. The main inconvenience is that thesmoothed signal is delayed compared to the original signal. However, thisdelay is often compensated by the fact that the price rate of change is mov-ing ahead of the original price signal. The net effect is therefore usuallyneutral.

Price averaging (50- or 200-day moving average) and the calculation ofthe price rate of change form the basic elements of price-based traditionaltechnical analysis tools.



FIGURE 3.5 Darden Restaurants: price rate of change smoothed with an expo-nential average function.

Effective Volume Trend

The same principle can be applied to the analysis of the rate of change ofthe Effective Volume flow. Let’s have a look at Figure 3.6, which representsthe total Effective Volume flow of Darden Restaurants for the past 60 days,and compare it to Figure 3.7, which separates the total Effective Volumeflow into the Large Effective Volume flow and the Small Effective Volumeflow. The main noticeable element of the two figures is that the total Ef-fective Volume flow in Figure 3.6 is almost a copy of the Large EffectiveVolume flow in Figure 3.7.

Now, if we look at Figure 3.8, we instantly see that the A, B, C, andD price trends do not always follow the corresponding Large EffectiveVolume flow trends of Figure 3.7. We can indeed see that trends C and

FIGURE 3.6 Darden Restaurants: total Effective Volume flow.


122 VALUE IN TIME

FIGURE 3.7 Darden Restaurants: Effective Volume flow separated by size.

D of both figures diverge. The following explanation is possible: At the endof price trend B the price was high enough. Large players ceased being netbuyers (trend C in Large Effective Volume is flat), and the price fell down(trend C in price) on its own weight due to lack of buying. Then, large play-ers started buying again (trend D in Large Effective Volume is increasingagain), which allowed the interruption of the price reversal and the forma-tion of the price trading range D.

As a reminder of Chapter 1, in which I revealed the procedure forcalculating the Large and the Small Effective Volume flow, we can see inTable 3.1 that the Effective Volume is the volume responsible for priceinflections. I have connected consecutive lines with either gray or blackarrows. The gray arrows represent positive price inflections, while theblack arrows represent negative price inflections. The last two columns ofTable 3.1 show that the Effective Volume is then separated into Large andSmall Effective Volume.

FIGURE 3.8 Darden Restaurants: stock price.


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124 VALUE IN TIME

TABLE 3.2 Darden Restaurants: 60 Days, Repartitionbetween Effective and Non-Effective Volume

Shares Percentage

Total Volume 80,529,000 100.0%Effective Volume 49,996,040 62.1%Non-Effective Volume 30,532,959 37.9%

In Table 3.2, we can see that for Darden Restaurants, the total Effec-tive Volume calculated during the past 60 trading days amounted to 62 per-cent of the total exchanged volume during that period. We can also see inTable 3.3 that the 49,996,040 shares that constitute the total number of Ef-fective Volume shares have been about evenly distributed between Largeand Small Effective Volume. As a matter of fact, the separation volume be-tween the large and the small size has been selected so that both groupshave about the same number of shares. This means that, in theory, eachgroup has the same power to move the share price. However, the last lineof Table 3.4 shows that small players were involved in 80 percent of theprice inflections that occurred during the 60 trading days. Furthermore,Table 3.5 shows that the price inflections in which small players were in-volved provoked price changes of 30,120 cents (a total of $301.20), whichis more than twice the number of cents changes generated by large play-ers (13,423 cents, or $134.23). Let’s now compare this ratio to the resultsof Table 3.6, which shows that the average level of price changes betweenconsecutive trading minutes is 3.91 cents for price inflections that are dueto large players, and only 2.19 cents for price inflections due to smallplayers. In military terms, you could say that small players have muchstronger firepower than large players, but those large players have astronger impact on each price change because they shoot bigger bullets.

What is really key is not just the firepower itself, but more importantlythe “aim to target.” Let’s have a look at Tables 3.7 through 3.10. We canfirst see from Table 3.7 that even if small players exchanged more than 24

TABLE 3.3 Darden Restaurants: 60 Days, Repartitionbetween Large and Small Effective Volume

Shares Percentage

Total Effective Volume 49,996,040 100.0%Large Effective Volume 25,365,552 50.7%Small Effective Volume 24,630,488 49.3%



TABLE 3.4 Darden Restaurants: 60 Days, Repartition of PriceInflections between large and small players

Price Inflections Percentage

Total number of price inflections 17,186 100.0%Price inflections due to large players 3,433 20.0%Price inflections due to small players 13,753 80.0%

TABLE 3.5Darden Restaurants: 60 Days, Repartition of CentsChanges during Price Inflections between Large andSmall Players

Cents Percentage

Total number of cents changes 43,543 100.0%Cents changes due to large players 13,423 30.8%Cents changes due to small players 30,120 69.2%

TABLE 3.6

Darden Restaurants: 60 Days,Average Number of Cents Changes byPrice Inflection for Large andSmall Players

Average Cents Change per Inflection Cents

Due to large players 3.91Due to small players 2.19

TABLE 3.7Darden Restaurants: 60 Days, Separation ofSmall Effective Volume Shares into Positive andNegative Price Inflections

Shares Percentage

Small Effective Volume 24,630,488 100.0%Small positive volume 12,362,157 50.2%Small negative volume 12,268,331 49.8%Small net volume 93,825 0.4%

million effective shares during the 60 trading days, the direction of theseshares was not clear. Indeed, about 12 million effective shares were linkedto negative price inflections, while about the same number were linked topositive price inflections. Small players are therefore directionless! This isusually the case when the stock is mainly traded by large funds.


126 VALUE IN TIME

TABLE 3.8Darden Restaurants: 60 Days, Separation ofLarge Effective Volume Shares into Positive andNegative Price Inflections

Shares Percentage

Large Effective Volume 25,365,552 100.0%Large positive volume 14,310,963 56.4%Large negative volume 11,054,589 43.6%Large net volume 3,256,373 12.8%

TABLE 3.9Darden Restaurants: 60 Days, Separation of SmallEffective Volume Shares into Number of Positive andNegative Cents Changes

Cents Percentage

Total cents change due to small players 30,120 100.0%Positive cents change 14,670 48.7%Negative cents change 15,450 51.3%Net cents change −780 −2.6%

TABLE 3.10Darden Restaurants: 60 Days, Separation of LargeEffective Volume Shares into Number of Positive andNegative Cents Changes

Cents Percentage

Total cents change due to large players 13,423 100.0%Positive cents change 7,403 55.2%Negative cents change 6,020 44.8%Net cents change 1,383 10.3%

By contrast, you can see from Table 3.8 that large players were netbuyers for 3,256,373 shares, or more than 12.8 percent of the total volumeexchanged by these large players. This is a clear picture of the “aim totarget” or intent of large players.

This difference between large and small players is also very apparentwhen you compare Tables 3.9 and 3.10. Table 3.9 shows that the 24 millionshares traded by small players had a net price influence of −780 cents.

However, Table 3.10 shows that the 25 million shares exchanged bylarge players resulted in moving the price up by 1,383 cents.

In short, small players had four times as many chances to move theprice up and down, because they were involved in four times as many



price inflections as large players (Table 3.4). However, during the 60 trad-ing days, the stock price was up about $6. Large players were net positiveprice movers for $13.83, while small players were net negative price moversfor −$7.80. We can conclude from this example that large players are theones who really move the markets, while small players are mainly makingnoise, statistically speaking.

What should be clear by now is that when large funds get involved in aspecific stock, they may easily influence the price direction. I would not saythat there is rampant manipulation, but when you know exactly the levelof shares accumulation you can achieve without moving the price, you alsoknow how to move the price up after you have finished accumulating.

Effective Ratio

Before proceeding in the comparison between price and Effective Volumetrends, let’s note that it is not entirely true that the Effective Volume is theonly force responsible for price changes. Indeed, Effective Volume is bydefinition the calculation of the number of shares that is responsible forprice inflections. In other words, Effective Volume measures the numberof shares that dynamically pushes the price up or down. These dynamicmovements are important, because they express a strong will of traders.What the Effective Volume has great difficulties measuring is the staticsupply of shares: It is the entire set of limit orders that slows down thedynamic movements of the strong-will traders. These limit orders are buyor sell orders entered in the order book at prices that are different fromthe market price. When the market price reaches these limit orders, theirexecution simply slows down the main direction movement; it then slowsthe momentum of strong-will traders. I show in Chapter 4 how to deal withthe measure of the supply of shares. However, for the purpose of our diver-gence analysis, we first need to adjust the Effective Volume to include theinfluence of the static players.

If we study a fixed trading period that I call the analysis window, weunderstand that the active buying pressure on a stock during that specificperiod is the difference between the Effective Volume flow at the end of theperiod and the Effective Volume flow at the beginning of the period. Thisis due to the fact that the Effective Volume flow has been built by addingall the Effective Volume responsible for positive price inflections and sub-tracting all the Effective Volume responsible for negative price inflections.

Force of active players = Positive Effective Volume

− Negative EffectiveVolume


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This force is what fuels or may change a price trend. As we have seenbefore, we also expect that this force will be slowed down if the number ofpassive traders increases. Therefore, we need to weight that force by thesum of the number of dynamic players and passive players, which is simplythe total number of shares exchanged during the analysis period.

I therefore define the Effective Ratio of a specific analysis window as:

Effective Ratio = Force of active players (during the analysis window)Total number of shares (during the analysis window)

For example, let’s suppose that for the past 30 minutes of trading, theforce of active players was a positive number of 50,000 shares (meaningthat there were 50,000 more shares that moved the price up than there wereshares that moved the price down). If we also suppose that during the same30-minute period the total exchanged volume was 500,000 shares, the realbuying force would be 10 percent of the total volume (50,000 shares dividedby 500,000 shares).

However, if during the following 30 minutes, the force of active play-ers was still 50,000 shares but the total volume was only 250,000 shares, itwould indicate a 20 percent total relative buying force during that period(50,000 shares divided by 250,000 shares). The reason for this increasedbuying force is the relative scarcity of shares available for sale during thissecond period.

In other words, the Effective Ratio is the rate of change of the propor-tion of Effective Volume flow to the total number of shares sold during aspecific trading period. For the purpose of divergence analysis, the reasonI prefer to use the Effective Ratio instead of the Effective Volume is thatwhen funds need to accumulate a large number of shares, the fund man-agers modulate their buying tactics according to the level of supply thatcomes into the market. If the supply of shares is very low, the fund man-agers will buy fewer shares; otherwise the price would be pressured up.

Figure 3.9 (a, b, and c) shows for Darden Restaurants the differ-ent stages of calculation, from the Large Effective Volume flow to thesmoothed Large Effective Ratio. A few comments on these differentfigures:

� The upper panel of Figure 3.9a shows the Large Effective Volume flow.� The lower panel of Figure 3.9a shows the rate of change of the Large

Effective Volume. It is obtained by subtracting (on the first panel) theLarge Effective Volume flow at the beginning of the analysis windowfrom the Large Effective Volume flow at the end of the analysis win-dow: C = B − A and C′ = B′ − A′.



FIGURE 3.9a DRI: calculation of the rate of change (lower panel) of the LargeEffective Volume flow (upper panel).

� Figure 3.9b shows the Large Effective Ratio. It is obtained by dividingthe result from the second panel of Figure 3.9a by the total number ofshares exchanged during the analysis period. As a matter of fact, this isthe real measure of the balance between large buyers and large sellersin percentage of the total number of shares.

� Figure 3.9c is obtained by smoothing Figure 3.9b with an exponentialmoving average.

How to Use the Effective Ratio The Effective Ratio is a measure ofthe buying/selling pressure during a fixed period of time called the analysiswindow. We can calculate either the Large Effective Ratio, which corre-sponds to only the large players, or the total Effective Ratio, which corre-sponds to both small and large players.


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FIGURE 3.9b DRI: Large Effective Ratio (a measure of the buying/selling strengthamong large players).

I use the Large Effective Ratio to discover entry points and the to-tal Effective Ratio to find exit points. Indeed, large players are usuallytrendsetters, and large players’ accumulation usually indicates a possiblefuture price move. However, I prefer to see the movements of all the play-ers in order to find exit points. Indeed, selling waves could be triggeredby some negative news, but they mainly come from the fact that a stockis overpriced, which triggers profit taking by both large and small players.It is when the majority of all the shareholders start selling that the pricemoves down.

The Large Effective Ratio can be used in stand-alone mode, to findout if the current accumulation/distribution by large players is strong

FIGURE 3.9c DRI: Large Effective Ratio smoothed by an exponential moving av-erage function. This set of figures (a, b, and c) shows the steps in calculating thebuying/selling force.



FIGURE 3.10a Tellabs: Effective Volume analysis.

compared to past accumulation/distribution (Chapter 6 uses this tool incombination with the Active Boundaries tool to form a trading strategy).Figure 3.10 (a, b, and c) shows such a comparison. We can see in Figure3.10a that for the company Tellabs, large players accumulated shares intwo phases: A and B. Phase A (shown by arrow A) was rather strong; Asa consequence, the Large Effective Ratio crossed over the average of thepeaks of past Large Effective Ratio trends, shown by the dotted horizon-tal line of 4.1 percent in Figure 3.10b. However, we can see at the right ofthe same Figure 3.10b that large players are becoming increasingly weaker,because of the weak B trend of Figure 3.10a. As a reference, Figure 3.10cshows the stock price.

FIGURE 3.10b Tellabs: Large Effective Ratio on a 3.3-day period.


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FIGURE 3.10c Tellabs: price evolution.

Comparing the Effective Ratio to the Chaikin Money Flow Indi-cator It is interesting to make a direct comparison between the EffectiveRatio indicator and standard indicators using end-of-day data, such as theChaikin money flow oscillator. The Chaikin money flow oscillator is calcu-lated by adding the daily readings of the accumulation/distribution signalduring a period of, for example, 21 days, and then dividing this numberby the total volume exchanged during that period. The daily accumula-tion/distribution signal is based on the hypothesis that the buying/sellingpressure on a stock is well measured by the location of the close rela-tive to the high and low for the day. This accumulation/distribution sig-nal simply weights the daily volume by the spread between the close andthe low prices divided by the spread between the high and low prices ofthe day.

In Figure 3.11, I analyzed the evolution of the Effective Ratio of Tellabsfor a 90-day period that included two large price gaps (A and B). Theseprice gaps were caused by positive and negative news. We can see thatprior to the positive news at point A in the upper panel of Figure 3.11, theEffective Ratio had been signaling a strong accumulation (lower panel).Also, prior to the negative news at point B, the Effective Ratio had beensignaling a strong distribution. Dashed lines labeled “strong accumulationlimit” and “strong distribution limit” are, respectively, the average of thepeaks and the average of the troughs of the Effective Ratio signal for thepast (for example, for the prior six months to two years). When the accu-mulation/distribution exceeds the limit, we may consider such accumula-tion/distribution as stronger than in the past.

As a comparison, the standard Chaikin money flow indicator shown inFigure 3.12 gave a positive but decreasing money flow indication leadingto point A (not specifically inviting us to buy). From point A, the Chaikin



FIGURE 3.11 Tellabs: Large Effective Ratio.

money flow indicator was showing a negative reading, indicating distribu-tion of shares, while the Large Effective Ratio indicator (Figure 3.11) wascontinuously above the 0 percent limit, indicating accumulation by largeplayers. At point B, however, while the Large Effective Ratio was urging usto sell, the Chaikin money flow indicator showed a rather strong positiveaccumulation reading, urging us instead to buy.

Why these differences? There are three reasons:

1. The Chaikin money flow indicator usually uses a wider analysis win-dow, such as 21 days, while the Effective Ratio uses a shorter analysiswindow of about three to five days. This is due to the fact that the Ef-fective Ratio tries to catch insiders’ and funds’ moves. We know thatinsiders get the news only a few days before its release to the public.Therefore, an analysis on a long period would not give a good signal onthe movements of the critical most recent trading days.


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FIGURE 3.12 Tellabs: the Chaikin money flow indicator.Source: Chart courtesy of StockCharts.com.

2. The Chaikin money flow indicator, like many other indicators, is basedon the closing price. We have seen in Chapter 1 that the close of the dayis heavily traded, so that large funds with deep pockets could eventu-ally tilt the close value in their favor.

3. You can see in the lower panel of Figure 3.11 that the buy limit istouched when the Large Effective Ratio crosses over the 4.1 percentline. This means that the imbalance between large buyers and largesellers is very small: Its average maximum compared to the total num-ber of shares exchanged is only 4.1 percent. This type of small imbal-ance is well below the measurement error rate of end-of-day tools suchas the Chaikin money flow indicator and can therefore be detected onlyby using more precise tools.

PRICE-VOLUME DIVERGENCE ANALYSIS

The next step is to compare price changes to volume changes. What do wewant to get out of that comparison? We want to have an early warning ofwhat is going on behind the scenes.

We already saw that large buying by funds at the bottom of a down-trend has the power to change the price trend direction. Therefore, thedivergence analysis between the Large Effective Ratio and price helps toindicate good entry points.



If buying points are relatively easy to find by studying large players’behavior, the detection of selling points requires studying the behavior ofall the players, not just the large trendsetters. There are two reasons tojustify such a different strategy between buying and selling.

First, large funds are much more cautious when they sell than whenthey buy. When a large fund buys a considerable amount of shares, this in-fluences the demand/supply, which could eventually push the price up. Ifthe price moves up before the fund has finished accumulating, the result isthat the fund will turn a paper profit on its previously acquired shares. Nofund manager will be blamed for making a profit. However, if the fund man-ager sells those shares too quickly, the demand/supply equilibrium couldchange so much as to push the price down. The fund manager will thenstart to incur a loss on the leftover holding position. For this reason, sell-ing usually takes place during a longer period of time than buying. More-over, the fund manager could be tempted to place shares at the ask price,in order not to pressure the price down by taking off the bid. This passivestrategy is more difficult to detect through volume analysis. Therefore, us-ing the total Effective Volume to evaluate selling signals will more oftengive better results than just taking the Large Effective Volume.

Second, shareholders usually sell to take their profits. This means thatsome start selling at a 10 percent profit, others at a 20 percent profit. Whenyou analyze the selling pattern, it is true that selling could start even whenthe price is still increasing, because shareholders do not always wait fortops before selling; they will start to sell when their profit target has beenreached. However, in the case of buying, the great majority buy when theydetect that the stock is a good value play. This is why finding bottoms iseasier than detecting tops.

Buying Pattern Analysis

When we are looking to buy a position, we want to know if at a given timethe accumulation of shares by large players during the analysis period canjustify the price change that occurred during the same period. Two casescan be pointed out:

1. If the accumulation of shares by large players is positive but the priceis moving down or sideways, then we may conclude that accumulationis under way, without knowing yet if this accumulation will be strongenough to move the price up at some point. However, by comparing topast divergences this positive divergence strength between large play-ers’ activity and the price, we may conclude that the actual divergenceis much higher than historical divergences, and therefore that it is agood time to buy the stock.


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2. If the accumulation of shares by large players is proportionallystronger than the price increase during the same period, we may con-clude that the uptrend will continue.

Figure 3.13 shows a divergence analysis example for Darden Restau-rants. In the lower panel of Figure 3.13 are represented both the Large Ef-fective Ratio of Figure 3.11 and the price rate of change of Figure 3.5, withsome scale adaptation.

The divergence between these two signals is represented in the upperpanel of Figure 3.13. For example, let’s consider the two divergence pointsD1 and D2.

D1 is calculated by subtracting P1 from ER1. This D1 measures thedifference between the evolution in large players’ activity and the price

FIGURE 3.13 Darden Restaurants: 60 days, divergence analysis, including pricegaps.



evolution during the 5.3 days that led to point D1. On point D1, we cansee that the Effective Ratio was in a downtrend, but still above 0 percent,indicating a positive total buying movement. However, the divergence be-tween the Effective Ratio and the price had been increasing up to pointD1 because the decrease of the price rate of change had been more impor-tant than the decrease of Effective Ratio. This indicated that the price dropwould be difficult to sustain for a long period if the accumulation by largeplayers was to continue.

By contrast, at the D2 point, it is clear that accumulation (ER2) is verystrong, even if the price rate of change (P2) is still close to 0 percent. Thisprobably indicates that the price will later on catch up with the strong largeplayers’ accumulation trend.

Price Gap Corrections

You may have also noted in the price rate of change signal in Figure 3.13one trough (T) followed by one peak (P). These sudden changes are onlydue to the large price gap that entered and exited the analysis window, aswas explained with Figure 3.4.

Price gaps represent a sudden reaction to news that occurred while themarkets were closed. This news induced a change of sentiment or a changein valuation, which is suddenly expressed in a price change. However, thissudden price change does not appear on the Effective Volume side for thefollowing three reasons:

1. The Effective Volume is calculated on a minute-by-minute basis. Sincewe must compare the price of the actual trading minute to the priceof the previous trading minute in order to know the direction of theEffective Volume, the first trading minute of the day is never taken intoaccount. To take into account the first minute of trading, we shouldcompare it to the last minute of trading of the previous day, whichwould be strange since so much time passed between these two tradingminutes. Therefore, the Effective Volume for the first trading minutemust be set to 0.

2. The second reason is linked to the separation between large playersand small players. The volume exchanged in the first minute of tradingis usually higher than the volume exchanged later, because a largenumber of individual investors would have placed their orders duringthe night, and many of these orders would be executed at the openingof the trading day. If we use this unusually high volume of the firstminute of trading, it will often be categorized as “large players” volumein the large/small players separation calculation. This calculation does


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not correspond to the reality, since the volume of the first tradingminute is the aggregate of a large number of small players’ decisionsthat took place during the night while the markets were closed.Therefore, this first trading minute should not be mixed with othertrading minutes.

3. The last reason is mathematical: Volume comes in bursts, which meansthat volume is very volatile in the short term (you could have 100,000shares exchanged in one minute, then 100 shares in the next). How-ever, price is nonvolatile in the short term: Between one minute andthe next, price usually moves by only a few cents. The problem is thatprice gaps introduce a very large peak in price change that is some-times hundreds of times larger than the usual price change that occursfrom one minute to the next. This greatly disturbs the results of themathematical calculation and could lead to misleading signals.

Figure 3.14 shows for Darden Restaurants the price rate of change thatwas represented in Figure 3.5, but for which price gaps have now beeneliminated. When applying this new price rate of change to the divergenceanalysis, we obtain Figure 3.15, which is very similar to Figure 3.13. Themajor difference is that because of the price gap, we can see that in Figure3.13 a strong selling divergence had been generated, while this is no longerthe case in Figure 3.15.

Historical Analysis of Buying Divergences

The objective in performing a historical divergence analysis is to detect thegeneral reversal pattern of the divergence signal in order to find out if thecurrent value of the signal is strong compared to historical signals.

FIGURE 3.14 Darden Restaurants: 60 days, gaps corrected price rate of changesmoothed with an exponential average function.



FIGURE 3.15 Darden Restaurants: 60 days, divergence analysis, excluding pricegaps.

Figure 3.16 shows the starting point of the historical divergence anal-ysis for Darden Restaurants. It is clear that buying around $34 and sellingaround $43 or $44 would have been a good trade.

In the middle panel of Figure 3.17, I have highlighted nine points thatrepresent peaks in divergence. This means that at these points, the diver-gence between the Large Effective Ratio and the price rate of change was atits maximum, indicating possible buying points. However, the really goodbuying points are those that show the strongest divergence among thosenine points. I have represented with a dashed line the average level of thepeaks of the divergence signals. Experience has shown me many times thatit pays to buy a stock when the divergence is higher than this average ofthe historical peaks.

I have pointed out in Figure 3.17 the buy zones as defined earlier. Notethat at the right of the graph, we are again in a buy zone.


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FIGURE 3.16 Darden Restaurants: price pattern.Source: Chart courtesy of StockCharts.com.

Selling Pattern Analysis

When selling, we want to know if at a given time, the distribution of sharesby all the players during the analysis period can justify the price changethat occurred during the same period. Two cases can be pointed out:

1. If the distribution of shares by all the players is strong but the priceis moving up or sideways, then we may conclude that selling is underway, without knowing yet if this selling will be strong enough to movethe price down at some point. However, the comparison of the presentselling divergence to historical selling divergences can lead us to a cor-rect conclusion regarding the timing of our own selling decision.

2. If the distribution of shares by all the players is proportionally strongerthan the price decrease during the same period, we may conclude thatthe downtrend will continue.

In Figure 3.18, I have highlighted nine points that represent troughs inselling divergence. This means that at these points, the divergence betweenthe total Effective Ratio and the price rate of change was at its minimum,indicating possible selling points. However, among these points, we have toeliminate the troughs that are above 0 percent, since those indicate a pos-itive divergence. Indeed, by definition, a positive divergence cannot leadus to sell the stock. A positive divergence between total Effective Ratioand the price indicates not only that the price has been moving down morequickly than the total Effective Ratio, but that the total Effective Ratio isquite strong; this could indicate a possible price reversal coming down the



FIGURE 3.17 Darden Restaurants: historical buy divergence analysis. Historicalbuy divergence analysis allows comparing the actual divergence signal between theLarge Effective Ratio and the price rate of change to the average of the positive pastpeaks of the divergence signals. A higher signal than the past average indicatesthat the divergence is much stronger than usual, signaling a probable good buyingopportunity.


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FIGURE 3.18 Darden Restaurants: historical sell divergence analysis. Historicalsell divergence analysis allows comparing the actual divergence signal between thetotal Effective Ratio and the price rate of change to the average of the past troughsof the divergence signals. A lower signal than the past average indicates that thedivergence is much weaker than usual, signaling a probable good selling opportunity.



FIGURE 3.19 Darden Restaurants: historical sell divergence analysis (price gapsnot corrected). When price gaps are not corrected, the divergence analysis can leadto wrong trading signals.

road. The troughs that are above 0 percent can be labeled as false sellingsignals.

However, after eliminating these false selling signals, the really effec-tive selling points are those that show the strongest divergence among thenegative troughs selling divergence points. In this case, we can see onlypoints 4 and 5. But, since the historical comparison is going back one year(points not represented on the graph), the real average level of the bottomselling divergence signals has been calculated at −1.2 percent.

As can be seen in Figure 3.18, there is no bottom selling divergencesignal that is lower than the average. This indicates that in the long term,the uptrend is still solid.

In Figure 3.19, I have represented the divergence signal that wouldhave been generated if the price gaps had not been corrected. In such acase, we can see that the average is slightly lower at −1.93 percent andthat points 4 and 5 are real selling signals that were generated by the pricegaps.

From now on, I will use the following notation on the divergencefigures:

� Buy zone limit—the average of the peaks of the past buying diver-gences.

� Strong buy zone limit—a limit that is 1.5 times higher than the averageof the peaks of the past buying divergences.

� Sell zone limit—the average of the troughs of the past selling diver-gences.


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� Strong sell zone limit—a limit that is 1.5 times lower than the averageof the troughs of the past selling divergences.

The strong buy and sell limits will allow us to sometimes consider onlythe strongest of the divergence signals.

EXAMPLES OF DIVERGENCE ANALYSIS

This section is divided into three parts:

1. The first part includes four straightforward examples on how to usedivergence analysis as a stand-alone tool.

2. In the second part, I use the trading examples from the previous chap-ters to see what the divergence analysis tells us for each of them. Theseexamples are somewhat more complex than the four straightforwardexamples and might be more suitable for a second reading of the book.

3. In the third part, I show a complete example involving both the di-vergence analysis and Active Boundaries indicators. In this example, Iguide you step by step in making trading decisions only on the basis ofthese two indicators.

Straightforward Signals

The basic objective of the divergence analysis tool is to give an automaticwarning when something strange is going on behind the scenes. This warn-ing is triggered only when what is happening is very unusual compared topast movements. Let’s look at four straightforward examples.

Westlake Chemical (WLK) Westlake Chemical Corporation is a man-ufacturer of basic chemical components (vinyls, polymers, etc.). The com-pany’s main costs are linked to oil costs. In fact, it is a pretty orthodox andstable business. Figure 3.20 shows the price trend for the past few months.The drop of February 20, 2007, was due to an earnings report that fell belowexpectations.

The upper and middle panels of Figure 3.21 show that large playersstarted to buy at the start of December 2006 (trend D in Large EffectiveVolume), when the price started to drop (trend A in price evolution). Thelower panel shows this divergence. This lower panel gives us two types ofinformation:

1. It indicates when the divergence is greater than the average past max-imum divergences (indicating that we are entering into a buy zone).



FIGURE 3.20 Westlake Chemical: buying opportunities.Source: Chart courtesy of StockCharts.com.

2. It indicates when the divergence is at its greatest (points 1, 2, and 3).

I would typically buy the stock if the following conditions are met:

� The divergence signal must be in a strong buy zone, meaning that itmust be at least 1.5 times stronger than past peaks of the signal. (In thecase of Westlake, the signal must reach 9 percent.)

� The Large Effective Volume is trending up (large players are buying).� The price moves up above the 9-day average. I am adding this last con-

dition in order to avoid investing too early; a retail investor is betteroff waiting for the trend to start. This condition may not apply to largefunds that need to invest well ahead of the new trend.

I have pointed out in Figure 3.20 the two buying opportunities (A andB) that met these conditions.

A naysayer might object at this point and state that by mid-March, thestock price was lower than the price of the A and B buying opportunities.This is true, but trading is also about selling at the right time. We can indeedsee in Figure 3.22 that by February 15, the Active Boundaries indicatorwas reaching the Upper Boundary, a place of trend reversal. Furthermore,we can see at the right of the graph in Figure 3.23 that on February 15,large players stopped buying and the price started to move down. If a stockbecomes expensive and funds are not buying any longer, the price is likelyto fall back down. It is difficult to miss such a selling opportunity.

Sierra Health Services, Inc. (SIE) Sierra Health Services, Inc. is amanaged health care organization—in other words, another orthodox and


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FIGURE 3.21 Westlake Chemical: buy divergence analysis.



FIGURE 3.22 Westlake Chemical: Active Boundaries.

FIGURE 3.23 Westlake Chemical: small selling divergence.


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FIGURE 3.24 Sierra Health Services: buying opportunities.Source: Chart courtesy of StockCharts.com.

stable business. Figure 3.24 shows the price trend for Sierra Health Ser-vices, indicating three buying opportunities (A, B, and C), which will bestudied next.

The upper and middle panels of Figure 3.25 show that SIE has beendisplaying a very strong divergence between large players and price. Youmight wonder why. The divergence analysis pointed out two buying oppor-tunities: A and B. The opportunity A was somewhat difficult to catch, sincethe stock price quickly moved back down below its 9-day moving average(see Figure 3.24). The B entry was easier to catch.

Another buying opportunity occurred on January 18 and 19, 2007 (op-portunity C), when the price moved above the 9-day average after the di-vergence analysis flashed a strong buying divergence signal (point X inFigure 3.26). Eventually, the company was bought on March 12, 2007. Itlooks like the negotiations lasted a good three months.

Celgene Corporation (CELG) Celgene Corporation is a biophar-maceutical company that develops medicines to treat cancer andimmune/inflammatory-related diseases. This is clearly a more exciting,higher-growth, and higher-risk business. Figure 3.27 shows the price trendfor Celgene Corporation, indicating two buying opportunities (A and B)and a selling opportunity (C), which will be studied next.

The upper and middle panels of Figure 3.28 show that Celgene Cor-poration was displaying some general divergence between the LargeEffective Volume and the price. The lower panel shows that this divergenceproduced a buy signal at point A. In August 2006, I presented for one of thefirst times the Effective Volume concepts. My presentation was to a train-



FIGURE 3.25 Sierra Health Services: buy divergence analysis (September to De-cember 2006).


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FIGURE 3.26 Sierra Health Services: buy divergence analysis (December 2006 toJanuary 2007).



FIGURE 3.27 Celgene Corporation: buying and selling opportunities.Source: Chart courtesy of StockCharts.com.

ing class that had been organized by Alexander Elder. (If you have neverparticipated in Dr. Elder’s seminars, I invite you to attend one of them. Theexperience is always fun and enlightening.) I communicated this stock pickto the class, as well as the next stock pick, American Airlines, which wasnot yet ready for a purchase.

For the sake of full disclosure, I bought at point A, but later had to sellbecause of a time-limit stop. A time-limit stop obliges a trader to sell if,after a fixed number of days, the stock does not move in the direction ofthe trade. In Chapter 5, I will fully show how this parameter allows you tomanage both the return and risk factors of your trades.

You will also notice in Figure 3.27 the possible buying opportunity atpoint B. I rejected this buying opportunity (to my later regret), however,because the divergence signal at that time had already sunk below the buylimit after having produced the buy alert X (see Figure 3.29). This exampleshows that in order to trade the divergence signal, you need to be in thebuying divergence territory while the price is above the 9-day average.

On December 8, 2006, to my surprise, I received an e-mail from Alexan-der Elder, who had followed my pick and had bought Celgene. He was con-sidering selling and wanted to see what my charts were saying. As you cansee from Figure 3.30, the divergence analysis indicated a sell signal at thattime. It is interesting to note that from the same original pick, an expe-rienced trader (Dr. Elder) netted a profit higher than 20 percent, while Iturned a 2 percent loss. This was a good lesson for me, however. It forcedme to study the optimum level of stop loss that gives the best risk/returnbalance for my trades. Chapter 5 is dedicated to the issue of the risk/returnbalance.


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FIGURE 3.28 Celgene Corporation: first buy divergence analysis.



FIGURE 3.29 Celgene Corporation: second buy divergence analysis.

AMR Corporation (AMR) AMR Corporation is a well-known airlinecompany (American Airlines). I also presented this possible trade dur-ing Dr. Elder’s seminar in mid-August 2006. Figure 3.31 shows the pricetrend for AMR Corporation, indicating two buying opportunities (A and B),which will be studied next.

As can be seen in Figure 3.32, the company experienced very heavybuying from large players while the price was still in a trading range. PointA indicates a maximum divergence, well above the buy limit. This triggeredan instantaneous buy signal, since the price was above its 9-day average.

Note also the second buying opportunity (at point B) that occurred ona price breakout movement, which the divergence analysis indicated (seelower panel of Figure 3.33).

More Complex Examples for a Second Reading

As this point in the chapter, I have introduced and shown examples of thebasic concepts of the Effective Ratio and the Divergence Analysis tools.I believe that studying more complex examples will be better suited to asecond reading of the book. I therefore strongly advise momentarily skip-ping not only this section but also the following three sections: “Combin-ing Divergence and Active Boundaries,” “How to Set the Optimal AnalysisWindow,” and “Empty Trading Minutes.” Indeed, I think that it would bemore enjoyable in a first reading to jump directly to the conclusion of thischapter, and then move on to Chapter 4, “Supply and Demand.”

Federated Investors Inc. (FII): The Standard Divergence PlayWe saw in Chapter 1 (Figures 1.17 to 1.20) that Federated Investors was


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FIGURE 3.30 Celgene Corporation: sell divergence analysis.



FIGURE 3.31 AMR Corporation: buying opportunities.Source: Chart courtesy of StockCharts.com.

showing a strong accumulation by large players before it jumped out of itstrading range on August 3, 2006.

Figure 3.34 shows the buy divergence analysis between May andSeptember 2006. As can be seen, this analysis produced three buy zones:

1. Buy zone A starts while the price is still in a trading range, on June 8,2006. If you have the patience to wait until the top of the divergenceis reached during that buy zone, you can see that this maximum diver-gence is reached on June 12 while the price is in free fall. This forbidsus to buy, because as a rule you may not buy or sell against the pricetrend.

2. Buy zone B is more interesting, because the price trading range iskept all through the buy zone. Therefore, buying at the maximum di-vergence on July 24 is a good decision.

3. Buy zone C is more problematic. If you did not buy during buy zoneB and are now receiving a new buy signal C that is as strong as the Bsignal, it is still less tempting, because in the meantime the price hasalready increased from $31 to $34, and the starting uptrend has nowbeen discovered by everyone.

Ariba, Inc. (ARBA): Difficulty of Detecting Insiders through Di-vergence Analysis We studied Ariba, Inc. in Chapter 1 (Figures 1.23and 1.24). We saw that during the three trading days leading to January 24,2006, large players were net buyers, while the stock price was declining.Then the January 24 earnings release was positive and pushed the price upby 19 percent overnight.


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FIGURE 3.32 AMR Corporation: first buy divergence analysis.



FIGURE 3.33 AMR Corporation: second buy divergence analysis.


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FIGURE 3.34 Federated Investors Inc.: buy divergence analysis.



As can be seen in Figure 3.35, the two buy zones A and B were goodbuy zones for the stock, since the price considerably appreciated after thebuys. However, buy zone C was not very good, since afterwards the pricedeclined.

As we can see in Figure 3.35, the three days leading to January 24 werenot within any of the A or B buy zones. It is important to understand thatthe divergence analysis is very good at finding out accumulation by largefunds, but is not reliable for finding insider activity such as the one that theEffective Volume analysis showed for Ariba on January 24, 2006. The rea-son is very simple: Insiders usually get their privileged information only afew days before the news hits the wires. Therefore, they cannot drasticallyinfluence the divergence pattern, since this pattern is formed through theanalysis of shares during a period that is longer than the time during whichinsiders accumulated their shares based on their privileged information.

Figure 3.36 represents the sell divergence signal. The sell divergenceis calculated on the comparison between the price rate of change and thetotal Effective Ratio. As can be seen, the average minimum of the sell sig-nals was at −2.6 percent. Whenever the divergence signal falls below thisaverage, we need to sell the stock that was bought earlier. In our case, theshares that we could have bought at buying point C would have been soldon March 23, 2006, at the start of the sell zone. Since the total EffectiveVolume usually changes its trend before the price starts to decline, the selldivergence analysis is a very good protection against bad trades. It oftentriggers before stop-loss limits.

I use stop losses only for protection against catastrophic situations: asudden market crash or very bad news that endangers the viability of thecompany.

Also note in Figure 3.36 the false buy signals. These are false signalsbecause their tops do not reach the 0 percent limit. A negative divergencealways means that the Effective Volume trend is weaker than the pricetrend. When the maximum of the divergence signal is negative, this is usu-ally a very bad sign for the stock.

Becton Dickinson (BDX): Forced to Sell by Divergence, Only toRepurchase Later at a Higher Price Becton, Dickinson was studiedin Chapter 2 when we discussed Active Boundaries (refer if necessary toFigures 2.3 through 2.6). As can be seen in Figure 3.37, the divergence anal-ysis produced two large buy zones (zone A and zone B). Buying in zone A ataround $60 was a good choice, since later on the price climbed to $70. TheActive Boundaries analysis was also showing that zone A correspondedto the Lower Boundary, from which the price usually rebounds. You willalso notice that the buy signal at the right of the graph in zone B is much


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FIGURE 3.35 Ariba, Inc.: buy divergence analysis.



FIGURE 3.36 Ariba, Inc.: sell divergence analysis.


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FIGURE 3.37 Becton, Dickinson: buy divergence analysis.

higher than historical signals, indicating a very unusual accumulation bylarge funds.

What is interesting to note, however, is the large sell signal that oc-curred on October 10, 2006, just before the stock climbed up again in buyzone B. This sell signal was due to an important distribution of shares by



FIGURE 3.38 Becton, Dickinson: a rare case where the price trend was leadingthe Effective Volume trend.

large players. We can indeed see in Figure 3.38 that leading up to October2 large players were heavy sellers, and that it took a strong price trend tomove the Large Effective Volume trend back up.

This clearly shows that large players are not always correct. As a mat-ter of fact, nobody could have predicted that the price would continue toappreciate, and the large sellers probably just wanted to take their profitsoff the table.

My policy, when facing such a heavy selling by large players just aftera price run, is to also sell and get my profits off the table, even if I will laterneed to buy the same stock back at a higher price. It is too risky to betagainst the large players’ move, even if it is later found to be wrong.

When I was starting to trade, I had difficulties coming back into a stockat a price higher than the one at which I had last sold it. It was some sort ofan admission of a failed decision to sell when it would have been wiser tokeep the stock. But in fact, the market does not care about the past. It does


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not care about your or my position. Therefore, there is no reason for meto care about such past positions. Once you sell a position, it is over. If theconditions change, it is perfectly correct to buy the same stock at a pricehigher than the one at which you previously sold it. You will come backinto the stock only if the probability of making a profit is high enough. Inother words, the technical analysis could very well tell you that it is lessrisky to buy a stock later on at a higher price than to hold the stock duringa period of uncertainty.

IMAX: How to Avoid Catastrophic Situations Using DivergenceAnalysis We studied the IMAX case in Chapter 2 (Figures 2.9 and 2.10).We can see in August 2006 a catastrophic gap downward that took outabout 40 percent of the value of the stock. The problem is that the di-vergence analysis was showing a strong buy signal just before the priceplunge. As shown in Figure 3.39, if you had bought during buy zone D,you would certainly have experienced a catastrophic loss. In Figure 3.39,I show the standard buy zone limit, as well as the strong buy zone limit,which is 1.5 times stronger. Let’s be clear: Divergence analysis will notprotect you from catastrophic losses all the time. The divergence analysishelps you determine if something abnormal is happening, something thatmay require you to buy or sell the stock. However, if bad news is closelyheld, you may not be able to detect it in advance through the divergenceor Large Effective Volume analysis. It is therefore a safer choice to makedecisions based on a combination of indicators. Let’s see how this workedfor IMAX during buy zone D.

� On July 28 (see Figure 2.9b), the Active Boundaries signal hit the UpperBoundary, a traditional place for reversal. Buying at the Upper Bound-ary must be done with extreme care.

� The Effective Volume analysis (see Figure 3.40) shows that beforereaching the price gap on the morning of July 27, large players wereaccumulating the shares, which is typical of information leaks beforepositive news (trend A in Large Effective Volume flow). However, twodays after the gap-up, large players started selling, and the price headeddown. As we saw in Chapter 2, it is forbidden to buy a stock against thelarge players’ trend.

� We can see in Figure 3.40 that price trend B was down from July 28.One of my rules is that I do not buy when the stock price is decreasing,and I do not short when it is increasing, because the price trend has itsown momentum that is often slow to change. As I wrote earlier, I evenprefer buying when the price crosses above its 9-day average. Buy-ing at the buy divergence signal would therefore go against this basictrading rule.



FIGURE 3.39 IMAX: buy divergence analysis.


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FIGURE 3.40 IMAX: Effective Volume analysis.

The whole problem of the false buy signal generated by the divergenceanalysis lies in the fact that we have made gap corrections. The conse-quence of these gap corrections is that we are viewing the price trend asif there had been no gap. However, after a large gap (such as the one onJuly 27), the market players’ reactions often change. This is why, after alarge gap, all the signals must be reevaluated for their correctness: The Ac-tive Boundaries could form new boundaries, the Large Effective Volumeanalysis may not be valid anymore, and the divergence analysis could giveopposite results depending on whether you include the price gaps.

Indeed, if you look at Figure 3.41, you can see that the middle panel(which calculated the divergence after gaps are corrected) gives a buysignal, while the lower panel (which calculated the divergence withoutgaps correction) produces a sell signal. Similarly, we can study the buyzones E and F in Figure 3.39. We can see that both zones E and F are false



FIGURE 3.41 IMAX: buy divergence analysis with or without gaps correction. Di-vergence analysis just after a large gap can give very different signals depending onwhether the gaps are corrected.


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FIGURE 3.42 IMAX: first false buy signal. This first false buy signal was indicatedby the divergence analysis on buy zone E in Figure 3.39, but contradicted by theEffective Volume analysis.

buy zones, because none of these two signals is sustained by the EffectiveVolume trends (see Figures 3.42 and 3.43).

Combining Divergence and Active Boundaries

In Chapter 2, we extensively studied how to monitor the trend of Tellabsusing the Active Boundaries (for reference, see Figures 2.7a and b, and2.11a and b). Let’s go again through the full up and down cycle of Tellabs(see Figure 3.44) and study what the divergence analysis is telling us inrelation to the Active Boundaries. The numbers in Figure 3.44 are dif-ferent points that will be discussed with only one focus: Should we buyor sell?



FIGURE 3.43 IMAX: second false buy signal. This second false buy signal indi-cated by the divergence analysis on buy zone F in Figure 3.39, but contradicted bythe Effective Volume analysis.

FIGURE 3.44 Tellabs: price cycle.Source: Chart courtesy of StockCharts.com.


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Let’s first summarize the four trading rules that we must follow:

1. Buy close to the Lower Boundary if:� The divergence analysis is in a buy zone and� The price is not decreasing (it is above the 9-day moving average)

and� The Large Effective Volume is increasing.

2. Buy close to the Upper Boundary if:� The divergence analysis is in a buy zone and� The price is not decreasing (it is above the 9-day moving average)

and� The Large Effective Volume is increasing and� The Active Boundaries signal did not cross below 0 percent between

the last time it hit the Upper Boundary and now.

3. Sell close to the Upper Boundary if:� The divergence analysis is in a sell zone or� The Large Effective Volume is not increasing.

4. When the price passes through the Lower Boundary, do not buy. In-stead, wait for new boundaries to be formed.

Please note among these rules that in order to buy, we need a combi-nation of all the conditions to be met, but in order to sell, we need onlyone of the conditions to be met. This means that we must use great cautionwhen buying and sell at the first sign of trouble.

Trade Analysis: A, B, and C Trends of Figure 3.44 We are nowgoing to review the important decision points that will lead us to successfultrading decisions.

� Points 1, 2, and 3 in Figure 2.7 were used to define the boundaries. Wetherefore start our analysis at point 4 in Figure 3.45, close to the LowerBoundary. The Large Effective Volume is not increasing (see Figure3.46), so even though we are in a buy zone (see Figure 3.47) and theprice is flat, we cannot buy here.

� At point 5, we just went through the Lower Boundary. It is a very badidea to buy here; some large player wanted badly to be out (see Figure3.46). Furthermore, we are in a sell zone (see Figure 3.48).

� At point 6, large players start moving up, but we are not in a buy zoneyet, and we are at the Upper Boundary, a traditional reversal point. Itis impossible to buy here.

� Point B (buy) in Figures 3.45, 3.46, and 3.47 is interesting. It lies closeto the Upper Boundary, in the buy zone, with Large Effective Volume



FIGURE 3.45 Tellabs: Active Boundaries for the A, B, and C trends in Figure 3.44.

FIGURE 3.46 Tellabs: Effective Volume analysis in the uptrend.


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FIGURE 3.47 Tellabs: buy divergence analysis in the uptrend.

increasing and the price trend not decreasing. This is a clear buy at$10.7.

� Since we bought, we now have to look for a selling point. Point 7 isdefinitively not a selling point, even if we are at the Upper Boundary.Indeed, large players are still heavily buying, and we are far from a sellzone on the divergence analysis chart (see Figure 3.48).

� Point 8 makes us very worried: We are back at $10.7, our buying price,even though we reached $11.88 at point 7. What should we do? Shouldwe sell in order not to turn our paper gain into a loss? However, at point8, we are located at the Lower Boundary, with large players heavilybuying, and the divergence analysis signal is at its highest. We keep the

FIGURE 3.48 Tellabs: sell divergence analysis in the uptrend.



stock. There is no reason to sell when large funds are buying on sucha scale.

� At point 9 we have a nice paper profit. What should we do? Should wetake our profit and “sell the news”? Large players are not selling thenews. We keep the stock.

� At point 10, we are somewhat higher than the Upper Boundary, an ex-uberance situation that cannot last. However, large players are still ac-cumulating, the price trend is healthy, and the divergence analysis doesnot show a sell signal. We keep the stock.

� At point 11, we are back to the Upper Boundary. However, the LargeEffective Volume flow is flat, and the divergence analysis signal isstrongly in the sell territory. We actually sell before reaching point 11,as soon as we reach the sell zone. We sell at point S (sell), for $14.71.

� At point 12, on the drop in price, even if the price returns to its posi-tive uptrend, the Large Effective Volume stays flat, and the divergenceanalysis signal is not back into the buy zone. We wait.

� Points 13 and 14 show the same pattern: Both lie deep in the sell zone.� Point 15 is interesting: We are back in the buy zone. Should we thus

buy? At that point, the Effective Volume flow is flat or slightly de-creasing. The divergence is producing a “buy” signal simply becausethe price dropped much more quickly than the drop in Effective Vol-ume. Furthermore, we passed through the Lower Boundary, indicatingthat something is happening. As a matter of fact, the large price drophas been attracting sellers wanting to protect their past gains or short-sellers wanting to profit from the shaky states of the shareholders. It issafer to wait.

Trade Analysis: D, E, and F Trends in Figure 3.44 As we finishedpoint 15 of the analysis without rebuying, we are still looking for a goodentry point, if ever. Just as a reminder, a good entry point is a value point(in terms of Active Boundaries) where large players are accumulating withabnormal strength.

� Since we crossed through the first Lower Boundary at point 15, we maythink that point 16 is forming a new set of boundaries (see the secondset of boundaries in Figure 3.49). Since we are not sure, it is better towait until the second set of boundaries is formed.

� At point 18, we may think that the second set of boundaries has beenfixed. However, the Large Effective Volume is trending down (Figure3.50), so even though point 18 is in a buy zone (Figure 3.51), we wait.

� Point 19 is still worse in terms of the Large Effective Volume trend.We are in a sell zone (Figure 3.52). The stock is now in a confirmeddowntrend. We continue to wait.


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FIGURE 3.49 Tellabs: Active Boundaries for the D, E, and F trends in Figure 3.44.

FIGURE 3.50 Tellabs: Effective Volume in the downtrend.



FIGURE 3.51 Tellabs: buy divergence analysis in the downtrend.

� Points 20 and 21 show false buying points (Figure 3.51). Indeed, atthese points, the divergence between Effective Volume and price ispositive simply because the price fell too sharply compared to the Ef-fective Volume. However, the Large Effective Volume trend is also neg-ative (Figure 3.50), forbidding us to buy.

� The next buying point is point 24 ($11), which combines a strong up-trend in Large Effective Volume with a flat price. But then, unfortu-nately, point 25 ($10.6) would be hit quite quickly thereafter, and wewould sell at a small loss (−3.6 percent).

� Buying at point 26 ($11.5) would also result in selling at point 27 ($11.3)for a small loss (−1.7 percent).

FIGURE 3.52 Tellabs: sell divergence analysis in the downtrend.


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Between points 23 and 27, the Large Effective Volume flow analysisshows a big battle that is taking place between funds. Some funds thatbought in at the beginning of the year and could not sell earlier in the down-trend are caught with losing positions, while their reporting period of theend of the year is approaching. Some of these funds are tempted to selltheir Tellabs holdings on any new price strength. In the meantime, otherfunds that sold at the top are encouraged to return to the stock, whosevaluation is still low compared to the previous high.

As you can see in this Tellabs example, a combination of the differentvolume-based tools produces very few good trading signals. This method isindeed much more restrictive than many other methods. As a consequence,by following this method, you will be invested in a specific stock for only asmall amount of time. In our example, we made a one-shot 37 percent gainby being invested only between the end of December 2005 and the end ofFebruary 2006 (a two-month period out of 13 months), and two small lossesfor a total loss of 5.3 percent.

What the high return on the short time period tells us is that if we needto be invested in a diversified portfolio of 20 different stocks, and if for eachstock our trading method allows us to stay invested for only 15 percent ofour trading time, then we will need to follow at least 133 stocks (20 ÷ 0.15= 133). In fact, you will likely need to follow many more, because tradingsignals coming from different stocks usually overlap. Nobody can follow somany stocks on a daily basis, except with the help of an automatic scanningsystem. I myself follow more than 800 stocks daily, but actually analyzefewer than 20 every day—the ones that are flagged by the system.

HOW TO SET THE OPTIMALANALYSIS WINDOW

So far in this chapter, we have followed a basic principle: We need to buywhen there is a strong positive divergence between the Large Effective Ra-tio and the price rate of change, and we need to sell when there is a strongnegative divergence between the total Effective Ratio and the price rate ofchange.

Unfortunately, life is not that simple, because volume and priceshave some very different characteristics, which makes divergence analy-sis rather tricky. The area in which they differ is called volatility. Pricevolatility refers to the number of price reversals within a given period oftime, together with the amplitude of such reversals. Thinly traded stocks(for example, those listed in the Pink Sheets) have higher price volatilitythan the well-traded blue chips.



Volume is much more volatile than price. The amplitudes of the vol-ume variations can be very significant to the point of rendering divergenceanalysis useless. The cause of such volatility comes from large funds thathave to trade a significant number of shares in order to make a meaningfulprofit. Since trading hours are limited, funds need to move large amountsof shares in and out during a short period of time. Even if the extreme vol-umes are filtered out, during a short period of time volume volatility will bemuch higher than price volatility.

Let’s come back to Figure 3.17 (Darden Restaurants Inc.). In Figure3.17, I took an analysis window of 5.3 days to calculate the Effective Ratioand the price rate of change as they are represented in the lower panelof Figure 3.17. In that case, the divergence analysis of the middle panelallowed us to make some trading decisions.

Let’s now take a much shorter analysis window of one day, and processagain the divergence analysis, as in Figure 3.53. You will at once noticethat the divergence signal closely follows the Large Effective Ratio signal,simply because with such a small analysis window, the gray signal thatrepresents the price rate of change is very small in amplitude. This onlyshows that for very short analysis windows, volume is much more volatilethan price.

If you follow the buy signals generated by this analysis, you can seethat you have a very large number of buy signals (you buy when divergenceis high). What do these signals represent? First, let’s remember that we arelooking at the large players only. That is, we are looking at the large spikesof volume that were responsible for a price change from one minute to thenext. What the model says is that these volume spikes come by waves, andsince the analysis window is short, a few consecutive volume spikes will beresponsible for these violent moves. It is also important to note that whenan analysis window moves minute by minute from older trading minutes tonewer trading minutes, we have to add new Effective Volume and subtractold Effective Volume. This has an important consequence: If a new spike ofEffective Volume must be added when we enter the corresponding tradingminute, this same spike will have a negative effect on the signal when leav-ing the analysis window. This largely explains the periodic ups and downsthat we see in the short-term divergence signal, such as the one in Figure3.54. This problem occurs only for small analysis windows, for which thevolume volatility is higher than price volatility.

You can see in the lower panel of Figure 3.53 that Large Effective Ratiospikes can lead to day-by-day variations going from −5 percent to +15 per-cent, for a total span of 20 percent. If you look at the price rate of changeduring the same one-day analysis period, you will notice that this rate ofchange is moving between −3 percent and +5 percent on average, for atotal span of 8 percent. On a one-day analysis window, the Large Effec-


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FIGURE 3.53 Darden Restaurants: divergence analysis for a very short analysisperiod.

tive Ratio signal offers an amplitude 2.5 times stronger than the price rateof change.

We now understand that on very short analysis windows, trading onthe divergence signal is identical to trading on the Large Effective Ratiosignal only. Is this what we need as traders? Would it be meaningful to buywhen some large player is buying and sell when some other large player isselling? This makes little sense. First, the buyers and the sellers are prob-ably not the same, and second, “large” does not especially mean “correct.”It is not because someone is buying one ton of oranges that we may con-clude that the price of oranges is going to increase. It is better to wait andsee if a sustained number of large-quantity orange buyers appears, withoutattracting more orange supplies.



In other words, when you use a short analysis window, you are say-ing: “Oh! Yesterday, large players were buying heavily. I’ll buy, too!” Say-ing that, you do not even know if the buyers are latecomers who want tocatch an uptrend, trendsetters who are accumulating before a price surge,or bargain hunters who are jumping in on a sudden price drop. Trading ona volume signal only is the worst method you could follow. Indeed, whendirectly investing in stocks, the only two ways to turn a profit are:

1. Buy at a low price and sell at a higher price.

2. Short at a high price and cover at a lower price.

If you read the previous statement again, you will notice that volumehas nothing to do with profit. Only price matters: To turn a profit, you needto have the price right more than you need to have the volume right.

Let’s not forget the goal of this model: We want to buy when the stockis cheap and when there is buying momentum by large players that is notyet built into the price. We want to sell when the stock price is higher(much higher, if possible) and when there is a selling momentum startingin the market.

In terms of the model, this simply means that price is the main indica-tor. Therefore, the price signal that we use in the model should be strongeror at least equal to the volume signal. You should not, under any circum-stances, use a volume signal that is much stronger than the price signal (asis shown in Figure 3.53).

Let’s examine Figure 3.54, for which I took an analysis window of 10days.

You can easily notice in the lower panel of Figure 3.54 that the pricerate of change is varying much more wildly than the Large Effective Ratio.The price rate of change is moving from −8 percent to +10 percent, whilethe Large Effective Ratio is moving only from +2 percent to +6 percent.For a 10-day analysis window, the price rate of change offers an amplitude2.25 times higher than that of the Large Effective Ratio. As a consequence,the divergence between the two (plotted on the upper panel) gives buyand sell signals that are mainly a consequence of the price rate of changevariations.

The problem with such a long analysis window (10 days) is not onlythat you get few signals, but that:

� These signals come too late. You will miss the moves by insiders, whotypically accumulate during only a few days before the news strikes.

� The limit between false and true signals is not reliable since we do nothave enough tops and bottoms to calculate an average that is statisti-cally valid. To get more tops or bottoms to calculate an average, we


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FIGURE 3.54 Darden Restaurants: divergence analysis for a very long analysisperiod.

would need much older data, which in fact does not properly reflectthe behavior of the actual pool of traders.

The conclusion is that primarily following the price rate of change ismuch more effective than following only the volume signal. However, thiswill not give you an edge compared to other indicators that also use mea-sures of price variations.

Selecting the right size for the analysis window is critical in order toget the right buy/sell signals. The right size is obviously between 1 day and10 days, such as the 5.3-day window in Figure 3.17.

Since I like to trade on the price trend but also get enough EffectiveVolume information to tell me if something is happening behind the scenes,I usually decide on the analysis window size in such a way that the maxi-mum amplitude of the Large Effective Ratio is between 75 percent and 100



percent of the maximum amplitude of the price rate of change pattern. InFigure 3.17, we can see that the Large Effective Ratio is evolving between−0.5 percent and +8.5 percent (amplitude of 9 percent), while the pricerate of change is evolving between −4 percent and +8 percent (amplitudeof 12 percent). Thus, we have 9 divided by 12, or 75 percent.

EMPTY TRADING MINUTES

If you look closely at Table 3.11, which represents the minute-by-minutedata that feeds all the tools presented in this book, you will notice thatthere are occasionally jumps between trading minutes. For example, youcan see that there are three lines lacking between 15:03 and 15:07. Weshould have seen lines for 15:04, 15:05, and 15:06. The fact is that no tradewas executed during these three minutes. Indeed, when there is no activityduring a specific trading minute, the data feed usually does not indicate “0”for that minute, but simply does not send data at all regarding that minute.We then end up with a data feed such as the one in Table 3.11. If we wantto be mathematically correct, we need to insert “0” lines for the minutesthat have not seen any trading, such as in Table 3.12 (see all the new lineswith “0” volume).

For the different tools that have been introduced so far, neither theEffective Volume Flow analysis nor the Active Boundaries analysis will beaffected by “0” or blank lines. However, it is clear that the tools that usea moving analysis window of fixed length will be affected, since including“0” lines will reduce the volume included in the analysis window. This willaffect the divergence signal, since the price and the Effective Volume havea very different volatility. In theory, adding “0” lines will slightly increase

TABLE 3.11 A Data Feed That Does Not Include Nontrading Minutes


4/10/2006 15:10 8.00 8.01 8.00 8.00 3004/10/2006 15:09 8.00 8.00 8.00 8.00 1,4174/10/2006 15:08 8.00 8.01 8.00 8.00 2,3034/10/2006 15:07 8.00 8.00 8.00 8.00 1004/10/2006 15:03 8.01 8.01 8.01 8.01 1,8004/10/2006 15:01 8.02 8.02 8.00 8.00 1,8844/10/2006 15:00 8.01 8.01 8.01 8.01 1,9924/10/2006 14:59 8.01 8.01 8.01 8.01 1,0004/10/2006 14:55 8.02 8.02 8.01 8.01 8,897


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TABLE 3.12 A Data Feed That Includes Nontrading Minutes


4/10/2006 15:10 8.00 8.01 8.00 8.00 3004/10/2006 15:09 8.00 8.00 8.00 8.00 1,4174/10/2006 15:08 8.00 8.01 8.00 8.00 2,3034/10/2006 15:07 8.00 8.00 8.00 8.00 1004/10/2006 15:06 8.01 8.01 8.01 8.01 04/10/2006 15:05 8.01 8.01 8.01 8.01 04/10/2006 15:04 8.01 8.01 8.01 8.01 04/10/2006 15:03 8.01 8.01 8.01 8.01 1,8004/10/2006 15:02 8.00 8.00 8.00 8.00 04/10/2006 15:01 8.02 8.02 8.00 8.00 1,8844/10/2006 15:00 8.01 8.01 8.01 8.01 1,9924/10/2006 14:59 8.01 8.01 8.01 8.01 1,0004/10/2006 14:58 8.01 8.01 8.01 8.01 04/10/2006 14:57 8.01 8.01 8.01 8.01 04/10/2006 14:56 8.01 8.01 8.01 8.01 04/10/2006 14:55 8.02 8.02 8.01 8.01 8,897

the amplitude of the price rate of change signal and slightly decrease theamplitude of the Effective Ratio signal.

However, as shown in Figure 3.55, these changes will have a very smalleffect on the divergence signal itself. This effect is indeed well within themeasurement error, and could not possibly affect trading decisions, at least

FIGURE 3.55 Comparison of divergence analysis signal with or without the inser-tion of blank or “0” lines for trading minutes for which there was no trading activity.



if these “0” lines are not too numerous. In the case of Ariba, Inc., the stockis traded only 77 percent of the time. This means that since every tradingday includes 390 trading minutes, the stock is traded on average duringonly 300 minutes. The 90 nontrading minutes are added as “0” lines, butthat addition hardly affects the divergence signal, as shown in Figure 3.55.

Although one example does not take the force of proof, it is quite obvi-ous that inserting or not inserting blank or “0” lines when a trading minutehas seen no trading activity will not affect the divergence signal of stocksthat are relatively well traded (well-traded stocks see trading activity formost of the trading minutes). Because the systematic insertion of blank or“0” lines uses a lot of computation power, I have made the decision in myday-to-day trading not to insert them.

WHAT WE LEARNED REGARDINGDIVERGENCE ANALYSIS

In this chapter, we learned that a high positive divergence between volumeand price trends indicates buying points. Such positive divergences occurfor one of two reasons:

1. The price is dropping more quickly than the Effective Ratio, meaningthat traders are selling at a slower pace than what the price movementmay indicate. In other words, the price drop is preventing an increasingnumber of traders from selling—more and more traders are thinkingthat the price is getting too cheap to sell.

2. The price is increasing more slowly than the Effective Ratio, meaningthat traders are buying at a quicker pace than what the price move-ment may indicate. This typically occurs when traders believe that theprice’s upward momentum will continue, attracting still more buyers,until the price becomes too high.

Trading decisions are made by comparing the divergence signal to theprice trend:

� If the price is decreasing but the divergence signal (between the Ef-fective Ratio and the price rate of change) is increasing, it means thatthe price is dropping only on a few sellers (meaning that nobody isbuying), or that bargain hunters have started to come in, slowing thedowntrend of the Effective Ratio. Simply put, we do not know what isgoing on. The best course of action is to wait for the price to stabilizeor reverse up.


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� If the price is stable but the divergence signal is increasing, this is asign of underlying change. However, accumulation may continue fordays before the price increases. In such a case, we should wait for thedivergence to become stronger than its historical average maximumbefore buying in; and, more important, I would advise waiting for theprice to cross above its 9-day average before buying.

We learned that trading rules must use a combination of complemen-tary indicators. My set of four trading rules is:

1. Buy close to the Lower Boundary if:� The divergence analysis is in a buy zone and� The price is not decreasing (it is above the 9-day moving average)

and� The Large Effective Volume is increasing.

2. Buy close to the Upper Boundary if:� The divergence analysis is in a buy zone and� The price is not decreasing (it is above the 9-day moving average)

and� The Large Effective Volume is increasing and� The Active Boundaries signal did not cross below 0 percent between

the last time it hit the Upper Boundary and now.

3. Sell close to the Upper Boundary if:� The divergence analysis is in a sell zone or� The Large Effective Volume is not increasing.

4. When the price passes through the Lower Boundary, do not buy. In-stead, wait for new boundaries to be formed.

We also learned that the divergence method when used in combinationwith the Active Boundaries signal, because it is very selective, does notprovide abundant trading signals. Therefore, this combination of methodsforces us to follow a large number of stocks. Some sort of scanning systemis thus necessary to automatically analyze a large number of stocks andproduce alerts on the stocks that offer good trading opportunities.


C H A P T E R 4

Supply andDemand

The Key to Trading

A t this stage in the book, you know what my basic hypothesis is: Inorder to make a profit trading, it is necessary to grasp how the sup-ply/demand equilibrium functions. We saw in the previous chapters

that this equilibrium is affected by a motive called profit (measured interms of price) and is moved by a force called strength (measured in termsof volume). There is, however, a third concept that influences the sup-ply/demand equilibrium. It is the resistance to change. It is a well-knownfact in physics that when you apply a force to an object, the object respondswith an opposite force. In the market, this means that when you apply aforce in one direction, you instantaneously generate a counterforce in theopposite direction. When the price of a stock increases, propelled by buy-ing power, that instantaneously attracts active sell market orders as wellas passive sell limit orders.

We have thoroughly studied the active players’ behavior using the Ef-fective Volume method, but we would do well to also study the behavior ofthe passive players, those who place limit orders. This is what this wholechapter is all about. The first part of the chapter deals with a measure ofthe overall supply of shares. We will see that when the supply of sharesdries up, the probability of a rebound in price is very high, especially whenlarge funds are net buyers.

The second part is somewhat more technical and deals with a minute-by-minute measure of the passive supply of shares. We will see that passiveplayers have only a minor influence on a stock’s price movements. We willsee that the market battle is mainly fought between large players—usually

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through very fast computer-based order-generation and routing systems.We will see how this fight prevents market manipulation.

This will lead us to the third part of the chapter: a comparison of thestrengths and weaknesses of funds compared to retail investors.

SUPPLY/DEMAND EQUILIBRIUM

In Chapter 2, we learned that during trends, traders’ expectation is cyclical,moving within two limits called the Upper and the Lower Boundaries. Thiscyclical movement is mainly influenced by the turnover of the pool of activetraders: New buyers lower the average profit of active traders and increasethe average expectation that the price will move up. The more numerousthe new buyers, the stronger the change in average expectation. When theaverage profit hits the Upper Boundary, the average expectation of activetraders for the price to further increase is at its lowest; at this point, newbuyers stop coming in and the price reverses down.

However, if you remember, in Chapter 1 (“Effective Volume”), welearned that the buyers/sellers equilibrium is influenced by the supply/demand equilibrium (Are shares available? Is money coming in?) andvolatility (How quickly are shares becoming available for sale? How

quickly is money becoming available to purchase shares?). We alsolearned that 25 percent of the volume involved in stock trading is respon-sible for 75 percent of the price movements. This 25 percent is called theLarge Effective Volume.

The buyers/sellers equilibrium depends primarily on the supply/demand equilibrium. Indeed, the stock market is above all a market (seeFigure 4.1). You therefore need to go to that market in order to find a buyerfor the shares that you want to sell and a seller for the shares you wantto buy.

The demand is a measure of the number of shares that people want tobuy or the incoming buying orders (see point 2 in Figure 4.1) that comeas a reaction to a price change. Only a part of the demand turns into realtransactions (see point 4 in Figure 4.1).

The supply is a measure of the number of shares that are for sale (seepoint 6 in Figure 4.1) or that come into the selling book as a reaction toa price change. Only a part of the supply turns into real transactions (seepoint 4 in Figure 4.1). Many orders that are placed too far outside of thebid/ask range are indeed never executed and will eventually be canceled.Furthermore, the supply of shares is represented not only by what lies inthe order book, but also by all the hidden limit orders that are waiting tobe placed once a certain price level is reached. To a larger extent, it is also


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FIGURE 4.1 How the stock market works.

1. Potential buyers follow a specific stock.

2. A buy decision is made; an order is sent.

3. The order reaches the book (market order or limit order).

4. Market orders are executed at market price.

5. The pool of stockholders follows the stock price.

6. Available shares are presented in the market by rank of expectation; the shareswith the lowest expectations are usually sold earlier than the shares with higherexpectations.

7. For large transactions, some orders can go directly between large holders.

represented by all the shares belonging to the float (the shares available fortrading) and by those that could be extended for sales if an attractive priceis offered.

We learned in the Introduction that decimalization has killed marketvisibility. Because, post-decimalization, traders no longer had an incentive


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to “show their hand,” it became quite difficult to evaluate the size of thesupply and the demand for shares. Normally, this equilibrium is indicatedin the order book, which lists the limit orders. Limit orders are the waitingbuy orders at and below the bid and the waiting sell orders at and above theask (see point 3 in Figure 4.1). As a trader, if you see a very large amount ofshares at the ask, you know that the market is offering shares for sale, andthat waiting a little could allow you to buy shares under better conditions.

Before decimalization, having access to the book of orders was veryhelpful and even critical for clearly understanding the buyers/sellers equi-librium. In my opinion, today, after decimalization, the order book is of notmuch help in figuring out the direction of the market (except in day-tradingactivities, where one cent can make a difference). This is because today in-stitutional players display only a part of their orders, and usually at the verylast moment.

The conclusion that we can draw from these observations is thatlooking at the order book is not very helpful in determining the sup-ply/demand equilibrium. It only gives an instantaneous picture of the mar-ket at a given time. In order to trade successfully, we need something morecomprehensive.

Furthermore, the difference between the buy/sell equilibrium conceptand the supply/demand equilibrium concept is not only the number ofbuy/sell orders that get executed compared to the size of the supply/demand; it is also the will that is behind each of the two concepts. Indeed,a share that was bought reflects a will, a taken decision, and carries anexpectation from the buyer (the buyer expects to make a profit). A sharethat is coveted (to be bought) is part of the demand and reflects either anintention or a potential expectation. It is not as strong as the real buying orselling act.

The same difference exists between placing a limit order and placinga market order. A limit order will be executed if a given price is reached.A limit order enters into the supply/demand basket, but is not immediatelyexecuted. Indeed, a limit order needs to meet a corresponding opposite or-der at the requested price to be executed. A market order is an order tosell or buy the stock at the current price, even if it causes some slippagebetween the displayed price and the executed price. A market order car-ries more will than a limit order. There is slippage when a market orderis executed at a price that is slightly different from the price at which theorder was placed. This small difference is the cost of having instantaneousexecution at the best available price.

Volatility is the second element that influences the supply/demandequilibrium. There are two types of volatility: the price volatility (How

quickly is the price changing?) and the volume volatility (How quickly

are shares being supplied and exchanged in the market?).



Volatility

Volatility is defined as the relative rate at which a variable (price orvolume) changes. Daily price volatility is mathematically calculated as thestandard deviation on an annual basis of daily price changes. We say thatthe price of a stock has a high volatility if it moves up and down quickly. Theone-minute volatility is often very different from the daily volatility, becauseprices move much less during one minute than during one trading day.When we talk about volatility, it is therefore important to state the periodof time we are referring to. Usually, we talk about short-term volatility asopposed to long-term volatility.

It is important to remember that, as we saw in Chapter 3, the pricepattern has a very small short-term volatility but a significant long-termvolatility. However, the volume pattern has an opposite volatility: a stock’sshort-term volatility is often significant (100 shares can be exchanged dur-ing one minute, and then 100,000 shares could be exchanged during thenext minute), while its long-term volatility is small (the 50-day average vol-ume variations are not so large).

The price and volume volatilities are related:

� The more quickly the buying volume appears, the more quickly theprice moves up.

� The stronger the price moves, the stronger the volume activity (strongprice movements attract stronger supply and demand).

How to Measure the Supply/Demand Equilibrium

There are two practical ways to evaluate the supply/demand of shares.The first one is static: a calculation of a price histogram corresponding

to the number of shares available for trading (the float). Each share car-ries its load of emotions, expectations, and so on. Therefore, knowing howmany shares have been bought and sold at each price level allows you toknow if many shares will be offered for sale when the price increases (indi-cating resistance), or if many buyers will appear when the price decreasesto a certain level (indicating support).

Figure 4.2 shows a support line that joins consecutive troughs for thecompany Reliant Energy Inc. (RRI). This line shows a support that is closeto $10.80. The interpretation of such a support line is that in the past, buy-ers were finding this price level to be cheap enough to buy, and sellersfound it to be too cheap to sell; hence, the price would reverse up. Because


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FIGURE 4.2 Support line.Source: Chart courtesy of StockCharts.com.

the pool of traders has not changed much since the last time the supportline was hit by the price, we may believe that this time buyers and sell-ers will make a similar analysis as to the value of the stock, and that theprice has a fair probability of moving up again from the support line if itfalls that far.

In Figure 4.3, I represented the volume histogram of all the last-tradedshares that belong to the float of Reliant Energy, as of September 25, 2006.Each vertical bar represents the number of shares that were exchanged atthe corresponding price. In such a volume histogram figure, the supportline is a vertical line that can be drawn at a price location were the volume

FIGURE 4.3 Price histogram.



histogram shows the lowest number of shares. In the case of Figure 4.3,the support line is set around $10.80. Note that the volume histogram figuregives an instantaneous picture of the repartition of shares on a given dateand at a specific time. As we will see later, the volume histogram figureevolves with time.

I call this measure of the supply/demand level static because it mea-sures only supply: how many shares will be available. It does not measurethe volatility (how quickly shares and money are coming in or out).

The second way to measure the supply/demand equilibrium is dy-namic, but is used only by players with access to large enough funds. It ishow you experimentally measure volume volatility. This dynamic methodis equivalent to market testing, and costs money to execute. Some marketmakers will indeed quickly push the price down and measure how fast anew supply of shares comes into the market or see if the new low price at-tracts many new buyers. This measure is often executed by trying to movethrough a support line. If the support line holds, this indicates that the mar-ket is ready for a new up leg. This is a well-known testing method. A sig-nificant price decline that attracts fewer sellers than an equivalent recentprice decline indicates that the supply of shares is drying up.

As you may notice, these two methods (either static or dynamic) onlymeasure one part of the supply/demand equilibrium: the supply side. Thesupply side is indeed easier to measure, since we have access to the pasttransactions. As we know how many shares have been bought at whatprice, we can draw some conclusions as to how many could eventuallybe sold at the current price. This is what I call the supply analysis.

The demand side is much more difficult to evaluate. We cannot lookinto the wallets of potential buyers and read their minds as to how theywill use their cash. However, as we saw in Chapter 3, a change in demandcan still be analyzed by looking at the divergence between the Large Effec-tive Ratio and the price. A strong positive divergence indeed indicates thatan unusual accumulation by large players is taking place. The accumula-tion itself originates from an increase on the demand side. If we catch thatdemand increase early enough, and if the supply analysis shows that onlya few sellers are present, a price increase will probably occur.

As we can see, the supply/demand equilibrium can be measuredthrough the use of two different tools: the supply analysis tool and the di-vergence analysis tool.

Supply Analysis Tool

Before explaining how the supply analysis tool works, it is important tocome back to the impact of the evolution of the volume/price repartition(histogram) to understand the dynamics of stock trading. Let’s look at


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FIGURE 4.4 IMAX: stock price evolution at the end of 2006.Source: Chart courtesy of StockCharts.com.

an example. In Chapter 2, I presented the case of the IMAX Corporation,which on August 10, 2006, saw its stock price cut in half overnight.

In Figure 4.4, I’ve pointed out three specific days that correspond todifferent sets of volume/price histograms. These three sets of histogramsare shown in Figure 4.5a, b, and c:

1. August 9 was the day prior to the big sell-off. The histogram is repre-sented in Figure 4.5a. We can see that as of August 9, all of the 40.21million shares that constitute the total issued shares of IMAX had beenexchanged at prices ranging between $8.17 and $10.92. During the last10 days preceding August 9, 545,000 shares on average had been ex-changed daily.

2. August 21 was the day that ended the big sell-off. During the eighttrading days between August 10 and August 21, 22.6 million shares ex-changed hands, or about 2.8 million shares per day (four times the dailyaverage of the 10 days preceding August 9). Figure 4.5b shows the vol-ume histogram as of August 21. As we can see, bargain investors ap-peared and created a new group of shareholders. Bargain investors aretypically investors who compare the present price to a past price andinvest “because the stock is now cheaper.”

3. September 5 was the day that ended the second sell-off. During the 10trading days between August 21 and September 5, 11.6 million sharesexchanged hands, or about 1.16 million shares per day. Figure 4.5cshows the volume histogram as of September 5. As we can see, valueinvestors appeared and created a third group of shareholders. After aprice drop, value investors typically wait longer than bargain investors



FIGURE 4.5a IMAX: volume histogram, August 9, 2006.

do to start purchasing shares. Typically, value investors do not reactto a price drop, but are attracted by the valuation of the company interms of its price-earnings (P/E) ratio. In November 2006, the averageshares exchanged per day fell to about 350,000 shares, indicating thatthe double waves of selling indeed killed the demand by antagonizingall the long-term shareholders.

The first lesson that we may learn about the analysis in Figure 4.5a, b,and c is this: When a company goes through a catastrophic situation thatinstantly affects all the shareholders, the probability that the company willlose its active shareholders is very high. Usually, when active shareholders

FIGURE 4.5b IMAX: volume histogram, August 21, 2006.


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FIGURE 4.5c IMAX: volume histogram, September 5, 2006. The evolution of thevolume histogram over the course of time indicates the positions of the differentgroups of shareholders and is the starting point in analyzing availability of shares.In Figure 4.5a, before the first sell-off, the last 40.21 million shareswere exchangedat prices ranging between $8.17 and $10.92. In Figure 4.5b, a large part of theseshares were sold and purchased by a group of bargain hunters at prices rangingbetween $5.45 and $6. Note at the right of Figure 4.5b that the initial group ofshareholders whose shares are shown in Figure 4.5a shrank and was replaced by thegroup of bargain hunters. After the second sell-off, the leftover of the initial groupof shareholders shrank further and was replaced by a group of value investors, asshown in Figure 4.5c.

are caught in such a bad position, they take their losses and leave the com-pany forever.

In the case of IMAX, bargain investors who were looking for a reversalreplaced the departing shareholders. However, that reversal did not mate-rialize, and continuous bad news forced both traditional shareholders andsome of the new bargain investors to sell at a loss. A new set of share-holders appeared: the value investors. Of course, after August 21, it is hardto know if the price really represented the value of the company. Nobodycould tell if more bad news was on the way. As we saw in Chapter 2, todetermine whether a share at a given time represents value, you have todetermine the probability that you will be able to sell it later on to some-one else at a higher price. To increase your chances of finding value, youmust find a buying price at which there will be very few sellers (the pricewill be so low that few are willing to sell at that price). At the same time,you also need to find buyers other than you who will push the price higher.As shown in Figure 4.6, large players have been keenly selling shares duringthe different sell-off phases. In addition, no accumulation by large playerscan be seen at any time, indicating that demand for the IMAX stock com-pletely dried up. Dried-up demand is not a place to find value, even if theprice itself is cheap compared to past prices.



FIGURE 4.6 IMAX: Effective Volume analysis. After a large price drop, the LargeEffective Volume will indicate if large players find value in the company. If they don’t,there is no specific reason to buy at this point.

The second (and more important) lesson that we may learn from theexample is this: Among the group of shareholders who follow a company,the set of shareholders effectively invested in the company stock is, atany given time, constantly changing. In the case of IMAX, the changeoccurred at a very quick pace, but this evolution in shareholders’ positionsis occurring for all companies’ stocks, admittedly at a somewhat lowerspeed. It is because of this evolution that the supply/demand equilibriumis continually evolving.

Where Does the Supply Come From?

In this section I show how the level of supply constantly evolves and howmonitoring it can lead to successful trading decisions. But first, we need toanswer a very simple question: Where does the supply come from?


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FIGURE 4.7 Tellabs: stock price leading to September 21, 2006.

This is quite simple: The supply comes from shareholders who are sell-ing their shares. Why would people want to sell their shares? Let’s have alook at Figure 4.7, which represents the share price evolution of the com-pany Tellabs for the last 448.5 million shares. This means that between June26, 2006 (the beginning of the graph) and September 21, 2006 (the end ofthe graph), 448.5 million shares were exchanged. This is exactly the num-ber of issued shares of Tellabs. The dotted line separates winners fromlosers, measured as of the price of the last trading day. Those who pur-chased their shares at a price lower than the closing price of September 21are earning money. The others are losing money, at least on paper.

To try to analyze the possible behavior of shareholders who purchasedthe Tellabs shares in the past, let’s turn to Figure 4.8. Figure 4.8 represents

FIGURE 4.8 Tellabs: shareholders’ profits/losses compared to stock price onSeptember 21, 2006.



the profits or losses on September 21, 2006, that shareholders who boughtin the past are experiencing, compared to the closing price at the rightof the graph. As you may have noticed, Figure 4.8 is the inverted imagein Figure 4.7. I have pointed out four zones of interest on which I willcomment. For each zone, we have to ask ourselves: Are the shareholders

who bought during this price zone ready to sell their shares? What is

the probability that they will consider selling their shares on the next

trading day? If nobody is ready to sell, then on the next day any buyingmove will push the price higher.

1. Zone A represents the profit/loss of the most recent buyers. Theserecent buyers are experiencing a profit ranging from 0 percent to15 percent. I do not believe that people who recently bought and areexperiencing a profit of between 0 percent and 5 percent would sell.They will probably wait. However, those who are experiencing a profitof between 5 percent and 15 percent are more likely to be ready tosell. I believe that their readiness to sell is proportional to their profit.Let’s assume that those who are experiencing a 5 percent profit are notready to sell, that those who experience a 15 percent profit are entirelyready to sell, and among those who experience, for example, a 10 per-cent profit, only half are ready to sell. This means that at a profit of15 percent or above, all the recent buyers would be potential sellers.This does not mean that they would be certain to sell. It means that theprobability of finding sellers among shareholders experiencing a profitof 15 percent or more is higher than among those experiencing only a5 percent profit.

2. Zone B represents the profit/loss of shareholders who bought earlierthan the most recent buyers. Zone B shareholders are experiencingbetween 15 percent and 20 percent profit. There are two interestingcharacteristics about zone B shareholders:� The first characteristic is that many of the zone B shareholders who

bought at that time have probably already sold their shares on theway to the current share price. Some of them indeed took their prof-its between 5 percent and 15 percent. We may therefore say that thefurther back we look into past buyers, the fewer shares will be foundfor sale, since many of these past buyers would have sold earlier.

� The second characteristic of past buyers compared to more recentones is that those who did not sell at an early stage are probablylooking for higher returns.

3. Zone C represents the profit/loss of still previous shareholders whobought earlier than the more recent buyers of zones A and B. Amongthis group, shareholders who regret not selling before the price drop of


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July 27 are now relieved that the price is coming back into their buyingrange, and may therefore be happy to sell their shares at around theirbuying price.

4. Finally, Zone D investors, if they did not cut their losses at the begin-ning of the price descent, are still locked in and will probably not sellbefore the price rises again.

In Figure 4.9, I show zones A, B, C, and D, but in terms of numberof shares exchanged during the corresponding periods. You can now seethat for each zone, it is possible to count the number of shares that wereexchanged and calculate a probability that these shares will be sold. Thetotal for the four zones forms the number of shares that could eventuallybecome available for sale on the day following September 21, 2006.

In fact, a true mathematical model is more complex, because we haveto consider that different price zones often overlap. Also, active traderswould usually sell more quickly than long-term investors. Therefore thereal mathematical model that I use separates both types of shareholders.

Although the model is quite complex, it simply counts the number ofshares that are available for trading and then weights this number witha probability that the shares will be offered for sale. Such a probabilityis calculated using mathematical expressions that factor in the sellingpattern distribution compared to the actual profit/loss and the delay sincethe purchase.

Let’s see how the model works on a few real-life examples.

Practical Examples

I would first like to come back to a comment that I usually read in traders’journals: “You must buy when everyone else is selling.” For me, this is a

FIGURE 4.9 Tellabs: volume histogram as of September 21, 2006.



sure recipe for financial disaster. There are only two clear times when youshould buy:

1. You buy when everybody else is buying, but you do it early in the trend.

2. You buy when everybody else has stopped selling. In other words, youbuy when the supply of shares has dried up, when only a few sharesare available for sale.

From what we learned in the previous section, it is obvious that at aspecific point of time, the only way to correctly evaluate the availabilityof shares is to count the number of shares that have a good probability ofbeing made available for sale at that point in time.

Supply analysis is important not because it allows us to discover value,but because it allows us to detect the right timing for when the selectedstock will be prone to rise. This price rise will occur if large players decidethat the stock price is compelling enough for them to take a significantposition. In other words, we need to wait to see that value is recognized bylarge players, being sure that our floor is protected by a lack of potentialsellers.

The Case of Reliant Energy (RRI) The lower panel in Figure 4.10shows a measure of the supply of the Reliant Energy stock (refer also toChapter 2, Figure 2.19a and b). You can see that the supply signal is verysensitive to small price variations: A price increase of a few percentagepoints could potentially attract a large number of new sellers, dependingon their relative profits.

Because that sensitivity could give misleading trend signals, Ismoothed the signal in the lower panel by using a four-day simple movingaverage.

As can be seen in the lower panel in Figure 4.10, I arbitrarily drew twohorizontal lines that separate the chart into three interesting supply zones:

1. The low supply level is set for a supply of less than 10 percent of thetotal number of issued shares. At such a low supply level, it is almostassured that any significant buying activity from a large fund will re-sult in a price increase. Do not forget that we can detect the largefunds’ buying patterns through the Effective Volume tool. However,a low supply level does not ensure that the price will not continue tofall. Indeed, if the prospects for the company are very bleak, no newinvestor will invest and any small selling pressure will push the pricefurther down. The low supply level determines a buying zone. (AfterI wrote this chapter, I did a sensitivity analysis on the supply level in


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FIGURE 4.10 Reliant Energy, supply analysis. The supply analysis will help topoint out unusually low supply patterns, where any significant purchase by a largefund will put pressure on the price to increase. Above the medium supply level of 20percent, shares are available for sale in high enough quantities to satisfy a large fund.Hence, the buying by large funds above the medium supply level may not necessarilyresult in a price increase.

terms of trading performance; I found out that the optimum low sup-ply level is not 10 percent, but rather between 5 percent and 7 percent.This sensitivity analysis is explained in Chapter 6.)

2. The medium supply level is set for a supply that is between 10 per-cent and 20 percent of the total issued shares. Twenty percent is



relatively low compared to the total number of issued shares, but itis already twice the low supply limit of 10 percent. By experience,this 20 percent level is the maximum limit at which one can say thatthere is a potential share availability problem. Do not forget that ifthere is a lack of shares available, any fund that wants to take asignificant position will need to push the price higher to attract thenew supply.

3. Above the 20 percent limit, we can say that a fund will be able to takea significant position without having a real impact on the price.

In Figure 4.10, I have also pointed out two zones of high price, zoneA and zone B. Although these zones correspond to high supply zones, it isdifficult in my experience to establish a correlation between the possibleselling pressure on a stock and the supply level. In other words, a 70 per-cent supply level does not offer a much stronger selling pressure than a50 percent supply level.

The Case of Tellabs (TLAB) Tellabs also shows two interesting lowsupply zones where buying would have led to a significant profit. (SeeFigure 4.11; refer also to Chapter 2, Figure 2.7a and b.) In this example,it is also important to note that the peaks of supply do not necessarily cor-respond to selling points. A peak of supply simply indicates that sharescould potentially be made available for sale. However, what will trigger aselling move is a price pullback. Indeed, when supply is high, it means thatmost shareholders are turning a paper profit of 15 percent or more. A pricepullback will attract these shareholders into selling, in order for them toprotect their profits. This selling pressure will be either a temporary pull-back or a real new downtrend. Once again, as we saw in Chapter 3, theEffective Volume tool and divergence analysis will give you a much clearerpicture of where you stand. In the lower panel of Figure 4.12, I indicate thebuy zone 2, as calculated by the supply analysis tool represented in Figure4.11. The upper panel in Figure 4.12 indicates that the downtrend 1 in LargeEffective Volume was preventing us from buying the stock, even at a lowsupply level. It is only when large players moved in (as indicated by thesmall uptrend 3) that the trading rules allowed us to start buying.

The Case of Openwave Systems (OPWV) Figure 4.13 shows howdangerous it can be to trade only on the basis of the supply signal. (Referalso to Chapter 2, Figure 2.16a and b.) You can indeed see in the lowerpanel that the two correct buy signals (buy zone 1 and buy zone 2) arefollowed by an incorrect buy signal (buy zone 3). A catastrophic drop inthe stock price of Openwave Systems triggered this incorrect buy signal.


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FIGURE 4.11 Tellabs: supply analysis. This example clearly shows that the peaksof supply do not automatically correspond to the peaks in prices. Also, notice thatthe buy zone 2 came too early in the downtrend. This shows that other tools suchas the Effective Volume tool are necessary to select the best entry point.

Unfortunately in this case, the Effective Volume could not prevent us fromentering during the Large Effective Volume uptrend B in the upper panel ofFigure 4.14, just before another price collapse (downtrend C in the lowerpanel). This is the reason I also use the rule to buy when the price is aboveits 9-day simple average. This avoids being trapped into most of the catas-trophic situations.



FIGURE 4.12 Tellabs: Effective Volume analysis. The Effective Volume tool is anexcellent complementary tool to the supply analysis tool.

One of the problems with the Large Effective Volume analysis is thatwe cannot discern if a buyer is buying the new-found value or if he is simplycovering a short position. We all know that large down-trends attract short-sellers. The owner of a share is always more eager to take action (sell)than someone who does not own it (yet who would be eager to buy it).Therefore it is probable that after a long down-trend, the first buying signswill be due to shorts covering their position. As we will see in Chapter 6,this is the reason why we need to look at the Large Effective Ratio signalthat we studied in Chapter 3. Indeed, the Large Effective Ratio allows us tocompare the present share accumulation to past accumulations, and thiscomparison allows us to judge the real strength of the buying movement:short covering must be accompanied by genuine buying in order to producea significant signal on the Large Effective Ratio tool.


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FIGURE 4.13 Openwave Systems: supply analysis. The supply analysis cannotdistinguish between a low supply signal that came from a normal pullback in priceand a low supply signal that happened because of a large catastrophic reversal. Othertools such as Effective Volume or the Effective Ratio are necessary to avoid untimelyentries.

The supply analysis signal is a very useful tool that gives unexpected(but good) results when used in combination with other tools. We will seein Chapter 6 how this signal can be used in a successful trading strategy.

Before going into the real world of how to make money, it is importantfor us to briefly study how funds get in and out of investment positions,since they are the ones that provide liquidity to the markets.



FIGURE 4.14 Openwave Systems: Effective Volume analysis. Figure 4.14 showsthat the Effective Volume analysis does not always prevent wrong calls. The LargeEffective Volume issued a buy signal inside of buy zone 3 indicated by the supplysignal. Both signals proved wrong later on.

FUNDS’ STRATEGIES

We saw in Chapter 1 that large players have a critical influence on the pricedirection, because funds have a propensity either to trade a large volumein the same price trend or to force a trend change.

However, if trading a large volume can give a large player real forceto move the market, it is also a giant weakness. Trading volume impliesthat a counterparty exists: If you want to buy one million shares, youneed someone ready to sell them to you. If the market is illiquid, largesales or purchases could have an impact on the price. This is the main


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reason there are parallel markets that allow funds to exchange sharesamong themselves.

For a retail investor, markets are fundamentally liquid. Retail investorscan indeed buy and sell shares at will without worrying about the in-fluence their trading will have on the share price. For funds, however,markets are in a constant illiquid situation: The size of the positionsthat funds must take is so large that they often need many days to loador unload positions. The supply/demand analysis is therefore critical forfunds.

We already know that managing a large position will have a significantimpact on the selection of tactics, depending on whether the fund wantsto buy or sell shares. Suppose that a fund wants to buy from the marketfive million shares of a company trading at $10. Suppose also that the av-erage number of shares traded is one million per day. If the fund does notwant to buy more than 10 percent of the daily volume, it can only purchasea maximum of 100,000 shares per day, and its accumulation tactics willbe carried out over 50 days. The Effective Volume tool will surely detectsuch tactics. However, such an accumulation could at some point start tohave an impact on price. Indeed, since the continuous buying could changethe balance between supply and demand, supply could dry up, forcing thestock price to increase.

Assume, for example, that after the purchase of three million shares,the price starts to increase, and is increased by 10 percent within a fewdays. Even if the fund did not get its targeted five million shares, it is al-ready showing a paper gain on the three million shares that have been pre-viously accumulated. The fund manager could then elect either to stop theshare accumulation or to continue to accumulate at a higher price at theask. This would push the price even higher and signal to the market that“a buyer is in town.” A new uptrend could then be triggered to the funds’advantage.

In summary, buying at the ask will work in the fund’s favor as long asthe fund has already accumulated a position in the stock.

Let’s now suppose that the fund needs to sell five million shares on themarket. It would be foolish to dispose of these shares in large blocks soldat the bid. This would certainly push down the price and the fund wouldincur an instant paper loss on its remaining shares. Therefore, selling alarge position into the market takes a lot of time and is more effectivelyachieved by increasing the shares offered at the ask without pushing theprice down, or by selling small quantities of stocks at the bid without totallyerasing the supply of shares.

In summary, selling blocks of stocks at the bid will work in a fund’sdisfavor if the fund still holds a large number of shares. The fund will bebetter off making sure that the bid/ask balance is not disturbed.



The question of supply is therefore critical for allowing large fundsto execute their moves. Funds need to start selling while the price is stillmoving up. If they wait too long, they could be forced to sell their large po-sitions into a downtrend, when the market is crowded with traders wishingto sell.

Position size has another important negative effect: Traditional fundsneed to take large positions, but they cannot use stop losses on these largepositions and therefore need to diversify to a great extent. Their extremediversification does not allow them to profit from good investment deci-sions in any significant way—hence the poor return of pension funds.

Let’s look at a few very tough situations that are in fact very commonoccurrences.

First, let’s have a look at Table 4.1. Every quarter, institutional playershave to report their buying/selling activity for all the securities in whichthey are active. These figures are readily accessible on the NASDAQ website (www.nasdaq.com). In Table 4.1, I’ve compiled the figures related tothe eight companies for which we studied the Active Boundaries indica-tor in Chapter 2. The first and the second columns represent the numberof outstanding shares and the percentage of institutional holdings, respec-tively, as reported by the NASDAQ web site at the end of June 2006. Thethird and fourth columns show, respectively, the number of shares thatwere traded during the prior quarter and the institutions’ position changesas reported by the NASDAQ. Finally, the last column gives a measure ofthe institutional activity during the last quarter. It is not entirely correct,because the institutions’ position changes are normally lower than theirreal activity. Indeed, if an institution buys one million shares during thequarter and sells it before the quarter ends, these two million shares willnot be reported. However, the last column gives you a good sense of theimportance of institutional activities for each stock.

Table 4.2 shows the detail of institutional activities. It is interestingto see that even though the global institutional activity is significant, thenet activity itself is small in percentage points compared to the numberof shares exchanged. For example, we can see that for Chico’s FAS, thenet activity was only –7.6 percent of all the shares exchanged during thequarter ending on June 30, 2006. As a comparison, the last column showsthe price change during the same period of time.

You can see by comparing the last two columns of Table 4.2 that thereis no clear correlation between a price drop and net selling activity by in-stitutions, or between a price increase and net buying activity.

Let’s take a closer look at Openwave Systems. During the quarter end-ing on June 30, 2006, institutions had been sellers of 30 million shares andbuyers of 24 million shares. During that period, Deutsche Bank AG wasthe largest buyer. This German bank increased its position in Openwave


TAB

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TAB

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209


210 VALUE IN TIME

Systems by 180 percent, adding 2.6 million shares. During that quarter, thestock fell from $22 to $11. Suppose that Deutsche Bank AG bought at $16.50on average. Since later on the stock price dropped to $6.10, we can easilycalculate that Deutsche Bank AG lost close to $10 per share, or $26 millionof paper loss. It may sound very large, but if we compare it to DeutscheBank AG’s total portfolio value of $168.4 billion, it is a loss of only 0.0154percent.

Let’s also have a look at Chico’s FAS. During the quarter ending onJune 30, 2006, institutions had been sellers of 49 million shares and buy-ers of 33 million shares. TIAA-CREF Investment Management LLC was thelargest buyer during that period. This fund increased its position in Chico’sFAS by 360 percent, adding 4.9 million shares. During that quarter, thestock fell from $40 to $27. Suppose that TIAA-CREF Investment Manage-ment bought at $33.50 on average. Since later on the stock price droppedto $18, we can conclude that TIAA-CREF lost close to $15.50 per share, ora $76 million paper loss. Compared to the total portfolio value of $127.7billion, it is a loss of only 0.059 percent.

Both institutions that are singled out run more than 4,000 positionssimultaneously, meaning that they are very well diversified. As a matter offact, they both manage a wide range of funds in different market sectors.A loss of $10 million on a position is probably compensated by a gain onanother position. Diversification is important if you cannot use stop-lossstrategies to get out of a losing position quickly. This is the case when youbuild a large position in a single stock, which is what funds do. However,because of their very large diversification, none of the large funds can everexpect to beat the markets on a consistent basis. They will gain when themarket increases and they will lose when the market decreases. The maingoal of diversification is risk control; the drawback is a lack of significantreturn. This is usually the case with pension funds, which follow strategiesthat are even more defensive.

The press is very fond of going after “the big, ugly market manipulatorsand rogue traders.” Everybody has read about such-and-such hedge fundgoing under because of the very large positions that were taken against anonconsenting market. I am in no position to praise or criticize; however,the reality is that managing a large fund is a very difficult task. That task isoften more about managing a sizable position than it is about the right entryand exit timing. I do not really sympathize with the two fund managerswho had to go through $26 million and $76 million of paper loss becausethey bought into a falling stock price. However, I think that fund managerswho can consistently beat the market managing a large fund deserve ourrespect.

Now, wait a minute! Didn’t I write in Chapter 1 that large players arethe ones responsible for price movements? In such a situation where even



large funds can routinely lose large amounts of money, is it worthwhile to

follow the moves of large players?

Yes, it is! At least if you know what you are looking at. I believe thatthe terms active and passive are indeed accurate for describing differenttypes of funds. Passive funds invest in baskets of shares that follow long-term sector trends. Active funds follow specific investment strategies thatinvolve both fundamental and technical analysis.

Active funds have a tendency to carefully select the stock in which theyare invested. The best active funds have key analysts who spend quite alarge amount of time studying the fundamentals of companies or checkingthe sales channels. Active funds invest both in blue-chip companies as wellas in midsize ones.

However, if you are a retail investor who wants to follow large players,you are better off investing mainly in midsize stocks. Indeed, blue chipsare traded by both active and passive funds. This makes it more difficult todistinguish Effective Volume movements triggered by active funds, sincemany passive funds will also trade large volumes. Passive funds seldominvest in midsize companies that are not part of the main stock indexes,because they would carry considerable volatility. Following the moves oflarge funds in these midsize companies provides many opportunities forprofit.

FUNDS AND MARKET MANIPULATION

Playing the market is very difficult for funds. By contrast, look at the manykey advantages that a retail investor can enjoy. Retail investors:

� Are always eager to study and gain knowledge, because it is theirmoney on the line.

� Can wait for a trend to start before entering a position.� Can quickly enter and exit positions.� Can concentrate on a few good plays.

In such a difficult environment for funds, the next natural question is:How do funds make money? Maybe it’s just an innocent question, but afterwhat we learned in the previous section, it is quite natural to wonder howthese traditional funds still give an acceptable return to their investors.

We all know that hedge funds use specific strategies that involve com-plex financial instruments in related markets. The question is more appli-cable to traditional funds: How do they make money? Funds that tradesuccessfully find pools of temporary illiquid situations from which they can


212 VALUE IN TIME

profit. The most common illiquid situations are those explained earlier inthis chapter, where the price of a stock has decreased more than what theunderlying fundamental value dictates, but where the strong decline haslocked most investors into the stock.

The supply analysis method described earlier in this chapter gives agood general view of the supply situation. However, one question has re-peatedly come to my mind: When a large fund accumulates a stock that is

in an illiquid situation, can this accumulation be conducted without a

manipulation that would keep the price low or cheap enough? Is it possi-ble for a fund to accumulate a significant number of shares on the marketwithout inducing a price increase? In other words, can funds manipulatethe market? Do they need to manipulate markets in order to make a profit?These are not innocent questions, since we know from Chapter 1 that ma-jor news is already shown in the Effective Volume signal before it becomespublic. This means that insider trading is a standard practice, not an ex-ception. My next innocent (but very scary) question, then, is: Is stock price

manipulation also a standard practice?

In the Introduction, I discussed the decimalization revolution that ren-dered the Effective Volume method possible. If you remember, I wrote:“Decimalization killed market visibility and, as some believe, may have en-couraged price manipulation.” What does this mean?

� Market visibility. Decimalization lowered the price spread by a factorof 6.25. This greatly reduced the disadvantage of placing market orders(buying at the bid or selling at the ask) compared to placing limit orders(placing a buy order at or below the bid or a sell order at or above theask). Large players therefore simply stopped placing large limit ordersand instead placed repetitive and fragmented market orders.

� Price manipulation. Since the price spread reduction caused by thedecimalization eliminated the incentive to place limit orders, it becametheoretically possible for a large fund to place a large buy market orderfollowed by a small sell market order that would push the price backdown. Before decimalization, price manipulations were quite difficultsince there were large buy and sell limit orders at each bid and askposition, respectively.

Because we’ve covered the different concepts of the book in so muchdetail, it is now time to come back to these bold statements regarding mar-ket visibility and price manipulation. To address these issues, we need tostudy the resistance to change that I introduced at the beginning of thischapter. The resistance to change is defined as the market’s natural reac-tion to a price change in a specific direction. To measure a resistance, wemust naturally first get a change.



Let’s look at the resistance to a buying pressure. We saw that the Ef-fective Volume is, by definition, the volume responsible for small pricechanges. When large players buy a stock, they must fight the resistanceof sellers who will place market sell orders. They must also fight againstthe resistance of sellers who placed or will place limit sell orders. I callthose who place market sell orders active sellers and those who place limitsell orders passive sellers.

The Effective Volume can be separated into two components: the pos-itive Effective Volume and the negative Effective Volume. The negative Ef-fective Volume will appropriately measure the active selling force. How-ever, how can we measure the passive selling force?

This force is clearly to be found in the “Non-Effective Volume.” ThisNon-Effective Volume is the volume that has no impact on price changes. Ibriefly inserted it into the divergence analysis (presented in Chapter 3) byusing the Effective Ratio. If you remember, the Effective Ratio measures achange in the ratio of the Effective Volume to the total volume. This totalvolume includes both the Effective and the Non-Effective Volume.

However, if you remember the details of Chapter 3, the level of theEffective Ratio usually stays within a few percentage points of the totalexchanged volume. This means that the equilibrium between buyers andsellers is rather thin. I have been wondering, then, if the divergence weoften see between the Effective Ratio and the price rate of change cannotalso be influenced by the passive players.

For example, suppose that a large fund places a very large number ofshares for sale at the ask. This means that these shares will simply waitfor buyers to come in, and it is clear that the price will not move up untilthe whole ask has been bought out by these buyers. Because of the largevolume at the ask, the only way for small sellers to sell their shares is tosell them at the bid, therefore lowering the price by one cent. However, ifthere is active buying on the stock, midsize buyers will come buy the stockavailable at the ask, increasing the price by one cent on midsize volume.In this example, this means that the Effective Volume pattern will be anincreasing Effective Volume; it represents the active buyers together witha flat price trend that is formed because a price limit was set by the largevolume at the ask. The same pattern would also appear if a large sellerwere to send a regular number of shares for sale at the ask.

This is an extreme case, of course, because I have not experiencedsuch behavior yet, but it may be worthwhile trying to measure it.

Recall that traditional technical analysis tools linking volume to priceusually weight volume by some price spread. The Effective Volume the-ory says, however, that since we are at the microscopic level, the pricespread itself is not important. The mere fact that the price changed fromone minute to the next is what is important. Therefore, the theory says that


214 VALUE IN TIME

if 10,000 shares move the price by one cent, it has the same importance asif the price had moved by five cents. Indeed, in terms of Effective Volume,if the 10,000 shares move the price up by five cents, it does not mean thatthese shares are five times stronger than if the price had moved by only onecent. It just indicates that someone has been buying.

It also indicates something different, though: The price moved up fivecents not because the 10,000 shares were exceptionally strong, but simplybecause the supply of shares was not enough to offset this buying push of10,000 shares. This gives us a clue about how to measure the static resis-tance to change.

Indeed, we have learned that a positive Large Effective Volume is whatreally moves the price up. For example, we saw in Table 3.6 in Chapter3 that for Darden Restaurants, the Large Effective Volume was pushingthe price up or down by almost four cents on average, while the SmallEffective Volume was moving the price by only a little more than two centson average.

Therefore, to measure the resistance to the buying power of large play-ers, we only need to measure for a fixed period of time the size of positiveLarge Effective volume necessary to move the price up by one cent. In-deed, if large players need fewer shares to push the price up by one centon average, it means that the resistance from passive players is decreasing.

The calculation method must use the following steps:

1. Take an analysis window of your choice (usually the same window asthe one used for the divergence analysis. In the case in Figure 4.15, Itook 3.3 days).

2. Add all the positive Large Effective Volume during the analysiswindow.

3. During the analysis window, add all the price increases that occurredduring the price inflections linked to the positive Large EffectiveVolume.

4. Divide the positive Large Effective Volume obtained in step 2 by theresult obtained in step 3.

5. Move the analysis window by one minute, redo the calculation, andplot the results as in Figure 4.15, which represents the resistance tochange.

Figure 4.15 must be compared to Figure 4.16, which represents boththe Large Effective Volume flow and the price during the same analysisperiod of 100 trading days.

When looking closely at Figure 4.15, we can see a strange pattern thatdoes not make much sense: The start of the buying activity by large players,



FIGURE 4.15 Tellabs: number of positive Large Effective Volume shares neces-sary to move the price up by one cent on average.

FIGURE 4.16 Tellabs: Large Effective Volume versus price analysis.


216 VALUE IN TIME

FIGURE 4.17 Tellabs: resistance to buying versus resistance to selling.

around December 2 (point A of Figure 4.16), corresponds to a strong re-sistance (R1 in Figure 4.15). Then, from December 2, it seems that theresistance progressively slows down (down arrow from R1). This is verycounterintuitive: If the price of a stock increases (up arrow from point Ain Figure 4.16), more passive sellers should normally appear and the resis-tance should increase. Why do we see the opposite?

Since I did not have a good explanation, I decided to do the same ex-ercise by measuring the resistance to the selling activity by large players. Ithen depicted those results with the results of the resistance to the buyingactivity by large players (see Figure 4.17). Figure 4.17 is rather interesting:It shows that the resistance to selling pressure is following the same pat-tern as the resistance to buying pressure. That goes against all commonsense, so I conclude that passive resistance is negligible compared to ac-tive resistance.

Indeed, when you look at the 3.3-day pattern of positive Large EffectiveVolume and you compare it to the negative Large Effective Volume pattern,you get the results shown in Figure 4.18a. You can see that the positiveEffective Volume and the negative Effective Volume are pushing the mar-ket in opposite directions at the same time:

� When large buyers are getting stronger (arrow is up on the positiveLarge Effective Volume), large sellers are also getting stronger (arrowis down on the negative Large Effective Volume).

� When large buyers are getting weaker (arrow is down on the positiveLarge Effective Volume), large sellers are also getting weaker (arrowis up on the negative Large Effective Volume).



FIGURE 4.18a Tellabs: positive and negative Large Effective Volume.

In practical terms, this means that the markets are very efficient: When-ever a large player buys enough shares to slightly move the price up bya few cents, another large player will sell almost an equivalent numberof shares that will bring the price back to its previous position. In lightof my statement that decimalization killed market visibility while favoringprice manipulations, I now need to comment on market visibility and pricemanipulation.

� Market visibility. The fact that it is very difficult to measure the pas-sive buying or selling resistance levels shows that limit orders are notan important component of the general resistance to change comparedto active resistance. This means that the order book does not influencethe market and does not give any visibility to the direction of thebalance between buyers and sellers. Decimalization indeed killedvisibility.

� Price manipulation. What Figure 4.18a and 4.18b tells us is that pricemanipulation is virtually impossible due to the very fine balance be-tween positive and negative Large Effective Volume. Indeed, with sucha balance, buying or selling of small amounts of shares is not likely topush the price up or down and therefore is not a good means for con-trolling the share price during accumulation or distribution. We cantherefore conclude that due to market forces, price manipulation isnot a possibility, at least for those stocks that trade significant volume.In order to confirm my findings quantitatively, I ran a detection algo-rithm on the minute data on many stocks that were in a price tradingrange, but could not find any mathematically significant discrepancythat could be explained by price manipulation.


218 VALUE IN TIME

FIGURE 4.18b Tellabs: Large Effective Volume balance. In a trading range, thefine balance between the positive and negative Large Effective Volume shows thatmarkets are efficient for very liquid stocks, and that therefore no significant pricemanipulation can take place.

WHAT WE LEARNED REGARDING THESUPPLY ANALYSIS

This chapter concludes the set of new concepts that I wanted to introduce:

� Chapter 1: Effective Volume.� Chapter 2: Active Boundaries.� Chapter 3: Effective Ratio and divergence analysis.� Chapter 4: Supply analysis.

We started this chapter by learning how important it is to measure thelevel of the supply of shares, and how to effectively perform such a mea-sure. We then moved on to study how funds must play in an illiquid environ-ment, and learned that funds have great difficulty making money primarilybecause the size of the position a fund must take is so large. We also discov-ered that markets are very efficient and that therefore price manipulationis not a likely scenario for funds.

This leads us to the next section of the book: how to make money, notonly for retail players, but also for traditional funds that need to managelarge positions. Indeed, we now understand how the market really works,and we know which tools can show us how it works. What we do not haveyet is a system that allows us to make money. It is a trading system that weneed, one that will constantly generate a profit.

In Part Two we will first review in Chapter 5 how to measure therisk/return balance of a trading strategy. We will then move on to the studyin Chapter 6 of a variety of successful trading strategies that are based ona combination of the tools presented in Part One.


P A R T T W O

Trading Strategies


C H A P T E R 5

PerformanceThe Risk/Return Balance

A t this point in the book, what we have is a set of trading signals.Before going on to the next chapter, which compares different trad-ing strategies, it is worthwhile to take a fresh look at the risk/return

balance. Indeed, it makes no sense to compare trading strategies in termsof the risk/return balance if we do not have a clear idea of what we arecomparing. “But wait a minute,” you might say. “It is simple: The returnis the money you expect to make, and the risk is the money you mayeventually lose.”

This is entirely true. There are many books that deal with portfolio per-formance analysis and comparison using the risk/return balance. You canindeed find many tools that allow the ranking of funds within that frame-work. (In this field, I highly recommend the book Hedge Funds: Quan-

titative Insights, by Francois-Serge Lhabitant.) However, the same toolscannot be used at the level of trading strategy, because a trading strategyproduces a set of investment opportunities from which the portfolio man-ager must choose. Therefore, the risk/return rating of a portfolio mixesboth the trading strategy’s efficiency and the portfolio manager’s stock-picking skills. In Chapter 6 we will be comparing different trading strate-gies, so it is important to first understand what we are measuring and howto measure it.

In this chapter, I review some of the formulas used to evaluate the per-formance of a trading strategy in terms of both risks and returns. Someof these formulas are complex and could be misleading, since they usehypotheses that are not always correct. Just because a formula is com-plex does not mean that it is useful. On the contrary, because the more

221


222 VALUE IN TIME

complex formulas do not necessarily reflect a trader’s everyday reality, Iprefer to use much simpler performance measures: the yearly expected re-turn (YER) and the monthly loss transferred (MLT) by the trading strategyto the portfolio. The YER is used as a measure for the return of the tradingstrategy, while the MLT is used to measure risk.

A successful trading strategy must not only generate good perfor-mance figures, but also be easily integrated into an automated tradingsystem.

I liken trading stocks to milking cows. When I was a boy I used to workon my uncle’s farm over the school holidays. My uncle had just bought anautomatic milking machine, and he explained that with it he could milk hiscows much more quickly. In other words, he got more milk per unit of timewith the machine than he could get milking by hand. Have you ever triedto milk a cow by hand? It is very difficult to do: You sit with your head onthe cow’s flank, firmly grabbing one teat in each hand. Then you pull onthe teats while gently squeezing them to get the milk out, all while the cowcontinuously hits your head with her tail. Trading stocks without a systemis a similarly tedious process.

Using a trading system to generate trades is similar to introducing anew milking machine on a farm: We need to evaluate its performance andthen try to optimize it. In the farm example, we need to rate the automaticmilking machine against what the herd can produce by hand-milking. In thesame fashion, when we evaluate our trading system, we also need to rateit against a buy-and-hold strategy executed on the same sample of stocksover the same time period as the one used by the trading system. Further-more, in order to test the efficacy of a milking machine, we could separatethe good milk-producing cows from the rest, to see if, compared to hand-milking, the machine gives better performance for each group of cows. Wecan do something similar with stocks. We can measure what a group ofstocks can produce with a buy-and-hold strategy versus some other con-trol group. This will allow us to rate the performance of our trading system.We will see how to do that later in this chapter. The term trading system

usually implies that the trading strategy is used in a systematic (almost me-chanical) way. However, in this chapter and the next I use the terms trad-

ing strategy and trading system interchangeably since I believe that a truetrading strategy must be turned into a mechanical system to be objectivelyeffective.

Let’s now examine what sorts of risks are inherent in a trading system.In the same way that we should not confuse the risk of a portfolio withthe risk of a trading strategy, we should not confuse the risks of the stockmarket in general with the risk of a trading strategy. The stock market risksare very well known, and measures of market volatility or periodicity ofcrash occurrences can indicate how risky a market is. In order to lower


Performance 223

our risk, we could select stocks from different sectors, with different betavalues. (William Sharpe defined the term beta in his capital asset pricingmodel theory. Without going into the detail of the formula, the beta valueof a stock is a measure of its volatility relative to the asset class. Stocksthat enjoy a beta value higher than 1 are more risky than the general stockmarket, but offer potentially higher returns. Stocks with a beta value lowerthan 1 are less risky than the general stock market, but offer lower returns.)However, this stock selection strategy has nothing to do with measuringthe risk of the trading system.

THE TRADING STRATEGY

Let’s first try to answer two basic questions: What is a trading strategy, andwhat are the objectives of a trading strategy?

What Is a Trading Strategy?

A trading strategy is a method that allows the production of a regular flowof trades in a repetitive process. It can be compared to a production linethat includes four components:

1. The raw materials. These are the trading signals that allow us to entera trade.

2. The tuning parameters. These are the different ways to control exitingof a trade. Stop loss levels, profit targets, and time limit levels are threetuning parameters that I use (the time limit level consists of selling thestock after a certain number of days).

3. The products. These are the trade returns, linked to the number of daysthat it took to produce each return.

4. The by-products. There are two types of by-products: the riskslinked to each trade and the pain generated by the temporary pricedrawdowns.

What Are the Objectives of a Trading Strategy?

The first objective of a trading strategy is to generate trades that will allowa better return, on average, than the returns produced by a buy-and-holdstrategy.


224 VALUE IN TIME

The second objective of a trading strategy is to shield us against a va-riety of risks:

� The first risk is simply the risk of not being able to reach our expectedreturn.

� The second risk is market risk. The trading strategy must provide goodprotection against the expected negative impact that a bad marketwould have on the group of stocks we are following.

� The third risk is the trader’s erratic reactions when facing conditionsof exuberant joy or excruciating pain.

� The fourth risk is simply the trader’s wrong analysis.

Applying the Strategy

My objective in this chapter is to help you study the products and by-products generated by the trading strategy and see how each of the tuningparameters may influence them. We need to understand how the machin-ery functions so that in the following chapter we will be able to select thebest trading strategies and optimize them.

It would be very difficult for me to explain the different ideas of thischapter without using practical examples. I have therefore generated asmany trades as possible using a sample trading strategy explained next. Wewill then be able to see by what mechanism and to what extent the differenttuning parameters help meet the sample trading strategy’s objectives.

For this work, I used the minute data of 159 stocks. As shown in Fig-ure 5.1, the majority of the data covers the years 2005 and 2006, a periodthat corresponds to two years of positive market trend (see Figure 5.2).Any trading strategy used during that period would naturally produce pos-itive returns and low risks. Finally, I sorted the 159 stocks by performance,collected the 69 worst-performing stocks in a laggards group, the 97 bestperformers in a highfliers group, and took some stocks from each group toform a standard group (see Table 5.1). Please note that I also included 7 ofthe worst “highfliers” among the “laggards” group, where they became thebest “laggards.”

Please note three points:

1. Even though 159 stocks may look like a large sample, it is still relativelysmall compared to the thousands of stocks and the different marketson which the method can be used.

2. Data availability dates back only to April 2001, a period that is not longenough to qualify the method during changing market conditions.

3. Past results do not indicate future results.


Performance 225

FIGURE 5.1 Distribution of the sample of stocks used to test the tradingstrategies.

Although the different trading methods presented in Chapter 6 shouldbe tested on more samples and over a longer period of time, I decided topublish these very promising preliminary results.

In this chapter, I use data from only the standard group of stocks. How-ever, in Chapter 6, the optimization process makes use of the three differ-ent groups.

FIGURE 5.2 S&P 500 trend during the last six years.


226 VALUE IN TIME

TABLE 5.1 Types of Stock Groups Used for the Analysis

Group of StocksNumber ofStocks

Number of TradingDays Analyzed

Average Number ofTrading Days perStock

Laggards group 69 35,023 508Standard group 101 41,843 414Highfliers group 97 47,584 491

The sample strategy that I use in this chapter consists of buying a stockwhenever the price is higher than its nine-day average, and keeping it un-less obliged to sell due to one of the tuning parameters. Since before in-vesting, the price was lower than the nine-day average, the objective of thisstrategy is to run price uptrends, catch them early enough, and hope thatthey develop into full long-term trends. The idea of using this very simpletrading strategy is to be able to study how the different tuning parameterswork, which are separate from the trading signals generated by the strategyitself. The goal of this chapter is to work out the relationship between thedifferent tuning parameters (the stop loss level, the time limit level, and theprofit target) and to study how they influence the performance of a tradingstrategy, in terms of both returns and risks. I believe that the behavior ofthe three tuning parameters is independent of the trading strategy and ismore related to the market cycle.

OPTIMIZING PROFITS

Makeup is to beauty as profit optimization is to trading strategies. Makeupon its own cannot turn a homely girl into a beauty queen; it only enhancesalready attractive features. Similarly, profit optimization only works with atrading strategy that is already robust. Let’s first define what we call profitand then see how to enhance it.

How to Measure the Return of a Trading Strategy

Running the sample strategy will produce a set of independent trades suchas those shown in Table 5.2. The buy order would be triggered by the trad-ing signal produced by the sample trading strategy, while the rightmostcolumn indicates the four reasons for selling the stock: reaching a stoploss level, a profit target, a time limit level, or a sell signal generated by theindicators.


Performance 227

TABLE 5.2 Trades Produced by a Trading Strategy

Buying Selling Reason forSymbol Buying Date Price Selling Date Price Selling

KG 10/25/2006 $16.94 12/15/2006 $16.31 Time limitRHT 10/24/2006 $19.42 10/26/2006 $14.83 Stop lossBDX 10/20/2006 $72.14 12/26/2006 $70.57 Time limitCTSH 10/18/2006 $77.41 11/8/2006 $77.12 Sell signalUPL 10/10/2006 $49.16 12/18/2006 $48.07 Time limitHAL 10/6/2006 $27.68 11/15/2006 $33.57 Profit targetCVO 10/4/2006 $19.02 12/26/2006 $19.85 Time limitCTSH 9/29/2006 $74.01 10/5/2006 $75.44 Sell signalCME 9/27/2006 $479.35 11/17/2006 $534.80 Sell signalCDNS 9/26/2006 $16.82 10/26/2006 $17.79 Sell signal

You do not need more information than what is stated in Table 5.2 inorder to measure the return of a trading strategy: You only need to knowhow much you are winning or losing and the number of days during whichyou held your shares.

There is a fascinating book written by Ralph Vince, called Portfolio

Management Formulas. In it he uses the mathematical methods employedin casino games and sports bets to rate stock-trading systems. The ideais that a stock-trading system produces a number of bets (or trades) thatgenerate either a positive or a negative return. By analyzing the past flow ofreturns, Vince says that it is possible to anticipate what the future returnsof the trading system will be.

What we are trying to do is to build a trading system that will giveus a positive mathematical expectation of winning. What does this mean?It means that if we invest $1,000 using the trading system, then we canreasonably expect the return on that $1,000 sum to be, on average, similarto the returns we had in the past using that system.

It is true that sometimes retail investors find themselves in the samesituation as the casino gambler, except that the odds are supposed to be intheir favor, while the odds of the casino gambler are in favor of thehouse. The similarities between casino gambling and trading are that thereare an almost unlimited number of bets and you can play any amountof money; you are only limited by the amount of your wealth. (This isvery reasonable for traders; otherwise they would not be traders, for in-dividuals who have a billion dollars would probably not spend their timetrading.)

Vince therefore defines the performance ratio (PR) and the pessimisticreturn ratio (PRR) as the two measures that will rate a trading system,


228 VALUE IN TIME

using only the number of trades and the return generated for each trade.He also introduces the optimum “f,” which is simply the optimum amountof money that we must risk on each trade in order to obtain the maximumPR or PRR. I advise day traders and short-term traders to have a good lookat what Ralph Vince says.

Using Vince’s formula, if our trading system generates 50 positivetrades with an average return of 12 percent and 40 negative trades withan average return of −8 percent, it is obvious that the mathematical expec-tation of winning is positive. It is indeed calculated as:

Performance ratio = Ratio of positive trades × Average positive returnRatio of negative trades × (−Average negative return)

Performance ratio = (50/90) × 12%(40/90) × 8%

= 1.875

However, if on average each positive trade lasted 20 days and eachnegative trade lasted 50 days, we can see that the trading method gener-ated 1,000 positive trading days with an average daily positive return of0.6 percent and 2,000 negative trading days with an average daily negativereturn of −0.16 percent.

50 × 20 = 1,000 positive trading days

with an average daily positive return of 12%/20 = 0.6% per day

40 × 50 = 2,000 negative trading days

with an average daily negative return of −8%/50 = −0.16% per dayWe could therefore say that the method produces a return of 0.0933

percent on average for each trading day, calculated using the followingformula:

(1,000 × 0.6%) − (2000 × 0.16%)3,000

= 0.0933%

If you consider that in one year there are about 250 trading days, themethod could statistically generate 0.0933% × 250 = 23.33% per year (with-out using the compounding effect). If you use calendar days, you need tomultiply the daily percentage by 365.


Performance 229

However, consider the reverse of the previous situation: If each posi-tive trade lasted 50 days and each negative trade lasted 20 days, the methodwould generate 2,500 positive trading days (with an average daily positivereturn of 12 percent) and 800 negative trading days (with an average dailynegative return of −8 percent).

50 × 50 = 2,500 positive trading days

with an average daily positive return of 12%/50 = 0.24% per day

40 × 20 = 800 negative trading days

with an average daily negative return of −8%/20 = −0.4% per dayWe could then expect a return of 0.08465 percent for each trading

day:

(2,500 × 0.24%) − (800 × 0.4%)3,300

= 0.08465%

This would statistically generate a yearly return of 21.21 percent, lowerthan the yearly return of 23.33 percent obtained when the negative tradestook longer than the positive trades.

We can easily see that two sets of trade productions that generate thesame performance ratio could perform differently in a portfolio, simply be-cause of the average time difference it takes between the sets to completeeach positive and negative trade.

As shown by Ralph Vince, the typical measure of performance in acasino is the performance ratio, which only involves the number of betsand the return on each bet. However, since the return on trading strategiesis not instantaneous, it is better to rate the trading strategy by measuringthe average return per holding day multiplied by the number of days dur-ing a reference period. Since the reference period is usually one year, I callthis measure the yearly expected return (YER). The YER is the ideal re-turn that you will have using the trading strategy if you are invested 100percent of the time. Since it is very hard to be invested 100 percent of thetime, you could consider yourself fortunate to generate 80 percent of theoptimal YER.


230 VALUE IN TIME

The Pessimistic Return Ratio

Ralph Vince defines the pessimistic return ratio (PRR) as the ideal measurefor ranking different trials during the optimization process of a tradingstrategy (Portfolio Management Formula, p. 46). The PRR is defined as theperformance ratio (PR) with one fewer square root winner and one moresquare root loser. This adjustment will give more weight on sets of tradesthat include more trades.

PRR =[(W − √

W)/T]

× AW[(L − √

L)/T]

× AL

where W = the number of winning or positive tradesL = the number of losing or negative trades

T = W + L = the total number of tradesAW = the average winning trade amountAL = the average losing trade amount

As indicated by Ralph Vince, a PRR value greater than 2.0 indicates a verygood system. A ratio greater than 2.5 is excellent.

My small comment on this PRR measure is that although it is excellentat measuring a set of trades, it does not include the time that it takes forthese trades to unwind. Hence the need for the YER measure.

YER = Average return per tradeAverage number of holding days per trade

× AD

AD is 250 (the number of trading days in one year) if you use the realnumber of days open for trading to calculate the average number of holdingdays per trade.

AD is 365 if you use the number of calendar days to calculate the aver-age number of holding days per trade.

Note that the YER figure does not include the compounding effect ofthe daily gain, since it considers each trade as independent from the next.It is not the same as when you are using a set of trades inside a portfolio.The compounding effect can be dealt with by using the average logarithmicdaily return for every day in the trade.

Let’s see how the YER is calculated in Table 5.3. In Table 5.3, I calcu-lated the return per trade, as well as the number of days it took to realizeeach of these returns. I then calculated the average number of days (42)


TAB

LE

5.3

Corr

ect

Cal

cula

tion

of

the

Yea

rly

Expec

ted

Ret

urn

(YER

)

Bu

yin

gB

uy

ing

Se

llin

gSe

llin

gR

ea

so

nfo

rIn

ve

ste

dP

rofi

t/Sy

mb

ol

Da

teP

rice

Da

teP

rice

Se

llin

gD

ay

sL

os

s

KG

10

/25

/20

06

$1

6.9

41

2/1

5/2

00

6$

16

.31

Tim

elim

it5

1−3

.72

%R

HT

10

/24

/20

06

$1

9.4

21

0/2

6/2

00

6$

14

.83

Stop

loss

2−2

3.6

4%

BDX

10

/20

/20

06

$7

2.1

41

2/2

6/2

00

6$

70

.57

Tim

elim

it6

7−2

.18

%C

TSH

10

/18

/20

06

$7

7.4

11

1/8

/20

06

$7

7.1

2Se

llsi

gnal

21

−0.3

7%

UPL

10

/10

/20

06

$4

9.1

61

2/1

8/2

00

6$

48

.07

Tim

elim

it6

9−2

.22

%H

AL

10

/6/2

00

6$

27

.68

11

/15

/20

06

$3

3.5

7Pr

ofi

tta

rget

40

21

.28

%C

VO

10

/4/2

00

6$

19

.02

12

/26

/20

06

$1

9.8

5T

ime

limit

83

4.3

6%

CT

SH9

/29

/20

06

$7

4.0

11

0/5

/20

06

$7

5.4

4Se

llsi

gnal

61

.93

%C

ME

9/2

7/2

00

6$

47

9.3

51

1/1

7/2

00

6$

53

4.8

0Se

llsi

gnal

51

11

.57

%C

DN

S9

/26

/20

06

$1

6.8

21

0/2

6/2

00

6$

17

.79

Sell

signal

30

5.7

7%

Av

era

ge

42

1.2

8%

YE

R1

1.1

1%

231


232 VALUE IN TIME

per trade as well as the average profit/loss generated per trade (1.28 per-cent). The YER is simply the 1.28 percent return obtained in 42 days, ex-trapolated to 365 days, which gives 11.11 percent. Of course, this is just anexample calculated for trades initiated between September 26, 2006, andOctober 25, 2006. In reality, the YER calculation is realized using hundredsof trades. What Table 5.3 means for practical purposes is that this trad-ing strategy generates trades that, on average, last 42 days and yield 1.28percent per trade. This is not a very good strategy, but it is interesting asan example, because of the −23.64 percent loss that occurred on RHT onOctober 24, 2006.

Another way to calculate the YER consists in first calculating the YERper trade, and then taking the average for all the trades. Table 5.4 showssuch a calculation, which yields a YER of −388.63 percent. This secondmethod is not valid, because it assigns more weight to the returns gener-ated during short-term trades than to the returns generated during longer-term trades. In fact, we see in the example of Table 5.4 that the RHT lossthat occurred during a very short period (two days) is measured as a tradecarrying a YER of −4313.47 percent. Since the Table 5.4 method allocatesthe same weight to the YER of each trade to calculate the average YER, weend up with an aberrant −388.63 percent YER for the method.

Furthermore, to make the simulation more realistic, I will also intro-duce a cost per trade of 0.25 percent of the amount invested, to cover thecommission and the slippage costs. This is to say that an investment of$10,000 would cost us $25 when we buy and $25 when we sell. Out of this$25 sum, a commission of $10 is normal for online trading, while $15 forslippage costs seems realistic. For example, if the stock we want to buy istrading at $15 and we want to buy at the ask, we will pay $0.01 more thanif we buy at the bid (if there are enough shares offered at the ask). For anamount of $10,000, we will have to purchase 667 shares, incurring a slip-page cost of $0.01 × 667 shares = $6.67. By taking instead a higher figureof $15 as slippage cost, I foresee that we might have to bid the price stillhigher to buy the shares.

Subtracting a 0.25 percent cost per trade means subtracting 0.5 percentfrom each line of Table 5.3, which produces the results shown in Table 5.5.

In order to analyze the influence of the three parameters (the profit tar-get, the stop loss, and the time limit) on the return of the trading strategy,independent of buy/sell signals that could be generated by the trading tools,the sample trading strategy was run over the three groups of stocks of Ta-ble 5.1: the laggards group, the standard group, and the highfliers group.

As a reference, Table 5.6 summarizes the return produced by using abuy-and-hold strategy on the three groups. A buy-and-hold strategy simplystates that we buy the stock on the first day and sell it at the last day forwhich we have valid data. I use the same method to calculate the return of


TAB

LE

5.4

Inco

rrec

tC

alcu

lati

on

of

the

Yea

rly

Expec

ted

Ret

urn

asth

eA

vera

ge

of

the

YER

per

Tra

de

Bu

yin

gB

uy

ing

Se

llin

gSe

llin

gR

ea

so

nfo

rIn

ve

ste

dP

rofi

t/P

rofi

t/L

os

sY

ER

pe

rSy

mb

ol

Da

teP

rice

Da

teP

rice

Se

llin

gD

ay

sL

os

sp

er

Da

yT

rad

e

KG

10

/25

/06

$1

6.9

41

2/1

5/0

6$

16

.31

Tim

elim

it5

1−3

.72

%−0

.07

%−2

6.6

2%

RH

T1

0/2

4/0

6$

19

.42

10

/26

/06

$1

4.8

3St

op

loss

2−2

3.6

4%

−11

.82

%−4

,31

3.4

7%

BDX

10

/20

/06

$7

2.1

41

2/2

6/0

6$

70

.57

Tim

elim

it6

7−2

.18

%−0

.03

%−1

1.8

6%

CT

SH1

0/1

8/0

6$

77

.41

11

/8/0

6$

77

.12

Sell

signal

21

−0.3

7%

−0.0

2%

−6.5

1%

UPL

10

/10

/06

$4

9.1

61

2/1

8/0

6$

48

.07

Tim

elim

it6

9−2

.22

%−0

.03

%−1

1.7

3%

HA

L1

0/6

/06

$2

7.6

81

1/1

5/0

6$

33

.57

Profi

tta

rget

40

21

.28

%0

.53

%1

94

.17

%C

VO

10

/4/0

6$

19

.02

12

/26

/06

$1

9.8

5T

ime

limit

83

4.3

6%

0.0

5%

19

.19

%C

TSH

9/2

9/0

6$

74

.01

10

/5/0

6$

75

.44

Sell

signal

61

.93

%0

.32

%1

17

.54

%C

ME

9/2

7/0

6$

47

9.3

51

1/1

7/0

6$

53

4.8

0Se

llsi

gnal

51

11

.57

%0

.23

%8

2.7

9%

CD

NS

9/2

6/0

6$

16

.82

10

/26

/06

$1

7.7

9Se

llsi

gnal

30

5.7

7%

0.1

9%

70

.16

%A

ve

rag

e−3

88

.63

%

233


TAB

LE

5.5

Cal

cula

tion

of

the

Yea

rly

Expec

ted

Ret

urn

(YER

)In

cludin

gT

radin

gC

ost

s

To

tal

Bu

yin

gB

uy

ing

Se

llin

gSe

llin

gR

ea

so

nfo

rIn

ve

ste

dP

rofi

t/T

rad

ing

pro

fit/

Sy

mb

ol

Da

teP

rice

Da

teP

rice

Se

llin

gD

ay

sL

os

sC

os

tsL

os

s

KG

10

/25

/20

06

$1

6.9

41

2/1

5/2

00

6$

16

.31

Tim

elim

it5

1−3

.72

%−0

.50

%−4

.22

%R

HT

10

/24

/20

06

$1

9.4

21

0/2

6/2

00

6$

14

.83

Stop

loss

2−2

3.6

4%

−0.5

0%

−24

.14

%BD

X1

0/2

0/2

00

6$

72

.14

12

/26

/20

06

$7

0.5

7T

ime

limit

67

−2.1

8%

−0.5

0%

−2.6

8%

CT

SH1

0/1

8/2

00

6$

77

.41

11

/8/2

00

6$

77

.12

Sell

signal

21

−0.3

7%

−0.5

0%

−0.8

7%

UPL

10

/10

/20

06

$4

9.1

61

2/1

8/2

00

6$

48

.07

Tim

elim

it6

9−2

.22

%−0

.50

%−2

.72

%H

AL

10

/6/2

00

6$

27

.68

11

/15

/20

06

$3

3.5

7Pr

ofi

tta

rget

40

21

.28

%−0

.50

%2

0.7

8%

CV

O1

0/4

/20

06

$1

9.0

21

2/2

6/2

00

6$

19

.85

Tim

elim

it8

34

.36

%−0

.50

%3

.86

%C

TSH

9/2

9/2

00

6$

74

.01

10

/5/2

00

6$

75

.44

Sell

signal

61

.93

%−0

.50

%1

.43

%C

ME

9/2

7/2

00

6$

47

9.3

51

1/1

7/2

00

6$

53

4.8

0Se

llsi

gnal

51

11

.57

%−0

.50

%1

1.0

7%

CD

NS

9/2

6/2

00

6$

16

.82

10

/26

/20

06

$1

7.7

9Se

llsi

gnal

30

5.7

7%

−0.5

0%

5.2

7%

Av

era

ge

42

1.2

8%

0.7

8%

YE

R6

.77

%

234


Performance 235

TABLE 5.6 Return of the Buy-and-Hold Strategy

Group of Stocks Yearly Buy/Hold Return

Laggards group −2.1%Standard group 13.6%Highfliers group 38.9%

the buy-and-hold strategy as the one I used to calculate the YER in Table5.3. The returns of Table 5.6 will be compared later to the return of thesample trading strategy.

How to Get the Best Return from aTrading Strategy

It is already well known to traders that by quickly taking profit, the risk ofa downturn is limited. However, we also limit our future profit potential,since the stock we sold at a profit could continue climbing up and wouldhave perhaps given us a better return.

To analyze the impact of the profit target level on the return of thetrading strategy, we need to disable the two other parameters (the stoploss level and the time limit level). We can then see in Figure 5.3 that profittargets have practically no influence on the yearly expected return (YER).Without any use of stop loss or time limit, the profit target of our sam-ple trading strategy will yield approximately what a buy-and-hold strategy

FIGURE 5.3 YER of the standard group of stocks, calculated for different profittargets, without using stop loss, time limit, or transaction cost.


236 VALUE IN TIME

FIGURE 5.4 YER of Figure 5.3, but including a 0.5 percent transaction cost.

could have produced. This is not only true for the standard group of stocksas shown in Figure 5.3, but also for the two other groups. This is due tothe fact that as soon as we take our profit, the trading strategy will quicklyindicate that the stock price is above the nine-day average and force us tobuy. In other words, the sample trading strategy is not much different froma buy-and-hold strategy.

Including the 0.5 percent trading cost per completed trade, the yearlyexpected return is getting weaker for lower profit targets, as shown in Fig-ure 5.4; this is logical, since lower profit targets imply a higher churn rate(we will buy and sell more frequently than when using higher profit tar-gets). From this point forward in Chapter 5 and 6, all the YER calculationswill include the 0.5 percent amount for transaction costs.

If we now use different stop loss levels on the same trading strategywhile also applying a 10 percent profit target and then a 20 percent profittarget, we obtain the interesting data shown in Figure 5.5. Figure 5.5 mainlyindicates that tighter stop loss levels are hurting profits, especially whenusing smaller profit targets. This is primarily due to the more numeroustrades generating a high number of transaction costs. The fact that lowerprofit targets generate lower returns is not something that can be general-ized to every trading strategy.

With this small example, we can already see why we need to use re-alistic transaction costs when analyzing the return of a trading strategy.Therefore, whenever you use the services of a stock-picking web site, besure that the historical performance includes realistic transaction costs. Ieven know of a “timely” stock-picking service that will send its pick onlyafter the trade has moved at least 0.5 percent in its direction. That stock-picking service uses a −2 percent stop loss and 5 percent to 10 percent


Performance 237

FIGURE 5.5 YER of the standard group of stocks, calculated for different stoploss levels, using no time limit.

profit targets, but beats the market by a jaw-dropping margin year afteryear. Needless to say, its performance does not include transaction costs.

Figure 5.5 also indicates that we cannot analyze profit target and stoploss levels independently of each other. It is their combination that willinfluence the return of the trading strategy.

Indeed, let’s have a look at Figures 5.6 and 5.7, which show the num-ber of positive and negative trades depending on different stop loss levelsand profit targets. We can clearly see that lower profit targets increase the

FIGURE 5.6 Number of winning versus losing trades as a function of the profittarget.


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FIGURE 5.7 Number of winning versus losing trades as a function of stop losslevels, for a 20 percent profit level.

number of winning trades (Figure 5.6), while tighter stop loss limits in-crease the number of losing trades (Figure 5.7). By comparing Figure 5.7 to5.8, we can also see that the level of stop loss that produces more winningthan losing trades varies according to the profit target we use: If we usethe 20 percent profit target of Figure 5.7, we will need to place relativelywide stop loss levels (higher than −13 percent) in order to generate morewinning than losing trades. By comparison, Figure 5.8 shows that using a

FIGURE 5.8 Number of winning versus losing trades as a function of stop losslevels, for a 10 percent profit level.


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10 percent profit target allows us to place tighter stop loss levels (higherthan −8 percent) to obtain more winning than losing trades.

However, it is clear that even if the tighter stop loss levels generatemore losing trades, the average loss per losing trade will be lower. Sym-metrically, if lower profit targets produce more winning trades, the averageprofit per winning trade will be lower. This is the reason why people likeRalph Vince use the pessimistic return ratio (PRR) to compare differenttrading strategies, since this measure includes both the number of tradesand the profit/loss per trade.

Figure 5.9, by contrast, shows that none of the varied stop loss lev-els leads to satisfactory PRR levels (a PRR of 2.0 or above is rated asvery good by Ralph Vince), while Figure 5.10 shows that any profit tar-get will give very good PRR figures when no stop loss or time limit isinvolved.

In reality, however, we also have to consider the duration of each trade.Indeed, if we let our negative trades run longer than the positive trades, wewill soon have a portfolio that will be riddled with negative trades. It iscalled the “loss of opportunity” factor. A higher loss of opportunity will begenerated by the trading strategy in which losing trades take more timethan winning trades. Indeed, in a portfolio we have a finite amount of cashto invest, and by investing a portion of our cash in a losing trade we willlose the opportunity to invest it in a winning trade. When we quickly cut ourlosing trades, we consequently increase the average proportion of winningagainst losing trades in our portfolio. As an example, Figure 5.11 showsthat beyond point A (a −12.5 percent stop loss level), losing trades will lastlonger than winning trades, directly hurting the portfolio. The wider the

FIGURE 5.9 Pessimistic return ratio as a function of stop loss level.


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FIGURE 5.10 Pessimistic return ratio as a function of profit target.

distance (S) between the two curves, the uglier our portfolio will becomeover time.

This is why, as shown in Figure 5.12, any level of profit target wouldquickly render a portfolio very ugly if the distance S is positive (losingtrades take longer to unwind than winning trades). This will be somewhatmitigated, however, if the number of winning trades generated is muchhigher than the number of losing trades.

As a rule of thumb, an acceptable trading strategy must generate tradeswhose average winning-versus-losing duration must be higher than 1.5. As

FIGURE 5.11 Average trade duration as a function of stop loss level.


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FIGURE 5.12 Average trade duration as a function of profit target.

shown in Figure 5.13, a fixed winning/losing duration ratio necessitates theuse of stricter stop loss levels (point B: −5.5 percent) when applying a 10percent profit target than when applying a 20 percent profit target (point A:−12.5 percent).

If you remember, however, Figure 5.5 was showing miserable returnsfor the sample trading strategy when applying a −5.5 percent stop loss tothe trading strategy while using a 10 percent profit target. This set of pa-rameters can therefore be eliminated, which leaves us with higher profit

FIGURE 5.13 Winning-to-losing trade duration ratio as a function of stop losslevel.


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targets combined with wider stop loss levels (a 20 percent profit target anda −12.5 percent stop loss level). It should be noted that other trading strate-gies could work well for low levels of profit target and stop loss, as we willsee in Chapter 6.

Figure 5.14 shows that not using any means to limit our losses (either atime limit or a stop loss level) will lead to very poor returns on the portfolio,as indicated by the low winning/losing duration ratio.

What about the Time Limit Parameter?

The time limit parameter works in a way that is similar to placing a marketorder on a fixed number of days after the purchase of the stock. As shownin Figure 5.15, this parameter does not improve the general profitability ofthe trading method. Nor, as shown in Figure 5.16, does it effectively cutthe number of losing trades the way the stop loss parameter did (see Fig-ure 5.7). Indeed, it is only after the time limit is reached that losing tradesare cut. Before that limit, losses are allowed to mount. The consequenceis the poor PRR obtained by the use of this time limit parameter (seeFigure 5.17).

In other words, compared to the use of stop loss, the time limit pa-rameter produces a smaller ratio of winning to losing trades. However,the winners will evolve in the portfolio for a long time while the losingtrades will be small and cut short. The time limit parameter thus has thepropensity to produce a good winning-to-losing trade duration ratio (seeFigure 5.18).

FIGURE 5.14 Winning-to-losing trade duration ratio as a function of profittargets.


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FIGURE 5.15 YER of the standard group of stocks as a function of time limitlevels.

This small exercise in trading logic using one simple example allowedus to understand the three different trade-offs that a trader is faced with:

1. The stop loss and time limit parameters work like an insurance policyagainst bad trades, but both hurt profitability. This is especially true fortighter stop loss or shorter time limit levels (see Figures 5.5 and 5.15).

2. However, looser stop loss levels hurt the ratio of the winning/losingtrade duration, leaving the portfolio open to too many losing trades(see Figure 5.13).

FIGURE 5.16 Number of winning versus losing trades as a function of time limitlevels, for a 20 percent profit target.


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FIGURE 5.17 Pessimistic return ratio as a function of time limit levels, for a 20percent profit target.

3. The time limit parameter is a good solution for the winning/losing tradeduration ratio (Figure 5.18), but produces many more losing than win-ning trades (Figure 5.16).

To conclude this section, it is obvious that:

� The adjustment of the trading parameters will not turn a losing tradingstrategy into a winning one.

FIGURE 5.18 Winning-to-losing trade duration ratio as a function of time limitlevels, for a 20 percent profit level.


Performance 245

� If you want to use an insurance policy against losing trades, the stoploss parameter will work better than the time limit parameter.

� If you use the stop loss parameter, you have to adjust its level depend-ing on your profit target (higher profit targets require wider stop losslevels).

MINIMIZING RISKS

Even the best plan sometimes fails. Traders are optimistic people, so plan-ning for failure is often a very difficult task: It forces us to look at the defi-ciencies of the trading strategy. We will first examine what we call risk, andthen study how to best tune the risk/return balance of a trading strategy.

How to Measure the Risks of a Trading Strategy

Of the four risks mentioned earlier (the risk of not being able to reachthe expected return, the market risk, the trader’s behavior risk, and thetrader’s wrong analysis risk), only the risk of being unable to reach theexpected return can be measured directly. All the other risks can only bemeasured indirectly. Indeed, when we make a bad trade, it is difficult toknow (for an outsider) whether the markets were responsible or the traderwas responsible for the negative outcome. For an outsider, the outcome isidentical: Time and money have been wasted.

It is also interesting to note that for all the risks mentioned (except thefirst risk, the risk that we will not be able to reach the expected return),there are trading parameters available to provide protection:

� The stop loss parameter provides some protection against marketdowntrends or against a sudden price drop of the stock we are trad-ing. Stop loss levels limit a trader’s pain and hence a trader’s potentialerratic decisions when experiencing large losses on a few positions.

� The profit target parameter forces the taking of profits on a regular ba-sis, avoiding the predominance of the trader’s optimism or exuberantemotions.

� The time limit parameter allows the trader to avoid continuation of ananalysis mistake, since an analysis is valid for only a limited period oftime.

The measure of the risk of not reaching the return objective is sim-ply a measure of the robustness of the trading strategy in terms of returnconsistency. This could be important, especially for a fund manager whose


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compensation is often proportional to the return above a certain bench-mark. The risk will then be measured by how much the return deviatesfrom that benchmark.

Measuring a deviation from an average is called standard deviation

and is usually referred to as a measure of volatility. The standard devi-ation measures how consistent the returns have been in the past. If wesuccessfully passed the optimization test, we may then suppose that suchconsistency will continue in the future. However, mathematically speak-ing, the standard deviation formula is valid only for normal or symmetricaldistributions of returns. Such distributions seldom occur in financial anal-ysis. This common simplification of assuming normal distributions makescalculations easier while research has not shown that this simplificationgenerates important errors—hence its wide acceptance.

Figure 5.19 shows the distribution of daily returns for the sample trad-ing strategy. Note how the daily return is calculated: For each trade, wedivide the return of the trade by the number of days it took to produce thatreturn.

In financial applications, many distributions are skewed either posi-tively or negatively. As an example, Figure 5.20 shows a positively skewed

FIGURE 5.19 Probability that any given day will produce a return that is withinthe stated bracket, for the sample trading strategy using a 20 percent profit targetand a −10 percent stop loss level.


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FIGURE 5.20 Probability that any given day will produce a return that is withinthe stated bracket, for a good trading method using a 20 percent profit target anda −10 percent stop loss level.

distribution of daily returns produced by a good trading strategy (thesupply/Large Effective Ratio trading strategy that we will study in Chapter6). Not only is the distribution’s center of gravity clearly on the right sideof the 0 percent value, but the right tail is fatter than the left tail, indicativeof a good trading strategy.

In order to see how to measure the robustness of a trading strategy,let’s revisit the example involving milking equipment. We know by expe-rience how much milk our herd of cows would produce by hand-milking.We also know that cows produce less milk during a dry summer than dur-ing a normal summer. If the milking machine yields a better productionthan hand-milking during either a dry or a normal summer, we would behappy about it, but this is to be expected. Otherwise, why would have weinvested in the equipment? What is not normal is to have cows that pro-duce less with the milking machine than with hand-milking. This is calledthe downside risk. It is the variability of the returns below a return deemednormal under the actual market conditions: the return of the buy-and-holdstrategy.

In our case, what we have to measure is the downside risk per tradingday. Indeed, our trading strategies are producing returns per trading day(i.e., daily returns).


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Downside Risk

Considering that BH is the average daily return of the buy-and-hold strategy,the downside risk is calculated by the following formula:

Daily downside risk =√√√√ n∑

i=1

(ri − BH)2/N

where ri = represents only the daily returns that were below the BH valueN = represents the total number of trading daysn = represents the total number of days with daily returns lower

than BH

The yearly downside risk of the trading strategy would then be cal-culated by multiplying the daily downside risk by the square root of thenumber of days in a year (250 if we count trading days, or 365 if we countthe total number of days).

To come back to our herd of cows, there are two other useful piecesof information that we would like to have:

First, in our herd, what is the average number of cows that are aller-gic to the milking machine, and thus will produce less milk than by hand-milking?

This is called the downside frequency. For our trading strategy, it issimply the ratio of the number of trading days that produce a daily returnlower than the daily buy-and-hold return to the total number of tradingdays. During the optimization process, the downside frequency is to beminimized.

Downside frequency = n

N

Average Downside Deviation

The average downside deviation is calculated as follows: For all the tradingdays that produce a daily return lower than the daily buy/hold return, weadd the sum of their difference from the BH return, and divide that sum bythe number of days that produce a return lower than BH.

Average downside deviation =n∑

i=1

(ri − BH)/n


Performance 249

Second, when a cow is allergic to the milking machine, how bad is it?Will she kick the machine and spill all the milk on the floor, or will shejust be uneasy and produce a little less milk than by hand-milking? This iscalled the average downside deviation.

The three risk measures that I have explained (the daily downside risk,the downside frequency, and the average downside deviation) are in myopinion useful only in two instances:

1. When we want to compare the risks of different trading strategies,these three measures give us rational points of comparison.

2. If you are a fund manager, it is always cool to show nice PowerPointpresentations demonstrating the risk/return evolution of your fund.

For the rest of us, these measures of risk are of very little use. First,except for the downside frequency, these measures use quite abstract con-cepts, and second, how significant are they really? Does it make a big dif-ference to know that 40 percent of your trades would be below the target?What is really important is to know that even in an adverse market con-dition, your trading strategy will meet your objectives and will shield youagainst bankruptcy risks.

This is the reason why, in terms of risk management, I prefer to usethe “average maximum drawdown” measure, which is close to the trader’sreality. The maximum drawdown related to a specific trade is the maxi-mum loss that we may experience between the time we buy and the timewe sell. For example, if we bought at $10 and sold at $15 but in the mean-time the stock retreated to a minimum of $9, the maximum drawdown is($10 − $9)/$10 = 10%. (Note that the definition is different from that ofmaximum drawdown in a portfolio, where we take a peak-to-valley mea-sure.) The average maximum drawdown gives a physical picture of thepain that, on average, we will have to endure when using a specific tradingstrategy.

The average maximum drawdown figures in Table 5.7 are the averagesof the maximum temporary loss that we take for each stock during thewhole period of time. It is the measure of the average maximum pain thatwe are going to endure for each group of stocks.

How to Tune the Risk/Return Balance

This part is far trickier, since, as we saw earlier, there are quite a few dif-ferent types of risks. Let’s look at the influence of the profit level on theaverage maximum drawdown, a realistic measure of risk. It looks as if


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TABLE 5.7 Risk/Return Produced by a Buy/Hold Strategy

Group of Stocks Yearly Buy/Hold ReturnAverage MaximumDrawdown

Laggards group −2.1% −32.2%Standard group 13.6% −18.2%Highfliers group 38.9% −13.5%

Figure 5.21 indicates that lower profit targets limit the risk, since the max-imum average drawdown is lower for smaller profit targets.

However, this may not be the case. We know that lower profit targetsreduce the average duration of each trade, and as a consequence also limitthe average maximum drawdown. In other words, we are probably replac-ing a few large drawdowns with more numerous smaller drawdowns. Thisalso means that the maximum average drawdown is probably not the per-fect measure of risk. There are indeed two events that could bankrupt usas traders: a small string of large losses occurring together or a large stringof small losses occurring together. If you ask me whether I would preferto be eaten by a tiger or by an army of ants, I’d say that except to shortenthe pain, I would not care much. However, I would certainly appreciateavoiding both scenarios.

Figure 5.22 shows the probability of occurrence of drawdowns for abuy-and-hold strategy applied to the standard group of stocks. We can seethat it would be wise to avoid the 5 percent of the drawdowns that are verysteep (between −50 percent and −99 percent) and the 22 percent of the

FIGURE 5.21 Drawdown of the standard group of stocks, calculated as a functionof profit targets, using neither stop loss nor time limit.


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FIGURE 5.22 Probability of occurrence of drawdowns.

drawdowns that lie between −25 percent and −50 percent. The maximumaverage drawdown is not a good measure of risk, because often in the stockmarket, very large drawdowns have a tendency of occurring concurrently.This is evidenced in Figure 5.23. Hence, when markets enter into a pricecorrection, the majority of the stocks are affected.

FIGURE 5.23 Time distribution of maximum drawdowns.


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Clearly we want to avoid tigers (a few large drawdowns occurring to-gether) and ants (many small losses occurring together). So what if we tryto measure their probability of occurrence depending on the different pa-rameters used?

First, we need to define what together is. If I were a short-term investor,with an average holding time of five days, together would mean a periodsomewhat longer than five days—for example, two weeks. If I were a long-term investor, I would need to use a longer analysis period such as onequarter. In this chapter, I use a period of one month. This means that I willlook at the drawdowns during consecutive one-month periods.

A Measure of Large Drawdowns Considering our standard groupthat includes 101 stocks, we need to measure the percentage of stocks thatwill find themselves hit at the same time by a drawdown larger than −25percent (tigers).

Please note here that to measure the risk linked to the largest losses,we need to measure it in terms of large drawdowns and compare it to thenumber of stocks that we are analyzing. This is the risk of what I call “trad-ing bankruptcy.” It is when the concurrent large drawdowns are so numer-ous that we risk completely crippling our portfolio.

What exactly is trading bankruptcy, anyway? I define trading bank-ruptcy as the moment when a trader has lost 50 percent of his capital. In-deed, someone who loses 50 percent of his capital will need to have a 100percent gain to come back to his original capital. Moreover, after emergingfrom trading bankruptcy, it will take that person 10 years of average annualreturns superior to 13 percent to reach the same no-risk return offered by 5percent Treasury bonds over the same 10-year period. How daunting a taskis it to emerge from trading bankruptcy? As a point of reference, considerthat over the 10 years ending in December 2006, the average hedge fund re-turned a little more than 10 percent annualized. In my opinion, it is highlyunlikely that someone who has just lost 50 percent of his portfolio capitalwill constantly beat the average hedge fund during the following 10 years.Because the road to recovery is very long, trading bankruptcy should beavoided.

To make this theory more understandable, let’s work on practical ex-amples. Figure 5.24 shows a time distribution of maximum drawdownslinked to the trades generated by our sample trading strategy when appliedto our standard group of stocks. In this example, I used only a 10 percentprofit target as a parameter—no stop loss or time limit. I am limiting theanalysis to the two years 2005 and 2006 since I have more data for thatperiod.

Figure 5.25 shows the proportion of occurrence of large drawdowns.This chart is better, in my opinion, than any mathematical formula at


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FIGURE 5.24 Time distribution of maximum drawdowns using a 10 percentprofit target.

FIGURE 5.25 Proportion of occurrence of drawdowns larger than −25 percentusing a 10 percent profit target, without stop loss or time limit.


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showing what the risk level of the trading strategy is, at least in terms ofoccurrence of possible disaster. For example, consider that in July 2006,during the market reversal illustrated in Figure 5.2, 11 percent of the gen-erated trades were showing a drawdown larger than −25 percent. This isquite a large figure, and it is to avoid such consecutive occurrence of lossesthat traders use stop-loss orders.

By comparison, consider Figures 5.26 and 5.27. They show what hap-pens when the same trading strategy is used, but with a −20 percent anda −10 percent stop loss, respectively. These figures show that the use ofstop-loss orders greatly reduces the risk of trading bankruptcy. Indeed, asshown in Figure 5.26, a −20 percent stop loss level would have reduced theworst occurrence of large drawdowns from 11 percent to 4.6 percent, whilea −10 percent stop loss level would have lowered it to almost 1 percent, orone occurrence in 100 trades (see Figure 5.27).

You may wonder why it is still possible to get drawdowns larger than−25 percent with stop loss levels of −10 percent or −20 percent. This isdue to the price gaps that may occur on bad news. A price gap could easilypass through the stop loss level and effectively force the trader to exit thestock at a lower price than the one indicated in the stop-loss order.

A Measure of Small Losses A measure of small temporary draw-downs is not very useful, because most of the stocks go through small

FIGURE 5.26 Proportion of occurrence of drawdowns larger than −25 percentusing a 10 percent profit target and a −20 percent stop loss level.


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FIGURE 5.27 Proportion of occurrence of drawdowns larger than −25 percentusing a 10 percent profit target and a −10 percent stop loss level.

drawdowns anyway after we purchase them. What is more useful is mea-suring the ratio of the small monthly losses—for example, those that arelarger than −5 percent—to total trades for the month. This is more useful,because these will be transferred to the portfolio. Our trading strategy willtherefore have to produce good enough profits to cover these losses.

This measure is calculated by adding all the losses larger than −5 per-cent for each month and then dividing that number by the number of tradesthat occurred during the same month. Using a stop loss level of −20 per-cent, we obtain the results shown in Figure 5.28. We can see that in April2005, 25 percent of all the ongoing trades during the month produced a losslarger than −5 percent. Is this significant? It depends on how much largerthan −5 percent the losses were. If the average loss is large, then there is a25 percent chance of transferring a high level of loss to the portfolio. Figure5.29 shows the level of such monthly losses. You can see that even if we callthem small losses, their average is somewhat larger than −20 percent. Wesee here that in April 2005, 25 percent of the ongoing trades for the monthgenerated a loss of −20 percent. If we multiply the two figures, we obtain−5 percent, which is the expected level of the loss that was transferred inApril 2005 from the trading strategy to the portfolio. This is not the realloss level that will hit the portfolio, since the portfolio loss also dependson positive trades of the month. However, I find this figure to be a good


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FIGURE 5.28 Proportion of occurrence of losses larger than −5 percent using a10 percent profit target and a −20 percent stop loss level.

FIGURE 5.29 Average monthly loss, for all trades ending with a loss higher than−5 percent, while using a 10 percent profit target and a −20 percent stop loss level.


Performance 257

FIGURE 5.30 Expected MLT, for all trades ending with a loss higher than −5percent, for a trading strategy using a 10 percent profit target and a −20 percentstop loss level.

reference for a loss that could hit, if profits do not show up as expected. Icall this calculation the monthly loss transferred (MLT) calculation.

We can see in Figure 5.30 that the average MLT is −1.22 percent. Thismeans that all the small losses higher than −5 percent generated by ourtrading strategy will transfer an average loss of −1.22 percent every monthto the portfolio, unless we generate positive trades to balance these losses.This figure shows the force we are fighting against.

What happens if we do the same work but with a −10 percent stop losslevel instead of a −20 percent stop loss level? We obtain a higher occur-rence of small losses (Figure 5.31) than what we saw in Figure 5.28 for a−20 percent stop loss. However, even if the −10 percent stop loss produceslower losses, their occurrence is so frequent that the MLT level worsens to−1.81 percent, as shown in Figure 5.33. This is very disturbing, because Ihad always believed that a stop-loss order was good protection. The use ofstop loss indeed offers good protection against larger hits, but the level ofthe stop loss itself will not help to better protect us against a string of smalllosses occurring during a market downturn.

As we have seen, tighter stop loss levels protect us well against largelosses, but either lose their efficiency against small repetitive losses or hurtour profit by forcing us to overtrade. This trade-off is shown in Figure 5.34.


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FIGURE 5.31 Proportion of occurrence of losses larger than −5 percent using a10 percent profit target and a −10 percent stop loss level.

FIGURE 5.32 Average monthly loss, for all trades ending with a loss higher than−5 percent, while using a 10 percent profit target and a −10 percent stop loss level.


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FIGURE 5.33 Expected MLT, for all trades ending with a loss higher than −5percent, for a trading strategy using a 10 percent profit target and a −10 percentstop loss level.

FIGURE 5.34 Expected MLT as a function of the stop loss level for a tradingstrategy using a 10 percent profit target.


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FIGURE 5.35 Expected MLT without stop loss or time limit levels for a tradingstrategy using a range of profit targets.

By experience, an MLT level greater than −1.5 percent does not bode wellfor our portfolio.

As a reference, Figure 5.35 shows the corresponding MLT for tradelosses larger than −25 percent when using a trading strategy without stoploss or time limit levels. This chart speaks for itself.

Does the Time Limit Parameter Help the MLT? As shown in Fig-ure 5.36, the time limit level performs better than the stop loss level, interms of expected MLT. For those who like stop loss, you can see in Figure5.37 that a combination of a 30-day time limit level with a stop loss strategyalso performs nicely in terms of expected MLT.

MEASURES OF RISK-ADJUSTEDPERFORMANCE: THE SHARPE ANDBURKE RATIOS

The Sharpe ratio, originated by the economist William Sharpe, is a mea-sure of the excess return achieved over a risk-free investment per unit ofvolatility. It is calculated as follows:

Sharpe ratio = Return of the trading strategy − Risk-free returnStandard deviation of return of the trading strategy


Performance 261

FIGURE 5.36 Expected MLT as a function of the time limit level for a tradingstrategy using a 10 percent profit target.

The higher the Sharpe ratio, the better the portfolio’s performance. Theinvestment firm Morningstar, Inc. states that a Sharpe ratio greater than1.0 is good; outstanding funds have a ratio greater than 2.0. In the past, theSharpe ratio of the S&P 500 has mostly been below 0.4. The Sharpe ratiois widely used to compare funds, which makes it very practical as a single

FIGURE 5.37 Expected MLT as a function of the stop loss level for a tradingstrategy using a 10 percent profit target and a 30-day time limit.


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comparative measure. This is the reason why, in the following chapter, Iuse mostly the Sharpe and Burke ratio (explained below) as a measure ofrisk, instead of the MLT.

Several other economists have proposed using the drawdown value onratios similar to the Sharpe ratio. From among them, I selected the Burke

ratio, devised by Gibbons Burke. Instead of using the standard deviation ofreturns to measure risk, the Burke ratio uses drawdowns, using the squareroot of the sum of the squares of each drawdown. This measure allows usto give more importance to large drawdowns than to numerous small ones.

Burke ratio = Return of the trading strategy − Risk-free returnDaily Downside Risk

Daily Downside Risk =√√√√[

n∑i=1

(Drawdown)2

]

In Chapter 6, where I use this Burke ratio, I take 5 percent as therisk-free return and consider only the drawdowns that are larger than −5percent.

The interested reader may refer to Francois-Serge Lhabitant’s bookHedge Funds: Quantitative Insights.

WHAT WE LEARNED IN THIS CHAPTER

As you know now, this chapter is the necessary foundation for a betterunderstanding of Chapter 6, which examines automated trading systems.In this chapter, I used a sample trading strategy to generate the buy signals.My objective was to show that after the trade is initiated, the managementof the trade itself using the different parameters that I described here isindependent of the generation of the signals (unless, of course, the LargeEffective Volume trading signal flashes that we need to quickly cash out,for example).

We saw that at the level of the trading strategy the risk/return balanceis well measured by:

� The yearly expected return (YER) of the trading strategy. The YERshould outperform the return of the buy-and-hold strategy.

� The monthly loss transferred (MLT) by the trading strategy to the port-folio, both for small −5 percent losses and for larger −25 percent tem-porary drawdowns.


Performance 263

Both the YER and the MLT, because they are commonsense perfor-mance measures that “speak to you,” should be preferred over mathemat-ical formulas. However, to be honest, both the Sharpe and Burke ratio areeasier to program than the MLT.

Most important, we also saw how the profit target, the stop loss, andthe time limit parameters can be used to manage an opened trade. Welearned how they mechanically work on the trades, and are able to be al-tered independently of the trading signals themselves.


C H A P T E R 6

AutomatedTrading Systems

S ome people think that automated systems are no good because theytake the decision making away from the investor. I for one do notwant to use a system that leaves the investor (me) powerless or igno-

rant. Since the money I invest is the money that I’ve earned from workinghard, it seems unnatural to me to let a bunch of wired transistors makedecisions in my stead. Besides, when I go to a cocktail party, I always havegood trades that I can talk about (let’s forget about the bad trades, whicharen’t cool enough for cocktail parties). I cannot imagine myself standingamong seasoned investors and telling them: “Yep! I am heavily invested inthe stock market, but I have no idea what sectors or what stocks I am in.My computer does the trading for me, and it beats the market quite consis-tently. By the way, my hobby is fishing. . . .”

Why, then, am I writing a chapter about automated systems? It is notbecause I’m concerned with how others perceive me at cocktail parties,but rather because automated systems are a very good way to generateprofits on a consistent basis while controlling risk. Human beings cannottrade profitably with consistency over a long period of time—unless theyare backed up by a trading system. One reason is that human beings aregreatly influenced by their emotions and memories of past trades (bothgood and bad). These memories carry an emotional impact that may even-tually influence their future decisions.

It is well known to traders that computers remove emotion from trad-ing. Computers, however, do much more than that. They:

� Can scan many more stocks than a human trader can.� Do not miss opportunities.

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� Act whenever necessary (for example, by taking profits or cuttinglosses).

� Have neither a boss nor a spouse to whom they need to report.� Allow you to back-test and optimize your ideas.

So despite my preference for human decision making over computer-ized decision making, there is a mathematical reality that I can’t ignore:Automated trading systems work.

In Chapters 1 through 4 we studied several new tools that generatevaluable and interesting trading signals. As we saw, one of these newtools—the Effective Volume signal—can greatly improve an existing trad-ing system that is based on a traditional method. It can be used as a confir-mation of other systems’ signals appearing simultaneously, but maybe alsoas a signal to turn a short-term trade into a long-term trade and switchingfrom one system to the other. I believe that it is the easiest tool for a traderwith a successful system to use in a practical way what I have discussed sofar in this book.

In this chapter we will see how the use of different combinations ofthe tools presented in the first part of the book allow us to develop trad-ing strategies that perform. These can be used both by dedicated privateinvestors and by fund managers.

As a reminder, each tool is based on a specific concept that alreadycarries with it the principle of a trading strategy:

� The Large Effective Volume signal tells us that it pays to buy a stockwhen that signal has been increasing for a few days while the stockprice is still within a trading range, because it indicates shares accu-mulation that does not yet appear in the price.

� The total Effective Volume signal tells us that it pays to buy whentraders are accumulating (for example, you could decide to buy whenthe total Effective Volume signal crosses over its 20-day average); thisindicates a change in the supply-balance equilibrium, which is a reasonfor a future price increase.

� The Active Boundaries signal tells us that it pays to buy at the LowerBoundary and sell at the Upper Boundary. This is because the UpperBoundary indicates the point at which the average shareholder doesnot expect the price to increase further, while the Lower Boundaryindicates the point at which the average shareholder expects the priceto begin increasing.

� The divergence analysis signal tells us that it pays to buy when thedivergence between the Large Effective Ratio and the price rate ofchange is greater than the average of its historical maximum. This


Automated Trading Systems 267

indicates that some unusual distortion is happening between the vol-ume accumulation trend and the price trend.

� The supply signal tells us that it pays to buy when that signal is lowerthan five to seven percent, indicating that few shareholders are readyto sell their shares.

� Finally, we should not forget the very important price trend signal,which states that “the trend is your friend” and that you should notbet against it.

Turning these various instruments into a profitable and automatedtrading system is no small task. I use such a system every day myself, butI seriously doubt that individual investors would be able to do, practicallyspeaking, what I do. That’s why this chapter is intended primarily for fundmanagers who enjoy better access to technical and human resources.

Applying the different tools on a daily basis is a very daunting task: Aone-day automated calculation of the different signals for 300 stocks takesabout six hours of computing time on a 3.2GHz dual-processor computer. Ithink that a standard trading platform would be even slower; it would prob-ably not be able to record intermediate calculations for hundreds of stocksand store them in a way for the user to be able to execute on a daily basisonly incremental calculations on newly downloaded data. In other words,unless the different tools that I present here are fully integrated in a moderntrading platform by an organization that offers the necessary professionaltraining and technical support, individual traders will most likely not havethe opportunity to use these tools to enhance their trading. The only excep-tion would be the Effective Volume Excel add-on (after Microsoft correctsthe execution speed problem on Excel 2007—an issue that has not beenresolved as of this writing; earlier versions of Excel work fine).

However, many funds do have the necessary computer power and in-house technical support. All they need to do is train one technician to man-age the set of computers that produce the trading signals. These signals canthen be fed either to an automated trading platform or to a team of traders.

This chapter is divided into two sections:

1. The production section deals with the questions of how to produce andhow to report the different trading signals. The production section willgive you the potential for money creation.

2. The trading strategies section shows how to combine these trading sig-nals to create successful trading systems. Each trading system will bedetermined by a fixed set of trading rules that can be unequivocallyinterpreted by a computer. This section shows how to make money inpractice.


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PRODUCTION OF TRADING SIGNALS

Most modern trading platforms allow users to define either alert rules ortrading rules. When certain conditions are met, the alert rules will trigger aninstant computer alert message (for example, in case of volume surge). Theset trading rules are used to scan a database and produce trading signalsfor the best matches. I will now review two screens that are based on thedifferent tools introduced in Chapters 1 through 4: the alert screen andthe production screen. The alert screen indicates something like: “Lookout—large players are moving in or out of a stock and this is not reflectedin the price.” The production screen sorts out the stocks that produce thebest trading signals.

Alert Screen

In terms of signal production, the first and most basic signal is an alert thatcalls for your attention: “Look here, maybe something is happening!” It isnot a trading signal per se, but simply an alert so that you will not misssomething that could be important. The question is: What are the things

you can’t afford to miss?

What you do not want to miss is something that is taking place underyour nose and that will come to light only later on when a price changehappens. In technical terms, you want to know if some large player hasbeen accumulating or distributing shares for a period of more than threedays while the price is trending in another direction or is still in a tradingrange—since funds need time to accumulate, taking a minimum of threedays allows you to focus on only sustained accumulation patterns.

This is useful for detecting:

� Accumulation while the stock is in a trading range or is finishing adowntrend.

� Distribution while the stock is in a trading range or is finishing anuptrend.

If you are not yet invested in the stock, this alert is not a signal for youto jump in; instead, it simply alerts you that the price could soon move out.Therefore, if the positive Effective Volume flow trend is continuing, youshould buy the stock as soon as the price breaks its base on the upside, asthe breakout is likely to be real.

However, if you are already invested in the stock and you see the LargeEffective Volume turning flat for a few consecutive days while the price isstill in an uptrend, this usually indicates that you should get out because



there is a lack of accumulation power. When you are already invested in astock, you should consider the Large Effective Volume signal to be a realtrading signal—it usually pays to err on the side of caution. (However, forshorting opportunities, the analysis of the total Effective Volume is moreappropriate, as we will see in Chapter 7.)

An example of an alert screen is shown in Table 6.1. As you can see,the rightmost three columns are the most important:

� The “Price 3 Days” column shows the price change within the pastthree days (between the closing price of the most recent day and theopening price three days before; positive price trends are shown ingray).

� The “LEV 3 Days” column shows the strength of Large Effective Vol-ume during the past three days, compared to the average three days’strength calculated over the previous 15 days (positive figures are ingray). This strength is defined as the ratio between the rate of changeof the Large Effective Volume flow during the past three days and therate of change over the previous 15 days.

� The last column is used as a ranking tool for the previous two columns.A positive number indicates that price and volume are trending in di-vergent directions. A negative number indicates that price and volumeare trending in the same direction.

The alert screen informs you of a developing situation: When LEV istrending positively while the price is still in a downtrend, it often indicates

TABLE 6.1 Alert Screen for Price/Effective Volume Divergences


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accumulation by large funds, which could be followed by a price trendchange; when LEV is trending negatively while the price is still in an up-trend, this indicates profit taking, which could also trigger a price trendchange later on. The lower part of Table 6.1, where the ranking figures arenegative, indicates that the accumulation/distribution trend by large play-ers is following the price trend. This is a trend confirmation signal. Everyday, about 10 percent of all stocks show an early alert signal, while about25 percent show a trend confirmation signal. The remaining 65 percent,which have been omitted from Table 6.1, do not indicate noteworthy in-formation in LEV (this occurs late in a price trend or during price tradingranges for which no accumulation/distribution was signaled by the LargeEffective Volume).

As an example of an early alert signal, we can see in Figure 6.1 thatlarge players are strong accumulators of Priceline shares even during theprice downtrend B. This could indicate that the price pullback will be

FIGURE 6.1 Priceline: a valid early alert signal.



short-lived. (When I wrote these lines on July 10, 2007, I did not foreseethat the price would jump and reach $80 one month later.)

In contrast, Figure 6.2 shows a false early alert signal. KB Home hasbeen experiencing a steep price downtrend, while large players were netbuyers (arrow B). This, however, does not indicate that the price down-trend will change anytime soon. First, the sector does not attract positiveattention from investors, and second, you can see in Figure 6.2 that arrowA has about the same strength as arrow B. This clearly shows that the largeand small players neutralize each other. A stronger LEV signal associatedwith a changing price trend will be necessary to buy that stock.

Production Screen

The production screen is the closest you can be to an automated tradingsystem if you do not want to use or build one. Table 6.2 shows one section

FIGURE 6.2 KB Home: a false early alert signal.


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TABLE 6.2 Production Screen

of my production screen. The original screen includes a pattern of colorsthat allows me to quickly see which indicators are positive. In Table 6.2, thecells that show a positive signal from the indicator are represented with agray background.

As indicated by its name, the first characteristic of a production screenis to show the level of the different indicators that are automatically pro-duced. For example, you can see in Table 6.2 that the production screenshows percentage numbers for the four indicators introduced in this book:

1. Expectation. This number originates from the Active Boundaries cal-culations (see Chapter 2). The number indicates the percentage priceincrease you may expect by investing at the current price level, untilthe price reaches the Upper Boundary. (In Table 6.2, the same numberindicates the percentage price decrease you may expect by going shortat the current price level, until the price reaches the Lower Bound-ary.) The color code indicates how far the signal is from the Upper orLower Boundary. (The gray cell indicates a signal close to the UpperBoundary for short trades and close to the Lower Boundary for longtrades. Short trade candidates are to be found among stocks that showa strongly negative global signal.)



2. Buy/sell divergence. This figure originates from the divergence anal-ysis calculation (see Chapter 3). It shows in percentage terms thestrength of the divergence between the Large Effective Ratio and theprice rate of change. The color code indicates how far the signal isfrom the average of the historical maxima. (The gray cell indicates asignal greater than 1.5 times the historical maxima for the buy diver-gence signal, and less than 1.5 times the historical minima for the selldivergence signal.)

3. Large Effective Ratio. This figure measures the balance between largebuyers and sellers, compared to the total volume exchanged duringthe analysis window (see Chapter 3). The color code indicates howfar the signal is from the average of the historical maxima. (The graycell indicates a signal greater than 1.5 times the historical maxima forthe Large Effective Ratio signal—historical minima and maxima aredefined in Chapter 3.)

4. Supply. This figure originates from the supply calculation model (seeChapter 4). It shows in percentage terms if the supply of shares istemporarily low enough to trigger a price increase as soon as thedemand starts to increase. The color code indicates how strong thesignal is. (The gray cell indicates a supply level that is lower than10 percent.)

A production screen should do two things. It should:

1. Produce individual indicators for each stock that allow the trader toquickly ascertain the stock situation without analyzing a graph.

2. Produce a global signal that allows a ranking of the different stocksand that automates trading decisions.

A production screen also allows us to quickly confirm or invalidate thesignals of the alert screen. For example, the KBH false signal of Figure 6.2is also invalidated by the Large Effective Ratio value for KBH that standsat 1 percent, which is well below the historical high. As for PCLN, evenif Figure 6.1 shows a valid alert signal, the expectation cell for PCLN (seeTable 6.2) indicates a stock price appreciation of only 4 percent, which isstill quite low.

Obviously, for the first four indicators, what is important is not the per-centage itself, but the color code that visually shows the relative strengthof the indicator signal compared either to a reference level (for the supplysignal) or to historical levels (for the other three indicators). This meansthat a trading method that combines several indicators first requires thecalculation of these reference, or historical, levels.


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If you remember, the various indicators introduced in this book tendto measure the behavior of different groups of shareholders:

� The Active Boundaries measure the limit levels of the expectation ofthe group of active shareholders.

� The Large Effective Volume measures the accumulation/distributionby large players.

� The divergence analysis measures how far the accumulation is builtinto the price.

� The supply indicator measures to what extent shareholders are readyto offer their shares for sale.

The fundamental hypothesis of this book is that the group of share-holders who follow a given stock varies slowly over time. All things beingequal, these shareholders will make similar buy or sell decisions. The cal-culation of the historical levels for a past period ranging between six and18 months tries to capture a reference value linked to past decisions.

This has two important consequences for the management of the signalproduction:

1. Some setup time must be allowed for each stock. This includes thedownloading and formatting of past data, as well as the detection ofhistorical levels. If we anticipate 30 minutes of setup time for eachstock, 1,000 stocks would require 500 hours of work, mainly computingtime.

2. The historical levels must be automatically recalculated at least everymonth in order to incorporate the data of the past month—more fre-quent adjustments are too computing-intensive for those following alarge number of stocks. In this way the trading system can adapt itselfto the changing behavior of the pool of shareholders.

TRADING STRATEGIES

As you can see in the rightmost column of Table 6.2, the production processalso generates a global trading signal that serves to sort the best tradingopportunities.

It would be a mistake to think that this global trading signal is just amathematical combination of all the previous indicators. What this globalsignal says is that, given (1) a trading strategy that has been formalizedinto a set of trading rules and (2) the weight that the trader puts on eachof these rules, the global signal represents the degree to which the stockmeets the predefined rules at this precise moment.



Does this mean that it is profitable to buy when the signal is high andsell when it is low? Assuming that the system works, it should be statisti-cally profitable, right? Not really. At this point, it is impossible to know ifthe trading strategy will yield a profit and, if so, to what extent. Optimiza-tion is necessary: what signal strength to take, what time frame, and so on.Also, buying is only half the work. You still need to sell, and as we saw inChapter 5, there are many reasons for selling: to buy a better play, to takeyour profit, to limit your losses, to shorten your investment time, or simplyto follow a selling signal.

Although short-term traders must carefully select the timing of theirentries and exits, what matters most for long-term traders is the quality ofentries. Short-term traders work with smaller profit margins. Therefore, abad exit makes a significant dent on the profitability of the trade. Indeed,if a short-term trader has a 5 percent profit target, a 2 percent miss on theexit lowers the profit by 40 percent (2 percent out of 5 percent), while fora long-term trader who is looking at 20 percent or more profit per trade, a2 percent miss on the exit of the trade lowers the profit by only 10 percent(2 percent out of 20 percent).

In other words, although the trading strategies explained in this chap-ter manage both entries and exits, their main difference is in the qualityof the entries. We will see that even if they use identical exit strategies,those trading strategies that have high-quality entries fare much better. Butwe will also see that for some strategies, early profit taking can greatlyimprove the results.

Before studying the different successful trading strategies, let’s comeback to Table 5.6 of Chapter 5. If you remember, I briefly introduced threedifferent groups of stocks, including their average yearly expected return(YER) using a buy-and-hold strategy. I will be using the buy-and-hold re-turn on these three groups of stocks as a benchmark against which we willcompare the different trading strategies.

What are the characteristics of a good entry strategy? My method is notdifferent from other methods in its requirements: We need to find long-termvalue, detect a change trigger, and use common sense.

Translated into Effective Volume vocabulary, the requirements are to:

� Find long-term value. The stock must be cheap, measured either interms of Active Boundaries (see Chapter 2) or in terms of supply level(see Chapter 4).

� Detect a change trigger. Large funds must be heavily buying, measuredin terms of Large Effective Volume flow (see Chapter 1), Large Effec-tive Ratio, or divergence analysis (see Chapter 3).

� Use common sense. If you decide to be long, the long-term pricetrend should be positive, and the short-term price trend should not benegative.


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Of course, these are obvious requirements, and you might think thatanybody who can use the trading signals corresponding to these require-ments would make a killing in the stock market. Unfortunately, this is notthe case. There are four reasons why:

1. We saw earlier that it is not because a stock is cheap that it cannotbecome cheaper. For example, a cheap uptrend reversal could easilybecome an expensive starting downtrend.

2. Large funds sometimes make large mistakes, especially during pricedowntrends. Price downtrends indeed attract large bargain hunterswho have a positive long-term view of a stock.

3. It is sometimes very difficult to predict if a new slope (a small trend)will develop into a new long trend.

4. Finally, the market as a whole could turn against us, which often hasnothing to do with the specific signals generated by our stocks.

Let’s now look at different trading strategies that meet these require-ments. For the different trading simulations, I evaluate the signals at theclose, but use the next day’s opening price as the buying price. I also takea 0.5 percent cost, including slippage, per round trade.

Trading Strategies Based on theActive Boundaries

We saw in Chapter 2 that the Active Boundaries tool is excellent for captur-ing price trends. The Upper and Lower Boundaries capture the change inexpectation of active traders. The Lower Boundary indicates that traders’expectation for a price increase is high; hence the probability is high thatthe price will reverse up, especially if large players are net buyers at thelevel of the Lower Boundary.

Let’s review how the trading strategy works, taking the example ofthe Todco company, a deepwater oil drilling company that was boughtout in mid-March 2007. Todco’s price movements, represented in Figure6.3, are captured within the Active Boundaries by Figure 6.4, in fact al-most copying exactly the price swings. The question is: Is it allowed tobuy at points A and B, which lie at the Lower Boundary? You can in-deed see in Figure 6.5 and 6.6—which represent the Effective Volume flowseparated by size leading to points A and B, respectively—that in bothcases, the Large Effective Volume flow during the last three days was pos-itive, while the price trend was not negative. The three requirements arethus met.



FIGURE 6.3 Stock price evolution for Todco.Source: Chart courtesy of StockCharts.com.

Let’s first study the influence of the Active Boundaries indicator with-out the use of the Effective Volume trigger. This gives the following tradingrules:

Buy� If the Active Boundaries signal is close to the Lower Boundary,� And the price trend is not negative.

Sell� If the Active Boundaries signal is close to the Upper Boundary,� Or if one of the following selling parameters is met: profit-taking limit

of 20 percent, stop loss limit of −10 percent, or time factor limit of50 days. (Figure 5.13 showed that, for the test trading strategy used inChapter 5, a 20 percent profit-taking limit combined with a −10 percentstop loss produced a winning/losing duration ratio higher than 1.5. The50-day time factor limit was selected to give time for the Active Bound-aries indicator to complete a swing from the Lower Boundary to theUpper Boundary.)

Note that the trading strategy discovers a price trend change beforethe price change actually occurs. However, it is sometimes wiser to waitfor the price trend to effectively begin before entering the trade.

Table 6.3 shows that the results of this specific trading strategy arevery good, especially in comparison to the return of the buy-and-hold


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FIGURE 6.4 Active Boundaries for Todco.

FIGURE 6.5 Effective Volume for Todco, leading to point A.



FIGURE 6.6 Effective Volume for Todco, leading to point B.

strategy. The rightmost column of Table 6.3 indicates the proportion ofinvested days compared to the total number of days analyzed. The lowerthe average invested time, the more stocks we will need to research inorder to find enough investment opportunities.

TABLE 6.3 Return of the Active Boundaries Trading Strategy

Group of StocksSharpeRatio

YearlyExpectedReturn

YearlyBuy/HoldReturn

Improvementover Buy/HoldReturn

AverageInvestedTime

Laggards group 2.00 36.3% −2.1% 38.4% 10.7%Standard group 1.30 21.9% 13.6% 8.3% 16.7%Highfliers group 3.14 50.5% 38.9% 11.6% 13.1%


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First Improvement: Addition of the Large Effective Volume Con-dition Let’s now add the Large Effective Volume trigger condition on thetrading rules: We buy only if the Large Effective Volume was positive dur-ing each of the previous three trading days. The trading rules are thereforeadapted as follows:

Buy� If the Active Boundaries signal is close to the Lower Boundary,� And the Large Effective Volume flow was positive during each of the

previous three trading days,� And the price trend is not negative.


of 20 percent, stop loss limit of −10 percent, or time factor limit of50 days.

As you can see in Table 6.4, this improved strategy gives better resultsfor the standard and the highfliers groups, but at the expense of the averageinvested time.

Although this trading strategy works very well, it is important to notetwo limitations that are inherent to the Active Boundaries indicator:

1. The Active Boundaries indicator catches trends between two parallellines, either for uptrends or for downtrends. Some people could beconfused and be tempted to be long in a downtrend, simply because thevisual pattern is similar: two parallel flat lines are less visually strikingthan two increasing or decreasing trend lines.

TABLE 6.4 Return of the Active Boundaries and Effective VolumeTrading Strategy





AverageInvestedTime




2. The Active Boundaries indicator is very bad at catching long-termtrend changes. At the Lower Boundary, it is sometimes difficult to de-tect whether the Active Boundaries signal will slide below the LowerBoundary or reverse up, even with the use of the Large Effective Vol-ume signal.

Let’s review these two limitations using the example of the companyLexmark, a computer printer manufacturer. We can see in Figure 6.7 thatLexmark experienced a strong price uptrend (uptrend A) in 2006 followedby a similarly strong price downtrend in 2007 (downtrend B). As you cansee in Figure 6.8, the Active Boundaries nicely captured these two trends.The trading strategy that says to buy at the Lower Boundary and sell at theUpper Boundary is valid only for uptrends, however. In downtrends, thetrading strategy should be to short at the Upper Boundary and to cover atthe Lower Boundary. It is evident that the uptrend A and the downtrend Bare both eye-catching in Figure 6.7: They are very easy to recognize. But thesame trends expressed in terms of Active Boundaries, as shown in Figure6.8, request our close attention in order to be recognized. If this differenceis true for the human eye, a computer will recognize the fixed levels of theActive Boundaries much more easily than a trend pattern.

As a reminder, we rate the trend strength by taking the midpoint be-tween the Upper Boundary and the Lower Boundary. For example, uptrendA was a 10 percent uptrend [15% − (15% − 5%)/2], while downtrend B wasa −9.75 percent downtrend [−6.5% − (−6.5% + 13%)/2].

The analysis of what happened at point 1 in Figure 6.8 will easily il-lustrate the second limitation of the Active Boundaries. We can see that at

FIGURE 6.7 Stock price evolution for Lexmark.Source: Chart courtesy of StockCharts.com.


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FIGURE 6.8 Active Boundaries for Lexmark.

point 1, we are at the Lower Boundary A, a location where the price shouldincrease again. However, if the Large Effective Volume flow is either neu-tral or negative at point 1, we may conclude that there is no buying pres-sure. Therefore, we can expect that the price will not increase, but ratherthat it will continue to move further down, pushed by its own downwardmomentum. This continuous fall would break the trend (we notice in Fig-ure 6.7 that at point 1, the uptrend seems already somewhat jeopardized).

However, Figure 6.9 was showing a strong accumulation by large play-ers (see arrow C), which in theory presages a new price surge. This wasclearly a false signal, because the large players’ stock accumulation wasnot strong enough to trigger a new uptrend. If you remember, in Chapter3, during the divergence analysis explanation, I introduced the Large Ef-fective Ratio concept. The Large Effective Ratio is simply the ratio of thenumber of shares accumulated by large players during a certain period oftime to the total number of shares that were exchanged during the sameperiod. The Large Effective Ratio allows the monitoring of the buying andselling waves generated by the activities of large players. The comparisonbetween the amplitude of the actual Large Effective Ratio and the past am-plitudes allows us to judge whether the actual buying is strong by histori-cal standards. Figure 6.10 shows that for Lexmark, on January 17, 2007, theLarge Effective Ratio was well below its average of past maxima, indicatingthat the large players’ accumulation was probably too weak to influence aprice trend change.

Second Improvement: Addition of the Large Effective RatioCondition Now let’s see how to improve the trading strategy by replac-ing the Large Effective Volume trading rule with a Large Effective Ratio



FIGURE 6.9 Lexmark: Effective Volume leading to January 17, 2007.

FIGURE 6.10 Lexmark: Large Effective Ratio, leading to January 17, 2007.


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trading rule. The idea is not just to catch Large Effective Volume signals,but to consider only those that are exceptionally good. The trading rulesare thus modified as follows:

Buy� If the Active Boundaries signal is close to the Lower Boundary,� And the Large Effective Ratio is higher than the past maxima,� And the price trend is not negative.



As can be seen in Table 6.5, the use of the Large Effective Ratio insteadof the Large Effective Volume has significantly improved the results of thistrading strategy, but at a cost in the average invested time.

Third Improvement: Elimination of the Price Trend ConditionThe drawback of the previous improvement is that the investment oppor-tunities that respond to this stricter condition are more difficult to find,although these opportunities are of better quality. To increase the numberof opportunities, we therefore need to reduce the strictness of the trad-ing rules. What about the elimination of the trading condition that statesthat the short-term price trend must not be negative? It is, of course, saferto wait for a price downtrend to stop or revert before buying a stock.However, because the Active Boundaries show us that the stock is cheapand the Large Effective Ratio shows us that there is a significant accu-mulation under way, the probability is high that the price trend will soonchange, even if it is still negative in the short term. The elimination of thisprice trend condition will also procure more time for shares accumulation,

TABLE 6.5 Return of the Active Boundaries and Large Effective RatioTrading Strategy





AverageInvestedTime




TABLE 6.6 Return of the Active Boundaries and Effective Ratio Trading Strategywithout Price Trend Condition





AverageInvestedTime


which is an interesting characteristic for funds, since funds need many daysto accumulate a position.

Table 6.6 shows that for the standard and the highfliers groups, thisstrategy is showing slightly better results than the previous one, while im-proving the average invested time.

Figures 6.11 through 6.15 compare the different improvements usingseveral measures. Figure 6.11 shows the progression of the yearly expectedreturn (YER) for the different improvements that have been brought tothe original trading strategy using the Active Boundaries. Notice that theoriginal Active Boundaries strategy offered a YER that was already higherthan the 13.6 percent YER of the buy-and-hold trading strategy (shown bythe dotted line in Figure 6.11). Except for the last one, each improvementbrought a substantial YER increase to the results of the previous stage.

FIGURE 6.11 YER for the different improvements on the Active Boundaries trad-ing strategies for the standard group.


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FIGURE 6.12 Drawdown measures for the different improvements on the ActiveBoundaries trading strategies for the standard group.

As we could have expected, the improvements that offer higher returnsare also those that procure lower risks. You will note that this contradictsthe general belief that higher returns usually also generate more risks. Wecan see in Figure 6.12 that the average drawdown per trade decreases to-gether with each improvement of the Active Boundaries trading strategy.As you will notice in Figure 6.13, the monthly loss transferred (MLT) to

FIGURE 6.13 MLT for the different improvements on the Active Boundaries trad-ing strategies for the standard group.



the portfolio gradually diminishes with each strategy improvement, exceptfor the last one. Consequently, both the Sharpe ratio (Figure 6.14) and theBurke ratio (Figure 6.15), which combine risk and return, show a strongincrease almost with each improvement, indicating an increased efficiencyfor the trading strategies.

Fourth Improvement: Early Profit Taking Now let’s turn to the sellside of the trade for which I have been using the trading rule to sell:

� If the Active Boundaries signal is close to the Upper Boundary,� Or if one of the following selling parameters is met: profit-taking limit


We already saw in Chapter 5 that a fine-tuning of the three selling pa-rameters will probably produce better returns. But let’s concentrate on thetrading signal itself: Do we really need to wait until we reach the UpperBoundary in order to activate a sell order? When we buy at the LowerBoundary and sell at the Upper Boundary, our goal is to take advantageof the full swing. However, the price movement from the Lower to theUpper Boundary is seldom linear. Traders quickly move into an emergingnew uptrend, strongly pushing the price up. But before the price reachesthe Upper Boundary, its rise starts to falter with the arrival of early profit

Active Boundaries Active BoundariesEffective Volume

Active BoundariesEffective Ratio


No Price Condition

FIGURE 6.14 Sharpe ratio for the different improvements on the Active Bound-aries trading strategies for the standard group.


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No Price Condition

FIGURE 6.15 Burke ratio for the different improvements on the Active Bound-aries trading strategies for the standard group, for drawdowns greater than−5 percent.

takers. Because most of the gain happens early in the trend, it is better tomodify the trading rules and sell at one-third of the distance to the UpperBoundary. The modified trading rules then become:

Buy� If the Active Boundaries signal is close to the Lower Boundary,� And the Large Effective Ratio is higher than the past maxima,� And the price trend is not negative.

Sell� If the Active Boundaries signal is rising to one-third of the distance that

separates the Lower from the Upper Boundary,� Or if one of the following selling parameters is met: profit-taking limit


As you can see in Table 6.7, this improved trading strategy gives stillbetter results, especially for the standard and the highfliers groups.

Furthermore, since we have improved the sell side of the trading rules,we can now apply this improvement to all of the previous versions of theActive Boundaries trading strategies:

� The original Active Boundaries strategy.� The Active Boundaries and Effective Volume strategy.



TABLE 6.7Return of the Active Boundaries and Effective Ratio TradingStrategy, without Price Trend Condition but Using an EarlyProfit-Taking Tactic





AverageInvestedTime


� The Active Boundaries and Effective Ratio strategy.� The Active Boundaries and Effective Ratio strategy with no trading

condition on the price trend.

The comparison of these four trading strategies using either a late oran early profit-taking tactic is shown in Figures 6.16 through 6.21. The dataset in gray (which is at the left side) displays the results for selling thestock close to the Upper Boundary—in other words, to take profit later inthe uptrend. The data set in black displays the results for selling the stockearlier in the uptrend, when the Active Boundaries signal is only one-thirdof the way between the Lower and the Upper Boundaries.


Yearly Expected Return (YER)



No Price Condition

FIGURE 6.16 YER comparison for the different improvements on the ActiveBoundaries trading strategies using either a late or an early profit-taking tactic.


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You can see in Figure 6.16 that rapidly selling the stock while it is stillearly in the uptrend produces much better results than if we want to ridethe complete trend. This is because in an uptrend the price increase is moreimportant at the beginning than at the end of the trend. Therefore, quicklyselling has a positive impact on the YER, and will be positive for the port-folio, if we can find enough trading opportunities to use our cash. Althoughthe YER improvement between the late and the early selling tactics is neverdouble, Figure 6.17 and 6.18 show that both the Sharpe ratio and the Burkeratio improve by a ratio that is often larger than the YER improvement.This means that the early selling strategies carry fewer risks than the lateselling strategies, and therefore are also more efficient.

This string of improvements, however, comes at a cost: a sharp de-crease in the average invested time. Indeed, except for the elimination ofthe price condition, since each improvement applies stricter trading condi-tions, it becomes more and more difficult to find good investment opportu-nities at any time.

Figure 6.19 shows the average time you can be invested in eachstock using the different versions of the Active Boundaries–based tradingstrategies.

For the original Active Boundaries trading strategy, Figure 6.19 showsthat the strategy produces an average invested time of 16.7 percent. Thismeans that, on average, if we select a stock from our standard group ofstocks, during one year we will be invested in that stock for 60.955 days




No Price Condition

FIGURE 6.17 Sharpe ratio comparison for the different improvements on the Ac-tive Boundaries trading strategies using either a late or an early profit-taking tactic.






No Price Condition

FIGURE 6.18 Burke ratio comparison for the different improvements on the Ac-tive Boundaries trading strategies using either a late or an early profit-taking tactic.

(365 days × 16.7%). If we select a second stock, we can also expect to beinvested during 60.955 days; for these two stocks, if we choose to be in-vested only in one stock at a time, we will be invested during 121.91 days(2 × 60.955 days). This is true only if these two investment opportunitiesdo not overlap in time. If we can find six investment opportunities that do

FIGURE 6.19 Ratio of time invested for the different improvements on the ActiveBoundaries trading strategies using either a late or an early profit-taking tactic.


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not overlap, we then can be invested 365.73 days (6 × 60.955 days). Consid-ering a 20-position portfolio, this would require us to find 120 investmentopportunities. Statistically, following 120 stocks using the original ActiveBoundaries trading strategy will provide such opportunities. If, however,we consider that 40 percent of these opportunities overlap, we will need toscan 200 stocks every day (120 divided by 60 percent).

If we perform the same type of calculation for the best improvement ofthe method (the combination of the Active Boundaries, the Large EffectiveRatio, the “no price trend” condition, and the early profit-taking tactic),we see in Figure 6.20 that we will need to follow more than 1,333 stockson a daily basis. We can therefore conclude that even if early profit-takingtactics offer an enhanced performance, they require much more work tofind investment opportunities. The early profit-taking tactics also naturallyshorten the trades and therefore require us to perform more trades. Thiswill not only increase the commission and slippage costs, but also thestress of performing more buy/sell operations (all the results include a0.5 percent trading cost per round trade). For example, Figure 6.21 showsthat for the late selling trading tactics, the average trade duration is about40 calendar days, which means that each position in the portfolio requiresabout 365/40 = 9.12 buy-and-sell trades per year. A 20-position portfoliorequires 182 trades per year. Figure 6.21 shows that moving to an early

FIGURE 6.20 Number of stocks to scan to support being invested in a 20-position portfolio on the Active Boundaries trading strategies using either a late oran early profit-taking tactic.






No Price Condition

FIGURE 6.21 Average trade duration in days for the different improvements onthe Active Boundaries trading strategies using either a late or an early profit-takingwindow tactic.

selling tactic shortens the average trade by more than half and forces us toperform more than twice the number of operations.

An early reader of this book told me that I should give advice on whatmethod is best to use. Well, the advice is straightforward: I go for the trad-ing strategy that has the potential to produce a 102 percent profit (seeFigure 6.16), even if this means a lot of work, especially if it is the com-puter that is working. In reality, the answer is straightforward only for aswing trader, a trader who closely follows the stocks’ swings. Large fundssometimes need many days to take a new position. Therefore, for them, theshorter the trade, the more difficult it will be simply to invest and get outof their positions later.

The case of short-term traders is still different: How would our strate-gies fare for short-term traders who like to switch within a few days fromone position to the next? To answer that question, it is important to seehow an average trade evolves for each trading strategy. For example, ifwe follow 10 trades, how much profit will we earn on average after oneday, after two days, and so on? For a given trading strategy, do we havea constant profit increase or a steeper one during the first days? In otherwords, do we earn 0.5 percent after one day, then 1 percent after two days,1.5 percent after three days, and so on, to reach 5 percent after 10 daysand 20 percent after 40 days? We can already sense that this is impossible,because on average, such a trading strategy would produce a YER of 182


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percent (365/40 × 20% = 182%). Note that I use calender days here not daysopened for trading. Figure 6.22 would have displayed a sharper rise of theaverage profit. Since the maximum profit reached as of now is 102 percent,it is clear that 182 percent is quite high. But is it really unattainable?

Before answering that question, we need to look at the evolution of theaverage profit produced by each trading strategy as each trade progressesin time. Such an evolution is shown in Figure 6.22. I have represented threestrategies that require about 40 days on average for trade completion. Fig-ure 6.22 shows the evolution of the first 20 days for the following threetrading strategies:

1. The AB strategy. This is the original Active Boundaries strategy, whoseresults are shown in Table 6.3.

2. The AB ER strategy. This is the Active Boundary strategy that uses theEffective Ratio as a trigger to decide on the entry timing. Results forthis strategy are shown in Table 6.5.

3. The AB ER NP strategy. This is the Active Boundary strategy that usesthe Effective Ratio, but without the condition that the short-term pricetrend should not be negative at the time of purchase. Results for thisstrategy are shown in Table 6.6.

The striking feature of Figure 6.22 is that all three trading strate-gies produce an average profit that is linearly increasing from the day of

FIGURE 6.22 Evolution of the average profit produced by each trading strategydepending on the progress of the trade from the date of initial purchase.



purchase but that suddenly stops progressing. For example, the AB strat-egy produces an average profit that increases regularly until day 11, andthen progress ceases. The other two strategies produce an average profitthat increases much more rapidly at the start of the trades, until day 5, andthen shows either no sign of progress or even some weakness after moredays pass.

The explanation for why the AB strategy takes more days to produceits average maximum profit is simple: For the AB strategy, we did not useany buying trigger. This means that the AB strategy offers a good measurefor the value of a stock (it shows when the stock is cheap), but is does notoffer any indication as to the timing of the purchase. By comparison, theother two trading strategies shown in Figure 6.22 indicate more preciselywhen to enter the trade, which allows them to quickly produce value foreach trade.

The real question that comes out of Figure 6.22 is: Why should we con-tinue to be invested in a stock after the trade has reached its full potential?For the AB ER NP strategy, keeping the stock longer than five days will notproduce additional profit. After day 5, the risk of incurring a loss on thattrade is now higher than the potential additional return.

Fifth Improvement: Imposing a Five-Day Time Limit Table 6.8shows the results after applying a five-day time limit rule to the tradingrules used to produce Table 6.7. This last improvement produces a trulyexceptional return, as you can see in Figure 6.23, which slows the YERevolution from one trading strategy to the next. In Figure 6.24 the Burkeratio, which is a measure of return by unit of risk (the risk being based onthe drawdown calculation), shows a fourfold amelioration over the previ-ous trading rule improvement.

But let’s come back down to earth. Whenever I see a trading strategythat publishes exceptional returns with very low risk, even if I do not doubt

TABLE 6.8Return of the Active Boundaries and Effective Ratio TradingStrategy, without Price Condition but Using an Early Profit-TakingTactic and a Five-Day Time Limit





AverageInvestedTime



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Active Boundaries

Yearly Expected Return (YER)

Active BoundariesEffective Volume



No Price Condition


No Price Condition5 Days Limit

FIGURE 6.23 YER evolution for the different improvements on the Active Bound-aries trading strategies using either a late or an early profit-taking tactic.

the published performance, I must ask myself: What are the costs linked tosuch a performance?

The main cost is shown in Figure 6.25: It is the very high number ofstocks that we need to scan in order to be fully invested in a 20-positionportfolio. Do not forget that the methods I am presenting in this book are




No Price Condition


No Price Condition5 Days Limit

FIGURE 6.24 Burke ratio for the different improvements on the Active Bound-aries trading strategies using either a late or an early profit-taking tactic.



FIGURE 6.25 Number of stocks to scan to support being invested in a 20-position portfolio with the Active Boundaries trading strategies using either a lateor an early profit-taking tactic.

very computing-intensive, since they go down to the level of the minutedata. For example, if it takes one minute of computing time to producethe trading signals for one stock, the 2,924 stocks that we need to scan(as shown in the last column of Figure 6.25) will require about 48.7 hoursof computing time. This would require a few computers working in par-allel, which is not a common practice among individual traders. Anotherway to attain such returns would be to reduce the portfolio from 20 to fivepositions. The 48.7 hours of computing time would then become a moremanageable 12.18 hours, which could quickly diminish with faster comput-ers coming out on the market. When you reduce the number of positions,though, you face two problems:

1. A large portfolio means more diversification, while a reduced portfo-lio means increased risk. For example, assume that we invest 20 per-cent of our capital in a single stock. If, because of very unfortunatecircumstances that even insiders could not have predicted, the stockprice drops by 50 percent overnight, we would lose 10 percent of ourcapital.

2. Less diversification also means that more capital must be allocated toeach single trade. However, we saw in Figure 6.22 that the majority


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of the gains are realized in the first few days of the trade. Therefore,it is clear that these larger amounts of capital must be invested verysoon after the appearance of the first buy signal; otherwise we willstart losing profit opportunities. For the AB ER NP strategy, if we areone day late, we lose a 1.2 percent gain opportunity. If we are two dayslate, we lose a 2.1 percent gain opportunity, or more than half of theaverage maximum 4 percent gain that the strategy can offer after fivedays. In other words, to reach the calculated returns, we must investthe whole allocation within a single day. For example, if we manage $1million in capital that must be allocated among five positions, we willneed to invest $200,000 within one day in a single stock. This seemsentirely possible. But what if we have $100 million in capital to invest?That would lead us to invest $20 million in one day in a single stock,something that is impossible for the majority of small- and mid-capstocks.

It is therefore clear that for large funds, the main limitation regardingthe use of the best-performing short-term trading strategy is the amountof capital that they need to invest. As a matter of fact, a fund that wouldlike to use one of the trading strategies presented here would have toadjust it considerably to allow for the possibility of investing in a largenumber of positions. The fund would also adjust the strategy so that it of-fers some time to invest in these positions before the price climbs againsignificantly.

Let’s come back to Figure 6.23 and analyze the “late profit-taking tac-tics” results that are represented by gray bars. We had four improvementsover the original Active Boundaries strategy. The Active Boundaries indi-cator is a value indicator. This indicator alone produces an acceptable re-turn of 21.9 percent, which is already higher than the 13.6 percent returnof the buy-and-hold method (a relative increase of 61.2 percent). The firstimprovement was to find a good trigger for entering the trade. The anal-ysis of the Large Effective Volume flow during the preceding three dayswas found to be a good enough trigger, improving the return from 21.9percent to 31.3 percent (a proportional increase of 42.9 percent). The sec-ond improvement was to instead use the Large Effective Ratio as a trig-ger. This increased the return from 31.3 percent to 52.9 percent (a pro-portional increase of 68.8 percent). The third improvement was a techni-cality regarding a trading condition about the price trend. This increasedthe return from 52.9 percent to 57.8 percent (a relatively small propor-tional increase of 9.3 percent). The most striking improvement on returnof the trading strategy is by limiting the duration of the trade. This in-creased the return from 57.8 percent to 156.5 percent (a relative increase of170.8 percent).



What we have just discovered here are the three pillars of successfultrading strategies:

1. The first pillar is to find value. If we misjudge the value of a stock com-pared to its current price, then it is very likely that we will miss thetrade. (The value is measured in terms of trading opportunity, which,with the Active Boundaries, is defined as traders’ collective expecta-tion of a future price increase.)

2. The second pillar is to detect a good time to enter the trade. I call it thetrigger. The trigger will produce an indication that the stock is readyto make a move.

3. The third pillar is the time management of the trade; it is the knowl-edge of the time it takes (1) between the signal from the trigger andthe beginning of the stock price movement, and (2) between the signalfrom the trigger and the reaching of the maximum average return ofthe trading strategy.

Figure 6.26 shows the YER improvement over the buy-and-holdmethod using the three pillars of successful trading. The value pillar im-proves the buy/hold return by 1.6 times, the trigger pillar improves thebuy/hold return by 3.9 times, and the time pillar improves it by 11.5 times.Of the three pillars, the most important is the one that brings the least

FIGURE 6.26 YER improvement over the buy-and-hold method using the threepillars of successful trading strategies.


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improvement: the value. Indeed, if we misjudge the value (we buy whenthe price is too high), the other two pillars will be of no use, because wewill most likely miss the trade.

In order to illustrate this, I ran three trading strategies using differenttrigger signals, first without the value signal from the Active Boundaries,and then in combination with this value signal. The trading rules for thestrategies are:

Buy� As soon as the buy trigger signal is set,� And the price trend was not negative during the past five days.

Sell� As soon as the sell trigger signal is set,� Or if one of the following selling parameters is met: profit-taking limit

of 20 percent, stop loss limit of −10 percent, or time factor limit of 50days.

The three trigger signals are the Large Effective Volume, the Large Ef-fective Ratio, and the buy divergence signal (the divergence between theLarge Effective Ratio and the price as explained in Chapter 3). The re-sults of these three trading strategies are shown in Figures 6.27 and 6.28.

FIGURE 6.27 YER produced by trading strategies based on different triggers,with or without the Active Boundaries value signal.



FIGURE 6.28 Ratio of invested days to total days available for the trading strate-gies based on different triggers, with or without the Active Boundaries value signal.

Figure 6.27 clearly demonstrates that without a value indicator, the buyingtriggers produce very poor results. The main reason for these poor resultsis that a buying trigger based on a measure of the accumulation of sharesby large players produces too many signals. Funds do indeed accumulateor sell shares on a constant basis, and since they often need many daysto take a position, this generates a large number of short-term Large Ef-fective Volume signals. But this accumulation does not mean that fundsare right to accumulate (i.e., that they correctly evaluated the value of thestock). If their evaluation was not correct, the high price will attract sell-ing by other players; hence the accumulation by large players will not besufficient to move the price upward. However, if such a strong accumula-tion occurs at a time of good value (low price), it is probable that this goodvalue will also be recognized by other players. At some point, the collectivebuying pressure will change the supply/demand equilibrium, and the pricewill have to increase. It can be seen in Figure 6.28 that the triggers, withoutuse of the Active Boundaries signal, generate so many trades as to be undis-cernibly invested in the stock: Triggers are not good for value assessmentand therefore cannot be relied upon without a very good value detectionmethod.

More stress must be put on the value than on the buy trigger; fur-thermore, I don’t think this depends on the type of indicators used. Af-ter I came to this “miracle” conclusion, I revisited Come into My Trading


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Room by Alexander Elder (p. 129). Dr. Elder explains his triple screenmethod:

The method of Triple Screen is to analyze markets in several time-

frames and use both the trend-following indicators and oscillators.

We make a strategic decision to trade long or short using trend-

following indicators on long-term charts. We make tactical decisions

to enter or exit using oscillators on shorter-term charts.

Dr. Elder continues:

The original method has not changed, but the system—the exact

choice of indicators—has evolved over years, as have the techniques.

Indeed, in this chapter, you did not find new ways to trade the market,but you saw how to best combine a new set of indicators to apply the trad-ing principles that have created wealth since human beings began tradingfor a profit: “Buy value at the right time.”

Trading Strategies Based on Supply Analysis

I promised in Chapter 4 that I would describe a trading strategy that usesthe supply analysis tool. If you remember, I wrote:

To determine whether a share at a given time represents value, you

have to determine the probability that you will be able to sell it later

on to someone else at a higher price. To increase your chances of

finding value, you must find a buying price at which there will be

very few sellers (the price will be so low that few are willing to sell at

that price). At the same time, you also need to find buyers other than

you who will push the price higher.

The general idea of the supply trading strategy is that:

� The price has fallen so much that most shareholders are locked in andare no longer willing to sell their shares at such a low price.

� The price has not risen far enough from its base to attract profit takingby more recent buyers.

Therefore, if the company is financially sound and in no danger ofbankruptcy, we may decide to step in as soon as the price downtrendis over.



TABLE 6.9 Return of the 10 Percent Supply Trading Strategy





AverageInvestedTime

Laggards group 0.38 11.0% −2.1% 13.2% 10.3%Standard group 1.22 21.4% 13.6% 7.8% 24.4%Highfliers group 2.45 37.7% 38.9% −1.2% 22.8%

This strategy is simply to buy the stock whenever the probabilisticmathematical model of the supply level shows a supply that is less thanan arbitrarily fixed ratio of the total number of issued shares. (In Chapter4, I showed examples using a 10 percent figure as a measure of a supplylevel.) We sell under the same conditions as those used in the previoustrading strategies (profit-taking limit of 20 percent, stop loss limit of −10percent, or time factor level of 50 days).

Table 6.9 shows that the results of the supply trading strategy using a10 percent level are not very good. This 10 percent supply strategy doesslightly better than the buy-and-hold strategy, but this is a poor reward forour efforts.

I initially selected the 10 percent level believing that it was a supplylevel low enough to trigger a buy signal. However, we can see in Figure6.29 that the supply method is very sensitive to the selection of the supplylevel, and it is clear that supply levels as low as 2 percent or 3 percent give

FIGURE 6.29 YER for the supply trading strategy calculated for various supplylevels.


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much better YER than higher supply levels. The cost for the use of very lowsupply levels is shown in Figure 6.30: the number of stocks that we need toscan in order to find trading opportunities. We can see in Figure 6.29 thatgoing from a 10 percent to a 5 percent supply level increases the YER from21.4 percent to 25 percent, while the number of stocks to scan is multipliedby three (Figure 6.30).

The supply trading strategy is similar to the Active Boundaries strategy,because both are based on the calculation of shareholders’ return since thetime they purchased their shares. To produce a buy signal on the supplystrategy, the price must have experienced a significant decrease, typicallybringing it well below the 50-day or the 200-day moving average. To pro-duce a buy signal on the Active Boundaries, the price simply has to pullback to the support line of the trend, which is the Lower Boundary. If theprice drops below the Lower Boundary, the Active Boundary method willstop issuing buy signals, since this means that there is a good chance thatthe situation for the company or the market has changed, and that the trendcould then also change its direction. It is usually when the Lower Bound-ary is broken that you may start looking to modify your trading stance froma long position to a short position. However, at the Lower Boundary, thesupply level itself is often still high. As a reference, I measured that at theLower Boundary 84 percent of the stocks had a supply level higher than5 percent, and 64 percent had a supply level higher than 10 percent. Thismeans that after the Lower Boundary is broken on the downside, the pricestill usually has a lot more to fall in order for the supply level to becomelow enough to trigger a buy signal. Many traders would not even buy in

FIGURE 6.30 Number of stocks to scan to support being invested in a 20-position portfolio using the supply trading strategy at various levels of supply.



such situations. Nor would funds chase after distressed stocks, which is thecommonly used term for stocks that have experienced a prolonged pricedecrease.

But, in most cases, the sharp overselling situation of a stock may nolonger accurately represent its true value or its potential.

We might then conclude that the price drop is temporary. In these typesof distressed plays, timing is everything. If we buy cheap at the wrong time,we will most certainly be stopped out in a matter of days, since the stockhas a nonnegligible probability of continuing its price decrease. In otherwords, the supply calculation tool is not as good a measure of value as theActive Boundaries tool.

Indeed, the biggest risk of this strategy is a value miscalculation: Thefact that the price has fallen a lot doesn’t mean that it will not continue fur-ther down. Let’s examine the risk of this trading strategy with an examplefrom the service and security company American Science & Engineering(ASEI). As we can see from Figure 6.31, around May 20, 2006, the supplymodel was indicating that the supply had fallen to less than 5 percent of thetotal number of issued shares, a level that can be seen as low enough fora price to increase as soon as buyers appear. However, this simple supplystrategy does not wait for buyers to step in; it dictates a buy decision assoon as the supply is lower than 5 percent. From $56, the lowest buyingpoint of the buy zone of Figure 6.31, the price dropped another 36 percent.This clearly shows that a large price drop does not guarantee that a pos-itive reversal is due to occur soon. For a reversal to happen, new buyersmust appear. This was not the case, as we can see in Figure 6.32. On thecontrary, Figure 6.32 shows a continuous selling pattern from large players(arrow A) even while the price trend turned flat (arrow B). This indicatesthat the selling pressure was far from over.

Based on this small example, it is evident that the supply model mustbe linked to an Effective Volume–based tool (Large Effective Volume,Large Effective Ratio, or divergence analysis) that offers a very good mea-sure of the demand strength. If the demand is indeed very strong at apoint where the supply of shares has dried up, the price, in theory, shouldincrease.

As shown in Table 6.10, the return produced by the 10 percent sup-ply trading strategy is already better when it is combined with the LargeEffective Ratio tool (which detects significant increases of large players’buying activity). Furthermore, Figure 6.33 shows that the returns producedby the combined supply/Large Effective Ratio trading strategy are alwayssuperior to the returns produced by the original supply trading strategy,independently of the supply level.

If we follow the same improvement gradation as we used for the ActiveBoundaries trading strategies, we could start using the time limit parameter


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FIGURE 6.31 Supply model for ASEI.

in the trades and see if a five-day time limit performs better than a 50-daytime limit. Unfortunately, the time limit factor does not work for the supply-based trading strategies, and the maximum YER that can be squeezed outof that strategy is 70 percent, as shown in Figure 6.33. If you remember,the very low supply levels are usually seen in distressed stocks, and we allknow that distressed stocks need a lot of time to come back to their orig-inal strength—if ever. You can see in Figure 6.34 that independent of thelevel of supply used, the progression in terms of average profit generated bythe trading strategy is very linear: We cannot say that the early portions ofthe trades perform better than later portions. This was not the case for the



FIGURE 6.32 Large Effective Volume and price trends for ASEI.

Active Boundaries–based strategies that were showing (see Figure 6.22) abetter performance in the early portions of the trades, which allowed usto use time limit parameters that produced much stronger general perfor-mances. But, at this point, we cannot say that a strategy that takes moretime than another strategy to produce its return is necessarily worse. Thisis especially true for funds that need more time to take or exit from tradingpositions: A slower-moving strategy would allow funds more time to playthe stock, which would in time produce a better return than fast-movingtrading strategies could allow them.

In order to compare the two types of strategies, the ActiveBoundaries–based strategy and the supply-based strategy, I selected fromFigure 6.16 three Active Boundaries strategies (see Figure 6.35), for whichI tried to find the equivalent strategies in terms of supply-based method-ology. The natural equivalent to the original Active Boundaries strategywas the original supply strategy, and the natural equivalent to the Active


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TABLE 6.10 Return of the 10 Percent Supply Trading Strategy Combined with theLarge Effective Ratio





AverageInvestedTime


Boundaries combined with the Effective Ratio strategy was the supplystrategy combined with the Effective Ratio. But, in both cases, what level ofsupply should be used? In order to compare two trading strategies, we needto find common ground, which I choose to be the ratio of invested days tothe total number of days available for trading. That is, if I am invested forthe same length of time using strategy A as strategy B, then, by comparingthe risk/return balance of the two strategies, I could decide which is better.The idea is, for each of the Active Boundaries trading strategies in Figure6.35, to find for the corresponding supply-based strategy the right supplylevel that will give it the same ratio of invested days as the correspondingActive Boundaries trading strategy. In other words, each group of two cor-responding trading strategies must display the same ratio of invested days(see Figure 6.36). Table 6.11 summarizes the correspondence between thetwo types of trading strategies.

FIGURE 6.33 YER for the supply trading strategy and the combined supply/LargeEffective Ratio trading strategy, calculated for various supply levels.



FIGURE 6.34 Evolution of the average profit produced by each trading strategydepending on the progress of the trade from the date of initial purchase.

Figures 6.37 through 6.39 compare the two types of trading strategies,separated into three sets. As you can see, the first two sets are almost equiv-alent, either in terms of return (YER), performance measured using thevariability of returns (Sharpe ratio), or performance measured using draw-downs (Burke ratio). For the third set (the 1.75 percent supply and the

FIGURE 6.35 YER for Active Boundaries–based trading strategies.


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FIGURE 6.36 Comparison of the ratio of invested days to the number of daysavailable, for the supply-based strategy versus the Active Boundaries–based strategy.

Active Boundaries with early profit taking, both using the Effective Ratio),the story is different:

� The Active Boundaries strategy produces a YER that is, in relativevalue, 35 percent higher than the returned produced by the supplystrategy (see Figure 6.37).

� The Active Boundaries strategy shows a performance that is 113 per-cent greater (more than double in performance) than the performanceproduced by the supply strategy (see Figure 6.38).

TABLE 6.11 Correspondance between supply and activeboundaries-based strategies



FIGURE 6.37 YER comparison between the supply-based strategy and the ActiveBoundaries–based strategy.

� In terms of Burke ratio, the Active Boundaries strategy shows a per-formance that is 33 percent better than the performance of the supplystrategy. This is mainly due to the fact that the Active Boundaries pro-duces a YER that is 35 percent higher than the YER of the supply strat-egy. Since the Burke ratio divides the YER by the risk level (measuredin terms of drawdown), this means that the risk is identical for bothstrategies (see Figure 6.39).

If we just follow the analysis of Figures 6.36 to 6.39 we can now saythat the two types of strategies are equivalent in terms of both risk andreturn, except when we start using time limit parameters or early profit-taking tactics (for which the Active Boundaries strategy combined withthe Effective Ratio and the time limit parameter performs much better).Such a conclusion is somewhat suspect, because it goes against a majordifference between the two types of strategies:

� The Active Boundaries strategy issues a buy signal as long as we areclose to the Lower Boundary. However, when we break through theLower Boundary, the buy signal is inhibited since this move oftenmeans a major trend change.

� However, the supply strategy issues a buy signal as soon as the supplylevel falls below a certain low level of supply, but also continues to


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FIGURE 6.38 Sharpe ratio comparison between the supply-based strategy andthe Active Boundaries–based strategy.

issue that buy signal as long as we are below that level (of course, theEffective Ratio must also be higher than its historical maximum). Thismeans that, in theory, if we misjudge a stock, after the price has fallenso much that the supply strategy issues its buy signal, the price couldstill fall to the ground, even if large players at some point are heavybuyers.

FIGURE 6.39 Burke ratio comparison between the supply-based strategy and theActive Boundaries–based strategy.



Therefore, the supply strategy is inherently more risky than the ActiveBoundaries strategy. As can be seen in Figure 6.40, for the supply-basedstrategy, there is indeed a larger proportion of trades that ended with a stoploss than for the Active Boundaries–based strategy. Let me explain Figure6.40, which is special because the horizontal axis means different thingsfor the supply and the Active Boundaries strategies. The upper line repre-sents the proportion of stop loss trades produced by the supply/EffectiveRatio–based strategy. From the left to the right, the level of supply is de-creasing, starting from 20 percent down to 2 percent. This trend corre-sponds to trades with increasing YER (as we saw in Figure 6.33). We cansee that the ratio of stop loss trades is diminishing with lower levels of sup-ply. A ratio of 25 percent means that one trade out of four was a bad trade,since we have been stopped out with a 10 percent loss.

The lower line represents the proportion of stop loss trades producedby the Active Boundaries/Effective Ratio–based strategy. From the left tothe right, the selling signal from that strategy comes more quickly, start-ing at the Upper Boundary on the left and progressing toward the LowerBoundary on the right (do not forget that with this strategy, we buy at theLower Boundary; selling close to the Lower Boundary means that we selljust a few days after buying). This trend also corresponds to trades with

FIGURE 6.40 Ratio of trades terminated with stop loss protection to the totalnumber of trades.


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higher YER. We can clearly see that the Active Boundaries–based strategyis generating a much lower ratio of stop loss trades than the supply-basedtrading strategy. A ratio of 10 percent means that only one trade out of 10was a poor trade.

It is interesting to see that the use of a stop loss feature has no effecton the YER, as can be seen in Figure 6.41: For both types of strategies,the YER does not change significantly regardless of whether we use stoploss protection. However, let’s look at the risk itself, measured by Burke’sdownside risk formula (refer to Chapter 5). Figure 6.42 shows that the useof stop loss protection significantly decreases the risk linked to the supply-based strategy (look at the down arrows), while it has almost no influenceon the risk linked to the Active Boundaries strategy.

Figure 6.42 is very important for those who like searching for the fallenangels or the distressed stocks: They will need to use stop loss protectionif they choose a supply-based strategy. As a consequence, the size of thefunds that they will be able to invest in will be limited, since in an emer-gency it is always more difficult to get out of a large position than a smallone. The second conclusion that we may draw is that if the trading strategyis producing reliable signals, you may avoid using stop loss tactics, such asin the case of the Active Boundaries trading strategy.

FIGURE 6.41 YER comparison with or without the use of stop loss protection.



FIGURE 6.42 Burke’s downside risk comparison with or without the use of stoploss protection.

Three Pillars of Successful Trading Strategies

We saw in Figure 6.26 that the three pillars of a successful trading strategyfor swing trading are:

1. Have a good assessment of value so that you select a stock that ischeap, with an upwards potential.

2. Find the right trigger to enter the trade at a good time.

3. Time management: manage the evolution of the trade, and shorten it ifpossible.

Let’s review the different traditional tools that could be used for thethree pillars:

Value Assessment As explained in Chapter 2, I refer to the assessmentof value in terms of trading opportunity. This is independent from the fun-damental or intrinsic value of a stock, which is usually measured in termsof price-earnings ratio, growth, cash flow, and the like.


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Here are a few well-known tools for detecting trading value:

� Price. The price must be in an uptrend, both in the weekly chart and inthe daily chart. The price must be above its 50-day moving average, andthe 50-day moving average must be above the 200-day moving average.

� Relative Strength Index (RSI). The RSI compares the recent pricegains to recent price losses and converts them into a number between0 and 100. The buying signal comes when the RSI comes back over the30 line that indicates an oversold level (see Chapter 2).

� Support line. This line indicates price congestions or the price levelswhere many buy/sell decisions were taken in the past. When the pricedeclines to its support line, it is prone to move back up if enough buy-ers appear.

� Price patterns. There are famous stock price patterns such as thehead-and-shoulders formation or the cup-and-handle pattern. Thesepatterns, which are abundantly described in the literature, allow us todetect when a stock is entering a zone of interest in terms of tradingvalue.

� Fibonacci retracements. These are also well documented in the liter-ature. The most commonly used numbers for retracements are 38.2percent and 61.8 percent. If the stock is in an uptrend, the Fibonaccitheory says that if the price goes down 38.2 percent of that uptrend, itwill likely move back up, and even more if it pulls back down to the61.8 percent level. Trading value is found at the Fibonacci pullbackpoint.

� Specific dates. If they can assess the fundamental value correctly, sometraders try to invest only before the quarterly earnings date; otherswant to capitalize on momentum created after earnings are reported.

� Active Boundaries and supply analysis. In this book, I introducedthe Active Boundaries tool (Chapter 2) and the supply analysis tool(Chapter 4) for value detection.

The Trigger, or Entry Timing The timing of trade entries is oftenfound in short-term pattern analysis or the use of oscillator-type indica-tors. Most of these indicators use only price, but Richard Wyckoff workedextensively on the price/volume relationship more than 85 years ago.

Here are a few well-known tools for entry timing:

� Candlestick analysis for the past few trading days (entire books arededicated to these patterns).

� Moving average convergence/divergence (MACD) or MACD his-

togram (MACDH). The MACD momentum oscillator was developedby Gerald Appel. It compares a fast and a slow moving average in order



to detect if the price change is quicker or slower than before. It com-pares the acceleration (rate of change) of the fast and the slow movingaverages. If the acceleration of the fast moving average is higher thanthe acceleration of the slow moving average, this indicates a positivemomentum in the price.

� Price trend. Price must ride between the 5-day moving average and the10-day moving average to be a good play; the 10-day moving average isthe first stop level in case of pullback. The 20-day moving average isthe “always” exit point.

� Price volatility. When the 50-day price volatility is above 0.4, look forpullbacks; when it is below 0.4, look for breakouts.

� Richard Wyckoff’s method. This is too long to explain here. I wouldsimply refer you to the set of DVDs of David Weis, the well-knownWyckoff advocate. David Weis shows in great detail how to look atthe combination of volume and price to search for the ease of move-ment as a buy trigger. These DVDs are available on Dr. Elder’s web site(www.elder.com).

� Effective Volume, Effective Ratio, and divergence analysis. I intro-duced in this book the Effective Volume tool (Chapter 1), the EffectiveRatio and the divergence analysis tools (Chapter 3) as trading triggers.

Time Management To my knowledge, there is no specific tool to man-age the time aspect of a trade. The simple idea is that some trading strate-gies allow you to enter just before an important move in price. In suchconditions, it is often better not to wait for the full swing before selling,because the price advance is often stronger during the earlier part of thetrade than during the later part. For example, I showed that the ActiveBoundaries–based strategies are good candidates with which to use timemanagement in the trade, while the supply analysis–based strategies arenot. Strategies that use volatility or specific earnings dates are very goodcandidates for fast profit taking.


The most important thing that we learned in this chapter is that nothinghas changed since human beings started trading for a profit: We must buyvalue at the right time. We also learned that if we misjudge the value,we will most probably lose money, even if we believe that the timing isgood.


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More specifically, we focused on two trading philosophies:

1. Swing trading, for which we used a combination of Active Boundariesand the Effective Ratio.

2. Picking distressed stocks, for which we used a combination of supplyanalysis and the Effective Ratio.

Both types of strategies produce much better returns than the buy-and-hold benchmark, but in both cases we noticed two things:

1. Getting higher returns from trading strategies also means having toanalyze a larger number of stocks in order to find trading opportunities.

2. Knowing how the trading strategy impacts the trade’s evolution in timeis essential, because, depending on the type of strategy, the right exittiming tactics can sometimes generate the most significant returns.

We then summarized the three pillars of a successful strategy: (1) thevalue assessment, (2) the entry trigger, and (3) the time management of thetrade evolution.

We also learned that the selection of the right trading strategy mainlydepends on the amount of capital to invest and on the trader’s ability toautomatically analyze a large number of stocks.


P A R T T H R E E

The Bonus Section


C H A P T E R 7

The Market Is aTwo-Way Street

Shorting Strategies

S horting consists of borrowing a stock and selling it in the market,hoping that the stock price will decline. After some time, the shortseller will repurchase the stock in the market (at a lower price, it

is hoped, than what the stock was sold for, pocketing the difference) andreturn the borrowed stock to the lender.

Over the past few years, the New York Stock Exchange has been re-porting that the short level for stocks has been situated, on average, be-tween only 5 percent and 7 percent of the average total daily volume oftrading. This relatively small figure indicates that short selling is not a com-mon trading practice. Some trading books advocate that good traders placeas many short plays as long ones, but since only 5 percent to 7 percent ofthe stocks exchanged are due to short traders, I cannot imagine that theremaining 93 percent to 95 percent are bad traders/investors. I know manygood traders who do not short stocks. It is a perfectly acceptable strategyto be mostly in cash during a bear market and wait for good investmentopportunities without actively shorting stocks.

THE SHORT SALE “TICK TEST” RULE

Before going into the technical analysis for short trading, I would like tocome back to the short sale “tick test” rule. This rule dates back to 1938. Itsimply stated that short sales were allowed only at a price above the last

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sale price, or if there was no change in the last sale price and this last saleprice was higher than the previous price, then short sales were allowedat the last sale price. This simply meant that it was not possible to pushthe price down when shorting. This rule has made things relatively difficultfor me in terms of Effective Volume, because it has made it impossible forthe Effective Volume tool to detect shorting activities. Indeed, the EffectiveVolume method records the volume that is responsible for a price changefrom one tick to the next. Since short traders were not allowed to pushthe price down but were only allowed to increase the ask price, the Effec-tive Volume could not detect them. However, the Securities and ExchangeCommission (SEC) came to the rescue of the Effective Volume method andabolished this “tick test” rule as of July 6, 2007. In other words, since July2007, the Effective Volume method has become even more effective.

HOW TO USE THIS BOOK’S TOOLS FORSHORT TRADING

This chapter is not intended to push you into short trading. My intention isto examine whether the tools introduced in this book can be combined tocreate successful shorting strategies.

Effective Volume

Throughout this book, I have repeatedly said that the Effective Volumeflow size separation between the Large Effective Volume and the SmallEffective Volume is the best way to measure funds’ activity, and that itpays to follow funds.

The advantage of the Effective Volume tools is that compared to stan-dard tools, they give you a much better view of what is happening in themarkets. This is especially true for long plays, as we have seen. We maythen ask the following question: When we are selling a long position in a

stock, should we directly turn into shorting the stock? As you may imag-ine, the answer is no: A shorting trade is not a carbon copy of a buyingtrade. You cannot simply take the buying strategies of Chapter 6 and turnthem into shorting strategies.

We will better understand this after studying the example of the en-ergy producer Reliant Energy. As you can see in Figure 7.1, at the end ofJanuary 2007, Reliant Energy broke out of its trading range and started along uptrend. Figure 7.2 shows a nice accumulation by large players, whilethe price was making a higher high and a higher low, indicating a possiblebreakout. Let’s suppose that we buy the stock on January 24.


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FIGURE 7.1 Reliant Energy: price trend.Source: Chart courtesy of StockCharts.com.

FIGURE 7.2 Reliant Energy: Large Effective Volume at the buying point.


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After buying on January 24, we can see in Figure 7.3 two very strongselling patterns:

1. On March 22, at point A, there was a strong price spike that exhaustedthe buyers. This spike came at the end of a profit-taking trend that wassignaled by the downtrend of large players (down arrow 1).

2. On April 5, at point B, we can see that the price is forming a dou-ble top, while large players are actively liquidating positions (downarrow 2).

There is no question that we need to sell either at A or at B. We haveindeed seen that it is not safe to bet against large players. The next questionis: Should we bet with these large players and short at point A or at point

B? Of course, when you look at Figure 7.1, you already know that shortingat the end of March 2007 or early April 2007 would have been a mistake.

FIGURE 7.3 Reliant Energy: Large Effective Volume at the selling point.



The problem of Reliant Energy is the contradiction between the largeplayers’ selling movement and the strong price uptrend. You may think thata strong selling pattern by large players is enough to break a price uptrend.However, this is usually not the case: Strong price uptrends do not easilybreak, the same way that strong price downtrends do not easily reverse. Itis mainly a question of value and the perception of value. Strong, lastinguptrends attract new investors up to the point where there will be an im-portant disconnect between the value and the stock price. Once a growingnumber of investors start noticing the overpricing of the stock, they willstart selling and the uptrend will begin to reverse down. This price reversalwill attract latecomers, and this pattern between late trend followers whopush the price up and early sellers who push the price down will continueuntil all the buyers are exhausted and a new downtrend can settle in. Thisis when you may go short.

In reality, a good short will be discovered if you are able to find a dis-connect between the value of a stock and how it is priced.

We already saw that the Effective Volume tool is not good for valueassessment. The Effective Volume tool is mainly used as a trigger, whenvalue has already been ascertained. I have also noticed that this triggerworks much better on the buy side than on the short side. There is indeedone main reason for buying a stock: to sell it later at a higher price. How-ever, there can be many reasons for selling: taking some profit off the table,decreasing the risk before an earnings release, rebalancing a portfolio, andso on. You cannot blindly follow large funds when you see them selling,since it is impossible to know the exact reasons for their moves. However,when large funds are heavily buying, you know that you should also buy,especially if the supply side has dried up or if you have correctly deter-mined the value of the stock.

In the case of Reliant Energy, you will notice that at the end of March,the price trend is well above the 50-day moving average (see Figure 7.1),indicating a very strong price move. This is not a place where you will findan easy short trade. Short trades are more commonly found in a weaken-ing price trend, which is typically seen as a price moving below its 50-daymoving average. In other words, the heavy selling trend from large playerscannot be trusted to signal a short play if it is not confirmed by a weakeningprice pattern.

Short-trading rule #1: The price must be below its 50-day moving

average.

It is very important to remember here that when selling, large fundsare very careful not to trigger a downtrend, since they would incur an in-stant paper loss on the shares they have not yet had the time to sell. Large


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funds are seldom trendsetters in a selling pattern. The selling trend will betriggered either by negative news, by a general market move down, or bya larger than usual wave of profit taking from all the players. To detect aselling wave, we therefore have to look at the weakening pattern in total Ef-fective Volume as opposed to the analysis of the strengthening of the LargeEffective Volume that is used to detect a buying pattern. The weakeningpattern in total Effective Volume must be compared to the price patternusing the divergence analysis tool. It can also be compared to past trendsusing the total Effective Ratio signal.

Divergence Analysis

The divergence analysis between the total Effective Ratio and the pricerate of change (refer to Chapter 3) is more relevant than the total Ef-fective Volume flow analysis, because the divergence analysis comparestoday’s selling pattern to the strength of past selling patterns, which pro-vides a better clue to where we stand in the selling wave. We will see that bystudying the case of the software company MicroStrategy. As you can seein Figure 7.4, this company experienced a healthy price uptrend betweenAugust 2006 and mid-November 2006 (point 1). Looking at Figure 7.4, it isdifficult to see at what point we should short: 1, 2, 3, 4, or 5. All of thesepoints seem to be good shorting spots, since the price was subsequentlyfalling. The dotted line shows the resistance level that is getting strongereach time it is hit without being broken. In Figure 7.5, which shows thetotal Effective Volume flow, I have also plotted the five points of interestthat we are now going to study.

� Point 1: This is the least likely place for a safe short. We are still ina strong uptrend, far above the 50-day average (see the length of thedouble arrow between point 1 and the 50-day average in Figure 7.4).We can also see in Figure 7.5 that at point 1, the total Effective Volumeflow is higher than its 20-day average and getting stronger—althoughit collapsed from point 1. You can see in Figure 7.4 that after point1 the price pattern breaks down below its 50-day average line on astrong selling move. Something happened. The investor’s confidence isin jeopardy.

� Point 2: For the first time, we are now reaching the resistance level(at point 1, the resistance line had not yet been created). But, since thetotal Effective Volume flow seems to have reversed up, as shown inFigure 7.5, we may not short.

� Point 3: The price (Figure 7.4), is back above the 50-day moving aver-age, slightly above the resistance line. The total Effective Volume flow



FIGURE 7.4 MicroStrategy: price pattern.Source: Chart courtesy of StockCharts.com.

is in a slight accumulation after heavy previous selling from point 2(Figure 7.5). This is a dangerous short. It is better to wait for the priceto move back down. In Figure 7.6, I have represented the Effective Vol-ume flow separated by size, both leading to points 2 and 3. You can seethat leading to point 2, the small players and the large players are mov-ing in opposite directions with the same strength, neutralizing eachother (arrows A and B). This is hardly a strong selling pattern. Regard-ing point 3, notice that even if the Large Effective Volume had shown

FIGURE 7.5 MicroStrategy: total Effective Volume flow.


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FIGURE 7.6 MicroStrategy: Effective Volume flow by size, February 2007.

a strong selling pattern since point 2, at the very right of Figure 7.6 (atpoint 3) there is some accumulation starting, after a flat Large Effec-tive Volume pattern (arrow C). This could signal a change of mood bylarge players. Since we are just crossing over the resistance line, wemay suspect that this new wave of buyers could break the resistanceat point 3 (Figure 7.4). This is not a safe short.

� Point 4: We are again back to the resistance line (Figure 7.4), whilethe total Effective Volume shows a very strong selling pattern (Figure7.5). This is a much safer short play than the possible short of point 3.Figure 7.7 details the Effective Volume flow separated by size, bothleading to points 3 and 4. You can see that even if the price trends lead-ing to points 3 and 4 (arrows D and F) are almost identical in strength,large players leading to point 3 (arrow C) were slightly positive buyers,while you can see strong large sellers leading to point 4 (steeper downarrow E). Point 4 is therefore a better short.



FIGURE 7.7 MicroStrategy: Effective Volume flow by size, March 2007.

� Point 5: I have to say that this is my favorite type of short. At point 5,we are in a price trading range, while the total Effective Volumeis heavily down. This pattern appeared after the price gap down ofApril 11 pushed the price below the 50-day moving average, shatteringthe hopes that the stock could come back above its resistance level.You can see in Figure 7.8, which shows the Effective Volume flow sep-arated by size, that large players were strong sellers (down arrow G)during the price trading range (flat arrow H). For me, shorting atpoint 5 is safer than shorting at point 4, because at point 5 you donot have to fight against a price trend, and you are below the 50-daymoving average.

Short-trading rule #2: The total Effective Volume flow must be be-

low its 20-day moving average.


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FIGURE 7.8 MicroStrategy: Effective Volume flow by size, April 2007.

If we now turn over to the divergence analysis tool (see Figure 7.9),we can see that the signal was indicating possible shorts close to points3, 4, and 5. The divergence analysis indicator gives more reliable signalsthan does the Effective Volume, but we can see that both are difficult touse without an assessment of value. The only indicators of value that Iused for the example of the MicroStrategy case study were the resistanceline and the 50-day moving average. Now let’s see if the Active Boundariesindicator is a good complementary tool for assessing value in case of shortselling.

Short-trading rule #3: The divergence between the total Effective

Ratio and the price rate of change must be below the sell divergence

limit.



FIGURE 7.9 MicroStrategy: divergence analysis.

Active Boundaries

Since it is an excellent indicator for trend monitoring, the Active Bound-aries indicator can be used to find shorting opportunities, especially whencombined with the Effective Volume or the divergence analysis tools. Star-bucks, the world-famous coffee shop chain, offers an interesting case studyfor an analysis of shorting opportunities using a combination of indicators.

We can see in Figure 7.10 that uptrend A, which occurred betweenAugust and November 2006, was followed by downtrend B. I markedfive points (1 through 5) of interest for possible shorting opportunities.These points have also been marked in Figure 7.11, which represents boththe Active Boundaries on the upper panel (the value), and the totalEffective Volume on the lower panel (the trigger).


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FIGURE 7.10 Starbucks: price pattern.Source: Chart courtesy of StockCharts.com.

FIGURE 7.11 Starbucks: Active Boundaries and total Effective Volume.



Let’s examine points 1 to 4:

� Point 1 is set on October 6, 2006, which is located far above the 50-dayaverage (Figure 7.10). The sudden price increase pushed the stock intoa new high, on heavy positive total Effective Volume flow. At point 1,the Active Boundaries indicator reaches the high level of 20 percent,but this type of average profit is very commonly seen and is not a suffi-cient reason to short the stock (although it is a good enough reason totake profit out if we are invested).

� Point 2 is set just after the price gap down of November 16, 2006. Thisgap down came as a surprise to all the investors who had been ac-tively buying shares since point 1, indicated by the increasing trendin total Effective Volume flow between points 1 and 2. Many of thosewho bought between points 1 and 2 lost a few percentage points ontheir investments. It usually is not enough to trigger an overall sellingspree, but at point 2, these shareholders doubt that their decision tobuy was correct. The price gap down of November 16 shattered theirconfidence, and they will now start selling.

Furthermore, those who bought before October 6 and did not sellafter the price surge of October 6 now regret that they did not, sinceon average at that time they were enjoying a 20 percent profit. Many ofthese are also going to sell to protect what is left of their profit. Inother words, a small gap down after a trading range that is topping aprice uptrend could be strong enough to trigger a sell-off. This sell-offcan be strong enough to change the mood of the majority of the activeshareholders, which will translate into a new trend and new ActiveBoundaries. It is therefore a good idea to short on the sell-off that wastriggered by the gap down at point 2.

� Point 3 has been set two days after a small price gap up that occurredon December 5, 2006, which was followed by renewed selling (decreas-ing Effective Volume flow). Note on the lower panel of Figure 7.11 thatat point 3, the 20-day total Effective Volume average is starting to trenddown. It is also interesting to note that on December 5, 2006, the pricegap up was so weak that the Active Boundaries returned only up to the0 percent level. From that point, we can already see the total EffectiveVolume flow decreasing together with the price decrease. Why wouldshareholders continue selling from an average return of 0 percent?Because they lost hope! The failure of the price gap up on December5 to bring the Active Boundaries above 0 percent is an indication ofrenewed selling. We can also see that a new Upper Boundary was cre-ated at point 3. Since the Lower Boundary is still at –15 percent, we arenow in a confirmed –7.5 percent downtrend (the midpoint between 0percent and –15 percent), just at the Upper Boundary, where the shareprice is expensive. It is therefore permissible to short at point 3.


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� Point 4 is set on the price spike of January 18, 2007, very close to thenew Upper Boundary of 0 percent. We can see in the lower panel ofFigure 7.11 that at point 4 the total Effective Volume flow was positiveand above its 20-day average, indicating an accumulation of shares.This accumulation is detailed in Figure 7.12, where you can see that atpoint 4, large players were still accumulating shares. Is point 4 a goodshorting point? No, because you absolutely may not bet against largeplayers, even if you think that you are right and they are wrong. Theyhave the purchasing power, which gives them the strength to move themarket in their direction. Point 4 is therefore not a good shorting point.You have to wait until point 5, which shows a strong selling trend bylarge players.

Short-trading rule #4: When close to the Upper Boundary, short

only when the downtrend is confirmed.

FIGURE 7.12 Starbucks: Effective Volume flow by size, January 2007.



The Supply Analysis Tool

In Chapter 4, we studied the level of supply with the objective of findingdisequilibrium points between a very low level of supply and an increasingdemand. At such points, we saw that a surge in demand, indicated by anincreasing Large Effective Volume flow, could trigger a new price uptrend.We also saw that a supply level of 45 percent is not very different froma supply level of 35 percent. It simply means that in both cases, a buyerwill easily find shares to buy. Figure 7.13 represents the supply level for

FIGURE 7.13 Starbucks: supply analysis.


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Starbucks, as measured using the supply analysis tool presented in Chap-ter 4. You can clearly see that for all potential shorting points 1 to 5 the levelof supply was quite high, indicating that there were, statistically speaking,enough shares available for sale. The only information of value that the sup-ply analysis tool is offering is the potential zones of short squeeze, whichare the zones of very low supply levels. It is very dangerous to short astock that is indicating very low supply levels, because after we short, westill need other shareholders to sell their shares in order for us to gain froma lower stock price. The probability of finding sellers in a low supply levelis quite small. Furthermore, we saw that even a modest surge in the buy-ers’ interest will push the price up, and could quickly force short playersto cover, fueling the price surge that will squeeze still more short playersand force them to cover. A low supply level indicates a situation that wecan qualify most of the time as static: The price decrease has locked manyshareholders into their positions, and these shareholders are waiting for aprice increase, which could take some time to occur.

However, what you are interested in as a short seller is not specificallythe level of the supply itself, but the dynamic reaction of the shareholderswhen the supply level is still high. Indeed, you want to know how share-holders will react to the most recent price action: Will they keep or selltheir shares? If you expect a sell-off, then it is a good idea to short aheadof the sell-off. One of the best methods for anticipating a sell-off is to studythe repartition of the shares by profit level at a point in time (the evolu-tion of the volume histogram), and see how this repartition evolves as areaction to a price change.

Let’s come back to the Starbucks case study. Figure 7.14 shows theshare price pattern up to October 6, 2006, just after a price increase of morethan 15 percent that occurred after October 2. We want to see whetherbetween October 2 and October 6 shareholders will be willing to keep their

FIGURE 7.14 Starbucks: price trend up to October 2006.



shares or they will prefer to sell them. In Figure 7.14, I identified two groupsof shareholders:

1. Group A is the regretful group of shareholders. This group boughtbefore August 2006 and therefore experienced the price drop thatoccurred in August. Some of them sold after the drop, but those whodid not sell experienced a sentiment of failure, which was followed bythe great relief of September, when the stock price rebounded closerto their purchasing price.

2. Group B is the winner group of shareholders, since most of them pur-chased their shares at a price lower than the October 4 price, just be-fore the price gap up of October 5.

We want to see how these two groups of shareholders could possiblyreact to the price gap up of October 5. In order to analyze the change, Ihave represented in the upper panel of Figure 7.15 the volume histogram of

FIGURE 7.15 Starbucks: volume histogram, October 2006.


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these two groups of shareholders, in profit/loss percentage terms measuredas of October 4, 2006 (before the price gap). The lower panel represents thesame volume histogram, but as of October 6, 2006 (after the price gap).

On October 4, many shareholders in group A were ready to sell in relief(since they first experienced a paper loss on a price drop that was laterfollowed by a price rebound), while some shareholders in group B wereready to take their profit. On October 6, you can see that all shareholdersin group A are now enjoying a small profit. This profit has come so quicklythat most would be inclined to wait for a continued price appreciation.On the contrary, shareholders in group B are experiencing a very healthyprofit and could decide to take money off the table. Then you have thenewcomers who create the new group C (at the left of the lower panel ofFigure 7.15), formed by the shareholders who bought on October 5 and 6.These shareholders have come in with high expectations and are not likelyto sell. Shorting after the price gap of October 5 would therefore not be agood idea: The number of hopeful shareholders is too high compared tothe possible number of profit takers.

Let’s now turn to November 16 and 17. As shown in Figure 7.16, a smallprice gap down occurred on November 17, which triggered a sell-off in thefollowing days. Since October 5, when the previous gap up occurred, thenumber of shareholders entering into group C has been increasing, whilethe number entering into group A has been decreasing. The upper panel ofFigure 7.17 shows the volume histogram of the three groups of sharehold-ers just before the price gap of November 17. As can be seen, most of theshareholders in group B are experiencing a slight profit. Their expectationfor a further price increase is still strong. This expectation is being rein-forced by the small price uptrend leading to November 17. Those in group

FIGURE 7.16 Starbucks: price trend up to November 2006.



FIGURE 7.17 Starbucks: volume histogram, November 2006.

A, who did not sell earlier, are now experiencing a relatively good profit (donot forget that this was a group of losers just two months earlier). GroupB is the most profitable, and a large number of these shareholders are ac-tively selling for profit taking. How is each of these groups going to reactto the price gap down of November 17?

� The A group is the die-hard group of long-term holders who alreadywent through one disappointment at the beginning of August. Thosewho did not sell at that time and still hold the stock may be slow to sellat this time. This group has become immune to pain.

� The B group that is enjoying a good profit (in the center of Figure 7.16)is already in a selling mood. The small drop on November 17 couldaccelerate their selling tendency.


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� Group C is now experiencing a slight loss. It is the group that camewith the highest and most recent expectation for a price increase. Thisgroup is certainly disappointed now, and many of them will sell theirstock in order to limit their losses.

Looking at the lower panel of Figure 7.17, we can see that the totalof the sellers (groups B and C) is much larger than the total number ofnonsellers, meaning that if new buyers do not come in force, the sellingmovement will accelerate.

We can see with this example that the supply analysis tool does notgive clear signals of when to short. However, it will show when shortingis too dangerous because of the low supply levels. What I like, however,regarding the supply analysis tool is that when we separate shareholders bytype (winners, losers, recent holders, less recent holders), the evolution ofthe volume histogram gives very good clues as to how actual shareholdersmay react in the event of a price increase or price decrease. This analysisis useful for both long and short plays.


We learned that shorting is possible using combinations of the tools thatwere introduced in the first section of this book. The four trading rules thatI use for shorting are:

Short-trading rule #1: The price must be below its 50-day movingaverage.

Short-trading rule #2: The total Effective Volume flow must be below its20-day moving average.

Short-trading rule #3: The divergence between the total Effective Ratioand the price rate of change must be below the sell divergence limit.

Short-trading rule #4: When close to the Upper Boundary, short onlywhen the downtrend is confirmed.

However, since I have not done any back-testing of shorting strategiesusing these rules, it is impossible to evaluate their true performance.


C H A P T E R 8

Market andSector Analysis

W hat is the market influence on the performance of a stock? Whatis the influence of the sector performance on the performance ofa stock? I do not know. However, I know that if I invest in a stock,

my investment will perform better if the stock is in a trendy sector and ifthe markets are in a positive trend. The corollary is that when a sector orthe markets are sharply down, even good stocks will go down in concert.

Market analysis and sector analysis are so important that I did not wantto finish the book without at least scratching the surface of these very largetopics. I will not talk about such items as the evolution of consumer spend-ing, commodity prices, interest rates, and the like, although they are criticalfor both market and sector analysis. However, I have been wondering if, onthe technical side only, my tools could be used to offer broader sector andmarket views.

This type of proposition looks both very attractive and dangerous: Howcan I apply some tools to a usage for which they were not developed? If youconsider, for example, the cyclical pattern of the Active Boundaries or thecomparison of the divergence analysis signal to historical references, bothare based on a very fundamental hypothesis: The composition of the groupof traders invested in a specific stock changes very slowly compared to thecycle of the stock price itself. The same traders will trade the same stockagain and again at different times; they will use the same analysis methodto take their trading decisions, forming a recurring pattern of trends andreversals. It is this pattern that is captured by both the Active Boundariesand the divergence analysis tools.

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Can we also find such a cyclical price pattern in both the sectorsand the market? Can we adapt the toolbox? The answer is yes, we canadapt the tools, and the preliminary results presented in this chapter arevery interesting, although not complete. This chapter is divided in two sec-tions: The first section applies both the Active Boundaries indicator andthe divergence analysis to market analysis, while the second section ap-plies the divergence analysis to an analysis of the different sectors.

WHEN IS THE MARKETBECOMING EXPENSIVE?

This question is a difficult one to answer. So many scholars and Nobel Prizewinners have been answering this question that I feel that my small contri-bution may not add anything of interest. The problem with that questionis that there is no reference level against which to measure the expensive-ness of the market. Let’s review some of the references that we could use,supposing that the stock market is well represented by the S&P 500 index:

� Monetary unit. Around June 2007, the S&P 500 was breaking a newhigh in U.S. dollars, but when we adjusted its value to the euro, then itwas still far from the highs of 2000. Was the S&P 500 therefore consid-ered cheap or expensive?

� Purchasing power. As of June 2007, the S&P 500 had been growingsince August 2002. Did it grow at the same pace as the gross domesticproduct (GDP) growth? Is it expensive compared to the growth of thewealth produced by the nation? Do we measure the wealth in termsof individual income growth or in terms of corporate income growth?If the purchasing power of both consumers and corporations has in-creased more quickly than the S&P 500 increase, may we conclude thatin June 2007 the stock market was still cheap?

� Future earnings growth. Can the growth in purchasing power con-tinue? Since the S&P 500 represents the index of the 500 largest U.S.companies, what could affect their earnings growth? If we believe thatthe lack of availability of labor and cheap natural resources will havea negative effect on the future earnings growth, then the stock marketcould already be expensive.

In Chapter 2, I evaluated the value of a stock in terms of the expec-tation of its current shareholders, and I said that shareholders’ expec-tation is inversely proportional to their gains. By measuring the averageprofit/loss of a group of shareholders (the shareholders who own the


Market and Sector Analysis 343

Active Float of the stock), we obtain a cyclical signal that I called the Ac-tive Boundaries. The Active Boundaries evolves between a Lower Bound-ary that indicates a cheap price and an Upper Boundary that indicates anexpensive price.

Unfortunately, this way of thinking may not be easily applied to an in-dex analysis such as the S&P 500, because the S&P 500 is not one singlecompany. Shareholders who have invested in these 500 different compa-nies have profit expectations linked to their specific investments, not tothe gain/loss of the index itself. Therefore, the hypothesis of a united groupof shareholders who have a cyclical decisional pattern cannot be appliedto a wide variety of shareholders who invested in companies that followdifferent cycles of their own.

I had another idea: Since the market is “made” by large funds, and sincethese funds are evaluated on a yearly basis, why not use the same 12-monthtime period for an Active Boundaries calculation on the S&P 500 index? Weknow how many shares are traded daily of the companies forming the S&P500. We could then calculate the average profit of all the shares related tothe S&P 500 index that were exchanged during the past year and see howthis figure evolved day by day. Such results are shown in Figure 8.1a. Youcan first see that the Active Boundaries catch the market value much betterthan the index trend itself or even the index rate of change over the sameperiod (see Figure 8.2). For example, you can see that since 1987, the S&P500 has been evolving between an Upper Boundary of 20 percent (UB1)and a Lower Boundary of –20 percent (LB1). You may notice that the Octo-ber 1987 market crash is more apparent in the lower panel of Figure 8.1than in the upper panel. The October 1987 crash moved the market al-most overnight from the expensive Upper Boundary 1 to the cheap LowerBoundary 1.

In Figure 8.1a, I have indicated three zones to which a set of boundarieseach correspond:

1. Zone A shows the 1995–2000 uptrend, which is well captured by UB2(+20 percent) and LB2 (0 percent).

2. Zone B covers the bubble crash that occurred between 2000 and 2003,and is captured by UB3 (0 percent) and LB3 (–20 percent).

3. Zone C relates to the most recent uptrend, which started in 2003. It iscaptured by UB4 (20 percent) and LB4 (0 percent).

As I write this update on January 18, 2008, the market has evolved con-siderably, as can be seen in Figure 8.1b.

As a reference, I also included in Figure 8.2 the price rate of changeusing a 12-month window. We can see that Figure 8.2 does not produce as


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FIGURE 8.1a S&P 500: index price pattern and Active Boundaries.

regular a pattern as the one in Figure 8.1a. One of the reasons for this isthat the price rate of change signal changes not only when a price changeoccurs, but also when that change is moving out of the 12-month analysiswindow. For example, we can see in Figure 8.2 that the 1987 crash pro-duced a mirror image (which is shown by a large increase in the price rateof change), simply for a mathematical reason: The mirror image occurswhen the price gap down of the 1987 crash leaves the analysis window(you may refer to the explanations of Figure 3.4 in Chapter 3, related tothe analysis window). This problem can be corrected using some weight-ing methods (linear or exponential), but these corrections serve only tomassage the data to obtain desirable results.



FIGURE 8.1b S&P 500: updated index price pattern and Active Boundaries.

The Active Boundaries indicator is much stronger, because it measuresthe evolution of the average profit of shareholders, which is the main factorbehind shareholders’ selling decisions. The strength of a value indicatorsuch as the Active Boundaries is that at the right side of the figure, we caninstantaneously see if we are close to one of the boundaries, and how closewe are to it. For example, at the right of the lower panel of Figure 8.1a,the Active Boundaries indicator is at 11.3 percent, corresponding to an S&P500 value of 1,536. (The method used is identical to the method describedin Chapter 2 for finding price targets at the boundaries.) The simulationshows that the 20 percent UB4 level will be reached for an S&P value of


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FIGURE 8.2 S&P 500: index rate of change.

1,650; at that point, the S&P will definitively be very expensive. On the otherhand, the 0 percent LB4 level will be reached for an S&P value of 1,380. Ifwe go down below 1,380, it will be the sign of a confirmed bear market. Thecalculation of the S&P values that corresponds to the different levels ofthe Active Boundaries must be reexecuted every day, because the tradingactivity during the day has an impact on the Active Boundaries indicatorand therefore on the S&P value calculated through simulation. In otherwords, if on July 30 the S&P 500 value corresponding to UB4 is 1,650, atthe end of July 31, this value will be slightly higher or lower dependingon (1) the number of shares that have been exchanged and (2) the pricechange between July 30 and July 31.

I wrote the previous paragraph at the end of August 2007. Since thenthe S&P 500 has gone decisively through its LB4 Lower Boundary (see Fig-ure 8.1b.) The next Lower Boundary level is LB3, which indicates an av-erage loss of –20 percent. That will happen for an S&P 500 value reaching1,150. If ever we go there, Figure 8.1a indicates that we will snap back up.

Another interesting research path regarding the application of the Ac-tive Boundaries to a measurement of the market’s strength could be tocalculate the S&P 500 Active Boundaries using the Active Boundaries cal-culations operated for each stock in the S&P 500. The idea is the following:Knowing that each stock moves within its own price cycle, we could as-sign a number with a value between –1 and +1 to each stock’s calculatedActive Boundaries. This value would depend on the position of the ActiveBoundaries signal within the boundaries (–1 when the signal is at the LowerBoundary, +1 when the signal is at the Upper Boundary, and between –1



and +1 depending on the situation of the signal between boundaries). If weweight this number by the S&P 500 weight, I believe that we could build anew Active Boundaries indicator that is closer to the value of each of thecomponents of the S&P 500, which would be more accurate than the ActiveBoundaries indicators in Figures 8.1a and 8.1b.

The real weakness of the Active Boundaries indicator is that it cannottell us if we will rebound at the boundary or break it. Indeed, whenever wetouch one of the boundaries, there is no way to predict the future move-ment. When we analyzed a single stock, it was quite natural to rely on theLarge Effective Volume as the trigger that would indicate the direction ofthe next probable move of the stock price. But, for the S&P 500, there are500 different stocks and, since each stock has a different face value, wecannot simply add up the Large Effective Volume flow of each stock hop-ing to obtain a total Large Effective Volume flow for the whole index. (Asa matter of fact, the Active Boundaries calculation on the S&P 500 itself isflawed, since a stock that is exchanged in a company with a share price of$100 carries the same weight in the calculation as one with a share priceof $10.) The best approach is to use the concept of money flow, whichallows us to measure the market evolution using the following six-stepprocedure:

1. For each stock of the index, calculate the minute money flow, whichis the minute-by-minute product of the Large Effective Volume and theaverage stock price during each minute.

2. Select an analysis period—for example, 20 days.

3. During this analysis period, calculate the Large Effective Money flow,which may be obtained by adding all the minute money flows calcu-lated in step 1.

4. During the same analysis period, calculate the total money flow, whichmay be obtained by tallying for each trading minute the total volumemultiplied by the average stock price. (Please note that the total moneyflow is always positive, because it represents all the money that is in-vested in the stock during the analysis period.) However, the LargeEffective Money flow is moving up and down, because the Large Ef-fective Volume could be positive or negative depending on whetherthe price inflection between the previous minute and the currentminute was positive or negative (see the Effective Volume definition inChapter 1).

5. For each stock, we divide the result of step 3 by the result of step 4.We then obtain for each stock what I call the “20-day large players’strength” expressed as a percentage of the money invested in each


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stock during that period. We now have a list of strength percentagescorresponding to each stock of the index.

6. Each of these percentages must be weighted by the stock’s marketvalue, since it is how the S&P 500 weights the 500 different stocks thatbuild the index.

We can then obtain what I call the “market money flow,” which is aminute-by-minute evolution of the flow of money that moves in or out ofthe stocks of the S&P 500 as part of large players’ activity. I am pretty surethat if we study the evolution of this money flow at critical positions of theActive Boundaries for the S&P 500, we will be able to create a tool thatcould possibly predict the next move of the index. I believe that applyingthis type of idea to different indexes could lead to interesting results.

As an example of such a calculation, I took 254 reference stocks from36 different sectors (these are in fact some of the stocks that I follow on adaily basis, and for which I have sufficient data for this example).

Figure 8.3 shows the evolution since August 1, 2006, of the price changeof this group of 254 reference stocks, compared to the evolution of the S&P500. We can see that until September 2007, both evolutions were closelyrelated—my reference list indeed includes fewer financial and more basicmaterial stocks than what is included in the S&P 500. We therefore maynot conclude that the market strength analysis performed on these 254reference stocks is a good indication of the global market strength. How-ever, Figure 8.4 shows interesting results that a further study—presentlyunderway—for the S&P 500 could confirm.

FIGURE 8.3 Price change for the 254 reference stocks compared to the S&P (be-tween August 1, 2006, and March 6, 2008).



The upper panel of Figure 8.4 shows the results of the market moneyflow calculation for these 254 reference stocks. Here are a few observationsabout Figure 8.4:

� The five negative divergences noted N1 to N5 were showing consec-utive lower highs in the total money flow, while the correspondingaverage stock price was increasing to a new high. This pointed to aweakening market that was soon followed by a significant price drop.

� Correspondingly, the two positive divergences noted P1 and P2 indi-cated that the money flow dropped to a higher low, while the pricedropped to a lower low. This indicated an oversold market that soonsnapped back up.

FIGURE 8.4 Price change compared to large players’ strength evolution (betweenAugust 1, 2006, and January 17, 2008).


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� From July 6, 2007, the “tick test” rule changed (see Chapter 7), whichmay have influenced the pattern of the signal from that point on.

(Note: this analysis was pointed out to me by Tim Ord, founder ofwww. ordoracle.com. Tim has an excellent daily newsletter specialized inthe gold market, that also applies price/volume techniques).

SECTOR ANALYSIS

There are two ways in which the sector analysis has been traditionallyused:

1. Stock strength comparison within a sector If the price of a specificstock is lagging its peer group, we can conclude that this stock is non-performing and therefore sell it. If an important company that is partof a sector declares an earnings warning, we may conclude that thewhole sector is under the same negative economic situation and willalso react negatively. In anticipation, we would sell the other compa-nies’ stocks that belong to the same sector.

2. Fund reallocation between sectors. It is well known that sectors havedifferent cycles. For example, the telecommunications sector will notbe likely to move in phase with the retail or housing sectors. Therefore,traditional defensive funds such as pension funds will reallocate theirfunds between sectors on a regular basis, depending on measures suchas the relative sector price performance or the change of interest rates.

The question is: Can the Effective Volume method improve how sectoranalysis has traditionally been performed?

Remember that the Effective Volume, when it is split into the Largeand the Small Effective Volume, allows us to detect when funds are accu-mulating or distributing shares of a certain company. Let’s review how wecan use that method:

� To detect the relative strength of stocks in terms of Large Effective Vol-ume (it could indeed be interesting to discover that a specific stock inthe sector is under heavy accumulation compared to the other stocksbelonging to the same sector).

� To calculate the total money flow entering or exiting a sector (on thatbasis, we could then perhaps predict the future price direction in whichthe sector will move).



Strength Comparison within a Sector

As usual, I prefer working on examples that we could eventually extendto a general approach. I will therefore first use the oil drilling sector andmore specifically the sector of “oil sea drilling.” Indeed, since land andsea drilling use different techniques and equipment, it is wiser to separatethe two fields and compare companies that are working in the same field.Table 8.1 gives the list of companies active within the oil sea drilling groupthat I will study from now on. The “capitalization” column represents themarket capitalization of each company, while the rightmost column repre-sents in monetary terms the total amount of money that was exchanged inorder for the daily average number of shares to be traded. For example, wecan see that for the company Transocean, an amount of $573.7 million incompany shares is exchanged daily.

Table 8.2 shows the proportion for each column compared to the totalof the column. We can easily see that:

� The first four companies (ATW, THE, OII, and RDC) are rather smallcompared to the last three (NE, GSF, and RIG). (GSF was later mergedwith RIG)

� For every stock except Rowan Companies, the proportion of the dailystock transactions is very close to the proportion of the capitalization.

In Figure 8.5, I have represented the profit/loss starting on June 19,2006. Although the black-and-white figure does not allow us to distinguisheach company clearly, we can easily notice that each stock’s profit/loss fol-lows the same cycle: They go up and down in unison, probably followingthe changes in the underlying commodity price: oil. In Figure 8.5, I have sin-gled out with a thicker line the company Todco (THE), which was boughtout on March 19, 2007. by Hercules Offshore, Inc.

TABLE 8.1 Group of Oil Sea Drilling Companies

Amount Linked toCapitalization Daily Stock

Company Symbol (Million $) Transactions (Million $)

Atwood Oceanics ATW 2,050 35.0Todco THE 2,790 72.3Oceaneering International OII 3,050 40.6Rowan Companies RDC 3,330 128.6Noble Corporation NE 12,290 260.4GlobalSantaFe GSF 15,520 313.7Transocean RIG 28,200 573.7

Total 67,230 1,424.3


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TABLE 8.2 Group of Oil Sea Drilling Companies, Proportional Figures

Company SymbolProportion ofCapitalization

Proportion ofDaily StockTransactions

Atwood Oceanics ATW 3.0% 2.5%Todco THE 4.1% 5.1%Oceaneering International OII 4.5% 2.8%Rowan Companies RDC 5.0% 9.0%Noble Corporation NE 18.3% 18.3%GlobalSantaFe GSF 23.1% 22.0%Transocean RIG 42.0% 40.3%

Total 100.0% 100.0%

Looking at Figure 8.5, it is difficult to decide which stock is performingbest. We can see that since November 2006, Rowan Companies (RDC) hasbeen the worst performer in the group.

Let’s now compare the signal of the Large Effective Volume flow foreach of the companies. Since each company stock is traded at a differ-ent price, comparing the Large Effective Volume flow does not providea good indication as to where the money is flowing. In order to see howmuch money is comparatively invested in each stock, it is better to multi-ply the Large Effective Volume flow by the stock price. Not surprisingly,the companies with larger capitalizations attract more money than thecompanies with lower capitalizations, as shown in Figure 8.6. If we com-pare Transocean (RIG) to Atwood Oceanics Inc. (ATW) (at the right of

FIGURE 8.5 Oil sea drillers: profit/loss comparison.



FIGURE 8.6 Oil sea drillers: large players’ money flow.

Figure 8.6), we can see that RIG shows a positive money flow by large play-ers of about $1.4 billion during the last year, while ATW shows a positivemoney flow of about $133 million. In other words, RIG is attracting 10.5times more money from large players than ATW is. But we saw in Table 8.1that RIG in general was attracting $573.7 million per trading day, which is16.4 times that of ATW, which was attracting only $35 million per tradingday. This means that if large players active in RIG had been accumulat-ing as much as in ATW (compared to the total money that the companyis attracting per day), they would have had to accumulate $1.4 billion ×(16.4/10.5) = $ 2.18 billion. In other words, RIG large players have been 36%weaker than ATW’s large players (10.5 compared to 16.4). If we now rebal-ance the large players’ money flow by the proportion of the total daily fundsthat each company attracts (as stated in the second column of Table 8.2),we can build a completely different figure for the money flow of large play-ers (see Figure 8.7). As we can see in Figure 8.7, Todco (THE) has beenthe company with the strongest rebalanced large players’ money flow be-fore being acquired. Of course, this is a pure coincidence. But, I still believethat if I wanted to invest in a sector by spreading the risk among differentcompanies, I would comparatively buy more shares from the companiesthat attract the most buying money from large players.

Reallocation of Funds between Sectors

The great principle of sector reallocation is to increase one’s positions insectors that are gaining momentum and reduce one’s positions in sectorsthat are starting a downward trend. A sensible question would then be to


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FIGURE 8.7 Oil sea drillers: rebalanced large players’ money flow.

wonder if the Effective Volume analysis applied to the stocks of one sectorcan help predict the future movements of that sector, just like for the 238reference stocks of Figure 8.4.

The test can be done in four simple steps, which must be performeddaily:

1. We first need to take for each stock in the sector a number of days dur-ing which we will count the money flow (Large Effective Volume flowmultiplied by the stock price) related to large players. For example,let’s take 50 days. (For Figure 8.4, I took instead 20 days, which movesmore quickly than the 50 days figure.)

2. The second step is to calculate for each stock the ratio between the50-day money flow related to large players and the total money thatwas invested in the corresponding stock during the same period of 50days. This gives us the strength of accumulation/distribution by largeplayers.

3. The third step is to weight this strength figure by the weight of thestock within the sector.

4. The final step is to sum up all the strength figures that we obtainedfor all the stocks in the sector. We thus obtain the accumulation/distribution strength related to the sector.

The results are represented in the upper panel of Figure 8.8. This isan image of the average strength used by large players to move the sector’s



FIGURE 8.8 Oil sea drillers: money flow–profit comparison.

profit up or down (the sector profit is defined as the change of the weightedprice of all the components of the sector since the start of the analysisperiod, which is June 19, 2006, in Figure 8.8). The lower panel of Figure 8.8shows the actual movements of the sector.

We can first see on the upper panel of Figure 8.8 that the sector’s largeplayers’ strength is sometimes close to 10 percent. This means that largeplayers have been very keen buyers of the stocks of the sector (there wasno heavy selling). If you remember Chapter 3, when we studied the Effec-tive Ratio, we saw that this Effective Ratio signal itself is usually rather low


356 VALUE IN TIME

(below 6 percent). This is due to the fine equilibrium between large buyersand large sellers: The difference between them is only a few percentagepoints of the total volume that was exchanged over the prior 50 days. Withthis 6 percent figure as a reference from my personal experience, 10 per-cent looks very strong.

The second important point about Figure 8.8 is that large players’strength seems to be a good predictor of the sector’s movements:

� Point A comes before the top in sector profit (the large players’ 50-daystrength trend turned down some days before the sector profit trend).

� Point B shows that strength is already growing before the sector profitstarts its new uptrend.

� Point C shows that large players started to become weak again, al-though that is not reflected in the sector profit.

I said large players’ strength seems to be a good predictor, because inreality, large players’ strength is not predicting anything! It is just showingwhen large players are getting stronger or weaker. Therefore, the way totime your entry into a sector is not by looking at the large players’ moves,but by looking at the sector’s moves and by complementing that informa-tion with large players’ strength shifts.

It is therefore useful to summarize five sector trading rules, and seehow they can be applied to a few examples:

1. Buy if the sector’s trend is flat or moving up from a bottom and if it waspreceded by a strength increase by large players.

2. Do not buy when large players are showing weakness.

3. Sell when you believe that you have enough profit.

4. Sell when the sector’s trend is turning down on weakness by largeplayers.

5. Do not sell yet if the sector’s trend is still positive, even if large playersare showing weakness, because funds take more time to get out ofpositions and they start doing it while the price is still in an uptrend.

Software Companies Figure 8.9 shows the price evolution—shownas the percentage profit/loss—of each company listed in Table 8.3. Thesecompanies are by no means a true representation of the software sector,but I have been following them for some time. (The fact that we cannotdistinguish the different companies in Figure 8.9 is unimportant. We maynotice, however, that stocks in this sector do not follow uniform move-ments like those of the oil sea drillers.)



FIGURE 8.9 Software: profit/loss comparison.

Let’s examine points A through D at the lower panel of Figure 8.10,which shows the comparison between the large players’ strength and theprofit evolution of the sector:

� Point A: The sector is turning up, while large players’ strength is firmlypositive. It is a good idea to buy.

� Point B: The sector is turning flat after a steep climb, with large play-ers’ strength slightly negative. We may wait or sell, but not buy.

� Point C: The sector is turning negative, with large players’ strengthstrongly negative. We must sell.

� Point D: The sector is turning positive after a three-month decrease,while large players’ strength has been strongly positive for about twomonths. We may buy.

TABLE 8.3 List of Software Companies

Company Ticker

Cadence Design Systems CDNSAdobe Systems ADBEElectronic Arts ERTSBMC Software BMCOracle ORCLSymantec Corp. SYMCVerisign VRSN


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FIGURE 8.10 Software: money flow–profit comparison.

Real Estate Figures 8.11 and 8.12 show the individual rebalancedmoney flow and the percentage profit/loss, respectively, for each of thehome builders (that I also refer to as the real estate list) listed in Table 8.4.

Figure 8.11 is difficult to use, because it doesn’t visually catch the ac-cumulation/distribution movements by large players. The change of largeplayers’ strength as shown in the upper panel of Figure 8.13 is easier tounderstand. We can indeed see that at the left of the curve, the largeplayers’ strength was the strongest when the profit was the lowest (the



FIGURE 8.11 Real estate: rebalanced large players’ money flow.

profit is calculated as a price change since June 8, 2006). This accumulationweakened together with the rise in profit. A weaker accumulation simplymeans that less money was moving in the sector, even if the sector’s profitmove was still positive. Note the increase in large players’ strength thatstarted about two weeks before point A. This renewed strength reversed

FIGURE 8.12 Real estate: profit/loss comparison.


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TABLE 8.4 List of Home Builders

Company Ticker

Centex Corp. CTXKB Home KBHLennar Corp. LENPulte Homes, Inc. PHMToll Brothers TOL

FIGURE 8.13 Real estate: money flow–profit comparison.



down at the end of April 2007, when some home builders published earn-ings lower than expected. At point B, the profit is again in a downtrend, butthis time backed by much weaker large players.


We learned that it is entirely possible to adapt the Active Boundariesand the Effective Volume tools to study both the market and the sectorevolution.

At this stage, more data and more computing power are necessary tocome up with the publication of real funds’ accumulation/distribution fig-ures linked to all of the existing sectors. I believe that such data couldbe useful for deciding how to reallocate funds between sectors. SinceI wrote this text, I executed a more thorough research in the filed. Re-cent results of sector and market analysis can be found on the web site:www.effectivevolume.eu.

conc JWBK129-Willain March 19, 2008 1:40 Char Count=

Conclusion

I n a speech given at the Monetary Economics Workshop of the NationalBureau of Economic Research Summer Institute in Cambridge, Mas-sachusetts, on Tuesday afternoon, July 10, 2007, the Federal Reserve

chairman, Ben Bernanke, stated: “Undoubtedly, the state of inflation ex-pectations greatly influences actual inflation and thus the central bank’sability to achieve price stability.” In other words, the Fed is monitoring in-flation expectations as much as inflation itself. This is also what the stockmarket is all about: expectation.

In writing this book, my goal was to explain how the market’s expecta-tion evolves using tools that stay as close as possible to fundamental mar-ket forces: the motives of market players that may explain why they buyand sell stocks at a specific moment, and the supply/demand balance.

A SHORT REVIEW

Having taken these principles as the basis for my research, I detailed ineach chapter at least one new concept (sometimes several), and then sum-marized it (or them) at the end of the chapter. After rereading these sum-maries chapter by chapter, I noticed that while factual, they do not includemy own opinion of what works and what doesn’t. I will now briefly reviewthese chapters and give you my opinion, which derives from my experi-ence in using the results of my own research. First of all, I need to saythat in my real, live trading, I do not produce the exceptional returns dis-played in Chapter 7. I hate losing the family money, and therefore use verytight stop loss levels of 2 percent to 4 percent. This allows me to pick up afew good opportunities, but I miss many others. Overall, the returns havebeen excellent, while keeping me out of danger during the most turbulentperiods—for example, during August 2007 and January 2008.

363


364 CONCLUSION

Large/Small Effective Volume Flow (Chapter 1)

This tool is excellent for detecting funds’ accumulation/distribution ofshares. However, used in the short term, I found it misleading: Indeed,markets are very efficient, meaning that strong buying is accompaniedby an almost equally strong selling force (refer to Chapter 4). The Effec-tive Volume tool measures the fine balance between buyers and sellers,which, as we saw, quite often represents only a few percentage pointsof the total exchanged volume. This means that in the short term, onefund could decide to sell more quickly than other funds are accumulat-ing. This could result in a small one- or two-day Effective Volume patternthat could eventually contradict a previously established stronger patternand that could eventually reverse. This is sometimes disturbing for tradersaccustomed to trading mainly on the price pattern. My advice when re-ceiving contrarian signals is to look at the long-term trend in Large Effec-tive Volume flow (the 40-day or even 60-day trends). This long-term trendin Effective Volume does not fail to show the underlying support of theprice trend.

Active Boundaries (Chapter 2)

This is one of my favorite tools, because it greatly helps in assessing theshort-term value of a stock. My preference is for stocks that are part of awell-known stock-picking list (such as the Investor’s Business Daily listcompiled by William J. O’Neil), entering buy orders on stocks that reachthe Lower Boundary during a price pullback.

You may remember that I explained in Chapter 2 that the adjustmentof the Active Float to define the initial Active Boundaries is a difficult pro-cess, one that requires consecutive, painstaking refining steps. This is trueif you do it manually, but this adjustment of the Active Float can be totallycomputer-generated.

Large Effective Ratio (Chapter 3)

The Large Effective Ratio is certainly a more useful tool than the EffectiveVolume, for two reasons:

1. The Large Effective Ratio measures the accumulation/distribution overa period of three to five days; it filters out the short term’s spikes inEffective Volume.


Conclusion 365

2. The current level can be compared to the historical levels of the LargeEffective Ratio, allowing us to judge if the buying/selling is importantby historical standards.

Divergence Analysis Tool (Chapter 3)

The divergence analysis tool measures for a fixed period of time the differ-ence in strength between the Effective Volume trend and the price trend.The size of this fixed period of time was set in order to adjust for the volatil-ity between price and volume, with the objective of producing a price signalthat is stronger than the volume signal. This tool therefore generates threedifferent types of trading signals:

1. When the price is in a strong uptrend that is stronger than—or inthe opposite direction of—the Effective Volume trend, this divergenceanalysis gives the trader the feeling that the price trend is not sustain-able, whereas in most cases it is. We saw many instances where strongprice uptrends or downtrends do not reverse easily. In such a case, thedivergence analysis signal could make the trader miss the possibilityof entering a long price uptrend in the middle of the trend, when thereis still lots of upside left.

2. When the price is in a strong downtrend that is stronger than—or inthe opposite direction of—the Effective Volume trend, the divergenceanalysis gives the trader the feeling that the price will soon reverseup, because of the volume accumulation that is taking place. Unfortu-nately, most of the time this reversal does not materialize, because ina steep downtrend, the first accumulation signs are often short traderswho are covering their positions. When short traders have finished, theprice may just continue its downtrend. This trading signal could thusbe misleading.

3. In my experience, the divergence analysis tool produces its best trad-ing signals when the price is in a trading range. In such a case, a strongdivergence simply indicates that strong accumulation or distribution istaking place and that the price trading range would probably break inthe direction of the Effective Volume trend.

In Figures 6.27 and 6.28 (Chapter 6), I briefly compared the results ofthe divergence analysis tool to the Effective Ratio tool when they are usedas buying triggers. It is clear from these charts that the divergence analysistool produces lower returns and generates fewer signals.


366 CONCLUSION

For these reasons, I use it as an alert signal that informs me when astrong divergence is taking place; it may be worthwhile to monitor, but Ido not use it as a trading signal.

The Supply Analysis Tool (Chapter 4)

The supply tool shows when a stock has been so strongly sold off thatthe probability of finding new sellers becomes increasingly low. Chapter 6has shown that this tool, combined with the Effective Ratio tool, providesfor very good trading opportunities. Because the Effective Ratio tool doesnot distinguish between the buying that results from short covering andthe buying that results from real buyers, I mainly use the supply tool incombination with a support line: I buy when the stock price forms a secondor third bottom in an increasing Effective Ratio trend.

The supply tool also renders other services that I did not elaborateon in Chapter 4. Indeed, since it is based on a mathematical model of thereadiness of shareholders to sell their shares, depending on the timing andthe price of their purchase, it can be used to operate a sensitivity analysis,more commonly known as a what-if analysis: What if the price increases ordecreases by 5 percent? Will this attract more or fewer sellers? This type ofanalysis, which may eventually be included in a subsequent book, is ratheruseful during the planning phase of a trade.

The Yearly Expected Return and the MonthlyLoss Transferred (Chapter 5)

The yearly expected return (YER) and the monthly loss transferred (MLT)to the portfolio, which measure the return and risk, respectively, of a trad-ing strategy, are very simple concepts. The YER is very useful for evaluatinga trading strategy independent of stock-picking skills. The MLT is closer tothe risk, because it uses the real drawdown of each trade. Once the tradingstrategy is selected, the YER and the MLT are of no use. I do not use themfor live trading.

Automated Trading Systems (Chapter 6)

This chapter is separated into two parts: the alert screens and the auto-mated trading systems. For my everyday trading, the alert screens are ex-tremely useful, because they automatically bring up the best trading oppor-tunities. However, I believe that the part on automated trading systems ismore valuable in the long term, because through the use of different com-binations of a new set of tools, we discover trading common sense: “Buyvalue at the right time.”


Conclusion 367

The Bonus Section (Chapters 7 and 8)

Out of these two chapters, I mainly use Chapter 8 for my everyday trading.The most important for me is Figure 8.4, since it gives a good picture ofthe underlying accumulation of funds in regard to the overall market pricemovements.

WHY I DISCLOSED MY METHODS

Some of my early readers thought that I disclose too much in the book.However, I think that it would be a mistake not to disclose my findings.The stock market has greatly changed in recent years: decimalization, high-speed communication, automatic program trading, and computing powerat the PC level have brought more changes to the stock market in the pastsix years than during the previous 60. Traditional trading tools will need tobe adapted to this new reality. Within a few years, technical analysis willbe very different from the technical analysis we have been accustomed toseeing in the preceding century—and that century ended just eight yearsago. Many more tools than those I have presented here will soon becomeavailable. For me, it is more important to be a part of that change than tomake money behind a computer screen.

MARKET MANIPULATIONS?

When I started my research, I had the strong feeling that markets wereheavily manipulated:

� I started my research at the time when the accounting improprieties ofEnron and WorldCom were unfolding.

� In general, companies did not disclose the various stock options incen-tives that they offered, and these were often not included in the earn-ings statement, which meant that these companies’ value was oftenmisrepresented—since part of that value had been promised to thirdparties through the options incentive programs.

� Insiders were continuously trading ahead of the news.� Naked short selling was authorized. (Naked short selling is the act

of selling a stock short without first borrowing it from anothershareholder.)

� Funds had the power to control the stock price in order to accumulatea position on the cheap.


368 CONCLUSION

� As of December 2007, problems with the subprime loan market areunfolding. Debt rating agencies had been generously labeling certaindebt packages as “investment-grade.” These debt packages, which wenow know are not investment-grade, were revealed to be carrying morerisk than was suggested by the rating.

Now, however, I feel more confident about the markets:

� In Chapter 4, Figure 4.17 shows that markets are very efficient. Thisrenders control of a stock price virtually impossible—at least for well-traded stocks.

� I now have the tools to trade ahead of the news. I will not be the lastone to know when something is happening in the market.

WHAT’S NEXT?

After reading this book, the question that you should ask yourself is: What’s

next?

� If you intend to buy a trading platform, it may be wise to wait until newplatforms come out that include modern technical analysis tools.

� In Chapter 6, I list the different traditional technical analysis tools thatcan be used to measure a stock’s value as well as the trading trig-gers. You may try to adapt my trading principles using these traditionaltools.

� After due consideration, I have decided to give free access to the Effec-tive Volume tool introduced in Chapter 1. More information is availableon the web site www.willain.com. I also intend to open a discussiongroup on the subject of volume analysis and might gradually disclosemy other tools to participants. If you want to participate, drop me aline. Knowledge is not a “zero sum game”: I believe that sharing knowl-edge will generate many new ideas. My dream is that, someday, thisdiscussion group evolves into a community of traders and researchersthat will collectively bring my work to another level that will benefiteveryone.


Conclusion 369

THE LAST WORD

Before closing this book, I have three important messages:

1. What counts is value: To make money, you must buy value at the righttime. Therefore, if you have some free time to study the market, pleasespend it by studying the value of the equity you buy. I did not writeabout fundamental analysis because it is a well-known subject, butvalue detection is generally how the money is made. It is sometimeswiser to buy a base metal mining stock that trades at a price-earningsratio of 3 than to find technical patterns that will net you a quick10 percent.

2. The market does not care about your opinion. Even if you think that itshould move in one direction, the market may not agree with you. It isbest not to have any opinion, but to stay open and keep your alert levelhigh to the changes that are about to come. Stay humble and listen tothe market.

3. Generating a profit should not be a goal of your trading activity, butrather a result of the improvements that you make in your tradingmethods. Do not be afraid to learn from others, but also to share yourideas. Sharing knowledge brings knowledge!

My wife Michiko has told me on several occasions that she wants meback in the family. She told her friends that over the past few years she hadbecome a “computer widow.” With this long research and writing periodfinished, it’s now time for me to focus again on my family.

Trading the market is always a pleasure, but where does it fall on mylist of priorities?

Where does it fall on your priority list?

data JWBK129-Willain March 19, 2008 1:48 Char Count=

Data Providers

T he United States is well-known for its openness in terms of supplyof data. The suppliers listed below offer minute data that can be im-ported into MS Excel.

Historical Quotes Downloader (HQD) is a software package sold for$49.95 by the company Ashkon Software L.L.C. (www.ashkon.com). HQDallows downloading minute data from up to 1,000 stock symbols, datingback 20 days. Data is 20 minutes delayed, and sometimes you need towait 8 hours after the market closes in order for HQD to collect the ex-act volume data from the different exchanges. The company announced atthe end of December 2007 that although the software sales activities willnot be interrupted, no further technical maintenance will be offered, sincethe company is changing its business model. I have been using HQD formany years.

Tickdata (www.tickdata.com), a service offered by Nexa Technolo-gies, Inc., a provider of online and direct access trading platforms and elec-tronic order routing solutions, offers historical intraday data for up to 16stock exchanges. The database dates back to many years. I have used Tick-data’s data for the backtesting of Chapter 6. I found their data to be verygood quality, although the importing and reformatting process is quite long.Historical data costs $18 per year and per stock symbol. Additional fees andstock exchange fees must be added for life data.

IQfeed (www.iqfeed.net) is a service offered by DTN Market Access,a provider of web content and data feeds for a variety of companies inthe agricultural, energy, and financial industries. IQfeed provides historicaland life intraday data for nine different stock exchanges. Minute data isavailable for the last eight months. The service is charged $55/month, towhich a monthly stock exchange fee must be added. The service must beprogrammed in order to provide a continuous data feed in the requestedformat. I am in the process of testing this service.

371

data JWBK129-Willain March 19, 2008 1:48 Char Count=

372 DATA PROVIDERS

Opentick Corporation (www.opentick.com) offers free real-time andhistorical market data for U.S. exchanges. Minute data is available forthe last five years. Users, however, must pay the monthly stock exchangefees. The service must be programmed in order to provide a continuousdata feed in the requested format. I am also in the process of testingthis service.

sources JWBK129-Willain March 19, 2008 2:34 Char Count=

Sources

Appel, Gerald. Technical Analysis: Power Tools for Active Investors. Upper SaddleRiver, NJ: Financial Times Prentice Hall/Pearson Education, 2005.

Elder, Alexander. Come into My Trading Room. New York: John Wiley & Sons,2002.

Elder, Alexander. Entries & Exits: Visits to Sixteen Trading Rooms. Hoboken, NJ:John Wiley & Sons, 2006.

Elder, Alexander. Trading for a Living. New York: John Wiley & Sons, 1993.

Elder, Alexander. Sell and Sell Short. Hoboken, NJ: John Wiley & Sons, 2008.

Harris, Larry. Trading & Exchange: Market Microstructure for Practitioners. NewYork: Oxford University Press, 2003.

Lhabitant, Francois-Serge. Hedge Funds: Quantitative Insights. Hoboken, NJ:John Wiley & Sons, 2004.

O’Neil, William J. Investor’s Business Daily. www.investors.com.

Ord, Tim. The Secret Science of Price and Volume: Techniques for Spotting Market

Trends, Hot Sectors, and the Best Stocks. New York: John Wiley & Sons, 2008.

Prigogine, Ilya, and Isabelle Stengers. La nouvelle Alliance. Paris: Gallimard, 1979.

Vince, Ralph. Portfolio Management Formulas: Mathematical Trading Methods

for the Futures, Options, and Stock Markets. New York: John Wiley & Sons, 1990.

Weis, David H. Catching Trend Reversals (DVD). www.Elder.com.

Williams, Larry. Long-Term Secrets to Short-Term Trading. New York: JohnWiley & Sons, 1990.

373

sources JWBK129-Willain March 19, 2008 2:34 Char Count=

abu JWBK129-Willain February 21, 2008 10:40 Char Count=

About the Author

P ascal Willain is a private trader who trades only his personal funds,and he uses the very tools presented in this book. He graduated in1983 with an engineering degree in telecommunications from the

Universite Catholique in Louvain, Belgium. Pascal then went on to earna master’s degree in applied mathematics in 1987 from the Universityof Electro-Communications in Tokyo, Japan. Two years later, he earnedan additional degree in business from the Universite Libre in Brussels,Belgium.

Dr. Alexander Elder featured some of the tools presented here in achapter of his own book, Entries & Exits: Visits to Sixteen Trading

Rooms. More about the Effective Volume tools can be found on the websites at www.willain.com and www.effectivevolume.com. The author canbe contacted by e-mail at [email protected].

Before becoming a trader, Pascal created three companies in such var-ied fields as consulting, computer-voice interfaces, and parking-garage sys-tems. These companies are now managed by partners.

With his wife Michiko, Pascal also created the Nello and PatrascheFoundation, which is dedicated to helping handicapped orphans. For moreinformation, go to www.multilines.be/np.

375

abu JWBK129-Willain February 21, 2008 10:40 Char Count=

ind JWBK129-Willain March 18, 2008 14:53 Char Count= 0

Index

Account churning, 15Accumulation/distribution tactics and

methods, 19, 24–32, 33, 64, 66, 85,116, 130–32, 206, 270, 274, 302, See

also Tactical movesActive and passive funds, 211, See also

InstitutionsActive and passive sellers, 213, 216Active Boundaries, 5, 82, 75–82, 82,

67–111, 266, 272, 274, 276–302,316, 304–16, 331–34, 341–48, 364

Lower Boundary, 5, 272, 287, 311,313

new Active Boundaries, 167, 184Upper Boundary, 5, 164, 272, 287–89

Active Boundaries, how to calculate,109

Active Boundaries, strategy, 310Active buying to accumulate a

position, 64, 127, 213Active sell market orders, 185Aite Group, 65Alert screen, 6, 366Alert signal, false early alert signal,

271, 366Alexander Elder Dr., 2–3, 30, 32, 50–52,

61, 151, 153, 302, 317American Science & Engineering

(ASEI), 305Amount of capital, 298AMR Corporation (AMR), 153Analysis window, 120, 127, 128, 129,

133, 137, 177, 180, 177–82, 214,273, 344

Analysts, 211

Ariba, Inc. (ARBA), 47, 159Ask-Bid, 16, 65, 66, 135, 186, 188, 206,

212, 213, 232Attractive price, 187Automated computer trading, 2, 65,

See Program tradingAutomated trading systems, 6, 51, 69,

264, 266, 366Average annual returns, 252Average downside deviation, 248, 249Average holding time, 252Average invested time, 279, 284, 290Average logarithmic daily return, 230Average maximum drawdown, 249–62Average profit per winning trade, 239Average ROI, 74–76, See also Float ROIAverage Separation Method, 52, See

Effective Volume:the separationvolume

Bargain hunters, 179, 184, 194, 276Bargain investors, 192–94, See also

Bargain huntersBear/bull equilibrium, 60Becton Dickinson (BDX), 78–80,

162–63Ben Bernanke, 362Benchmark, 246, 275Beta value of a stock, 223Blue-chip companies, 211Burke ratio, 262, 287, 290, 295, 309,

314, See also Risk:risk-adjustedperformance

Buy value at the right time, 302, 317,366

377


378 INDEX

Buy/sell balance, 63, 129, 132, 186, 188Buy/sell operations, 292Buy-and-hold strategy, 222, 223, 232,

247, 250, 275, 298Buyers/Sellers equilibrium, 213, See

also Buy/Sell balanceBuying momentum, 180Buying Pattern Analysis, 135Buying trigger, 6, 295, 301, 303, 315

Calculating the Large and the SmallEffective Volume flow, 122

Candlestick analysis, 1, 316Casino games, 227Catastrophic losses, 164Catastrophic situation, 193, See also

Catastrophic lossesCelgene Corporation (CELG), 151Chaikin money flow indicator, 132–34,

See also Trading:traditionalanalysis tools

Cheap uptrend reversal, 276Chesapeake Energy (CHK), 116Chico’s FAS (CHS), 102–4, 207, 210Churn rate, 235Cognizant Technology Solutions

(CTSH), 49Combining Divergence and Active

Boundaries, 155, 167Comparing the divergence signal to

the price trend, 184Comparing the present price to a past

price, 192Compounding effect, 230Computing time, 267, 274, 297Consecutive occurrence of losses, 254,

See also Catastrophic lossesConvertible debenture, 76Correct buy signals, 201

Darden Restaurants Inc. (DRI), 124,118–29, 136–44, 177, 178, 179, 214

Dead Cat Bounce, 107, See also

Downtrends and ActiveBoundaries

Decimalization, 1–2, 7, 16, 17, 15–19,54, 64, 187–91, 212, 217–18, 367

Demand/supply equilibrium, 12, 34,101, 114, 135, 186

Departing shareholders, 103, 194Detect a change trigger, 275, See also

Large Effective Volume, See also

Large Effective RatioDetecting tops, 135Determination of the overall trend, 38Deutsche Bank AG, 207, 210Diluted number of shares, 76, See also

Float:available floatDiscovery of value, 6Distressed stocks, 305–6, 314, 318Distribution of returns, 246Divergence

buy zone limit, 142, 164, 166divergence analysis examples, 144divergence peaks, 138, 153, 155divergence troughs, 142historical maxima, 273, See also

Divergence:buy zone limithistorical minima, 273, See also

Divergence:sell zone limitsell zone limit, 142selling pattern analysis, 139

Divergence Analysis, 5, 58, 86, 113, 114,127, 266, 273–74, 300, 326–31, 365

Divergencescomparison of past divergences,

135, 184Diversification, 207, 210, 297Don Worden, 65Downside frequency, 248Downside risk, 247Downtrends and Active Boundaries,

102–8, 114, 137Downtrends and divergence analysis,

176, 201Downtrends and Effective Volume,

202Drawdowns, 250, 252, 286, 295, 309Dried-up demand, 194Dynamic method to measure the

supply/demand equilibrium, 191

Early profit taking, 287Early profit-taking tactics, 311


Index 379

Earnings per share (EPS), 76Earnings surprise, 67, 88Earnings-related insider move, 47Effective Ratio, 58, 114, 127, 128,

365–66Large Effective Ratio, 178, 181, 203,

273, 282, 300, 364total Effective Ratio, 129, 142, 143,

159, 177, 326, 330, 340Effective Volume, 40–41, 54, 66, 114,

129, 131, 216, 266, 296, 326,321–26, 364

Large Effective Volume, 35, 40, 266,268, 274, 280, 300

Large Effective Volume flow, 282separation volume, 34, 38, 52, 53, 56,

124Small Effective Volume, 35Small Effective Volume, 214total Effective Volume, 266

Effective Volume at key turningpoints, 79

Effective Volume flow, 121Effective Volume trigger, 277Elimination of the Price Trend

Condition, 284Elliott wave analysis, 1Empty trading minutes, 183End-of-day data, 12, 26, 29, 59, 64, 132,

See also Minutes dataEnd-of-day indicators, 20, 63, 64, See

also Trading:traditional analysistools

Entry strategies, 275Envoy Communications Group, 107,

108Equi-Power Separation Method, 54, 56,

See Effective Volume:theseparation volume

Excel add-on, 267Exit strategies, 275Expectation, 272, See Traders:trader’s

expectationExpensive starting downtrend, 276Exponential average function, 120,

121, 139Extreme volumes, 177

Fallen angels See also Distressedstocks

Falling Knife, 105, See also

Downtrends and ActiveBoundaries

Federated Investors Inc. (FII), 41,155

Fibonacci retracements, 1, 316, See

also Trading:traditional toolsFinancial disaster, 199Finding bottoms, 135Finding long-term value, 275, See also

Active Boundaries, See also

Supply AnalysisFinisar Corporation, 101Fixed Separation Method, 52, See

Effective Volume:the separationvolume

Floatactive float, 76–101, 101, 104, 109,

343, 364available float, 76, 77, 199, 201, 303,

305float ROI, 74, 77

Force Index, 61, See also

Trading:traditional analysis tools,See also Alexander Elder Dr.

Francois-Serge Lhabitant, 221, 262Fund managers, 4, 114, 128, 135, 206,

210, 245, 249, 266, 267Fund reallocation between sectors,

350, 353Fundamental analysis, 11, 39, 211,

369Funds, 3, 4, 5, 6, 72, 135, 204, 276, 285,

293, 298, 301, 307, See also

InstitutionsFuture earnings estimates, 67

Gerald Appel, 51, 316, See also MovingAverage Convergence/Divergence(MACD)

Gibbons Burke, 262, See also Burkeratio

Good trading strategy, 247Grandmother analogy, 27Group of shareholders, 274


380 INDEX

Hedge funds, 19, 211Herd behavior, 58Highfliers group, 224Historical divergences, 136, 138Historical reference levels, 273, 274,

365How do funds make money, 211How to antagonize all the long-term

shareholders., 193How to turn a profit, 180

Illiquid markets, 205, See also FundsIlya Prigogine, 13, 20IMAX Corporation, 88, 89, 90, 164–70,

192, 193, 194, 195Imposing a five-day time limit, 295Incorrect buy signals, 201Incremental calculations, 267Insider trading, 47, 117, 212Insiders, 3, 5, 7, 43, 66, 159, 367, See

also Insiders tradinganalysis of different types of news,

44Instantaneous trade execution, 188Institutional activity, 207Institutional investors, 1, 3, 19, 20, 63,

66, 76, 106, See Institutionsstock accumulation by, 64, 206, 285

Institutions, 22, 33, 57, 207, 210, See

Institutional players, See

Institutional investorsInsurance policy against bad trades,

243Investment

investment style, 87longterm investment, 87

Investment opportunities, 221, 279,284, 290, 291, 292, 320

Investor’s Business Daily, 364Investors

long-term value investors, 86, 87, 252short-term investors, 252

KB Home (KBH), 271, 360

Lack of shares, 201Laggards group, 224

Large drawdowns a measure of, 252,254, 262

Large Effective Money flow, 347Larry Williams, 24, 25, 26, 27, 30, 31, 32Laszlo Biriny, 65Leaving a losing position, 210LEV, 270, 271, See Large Effective

VolumeLexmark (LXK), 281Limit orders, 127, 188Limiting the duration of the trade, 298Liquidity, 16, 18, 19, 76, 84, 204

illiquid environment, 5Look into the wallets of potential

buyers, 191Losing trades number of, 238, 242Loss of opportunity, 239Louis Pasteur, 1Low supply limit, 201, See also Supply

level

Manipulation, 4, 17, 18, 26, 54, 127, 186,212, 218, See also Price:pricemanipulation

Market analysis, 342–50Market forces, 5Market maker, 22Market orders, 188Market players, 2Market testing, 191Market trend, 7Market visibility, 1, 16, 17, 54, 187, 212,

217Mathematical expectation, 227, 228Mathematical operations necessary for

modern trading tools, 75Maximum drawdowns, 252Memories of past trades, 264Meridian Resource Corporation

(TMR), 92, 94, 114MicroStrategy (MSTR), 326, 327, 328,

329, 330Midsize volume, 213Milking cows, 222Milking equipment, 247Model, 58, 60, 64, 177, 180, 198, 223,

273, 303, 305, 306, 366


Index 381

Momentum oscillator, 51Money Flow Index, 65Monthly loss transferred (MLT), 6, 257,

366MONTHLY LOSS TRANSFERRED

(MLT), 6, 222, 260, 286, See also

Monthly loss transferredMorningstar, Inc, 261Most recent buyers, 197Moving average (MA), 69, See also

Trading:traditional analysis toolsMOVING AVERAGE (MA), 69Moving Average Convergence/

Divergence (MACD), 1, 41, 51, 58,59, 316

Naked short selling, 367NASDAQ, 14, 107, 207News positive or negative, 3, 88, 105,

130, 132, 159, 164, 194, 326Nonconsenting market, 210Nontrading Minutes, 181, 182Normal distributions of returns,

246Number of outstanding shares, 207

Offering price, 72Oil drilling sector, 351On-balance volume, 64, See also

Trading:traditional analysis toolsOpenwave Systems (OPWV), 97, 101,

201, 207Optimizing a trading strategy, 225, 230,

248, 275Optimum, 228Order book, 16, 18, 127, 186, 188, 217Order fragmentation, 19, 65Order-placing algorithms, 22Overbought or oversold stocks, 12, 41,

51, 68, 69, 316

P/E ratio, 67, See Price-earnings(P/E)

Paper gain, 72Paper loss, 71, 325Passive buying to accumulate a

position, 64, 135, 217

Past results, 224Past share accumulations, 117, 203Pension funds, 207, 210, 350Percentage of institutional holdings,

207, See also InstitutionsPerception of the reality, 70, See

Traders:trader’s expectationPerformance ratio, 227Pessimistic return ratio, 227PetroQuest Energy, Inc. (PQUE), 45Pillar of successful trading, 12, See

also Trading:trading pillarsPlayground Analogy, 38Pool of traders, 180, 190Portfolio, 221, 249, 260, 287, 292Position size, 206Positive or negative trades, 228, 229,

230, 239, 255, 257Positive or negative trading days, 228Potential sellers, 199Predefined algorithms, 65, See

Program tradingPresent share accumulation, 203Price

catastrophic price drop, 201, 202cheap stocks, 3current price, 1overlapping price zones, 198price adjustments, 19price bounce, 107, 185price breakout, 153, 268, 321price cycle, 77, 170, 346price evolution, 1, 5price fluctuations, 1price gap corrections, 137, 166, See

also Price:Price Gapsprice Gaps, 78, 79, 84, 88–91, 92, 103,

120, 132, 136–44, 164, 167, 254,344, See also Price GapCorrections

price histogram, 189, 191price inflections, 21, 22, 35, 36, 40,

52, 55, 114, 123–27, 127, 214, 347price manipulation, 2, 6, 17, 63, 212,

217, 218price patterns, 316price precedence trading rule, 14


382 INDEX

Price (Continued)price rate of change (ROC), 119, 120,

121, 136, 137, 136–43, 159, 176–84,213, 266, 273, 326, 330, 340, 343,344

price repartition of volume, 27price reversal, 194, 305, See also

Price:price bounceprice spread, 24, 26, 59, 64, 212, 213price volatility, 138price/volume based indicators, 58,

59, 64, 132price-based technical indicators, 58,

59, 69, See Trading:traditionalanalysis tools, See Trading:classictechnical tools

Price-earnings (P/E), 67, 76, 193trend above the 9-day average, 145,

147, 153, 166, 184Price/Volume relative strength, 113Priceline (PCLN), 270Privileged information, 41, 159Probability of making a profit, 164Probability of selling, 197Production Screen, 271Profit

early profit taking, 289, 292evolution of the average profit, 294,

345in relation to expectation, 74linearly increasing average profit,

294profit growth, 11profit optimization, 226profit target, 6, 275profit targets, 223, 235

Profitability of the trade, 275Program trading, 17, 77, 111, 367PRR, 242, See Pessimistic return ratioPublic order precedence, 14Pullbacks, 85, 112, See also

Trend:trend retracement

Quarterly earnings dates, 316

Ralph Vince, 227, 239Ratio of invested days, 308

Ratio of stop loss trades, 313, See Stoploss

Readiness of shareholders to sell theirshares, 366

Real estate sector, 358Relative sector price performance, 350Relative Strength Index (RSI):, 41, 51,

58, 59, 68, 316Reliant Energy (RRI), 105, 106, 189,

190, 199, 200, 321–26Resistance to change, 185, 212, 214,

217Retail investor, 145, 206, 211, See also

Traders:retail playersReturn consistency, 245Richard Wyckoff, 59, 316, 317, See also

Price:price/volume relationship-based tools

Riskbankruptcy risk, 249, 252, 254risk control, 210risk of a portfolio, 222risk of a trading strategy, 222, 366risk-adjusted performance, 260rsik management policy, 73, 87stock market risk, 222trader’s behavior risk, 245trader’s wrong analysis, 224, 245

Risk/return balance, 6, 153, 218, 221,245, 249–62, 308

Robustness of the trading strategy,245, 247

Same purchasing or selling power, 38Sample strategy, 226Scanning systems, 268–74Sector analysis, 7, 341, 361Securities and Exchange Commission

(SEC), 321Selling activity by institutions, 207Selling momentum, 180Selling parameters, 278, 280, 284, 287,

288, 300Selling pattern, 326Selling pressure, 91Selling reasons, 226, 325Sell-off, 192, 194, 333, 336, 338


Index 383

Sensitivity analysis, 199, 200, 366Separation volume, 137, See also

Effective Volume:the separationvolume, See also EffectiveVolume:separation volume

Share availability problem, 201Shares

shares accumulation, 5, 24, 268shares availability, 72shares distribution, 5, 24

Sharpe ratio, 260, 261, 287, 290, 309,See also Risk:risk-adjustedperformance

Short or long-term trade, 266Shorts

covering a short position, 203short plays, 49, 72, 269, 331shorting strategies, 7, 86, 304,

320–40Sierra Health Services, Inc. (SIE), 147Significant position taken by funds, 23,

199, 201, See also FundsSlippage, 86, 188, 232, 276, 292Small drawdowns a measure of, 254Software sector, 356Spread cost, 1, 16, 18Standard deviation, 246Standard group, 224, 250Starbucks (SBUX), 331, 332, 334, 335,

336, 337, 338, 339Static resistance, 214Static supply of shares, 127Stochastics, 1Stock strength comparison within a

sector, 350, 351Stock trading new vision of, 2Stock-picking skills, 221Stocks That Need to Rest, 102, See also

Active BoundariesStop loss, 6, 71, 85, 159, 207, 245, 257,

313, 314Stop Loss

stop loss levels, 223, 235Stop-loss limit order, 68Straight average function, 120Strategic buying decisions how to

catch, 64

Strategic moves, 19, 65String of small losses, 257Strong accumulation limit, 132, See

also Effective Ratio, See also

Effective RatioStrong distribution limit, 132, See also

Effective RatioSuccessful trading strategies, 299Supply Analysis, 5, 191, 198, 267, 273,

302–14, 316, 335–40, 366Supply and Demand, 3, 5, 59, 60, 86,

117, 185, 188, 191, 195, 301Supply indicator, 274, See also Supply

analysisSupply level, 199, 201, 303Supply of shares, 23, 62, 63, 64, 73, 106,

107, 116, 127, 128, 185, 186, 191,199, 206, 214, 218, 273, 305

Supply/Demand Equilibrium, 189, See

also Supply and demand, See also

Dynamic method to measure thesupply/demand equilibrium

Supply-based strategy, 302–14Support/Resistance Lines, 51, 59, 68,

189, 190, 304, 316, 366

Tactical moves, 2, 5, 65, 66, 128Technical analysis tools, 1, 2, 3, 5, 12,

13, 19, 39, 60, 119, 211, 213, 320,367, 368

Tellabs (TLAB), 27, 34–37, 52, 53, 81,82, 83, 90, 91, 92, 131–34, 167–76,196, 198, 201, 202, 203, 215, 216,217

Temporary illiquid situations, 211The first trading minute of the day,

137TIAA-CREF Investment Management

LLC, 210Tick test rule, 7, 321, 349Tick volume Analysis, 20, 58Tick Volume Analysis, 65, See also

Trading:traditional analysis toolsTime frame evaluation, 64, 118, 275

one-minute time range, 22, 64Time limit trading parameter, 6, 151,

223, 242–45, 260, 305


384 INDEX

Time precedence trading rule, 14, 16Time repartition of volume, 28Timing detection, 199Todco (THE), 276Total exchanged volume, 19, 124, 128,

213, 364Trade

time management, 299, 315, 317trade duration, 239, 250trade evolution, 293trades production systems, 268–74trigger, 299

Tradersactive traders, 5, 11, 20, 76, 77–93,

104, 110, 186, 198, 276beginning traders, 2confirmed traders, 2how to determine the behavior of

traders, 59individual traders, 4, 267, 297, See

also Traders:retail playersinvestors VS traders, 76latecomers, 179locked in trader, 71, 302long-term traders, 275, See also

Traders:investors VS tradersmanipulators, 7momentum players, 58passive traders, 128, 185, 213, 214position trader, 64, 111professional traders, 1profit takers, 82, 338retail players, 53, See Institutional

investorsshort-sellers, 203short-term traders, 69, 228, 275,

293skilled traders, 4speculator, 69swing trader, 86, 293, See

Traders:position traderteam of traders, 267traders erratic decisions, 245trader’s expectation, 3, 5, 11, 12, 20,

70–89, 104, 107, 186, 188, 342, 362,See also Float:active float

traders’ exuberance, 107, 108, 173,See also Uptrends and ActiveBoundaries

traders’ movements, 2traders’ optimism, 245traders’ pain, 245traders’ personalities, 77traders’ will or intent, 188trading artists, 2trend followers, 58trendsetters, 5, 58, 66, 130, 179,

326Trading

back-test trading ideas, 266best trades ranking tool, 269buy/hold trading method, 6classic technical tools, 27, 50, See

Trading:traditional analysis toolsday-trading, 188position size, 6production screen, 6swing trading, 86, 87, 315, 318, See

also Traders:swing tradertrade evolution, 6trading day, 21, 59, 63, 137, 189, 196,

197, 228, 247, 353trading opportunities, 6, 108, 184,

274, 290, 304, 318, 366trading patterns, 1, 2trading platform, 1, 2, 267, 368trading range, 17, 40, 41, 91, 112trading rules, 5, 94, 167, 184, 201,

267, 268, 274, 277, 280, 284, 288,295, 300, 276–317, 340, 356

trading session, 12trading strategies, 4, 6, 223, 275–317,

366trading system, 222trading tools, 2, 3, 4, 5, 6, 7trading transactions, 186traditional analysis tools, 12, 64, 120,

See also Traditional chartiststransactional level, 20, 64, 65use common sense, 107, 216, 275,

366Traditional chartists, 2, 4


Index 385

Transaction costs, 235, 236, 237, See

also Commissions, See also

SlippageTransactional data, 65Trends

betting agaisnt a price trend, 112comparing trend direction, 115comparing trend strength, 115Effective Ratio trends, 131Effective Volume Trend, 121long-lasting trend, 85price trends, 3, 5, 67, 118, 166, 267trend confirmation signal, 270trend line, 68, 69trend retracement, 78, 79trend reversals, 12, 61, 79, 89, 91, 95,

96, 97, 100, 177, 341Tuning parameters, 223, 226–63

Uptrends and Active Boundaries, 81,82, 84, 86, 103

Value, 5, 67, 70, 89, 199, 298, 299, 305,315, 325, 364, 368

Value investors, 192, 193, 194Volatility, 5, 19, 29, 52–55, 63, 65, 101,

114, 120, 176–89, 211, 222, 223,246, 260, 317, 365

volatility at the one-minute bar level,65

volume volatility, 65, 138

Volumevolume data, 5volume histogram, 190volume spikes, 65, 177, See also

Volatilityvolume Weighted by the Price

Spread, 63, See also

Trading:traditional analysis toolsvolume-based tools, 59

Weakness Index, 62Welles Wilder, 51Westlake Chemical (WLK), 145What-if analysis, 366When to buy, 199Where does the supply come from,

195Why trends exist, 67, 112, See also

Active BoundariesWilliam J. O’Neil, 364William Sharpe, 223, 260, See also

Sharpe ratioWinners and losers, 196Winning trades number of, 238Winning/losing duration ratio, 241, 242,

278Winning/losing trades ratio, 242

Yearly expected return (YER), 6, 222,229, 285, 290, 293, 295, 299, 304,306, 313, 36

value in time

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