Data Analytics for Corporate Debt Markets

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Data Analytics for Corporate Debt Markets

Using Data for Investing, Trading, Capital Markets, and Portfolio Management

Robert S. Kricheff

Vice President, Publisher: Tim Moore Associate Publisher and Director of Marketing: Amy Neidlinger Executive Editor: Jeanne Glasser Levine Development Editor: Natasha Torres Operations Specialist: Jodi Kemper Cover Designer: Alan Clements Managing Editor: Kristy Hart Senior Project Editor: Betsy Gratner Copy Editor: Karen Annett Proofreader: Williams Woods Publishing Indexer: Tim Wright Compositor: Nonie Ratcliff Manufacturing Buyer: Dan Uhrig

© 2014 by Robert S. Kricheff Published by Pearson Education, Inc. Upper Saddle River, New Jersey 07458

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All rights reserved. No part of this book may be reproduced, in any form or by any means, without permission in writing from the publisher.

Printed in the United States of America

First Printing February 2014

ISBN-10: 0-13-355365-5 ISBN-13: 978-0-13-355365-9

Pearson Education LTD. Pearson Education Australia PTY, Limited. Pearson Education Singapore, Pte. Ltd. Pearson Education Asia, Ltd. Pearson Education Canada, Ltd. Pearson Educación de Mexico, S.A. de C.V. Pearson Education—Japan Pearson Education Malaysia, Pte. Ltd.

Library of Congress Control Number: 2013953301

I would like to dedicate this book to my wonderful mother-in-law Hope Mullen Bowe and also to the

memories of my father-in-law Dr. Edward T. Bowe and my Uncle Bill Glou, both of whom were great men with wonderful souls. All of them have made

my life, and many others, so much better.

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Contents

Section I Introduction to Data Analytics for Corporate Debt Markets . . . . . . . . . . . . . . . . . . 1

Chapter 1 The Basics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Why Use Analytics? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3What Is Data Analytics? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4How Data Analytics Is Used and How It Differs

from Credit Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5An Example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6How This Book Is Structured. . . . . . . . . . . . . . . . . . . . . . . . 8

Chapter 2 Corporate Debt Is Different . . . . . . . . . . . . . . . . . . . . . . 11

The Unique Nature of Corporate Debt. . . . . . . . . . . . . . . 11Sources of Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16Pricing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18An Example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19Endnotes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Chapter 3 Managing Projects and Managing People. . . . . . . . . . . . 23

The Basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27Endnote. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Closing Comments on Section I . . . . . . . . . . . . . . . . . . . 29

Section II Terminology and Basic Tools. . . . . . . . . . . . . . . . . 31

Chapter 4 Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Valuation Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33When and How to Use Yield and Spread . . . . . . . . . . . . . 34Volatility Terms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36Further Discussion on Duration . . . . . . . . . . . . . . . . . . . . 38Debt-Ranking Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39Credit Ratings Terms and Usage . . . . . . . . . . . . . . . . . . . . 41Industry Group Terms and Definitions . . . . . . . . . . . . . . . 42Endnotes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

viii DATA ANALYTICS FOR CORPORATE DEBT MARKETS

Chapter 5 Basic Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45Graphical Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46Trend Lines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51Backing Up Graphs with the Data in Tabular Form. . . . . 51Queries and Sorts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52Endnotes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Chapter 6 Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

What Is Data Mining? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55Neighbors and Neighborhoods . . . . . . . . . . . . . . . . . . . . . 57Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57Decision Trees and Neural Networks . . . . . . . . . . . . . . . . 58Summary Comments on Data Mining. . . . . . . . . . . . . . . . 60

Closing Comments on Section II . . . . . . . . . . . . . . . . . . 61

Section III The Markets and the Players . . . . . . . . . . . . . . . . . 63

Chapter 7 The Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

Investment-Grade Corporate Bonds . . . . . . . . . . . . . . . . . 65High-Yield Corporate Bonds . . . . . . . . . . . . . . . . . . . . . . . 66Leveraged Loans. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67Emerging Markets and International Bonds

and Loans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68Credit Default Swaps (CDSs) . . . . . . . . . . . . . . . . . . . . . . 69

Chapter 8 The Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71The Issuers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72Investment Banks and Broker-Dealers . . . . . . . . . . . . . . . 73Money Managers and Institutional Investors . . . . . . . . . . 77Asset Allocators and Consultants . . . . . . . . . . . . . . . . . . . . 80Systems Managers and Programmers . . . . . . . . . . . . . . . . 80

Closing Comments on Section III. . . . . . . . . . . . . . . . . . 83

CONTENTS ix

Section IV Indexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Chapter 9 Index Basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

Why Do Indexes Matter? . . . . . . . . . . . . . . . . . . . . . . . . . . 87Calculation Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . 91

Chapter 10 Index Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97Index Construction: Selection Criteria . . . . . . . . . . . . . . 100Index Construction: Requirements . . . . . . . . . . . . . . . . . 102

Chapter 11 Other Topics in Corporate Bond Indexes. . . . . . . . . . . 109

New Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109Defaults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110Issuer Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111Liquid Indexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113Investable Indexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114Indexes Versus Portfolios . . . . . . . . . . . . . . . . . . . . . . . . . 115

Closing Comments on Section IV . . . . . . . . . . . . . . . . . 117

Section V Analytics from Macro Market Data to Credit Selection . . . . . . . . . . . . . . . . . . . . . . . . 119

Chapter 12 Top-Down Basics—Looking for Investment Themes Between Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

Market Comparisons—Returns . . . . . . . . . . . . . . . . . . . . 121Volatility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122Correlations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123Market Comparisons—Relative Value. . . . . . . . . . . . . . . 125Dispersion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126Duration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126Some Other Comments—On Using Historical Data . . . 127Some Other Comments—Weightings . . . . . . . . . . . . . . . 128

Chapter 13 The Next Layer—Analyzing a Market . . . . . . . . . . . . . 131

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131Risk Segments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132Bucketing Sectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133Industry Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

x DATA ANALYTICS FOR CORPORATE DEBT MARKETS

Building Industry Equity Monitors . . . . . . . . . . . . . . . . . 135What Can Be Learned from Market Shocks . . . . . . . . . . 136The Crowded Trade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137Endnote. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

Chapter 14 Data Analytics for Credit Selection . . . . . . . . . . . . . . . 139

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139Data for Credit Selection . . . . . . . . . . . . . . . . . . . . . . . . . 140Comments about Sorts and Queries . . . . . . . . . . . . . . . . 142An Example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143Financial Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145Operational Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148Financial Liquidity and Some Differences

Between Credit Analysis and Data Analytics . . . . . . . 149Credit Scoring. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152Analytics Used in Relative Value . . . . . . . . . . . . . . . . . . . 155Price Movements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160Using Equity Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161Maintenance Covenants . . . . . . . . . . . . . . . . . . . . . . . . . . 164Analytics and Nonfinancial Information . . . . . . . . . . . . . 165Endnotes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167

Closing Comments on Section V. . . . . . . . . . . . . . . . . . 169

Section VI Analysis of Market Technicals . . . . . . . . . . . . . . . 171

Chapter 15 Market Demand Technicals . . . . . . . . . . . . . . . . . . . . . 173

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173Demand Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174Other Demand Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . 175

Chapter 16 Market Supply Technicals . . . . . . . . . . . . . . . . . . . . . . . 177

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177Use of Proceeds and Other Ways to Analyze Supply . . . 178Analyzing Price Talk and Pricing . . . . . . . . . . . . . . . . . . . 182Postplacement Trading. . . . . . . . . . . . . . . . . . . . . . . . . . . 184Supply and Demand Impact the Face of the Market . . . 186Endnote. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187

Closing Comments on Section VI . . . . . . . . . . . . . . . . . 189

CONTENTS xi

Section VII Special Vehicles—Liquid Bond Indexes, Credit Default Swaps, Indexes, and Exchange-Traded Funds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191

Chapter 17 Liquid Bond Indexes . . . . . . . . . . . . . . . . . . . . . . . . . . . 193

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193Why a Liquid Bond Index and What Is It? . . . . . . . . . . . 194What Are Benefits and Drawbacks of a Liquid Bond

Index Versus a Full Index? . . . . . . . . . . . . . . . . . . . . . 194

Chapter 18 Credit Default Swaps and Indexes . . . . . . . . . . . . . . . . 199

What Other Tools Do Investors Use to Measure the Corporate Bond Market? . . . . . . . . . . . . . . . . . . . 199

What Is CDS and What Is a CDS Index? . . . . . . . . . . . . 200Understanding CDS Pricing—Spreads Versus

Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203CDS Indexes—How Are They Constructed? . . . . . . . . . 204What Can We Gauge from CDX Pricing and Skew? . . . 206Who Are the Participants? . . . . . . . . . . . . . . . . . . . . . . . . 207Implications and Limitations of CDX . . . . . . . . . . . . . . . 210

Chapter 19 Corporate Debt Exchange-Traded Funds (ETFs) . . . . 213

What Are ETFs? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213Mechanics of ETF Construction . . . . . . . . . . . . . . . . . . . 215ETFs in Asset Allocation . . . . . . . . . . . . . . . . . . . . . . . . . 218ETFs Used as a Measure of the Market—

What about Technicals? . . . . . . . . . . . . . . . . . . . . . . . 219

Closing Comments on Section VII . . . . . . . . . . . . . . . . 221

Section VIII Collateralized Loan Obligations (CLOs). . . . . . . 223

Chapter 20 Introduction to CLOs . . . . . . . . . . . . . . . . . . . . . . . . . . 225

What Is a CLO? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225Why Do They Matter for You? . . . . . . . . . . . . . . . . . . . . 226Some Basics Affecting CLO Issuance . . . . . . . . . . . . . . . 227Types of CLOs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228

xii DATA ANALYTICS FOR CORPORATE DEBT MARKETS

Chapter 21 Structure of Typical CLOs . . . . . . . . . . . . . . . . . . . . . . 231

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231More Analytics: Tests and Measures . . . . . . . . . . . . . . . . 232Endnotes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234

Closing Comments on Section VIII . . . . . . . . . . . . . . . 235

What Does Analyzing CLO Data Tell Us? . . . . . . . . . . . 236Endnote. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236

Section IX Tools for Portfolio Analysis . . . . . . . . . . . . . . . . . 237

Chapter 22 The Why, What, and How of Portfolio Analysis . . . . . . 239

Goals of Portfolio Analysis . . . . . . . . . . . . . . . . . . . . . . . . 239What Are Your Investment Goals and Objectives?. . . . . 242Components of a Portfolio Analysis/Performance

Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243Endnotes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247

Chapter 23 Performance Attribution . . . . . . . . . . . . . . . . . . . . . . . . 249

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249Allocation Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251Selection Effect. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254Interaction Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256Interpreting the Total Effect . . . . . . . . . . . . . . . . . . . . . . 257Two-Factor Approach to Performance Attribution. . . . . 258Challenges of Sector-Based Performance

Attribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259Endnotes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260

Closing Comments on Section IX . . . . . . . . . . . . . . . . . 263

Section X The Future of Data Analytics and Closing Comments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265

Chapter 24 Some Thoughts on the Future of Data Analytics in Corporate Debt Markets . . . . . . . . . . . . . . . . . . . . . . . . 267

Bond Data and Fundamental Data . . . . . . . . . . . . . . . . . 267Third-Party Vendors of Financial Data . . . . . . . . . . . . . . 268Growing Use of Word Recognition . . . . . . . . . . . . . . . . . 270Covenant Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270Multiple Scenario Analysis . . . . . . . . . . . . . . . . . . . . . . . . 271

CONTENTS xiii

Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272Indexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272Pricing and Liquidity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273A Final Concern about the Future

of Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274Endnote. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274

Chapter 25 Closing Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277

About the Author

Robert S. Kricheff (Bob) is a senior vice president and portfolio manager at Shenkman Capital Management. Before joining Shenk-man Capital, he worked for more than 25 years at Credit Suisse in Leveraged Finance. Prior to leaving Credit Suisse, he was a managing director and head of the Americas High Yield Sector Strategy.

He has worked doing credit analysis in several industries, includ-ing media, cable, satellite, telecommunications, health care, gaming, and energy, and has worked with corporate bonds, loans, convert-ibles, preferred stocks, and credit default swaps as well as emerging market corporate bonds. He has also run strategy and has overseen portfolio analytics.

Bob is the author of A Pragmatist’s Guide to Leveraged Finance: Credit Analysis for Bonds and Bank Debt and two e-book shorts, The Role of Credit Default Swaps in Leveraged Finance Analysis with Joel S. Kent and How to Analyze and Use Leveraged Finance Bonds for Project Finance , all published by FT Press. He also contributed to the book High-Yield Bonds: Market Structure, Valuation, and Port-folio Strategies by Theodore M. Barnhill Jr., William F. Maxwell, and Mark R. Shenkman, published by McGraw-Hill.

Bob graduated from New York University School of Arts & Sci-ence with a BA in journalism and economics and received an MSc in financial economics from the University of London School of Oriental and African Studies.

About the Contributors

Jonathan Blau is a managing director of Credit Suisse and head of Global Leveraged Finance Strategy within the Investment Banking Division, based in New York. In this role, Mr. Blau is the strategist for leveraged finance, covering the drivers of risk and return for high-yield and leveraged loans in the United States and Europe.

Jonathan joined Credit Suisse in November 2000 when the bank merged with Donaldson, Lufkin & Jenrette (DLJ), where he was a senior vice president in High Yield Research. Jonathan joined DLJ in 1997 from Chase Securities. Prior to Chase, he worked at First Bos-ton, which he joined in 1992.

Before he began his career in finance, Jonathan was a computer design engineer at Data General, Tandem Computers, and Alliant Computer Systems. He holds five U.S. patents, and his work at Data General was featured in the book The Soul of a New Machine by Tracy Kidder.

Mr. Blau received a BS in computer science and engineering from the Massachusetts Institute of Technology.

Miranda Chen is a member of the US Credit Strategy team at Credit Suisse based in New York. Her work covers both investment-grade and high-yield markets and spans cash, credit derivative, and structured credit trading strategy, incorporating fundamental, struc-tural, and technical analysis as well as macro and cross asset themes. Previously, Miranda was a member of the Leveraged Finance Strat-egy team at Credit Suisse where she covered high-yield, leveraged loans, and collateralized loan obligations (CLOs). Miranda is a Char-tered Financial Analyst (CFA) Charterholder and holds a dual degree from the University of Pennsylvania in economics (finance) and elec-trical engineering.

xvi DATA ANALYTICS FOR CORPORATE DEBT MARKETS

Alexander Chan joined Shenkman Capital in 2010 as vice presi-dent of Quantitative Strategy and Portfolio Analytics. Prior to joining Shenkman Capital, Mr. Chan was the leveraged finance strategist at Nomura Securities publishing on high-yield bonds, leveraged loans, and CLOs. He also had a similar role at Barclays Capital for several years prior to Nomura. Mr. Chan spent eight years at Credit Suisse/Donaldson, Lufkin & Jenrette (DLJ), where he was a member of the Institutional Investor ranked Leveraged Finance Strategy team. Dur-ing his time at Credit Suisse, Mr. Chan held a variety of roles, includ-ing publishing strategist, advising institutional investors on manager selection, and CLO sales. Mr. Chan received a BA from Tufts Univer-sity in quantitative economics and international relations.

3

1 The Basics

Why Use Analytics?

Using data analytics is absolutely necessary in the modern cor-porate debt markets; the sheer growth and complexity of the mar-ket make it almost impossible to do any major role in these markets without at least some use of analytics. It is required to compete in the modern markets and, with increasing focus on managing risk, it is a necessary tool to manage large trading desks at investment banks and portfolios at money management firms. The corporate debt markets are dynamic, not static, and analytics is necessary to see how the mar-kets are changing.

New entrants into this market should be aware of the basic tools used for analyzing data in the markets, and more seasoned practitio-ners should also be aware of how others in the markets may be using analytics. Everyone should be looking at new ways to analyze the mar-kets and be looking for ways to use new developments in the market in his or her analytics.

From risk managers, to credit analysts, portfolio managers, investment bankers, capital markets and syndicate personnel as well as traders, salespeople, and asset allocators within the multitrillion dollar international corporate debt market, if you do not use data ana-lytics in your business and investing decisions, you will be at a massive disadvantage.

4 DATA ANALYTICS FOR CORPORATE DEBT MARKETS

Typically, numerous programmers, system designers, and man-agers work on building and maintaining these systems. If they want to excel, they also should have a strong understanding of the unique nature of corporate debt products and the type of analysis that end users want to undertake.

What Is Data Analytics?

What is data analytics? It consists of gathering the proper data and then manipulating and examining it so that you can reach logical con-clusions about the data you are looking at. In the case of this book’s topic, it involves data about the corporate debt markets and seeing what conclusions you can derive about the various markets relative to each other and the subsets of each market and their relationships. Ideally, you will see trends developing over time or relationships that do not seem to make sense and these can create opportunities. Sev-eral products have been developed in the marketplace, which depend on these analytics as well.

Many tools are used in analytics and you can try to achieve many different goals with the analytics. This book is an introduction that focuses on data and analytics used in the corporate debt markets. It is not intended to be a detailed how-to book, but it does outline how analytics is typically used by the major players in the corporate debt markets as well as the major tools and methods currently used in data analytics in these markets. This book sheds some light on ways these tools are designed, points out some of their shortcomings, and offers a glimpse into where analytics in this field may be starting to go in the future.

Many of the analytical tools used are similar or even exactly the same as those used in the traditional government debt market. Also utilized are many features from the equity markets. Blending these features and detailed macro- and microanalysis creates unique ana-lytical tools for corporate debt instruments.

CHAPTER 1 • THE BASICS 5

How Data Analytics Is Used and How It Differs from Credit Analysis

If the fundamental credit analysis, which is so important in mak-ing proper investment decisions, starts from the bottom up, data ana-lytics can best be viewed as working from the top down. The work can start from examining some of the macro data on the various fixed-income markets and comparing data across these markets. The next step down the ladder would be to start examining the various sub-sets of a given market. All of this analysis should encompass return analysis as well as comparisons of relative value and volatility. You will likely run this analysis through historical cycles and be trying to extract how past trends might give you a road map into the future or what relationships seem out of line from historic trends and present opportunities or risks. You may spend a particular amount of time examining specific periods of heightened volatility and how various market segments reacted during these times.

This macro work will help you to develop themes and strategies that you and your team will need to follow in your investing or trading strategies. It will also help you understand where your current strate-gies have you positioned. This may all lead to your developing invest-ment themes you want to pursue.

To find the specific investment ideas that you want to delve into, you will likely use analytics on a database using queries and data sorts to develop narrower and narrower lists of securities that meet the criteria for your investment themes. One of the keys to the quality of the analytics is how robust the database is.

After you develop these lists, the detailed fundamental and rela-tive value analysis can kick in for credit selection. A portion of the analysis that you need to do to best understand when and where to invest includes technical analysis of supply and demand in the mar-ketplace. Many of the items that you will use for your analysis have

6 DATA ANALYTICS FOR CORPORATE DEBT MARKETS

their own idiosyncrasies that you need to understand to best do your analysis.

One of the most widely used tools is market indexes, which each have benefits and drawbacks that need to be understood. It is also critical to understand credit default swap (CDS) indexes, exchange-traded funds (ETFs), and collateralized loan obligations (CLOs), all of which rely heavily on data analytics to be run and managed. In doing all this analysis, you need to understand who the end user is as well as the other players who can influence the marketplace. You also need to understand the basics of bond math as well as statistics—both of which are necessary for undertaking this work.

If much of the analytic work just described is to reach a conclusion about where you should be best positioned for optimal performance, performance attribution shows you how you have been positioned and how that has impacted you. Performance attribution analysis is a culmination of much of the data analytics tools that are used across the marketplace.

An Example

This example starts with a look from the top down, starting with some macro analytics, and then brings it down to a more micro level of actual credit selection possibilities that can fit the theme developed by the macro analytics.

A macroanalysis might include looking at how the average yields on the leveraged loan market are trading relative to the average yields in the high-yield bond market versus historic trading patterns. In this analysis, you would consider many factors, including the following:

• The bulk of the loan market has floating-rate coupons while the high-yield bond market does not.

• During various historic cycles, what was the economy doing versus the current environment?

CHAPTER 1 • THE BASICS 7

• During various historic cycles, what direction were interest rates going versus the current environment?

• How has the makeup of the two markets changed over time? (For example, does the high-yield market now have significantly more secured bonds than it did during some other cycle?)

In addition, you would likely consider and address many other factors in the analysis.

In the preceding example, let’s assume you see a historical pattern that during a period of soft gross domestic product (GDP) growth, you actually see that yields on the high-yield bond market go up mate-rially more than on loans. You then might want to take this to another level and see if this pattern is true for all bonds or just certain types. Perhaps it is most pronounced in notes that have a subordinated rank-ing and that were issued by companies that are in cyclical industries.

If you are worried about entering a period of soft GDP, you might then want to analyze which bonds fit the criteria to make sure you do not have exposure to them. You can then use a database with a query system to develop a list of debt issuers that are in cyclical industries and have subordinated bonds outstanding. You might then want to take this list of subordinated bonds, compare it with the spread on the same company’s loans, and compare this spread to historical trends while also having a credit analyst explore the overall credit strength of the company.

This is just a simple example of how data analytics can be used in the corporate debt market.

I believe that analytics in the corporate debt market is still in the early stages of development and usage. There will be significant advances in the use of analytics going forward and this will lead to an increase in spending in coming years. Money will need to be spent to improve risk management and develop new analytical tools for the markets. Data management and analysis techniques that are being

8 DATA ANALYTICS FOR CORPORATE DEBT MARKETS

used in other fields, such as in marketing, are likely to be adapted for these markets.

How This Book Is Structured

Section I outlines why the corporate debt markets are so different from other asset classes. This section highlights some of the problems and difficulties with trying to undertake analytics in the corporate debt markets. It also discusses some of the common data sources.

Section II goes over some of the key terms used in the market-place and common to analytics and also reviews typical tools that are used in the markets. If you are familiar with the markets, you might choose to skim this section. If you are relatively new to the markets or are coming from the programming and system side, you might not be familiar with the topics covered in this section and should find it helpful.

Section III summarizes the definitions of the various markets within the overall spectrum of corporate debt. It also outlines who the major players are in the corporate debt market and how they utilize data analytics.

Section IV covers indexes. Jonathan Blau discusses the details and design of corporate debt market indexes. Indexes are the most widely used source for performance comparisons of various markets and for portfolio performance attribution and comparison. Understanding how these indexes work, the different methodologies, and their short-comings is key in understanding much of the everyday analytics that goes on regarding market data.

Section V examines how data analytics is used starting from a top-down approach. This macro approach starts with examining perfor-mance and relative value at the market level and then works its way down to analyzing key subsectors of the markets to develop invest-ment themes and capture trends that might be occurring within a

CHAPTER 1 • THE BASICS 9

given market. We then explore some of the tools used to derive lists of possible credits to select that can meet the investment themes that are developed.

Section VI focuses on analyzing supply-and-demand trends in the marketplace, known commonly as technicals. Understanding the trends in market technicals can be critical in helping to make timing decisions and weighting decisions in the marketplace.

Section VII explores special vehicles that have evolved in the mar-ket. Miranda Chen, an expert on these products, authored this sec-tion, which outlines liquid bond indexes, credit default swap indexes, and exchange-traded funds and shows how they depend on analytics for their construction. This section also outlines why monitoring these vehicles can help give insight into market trends and technical more quickly than some other sources of data.

Section VIII explores collateralized loan obligations. These struc-tured products also utilize analytics systems to be structured and to maintain their portfolios within the rules that they have to operate. Similar to the other structures’ vehicles, understanding and monitor-ing data on these products can add insights into analyzing trends in the corporate bank loan markets.

Section IX outlines the key features of portfolio analysis and per-formance attribution. This is one of the most developed uses of analyt-ics in the market and we would expect to see the use of such products expand and evolve. This section is written by Alexander Chan, who has worked on developing and running several of the early attribution systems and some of the most current and up-to-date systems.

Section X takes a look at some of the possibilities of where data analytics for corporate debt might be heading in the coming years.

We then include some closing remarks.

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Index

277

liquid bond indexes benchmarks, 196-197 benefits of, 194 - 197 ETFs, 195

market indexes, 6 Morningstar, 17 neural networks, 59 - 60 performance attribution analysis, 6 regression

colinearity, 50 linear relationships, 50 smoothing data, 50 variables, 49

scatter diagrams, 48 trend lines, 47 - 48

analyzing portfolios, goals of, 239 - 241 APs (authorized participants), 216 arbitrage CLOs, 228 asset allocators, 80

ETFs, 218 - 219 asset classes

returns, comparing, 121 - 122 risk, 122 weightings, comparing, 128 - 129

asset managers, 77 - 79 attribution analysis, 94 averages

calculating, 93 market weighted average, 92

A absolute attribution, 249 alerts for price movements, 161 algorithms, neural networks, 59 - 60 allocation effect, 251 - 254 alpha, 37 analytics tools, 4

Bloomberg system, 45 CDS, 199 - 200 clustering, 57 - 58 correlation, 51 data mining

in finance, 56 in marketing, 56 neighbors, 57

databases queries, 52 - 53 robustness of, 5 sorting, 53

decision trees, 58 - 59 Dow Jones Industrial Average, 11 ETFs, 199 - 200 graphical data, 46 - 47

historical data, 46-47 time series, 46

indexes, 17 attribution analysis, 94 constituents, grouping, 94 constructing, 97 - 100 investable indexes, 114 - 115

Lipper, 17

278 INDEX

B backing up graphical data in

tables, 51 - 52 balance sheet CLOs, 228 bank loans

leveraged loans, 67 - 68 LIBOR floor, 36 yields, 35-36

benchmarks, 87 allocation effect, 251 - 254 indexes as, 89 - 90 for investors, 242 - 243 for liquid bond indexes, 196-197 in portfolio analysis, 240

benefits of ETFs, 213 - 215 of liquid bond indexes, 194 - 197

beta, 37 BHB (Brinson, Hood, and Beebower)

allocation effect, 252 - 254 Blackrock iShares, 195 Bloomberg Professional CDSW

screen, 204 Bloomberg system, 45 bonds

CBOs, 175 CDSs, 69 - 70 credit ratings, 41- 42 debt rankings, 39 - 41 duration, 38 - 39 , 126 - 127 face amount, 33 fallen angels, 42 high-yield corporate bonds, 66 - 67 HoldCo, 40-41 industry groups, 42 - 43 investment-grade, spread, 34 investment-grade bonds, 65 - 66 liquid bond indexes, 194 Non-Trace, 18 OpCo, 40- 41 pricing data, 18 - 19 ratings, tracking, 20 - 21 rising stars, 42

round lot trades, 33 STW, 34

when to use, 34 - 36 yields, when to use, 34 - 36 YTM, 34

Brinson method, 251 Brinson-Fachler model, 251 broad rules-based indexes, 100 broker-dealers, 73 - 77

interdealer brokers, 209 bucketed credit scoring systems, 153 bucketing sectors, 133 - 134 buy-side, 77 - 79

C calculating

average total return, 92 default rate, 93 interaction effect (performance

attribution), 256 - 257 NAV for ETFs, 219 portfolio performance statistics,

245 - 246 selection effect (performance

attribution), 254 - 256 capitalization, 91 CAPM (Capital Asset Pricing

Model), 37 cash CLOs, 228 cash flow, EBITDA as measure of,

163 - 164 CBOs (collateralized bond

obligations), 175 CDOs (collateralized debt

obligations), 175 CDS (credit default swap) indexes, 6 ,

69 - 70 , 199 - 200 construction, 204 - 205 participants, 207 - 210

CDSs (credit default swaps), 200 - 203 DTCC, 205 pricing, 203 - 204 spreads, 203 - 204 standardization, 202 - 203

INDEX 279

CDX (credit default indexes), 202 limitations of, 210 - 211 Maiden Lane case, 210 participants, 207 - 210 pricing, 206 - 207

characteristics of indexes, 87 - 91 classifying securities for inclusion in

indexes, 104 - 107 CLOs (collateralized loan obligations),

175 , 225 analyzing, 236 arbitrage CLOs, 228 balance sheet CLOs, 228 issuance, 227 market-value CLOs, 228 quality measurements, 233 - 234 static CLOs, 228 structure, 231 - 232 structuring, 226 - 227 synthetic CLOs, 228 types of, 228 - 229

clustering, 57 - 58 coefficient of determination, 51 colinearity, regression analysis, 50 communication within projects

importance of, 23 - 24 scope of, 25

comparing credit analysis and data analytics,

5 - 6 , 149 - 152 liquid bond indexes with full

indexes, 194 - 197 market indexes, 90 - 91

correlations, 123 - 125 relative value, 125 - 126 returns, 121 - 122 risk, 122 weightings, 128 - 129

portfolios and indexes, 115 complexity of corporate debt

market, 13 - 15 covenants, 14 databases, 14

constraining exposure of large issuers, 112 - 113

constructing indexes, 97 - 100 classifying securities for index

inclusion, 104 - 107 constituents, selecting, 102 defaults, including, 110 - 111 liquid indexes, 113 - 114 new issues, 109 - 110 objectives of index, identifying, 97 requirements, 102 - 107 sectors, 99 selection criteria, 100 - 102

consultants, 80 contracts

CDS pricing, 203 - 204 standardization of, 202 - 203

CDX, 202 corporate debt market

asset allocators, 80 CDSs, 69 - 70 complexity of, 13 - 15

covenants, 14 databases, 14 yields, 13

consultants, 80 data analytics, example of, 6 - 8 emerging markets, 68 - 69 high-yield corporate bonds, 66 - 67 indexes, sectors used in, 88 as institutional market, 72 investment banks, 73 - 77 investment-grade bonds, 65 - 66 issuers, 72 - 73 leveraged loans, 67 - 68 money managers, 77 - 79 players in, 71 rankings, 39 - 41 segmenting, 131 - 132

industry analysis, 134 - 135 by risk, 132 - 133 by sectors, 133 - 134

systems managers, 80 - 81 unique nature of, 11 - 15

280 INDEX

correlation, 51 correlations between markets,

comparing, 123 - 125 cost centers, importance of

communication, 26 covenants, 14

analyzing, 270 - 271 maintenance covenants, 164 - 165

credit analysis versus data analytics, 5 - 6

credit ETFs, 213 APs, 216 constructing, 215 - 217

credit ratings, 41 - 42 credit scoring, 151 - 155

bucketed scoring systems, 153 credit selection, 139 - 140 , 149 - 152

credit analysis versus data analytics, 149 - 152

database fields for, 140 - 142 financial metrics, 145 - 148 queries, 142 - 145 sorting, 142 - 145

financial liquidity, 149 - 152 maintenance covenants, monitoring,

164 - 165 nonfinancial information, 165 - 166 price movements, 160 - 161 relative value, 155 - 160 using equity data, 161 - 164

crowded trades, 137 - 138 currencies, local currency issues, 68 current yield, 34

D data analytics, 5 - 4 , 275 - 276

analytics tools, 4 for asset managers, 78 - 79 versus credit analysis, 5 - 6 crowded trade, 137 - 138 future of, 267

bond data, 267 - 268 covenant analysis, 270 - 271

data mining, 272 fundamental data, 267 - 268 indexes, 272 - 273 liquidity, 273 - 274 multiple scenario analysis, 271 third-party vendors of financial

data, 268 - 269 word recognition, 270

graphical data, 46 - 47 historical data, 46-47 scatter diagrams, 48 time series, 46

indexes, 17 macro analytics, 6 - 8 market technicals, 75 pricing data, 18 - 19

bonds, 18 loans, 19

projects documentation, 27 - 28 importance of communication,

23 - 24 knowing goals of, 19 - 21 managing, 26 - 27

relative value, 155 - 160 sources of data, 16 - 18 top-down analytics, 6 - 8

data mining, 55 - 56 , 272 in finance, 56 in marketing, 56 neighbors, 57

databases bonds, tracking ratings, 20 - 21 for corporate debt analysis, 14 fields for credit selection, 140 - 142 flexibility of, 13 operational data for credit

selection, 148 queries, 52 - 53

for credit selection, 142 - 145 robustness of, 5 sorting, 53

credit selection data, 142 - 143

INDEX 281

debt CLOs, 225

analyzing, 236 arbitrage CLOs, 228 balance sheet CLOs, 228 issuance, 227 market-value CLOs, 228 quality measurements, 233 - 234 static CLOs, 228 structure, 231 - 232 structuring, 226 - 227 synthetic CLOs, 228 types of, 228 - 229

deleveraging, 176 refinancing, 179 - 180

debt rankings, 39 - 41 HoldCo bonds, 40-41 OpCo bonds, 40-41

decision trees, 58 - 59 default rate, calculating, 93 deleveraging debt, 176 demand technicals, 173 - 176

public disclosure, 174 - 175 dependent variables, regression

analysis, 49 dispersion, 126 Diversity Score, 233 Dow Jones Industrial Average, 11 drawdowns, 245 DTCC (Depository Trust & Clearing

Corporation), 205 , 208 duration, 38 - 39 , 126 - 127

E EBITDA, as measure of cash flow,

163 - 164 equity ETFs, 213 equity markets

as credit selection criteria, 161 - 164 industry groups, 42 - 43 valuation, monitoring, 136

ETFs (exchange-traded funds), 195 , 199 - 200 , 213 - 215

advantages of, 213 - 215 APs, 216

in asset allocation, 218 - 219 construction, 215 - 217 credit ETFs, 213 equity ETFs, 213 fixed-income ETFs, 213 HY ETFs, 217-218 leveraged loan ETFs, 217-218 NAV, calculating, 219

exposure of large issuers, constraining, 112 - 113

extension risk, 38

F face amount, 33 factor-based performance

attribution, 250 fallen angels, 42 , 107 fields (database) for credit selection,

140 - 142 sorting, 142 - 145

financial liquidity, 149 - 152 financial metrics for credit selection,

145 - 148 FINRA (Financial Industry

Regulatory Authority), 241 fixed-income ETFs, 213 flexibility of databases, 13 forward supply table, 180 - 181 full indexes, comparing with liquid

bond indexes, 194 - 197 future behavior of markets,

predicting, 87 future of data analytics, 267

bond data, 267 - 268 covenant analysis, 270 - 271 data mining, 272 fundamental data, 267 - 268 indexes, 272 - 273 liquidity, 273 - 274 multiple scenario analysis, 271 third-party vendors of financial

data, 268 - 269 word recognition, 270

282 INDEX

G goals

of analytics projects, 19 - 21 investor benchmarks, 242 - 243 of performance attribution, 249 of portfolio analysis, 239 - 241

graphical data, 46 - 47 backing up in tables, 51 - 52 historical data, 46-47 scatter diagrams, 48 time series, 46 trend lines, 47 - 48

grouping index constituents, 94

H high-yield corporate bonds, 66 - 67 historical data, 46-47 , 127 - 128 HoldCo (holding company) bonds,

40-41 HY (high-yield) ETFs, versus loan

ETFs, 217-218

I I/C (interest coverage) test, 234 identifying

index objectives, 97 relationships

with neural networks, 59 - 60 through data mining, 55 - 56

idiosyncratic risk, 88 - 89 of new issues, 109

illiquid securities, 114 implications of CDX indexes, 210 - 211 importance of communication in

projects, 23 - 24 importance of indexes, 87 - 91 independent variables, regression

analysis, 49 index skew, 207 indexes

averages, 91 - 92 as benchmark, 89 - 90

benchmarks, 87 CDS, 199 - 200 CDS indexes, 69 - 70

construction, 204 - 205 DTCC, 205

CDX indexes limitations of, 210 - 211 Maiden Lane case, 210 participants, 207 - 210 pricing, 206 - 207

characteristics of, 87 - 91 constituents, grouping, 94 constructing, 97 - 102

constituents, selecting, 102 defaults, including, 110 - 111 issuer size of securities as

criteria, 109 - 113 new issues, including, 109 - 110 requirements, 102 - 107 rules-based indexes, 100 - 102

corporate bond indexes, sectors, 88 importance of, 87 - 91 investable indexes, 114 - 115 liquid bond indexes, 193

benchmarks, 196-197 benefits of, 194 - 197 ETFs, 195 reasons for, 194

liquid indexes, 113 - 114 markets, comparing, 90 - 91 objective of, identifying, 97 versus portfolios, 115 sectors, 99

attribution analysis, 94 securities, classifying for inclusion,

104 - 107 as source of data, 17 stock indexes, 11 - 13

industry equity monitors, building, 135 - 136

industry groups, 42 - 43 , 135 - 136 interaction effect (performance

attribution), 256 - 257 interdealer brokers, 209

INDEX 283

interest rates, duration, 38 - 39 , 126 - 127

international investing, 69 interpreting total effect of

performance attribution, 257 - 258 investable indexes, 114 - 115 investing internationally, 69 investment banks, 73 - 77

pack mentality of, 187 sell-side, 76-77

investment-grade bonds, 65 - 66 credit ratings, 41 - 42 spread, 34

investor benchmarks, 242 - 243 issuers

of corporate debt, 72 - 73 of securities

constraining exposure of, 112 - 113

as criteria for index inclusion, 109 - 113

J-K-L large issuers, constraining exposure

of, 112 - 113 LBOs (leveraged buyouts), 179 LCDSs (loan credit default swaps), 69 learning from market shocks, 136 - 137 leverage ratio, 145 - 148 leveraged loans, 67 - 68

ETFs, 217-218 LIBOR (London Interbank Overnight

Rate), 36 limitations of CDX indexes, 210 - 211 linear relationships in regression

analysis, 50 Lipper, 17 liquid bond indexes, 193

benchmarks, 196-197 benefits of, 194 - 197 constructing, 113 - 114 ETFs, 195 reasons for, 194

loans CDSs, 69 - 70 CLOs, 175 , 225

analyzing, 236 arbitrage CLOs, 228 balance sheet CLOs, 228 issuance, 227 market-value CLOs, 228 quality measurements, 233 - 234 static CLOs, 228 structure, 231 - 232 structuring, 226 - 227 synthetic CLOs, 228 types of, 228 - 229

leveraged loan ETFs, 217- 218 leveraged loans, 67 - 68 pricing data, 19 yields, 36

local currency issues, 68

M macro analytics, 6 - 8 Maiden Lane case, 210 maintenance covenants, 164 - 165 managing projects, 26 - 27 market indexes, 6

CDS, 6 correlations, comparing, 123 - 125 predicting future behavior of

markets, 87 relative value, comparing, 125 - 126 returns, comparing, 121 - 122 risk, comparing, 122 weightings, 128 - 129

market strength indicators postplacement trading, 184 - 186 price talk, 182 - 184

market technicals, 75 market weight, 91 - 93 market weighted average, 92 marketing, data mining, 56 market-value CLOs, 228 maturity buckets, 134

284 INDEX

measurements of CLO quality, 233 - 234

metrics, financial metrics for credit selection, 145 - 148

Microsoft Excel, regression analysis, 49 - 50

miniTab, 50 money managers, 77 - 79 monitoring

equity valuations of industries, 135 - 136

maintenance covenants, 164 - 165 price movements, 161

monitoring performance, 17 - 18 with indexes, 17

Morningstar, as source of data, 17 multiple scenario analysis, 271

N narrow rules-based indexes, 102 NAV (net asset value) of ETFs, 219 neighbors, 57 neural networks, 59 - 60 new issues

including in indexes, 109 - 110 price talk, 182 - 184

nonfinancial information as credit selection criteria, 165 - 166

nonlinear relationships in regression analysis, 50

Non-Trace bonds, 18

O objectives of indexes, identifying, 97 O/C (overcollateralization) test,

233 - 234 OpCo (operating company)

bonds, 40-41 operational data for credit

selection, 148 OTC (over-the-counter)

transactions, 202 out-of-index bets, 243

P pack mentality of investment

bankers, 187 par weight, 93 participants

APs, 216 in CDX indexes, 207 - 210 in corporate debt market, 71

asset allocators, 80 consultants, 80 investment banks, 73 - 77 issuers, 72 - 73 money managers, 77 - 79 programmers, 80 - 81 systems managers, 80 - 81

performance . See also performance attribution analysis

attribution analysis, 94 benchmarks, 87

indexes as, 89 - 90 investor benchmarks, 242 - 243 for liquid bond indexes,

196-197 monitoring, 17 - 18

with indexes, 17 , 90 - 91 of portfolios, measuring, 243 - 246

performance attribution analysis, 246

portfolio statistics report, 244 total returns report, 244

performance attribution analysis, 6 , 246 , 249 - 251

allocation effect, 251 - 254 factor-based methodology, 250 - 251 interaction effect, 256 - 257 sector-based methodology, 251 ,

259 - 260 selection effect, 254 - 256 total effect, interpreting, 257 - 258 two-factor approach, 258 - 259

portfolio statistics report, 244

INDEX 285

portfolios analysis . See also performance

attribution analysis goals of, 239 - 241 performance attribution

analysis, 246 performance measures, 243 - 246 statistics, calculating, 245 - 246

attribution analysis, 94 versus indexes, 115

postplacement trading, 184 - 186 predicting future behavior of

markets, 87 price movements, monitoring, 161 price talk, 182 - 184 pricing data, 18 - 19

CDSs, 203 - 204 CDX indexes, 206 - 207 loans, 19 price talk, 182 - 184 securities, 99 - 100

programmers, 80 - 81 projects

communication importance of, 23 - 24 scope of, 25

documentation, 27 - 28 knowing goals of, 19 - 21 managing, 26 - 27

public disclosure of demand data, 174 - 175

Q-R quality measurements for CLOs,

233 - 234 queries, 52 - 53

for credit selection, 142 - 145 rankings, debt rankings, 39 - 41 rating categories, spreads, 20 - 21 Rattle, 56 RBSA (returns-based style

analysis), 246 refinancing, 179 - 180

regression, 47 - 50 colinearity, 50 linear relationships, 50 smoothing data, 50

relationships clustering, 57 - 58 identifying

with neural networks, 59 - 60 through data mining, 55 - 56

between markets, comparing, 123 - 125

relative value, 155 - 160 comparing between markets,

125 - 126 reports, measuring portfolio

performance, 244 - 245 requests, documenting, 27 - 28 requirements for index construction,

102 - 107 return contribution, 249 returns, comparing between markets,

121 - 122 rising stars, 42 risk

comparing markets, 122 credit ratings, 41 - 42 extension risk, 38 idiosyncratic risk, 88 - 89

of new issues, 109 relative value, 155 - 160 segmenting corporate debt market

by, 132 - 133 systemic risk, 88 - 89 volatility, 36 - 38

alpha, 37 beta, 37 CAPM, 37 duration, 38 standard deviation, 36-37 total return, 36

risk analysis, 76 round lot trades, 33 rules-based indexes, constructing,

100 - 102

286 INDEX

S SAS programs, 50 scatter diagrams, 48 scope of communication within

projects, 25 scoring credit, 151 - 155

bucketed credit scoring systems, 153 sector-based performance attribution,

251 , 259 - 260 sectors

attribution analysis, 94 in corporate bond indexes, 88 indexes, constructing, 99 industry groups, 42 - 43 segmenting the corporate debt

market, 133 - 134 securities

broker-dealers, 73 - 77 classifying for inclusion in indexes,

104 - 107 defaults, including in indexes,

110 - 111 illiquid securities, 114 portfolios, attribution analysis, 94 pricing, 99 - 100

segmenting the corporate debt market, 131 - 132

industry analysis, 134 - 135 by risk, 132 - 133 by sectors, 133 - 134

selecting constituents for index construction, 102

selection criteria for index construction

issuer size, 109 - 113 liquid indexes, 113 - 114 requirements, 102 - 107 rules-based indexes, 100 - 102

selection effect (performance attribution), 254 - 256

sell-side, 75 - 77 Senior ranking, 39 - 41

Senior Secured ranking, 39 - 41 Senior Subordinated ranking, 39 - 41 Sharpe ratio, 36 , 246 shocks, learning from, 136 - 137 skew, 207 smoothing data, 50 sorting data, 53

for credit selection, 142 - 145 sources of data, 16 - 18

indexes, 17 Lipper, 17 Morningstar, 17

specifications of CDS contracts, 202 - 203

spreads for CDSs, 203 - 204 dispersion, 126 rating categories, 20 - 21

SPSS (Statistical Package for the Social Sciences), 56

standard deviation, 36-37 standardization of CDS contracts,

202 - 203 State Street Global Advisors, 195 static CLOs, 228 statistics

coefficient of determination, 51 correlation, 51 data mining, 55 - 56 portfolio statistics report, 244 regression analysis, 49 - 50

stocks, 11 - 13 databases, 11 - 13

storing documentation, 28 strength of market indicators

postplacement trading, 184 - 186 price talk, 182 - 184

structuring CLOs, 226 - 227 STW (spread to worst), 34

when to use, 34 - 36 Subordinated ranking, 39 - 41

INDEX 287

supply technicals, 177 - 178 effect of supply changes on market

risks, 186 - 187 forward supply table, 180 - 181 postplacement trading, 184 - 186 price talk, 182 - 184 pricing, 182 use of proceeds, 178 - 179

synthetic CLOs, 228 systemic risk, 88 - 89 systems managers, 80 - 81

T tables, backing up graphical data,

51 - 52 tactical money, 175 technicals, 211

demand technicals, 173 - 176 public disclosure, 174 - 175

impact on markets , 172 supply technicals, 177 - 178

effect of supply changes on market risks, 186 - 187

forward supply table, 180 - 181 postplacement trading, 184 - 186 price talk, 182 - 184 use of proceeds, 178 - 179

third-party vendors of financial data, 268 - 269

time series, 46 top-down analytics, 6 - 8

using historical data, 127 - 128 total effect (performance attribution),

interpreting, 257 - 258 total return, 36

calculating, 92 total returns report, measuring

portfolio performance, 244 TRACE (Trade Reporting and

Compliance Engine) system, 18 tracking bond ratings, 20 - 21

transaction-oriented investment banks, 76-77

trend lines, 47 - 48 two-factor approach to performance

attribution, 258 - 259

U-V unique nature of corporate debt

market, 11 - 15 use of proceeds, supply technicals,

178 - 179 valuation

of equity, 136 face amount, 33 round lot trades, 33 STW, 34 YTM, 34

Vanguard High Yield Corporate Fund, 240

variables, regression analysis, 49 volatility, 36 - 38

alpha, 37 beta, 37 CAPM, 37 comparing markets, 122 duration, 38 - 39 learning from market shocks,

136 - 137 standard deviation, 36-37 total return, 36

W WARF (Weighted Average Rating

Factor), 233 weightings, 128 - 129

allocation effect, 251 - 254 weights report, 244 word recognition, applications for

data analysis, 270

288 INDEX

X-Y-Z yields

for bank loans, 35-36 for corporate debt instruments, 13 current yield, 34 dispersion, 126 when to use, 34 - 36 YTM, 34

YTM (yield to maturity), 34