Chapter Three

LITERATURE REVIEW

Chapter Three

LITERATURE REVIEW

It is better to go through the articles, papers and other literary worksof scholars who spent nights without sleep over the subject Stock PriceMovement as it would help the present study. So here an attempt is made tosurvey the literature related to the stock market.

The important issues currently in the discussion board are the markethypothesis. Lots of writings are found in this realm and every day new ideasand revelations come in to existence. Most of them either support theefficient market hypothesis or poses challenges to the contents of it. Thosewho are interested in the study of stock price movement cannot skip withoutlooking into it.

Similarly, another important issue lying alive in the area of stockmarket movement is the price and return volatility. Many researches havebeen going on in it and wide controversies are coming up on the subject.

Another important issue with the stock market is about the problem ofbeta. Beta’s efficiency to reveal the market related risk of stocks is beingquestioned by many writers.

A review of such writings and a brief description of the same areundertaken here in this chapter.

The Efficient Market on Trial- A survey:1The paper had leveled seriouscriticism against the validity of the Efficient Market Hypothesis proposed byEugene Fama. The view can be put briefly.The hitherto dominant paradigmin financial market research, the Efficient Market Hypothesis (EMH), hasbeen put on trial recently and subjected to critical re-examination. Thepreliminary evidence indicates that the initial confidence in the EfficientMarket Hypothesis might have been misplaced. It is observed that financialequilibrium models based on EMH fail to depict trading operations in thereal world. Various anomalies and inconsistent results call for refinement ofthe existing paradigm. It is proposed in this article that a more coherenttheory of stock market behavior can be developed by incorporatingKeynesian ideologies on the speculative behavior of investors.

Important criticisms

1. Fama had classified the market in to three namely, Strong, semi-strong and weak. Fama said that in the strong market all theavailable information –whether public or private would bereflected by the market prices. Therefore an investor with insiderknowledge cannot make more profit than a naïve investor. Thecontent is that the information would reach the marketimmediately and the market is very strong enough to assimilatesuch information outright. But the authors point out that therewere lots of studies which provide evidence of making profits onsuch information which were not incorporated by the market. Itmeans markets usually take time to adjust with newannouncements of the events before incorporating the same.Sehun’s study in 1986 and 1998 were studies of such types.

2. In the semi-strong market Fama says that the market manifest allpublicly available information then and there precluding any oneto make profit with insider knowledge. But the authors state thatwhen Patell and Wolfson studied in 1984 they said to have foundthat in the intra- day trades the information impacting stock pricestakes time to reach the market.

3. Weak form of market hypothesis maintains that the stock pricesmanifests all past prices and returns immediately. But the counterargument posed by Russel and Torbey was that even before themarket price discloses it the price can be predicted withaccounting and macro economic variables.

Contrary to the hypothesis Russel and Torbey have shown evidencesof inefficiencies in the real market

Evidences of inefficiences:

The Initial Euphoria and Subsequent Discontentment

EMH was the theoretical basis in the 70s and 80s. In the past prices

were predicted on the basis of past prices, P/E ratios, dividend yields and soon. Stock prices react to many events such as declaration of dividend, stocksplit, takeovers, amalgamation, divestitures, capital expenditure and so on. Itwas also true that stock prices assimilate such information consequent to theannouncement of such events within one or two days. Authors agree that tothat extent the theory of EMH is valid. But legitimately they raise thequestion that can be the movement of prices fully attributed to theannouncement of events? Is there any other factors affecting the pricemovement? Do public announcements affect the price at all? what could besome of other factors affecting prices? Roll(1988) states that most of theprice movements of individual stocks are not affected by publicannouncements. Cutler, Poterba and Summers (1989) reach similarconclusions. They report that there is little, if any, correlation between thegreatest aggregate market movement and public release of importantinformation.

The Current Debate

The authors state that current debate among the financial economistsis over the predictability of stock prices. The accumulating evidencesuggests that stock prices can be predicted with a fair degree of reliability.Critics of EMH (e.g. La Porta, Lakonishok, Shliefer, and Vishny [1997])argue that the predictability of stock returns reflects the psychologicalfactors, social movements, noise trading, and fashions or "fads" of irrationalinvestors in a speculative market. Any analysts believe after their empiricalstudies that predictability of stock prices is facilitated not only by therational factors but also by irrational. This view is totally strange to EfficientMarket Hypothesis.

They argue that many anomalies to EMH hypothesis are found toprevail in the market. Investors make abnormal profit out of such situationsis absolutely make Fama a false prophet. Otherwise there is no justificationfor the Market Anomalies like January effect, the week end effect orMonday effect, season effects, small firm effect, E/P ratio effect, Value-lineenigma, Over/Under Reaction of Stock Prices to Earnings Announcements,Standard & Poor’s (S&P) Index effect, The Distressed Securities Marketeffect and weather effect. These phenomena have been rightly referred to asanomalies because they cannot be explained within the existing paradigm ofEMH. It clearly suggests that information alone is not moving the prices.

The authors argue that according to EMH, in an efficient marketprices reflect investment value. Investment value implies economic value orintrinsic value of a stock. Intrinsic value of a stock is the discounted streamsof dividends over period according to the fundamental factors of the stock.But most often the price reflected by the market will be far from the intrinsicvalue. It is clear evidence that over and above the fundamental factors someirrational, psychological and noise factors also affect the market price. Theempirical evidence provided by volatility tests conducted by Shiller in 1981,LeRoy and Porter (1981) suggests that movements in stock prices cannot beattributed merely to the rational expectations of investors, but also involvean irrational component.

In the EMH, investors have a long-term perspective and return oninvestment is determined by a rational calculation based on changes in thelong-run income flows. However, in the Keynesian analysis, investors haveshorter horizons and returns represent changes in short-run pricefluctuations. If we regard the rational decision making process of the EMHas one that is guided by a complete knowledge of factors governing thedecision, it is immediately seen that the EMH is flawed. It fails to provide arealistic framework for the formation of expectations. It is difficult to arguefor investor decision making being rational under EMH, given theuncertainty factor. To make a rational decision would involve knowledge offuture income flows and also the appropriate discount factor, both of whichare unknowable. Like Keynes, many people would agree that few, if any,have sufficient knowledge to make it possible to forecast investment yields.Investor is not faced with risk as maintained by EMH, on the contrary byuncertainty. According to Keynes future is uncertain. Future returns cannotbe accurately determined under conditions of uncertainty. Uncertainty andprobability are different. Without objective evidence on which to base theirexpectation of prices, it becomes intuitively appealing that individuals wouldbase their opinions on other members of their group, an idea emphasized byKeynes.

Undoubtedly, the studies based on EMH have made an invaluablecontribution to our understanding of the securities market. However, thereseems to be growing discontentment with the theory. A limited survey of thecontemporary literature shows that criticism of EMH has gained both voiceand momentum during recent years. While it is true that the market respondsto new information, it is now clear that information is not the only variableaffecting security valuation. Recent years have witnessed a new wave of

researchers who have provided thought provoking, theoretical argumentsand supporting empirical evidence to show that security prices could deviatefrom their equilibrium values due to psychological factors, fads, and noisetrading.

Noise2 : Fischer Black in his paper describes the effect of noise trading inthe stock market with alarming cautiousness and unexpected bewilderment.He admits in his paper the existence of noise as a valid force that affects thestock price movement consequently affecting the expected return of theinvestors. By this he is supporting the views of Warren Buffet, Russel andTorbey. The quintessence of his paper is as below:

The effects of noise on the world, and on our views of the world, areprofound. Noise in the sense of a large number of small events is often acausal factor much more powerful than a small number of large events canbe. Noise makes trading in financial markets possible, and thus allows us toobserve prices for financial assets. Noise causes markets to be somewhatinefficient, but often prevents us from taking advantage of inefficiencies.Noise in the form of uncertainty about future tastes and technology by sectorcauses business cycles, and makes them highly resistant to improvementthrough government intervention. Noise in the form of expectations thatneed not follow rational rules causes inflation to be what it is, at least in theabsence of a gold standard or fixed exchange rates. Noise in the form ofuncertainty about what relative prices would be with other exchange ratesmake us think incorrectly that changes in exchange rates or inflation ratescause changes in trade or investment flows or economic activity. Mostgenerally, noise makes it very difficult to test either practical or academictheories about the way that financial or economic markets work. We areforced to act largely in the dark.

J.M Keynes was also of the view that the market prices are determinedby the irrational noises in the market. The paper vociferously speaks aboutthe psychological and other sentimental factors which affect the stock pricesrather than rational factors. But at the same time he is not completelydenying the importance of the validity of Fama’s EMH. The critics includingFischer Black pointed out many anomalies in the random walk hypothesis.According to Fischer the stock prices are not exactly random but rational asmaintained by Fama. On the contrary it is affected by a myriad of factorslike rational and irrational. As Keynes stated investors confront a lot ofuncertainties besides risks. Risks and uncertainties are not one and the same.

They are different. There is fear psychosis, sentiments, fashion etc thatcreate noise. The importance of such noise cannot be neglected. But itdoesn’t mean the EMH is not valid. The tenet that in the market, pricesreflect all information so that an insider cannot make more profit by virtue ofbeing an insider than a naïve investor is golden. If the market is efficient ofcourse it ought to happen. Inefficiency of the market is a constant concernand worry of the regulatory authorities.

Macroeconomic volatility and stock market volatility world wide3: Inthe paper Diebold and Yilmaz attempt to establish the fact that considerablestudies are not done to study the stock market volatility in response to thechanges in the macroeconomic factors. Financial economics isoverwhelmingly concerned with the valuation of the assets and in theprediction of asset prices and expected return from assets on the basis of thevolatility in asset’s prices and expected return for changes in thefundamentals of the market. Even though the theory envelops themacroeconomic components as important factors which influence the stockprice movement it remains only in theory and practically when stockvolatility becomes a matter of discussion, the macro component is neglected.In view of the authors of the Paper, “notwithstanding its impressivecontributions to empirical financial economics, there remains a significantgap in the volatility literature, namely its relative neglect of the connectionbetween macroeconomic fundamentals and asset return volatility. Weprogress by analyzing a broad international cross section of stock marketscovering approximately forty countries. We find a clear link betweenmacroeconomic fundamentals and stock market volatilities, with volatilefundamentals translating into volatile stock markets.”

The financial econometrics literature has been strikingly successful atmeasuring, modeling, and forecasting time-varying return volatility,contributing to improved asset pricing, portfolio management, and riskmanagement. Interestingly, the subsequent financial econometric volatility,although massive, is largely silent on the links between asset return volatilityand its underlying determinants. Instead, one typically proceeds in reduced-form fashion, modeling and forecasting volatility but not modeling orforecasting the effects of fundamental macroeconomic developments inparticular, the links between asset market volatility and fundamentalvolatility remain largely unstudied; effectively, asset market volatility ismodeled in isolation of fundamental volatility.

Here the focus is on stock market volatility. The general failure to linkmacroeconomic Fundamentals to asset return volatility certainly holds truefor the case of stock returns. In this paper an empirical investigation of thelinks between fundamental Volatility and stock market volatility is provided.The exploration is motivated by financial economic theory, which suggeststhat the volatility of real activity should be related to stock market volatility,as in the empirical studies of Shiller (1981) and Hansen and Jagannathan(1991). The paper is successful in establishing a clear positive relationshipbetween stock return and GDP volatilities. GDP is an importantmacroeconomic factor that will also influence the stock volatility. When theGDP of a country increases the economic activities will flourish.Consequently enterprises performance will be enhanced to result inprosperity. This will cause the stock volatility. But usually this factor isfound ignored or neglected is highlighted by the authors in this papernotably.

This paper is part of a broader movement focusing on the macro-finance interface. Much recent work focuses on high-frequency data, andsome of that work focuses on the high frequency relationships amongreturns, return volatilities and fundamentals (e.g., Andersen, Bollerslev,Diebold and Vega, 2003, 2007). Here, in contrast, the focus on internationalcross sections obtained by averaging over time. Hence this paper can beinterpreted not only as advocating more exploration of the fundamentalvolatility / return volatility interface, but also in particular as a call for moreexploration of volatility at medium (e.g., business cycle) frequencies.

Stock market volatility: Ten years after the crash4: A paper authored byG.William Schwert of University of Rochester in December 1997. In thepaper Schwert was speaking about Stock market volatility since the USstock market crash in October, 1987. He finds that the stock marketvolatility is unusually low from 1987 onwards. The large increase in stockprices during since 1987 means that many days during 1996 and 1997 haveexperienced near record changes in the Dow Jones Industrial Average, eventhough the volatility of stock returns has not been high by historicalstandards. He compared volatility of returns to US stock indexes atmonthly, daily, and intra-day intervals and he also showed the volatility ofreturns to stock indexes implied by traded option contracts. He alsocompared the volatility of US stock market returns with volatility of returnsto stock markets in the United Kingdom, Germany, Japan, Australia andCanada. All of the evidence leads to the conclusion that volatility has been

very low in the decade since the 1987 crash. The mini-crash of October 27,1997 reinforces the need to reevaluate the current system of circuit breakersso that they are triggered less easily. According to Schwert part of theproblem is caused by trigger points that are expressed as absolute, ratherthan percentage, changes in market indexes.

The recent events in the US stock market have renewed his interest instock market volatility. Schwert says that the stock market has risen lastdecade. Consequently frequency of large absolute changes in market indexessuch as DJIA has increased. Schwert is doubtful of whether the recentmarket movements have been unusually large. People cannot understand thereality of stock market volatility due to scale used by the raters. Schwert hasof the view that the problem of scale illusion remains a serious impedimentto public understanding of stock market volatility. According to himvolatility should be measured in percentage changes or rates of returns. Heblames the DJIA for the technique used by it in expressing the volatility inabsolute terms so that the press and the public exaggerate the severity ofrecent volatility.

Schwert examines the historical evidence of the US stock marketvolatility. He finds that the standard deviation after 1987 on the monthlyreturn was only 4% in contrast to 20% during the great depression during1930. Therefore he concludes that there is no volatility after 1987 in the USstock market. He confirms that since 1987 volatility became low and stableall over the world except in Japan, which experienced a substantial decreasein stock values and stock return volatility in the early 1990. Investors,regulators, brokers, dealers and the press are all concerned with stockvolatility. A large part of the problem is a perception that the prices move alot simply because the level of stock indexes like the DJIA is historicallyhigh.

Schwert points out that one of the consequences of the 1987 crash isthe legacy of rules and regulations that were promulgated to prevent arecurrence of this event. He concludes the paper by saying that the biggestchange that occurred in recent years is the ease with which the genral publiclearns about the intraday movements of stock market prices. Cabletelevision, internet, and other forms of low cost and high speedcommunication provide much more information about stock volatility thanhas been available in the past. Thus the public perceptions of volatility areheightened, even if volatility itself is not unusually high.

Efficient Market Hypothesis:5 A paper jointly authored by JonathanClarke, Thomas Jandik, Gershon Mandelkar. The paper is about the EfficientMarket Hypothesis propounded by Eugene F.Fama. The authors in thispaper are attempting to extent full support to the content of the hypothesis.They evaluate the tenets of the hypothesis and find themselves fully satisfiedwith the theory. According to them Efficient Market Hypothesis(EMH)deals with one of the most fundamental and exciting issues in finance – whyprices change in security markets and how those changes take place. It hasvery important implications for investors as well as for financial managers.The efficient markets hypothesis (EMH) suggests that profiting frompredicting price movements is very difficult and unlikely. The main enginebehind price changes is the arrival of new information. A market is said tobe “efficient” if prices adjust quickly and, on average, without bias, to newinformation. As a result, the current prices of securities reflect all availableinformation at any given point in time. Consequently, there is no reason tobelieve that prices are too high or too low. Security prices adjust before aninvestor has time to trade on and profit from a new a piece of information.

Three versions of the efficient markets hypothesis

The authors say that Fama has classified the efficient market in tothree forms on the basis of the information the market price reflects. Theyare as below:

Weak Form Efficiency

The weak form of the efficient markets hypothesis asserts that thecurrent price fully incorporates information contained in the past history ofprices only. That is, nobody can detect mis-priced securities and “beat” themarket by analyzing past prices. The weak form of the hypothesis got itsname for a reason – security prices are arguably the most public as well asthe most easily available pieces of information. Thus, one should not be ableto profit from using something that “everybody else knows”. On the otherhand, many financial analysts attempt to generate profits by studying exactlywhat this hypothesis asserts is of no value - past stock price series andtrading volume data. This technique is called technical analysis.

Semi-strong Form Efficiency

The semi-strong-form of market efficiency hypothesis suggests thatthe current price fully incorporates all publicly available information. Publicinformation includes not only past prices, but also data reported in acompany’s financial statements (annual reports, income statements, filingsfor the Security and Exchange Commission, etc.), earnings and dividendannouncements, announced merger plans, the financial situation ofcompany’s competitors, expectations regarding macroeconomic factors(such as inflation, unemployment), etc. In fact, the public information doesnot even have to be of a strictly financial nature. For example, for theanalysis of pharmaceutical companies, the relevant public information mayinclude the current (published) state of research in pain-relieving drugs.

Strong Form Efficiency

The strong form of market efficiency hypothesis states that the currentprice fully incorporates all existing information, both public and private(sometimes called inside information). The main difference between thesemi-strong and strong efficiency hypotheses is that in the latter case,nobody should be able to systematically generate profits even if trading oninformation not publicly known at the time. In other words, the strong formof EMH states that a company’s management (insiders) are not be able tosystematically gain from inside information by buying company’s shares tenminutes after they decided (but did not publicly announce) to pursue whatthey perceive to be a very profitable acquisition. Similarly, the members ofthe company’s research department are not able to profit from theinformation about the new revolutionary discovery they completed half anhour ago. The rationale for strong-form market efficiency is that the marketanticipates, in an unbiased manner, future developments and therefore thestock price may have incorporated the information and evaluated in a muchmore objective and informative way than the insiders. Not surprisingly,though, empirical research in finance has found evidence that is inconsistentwith the strong form of the EMH.

Common misconceptions about the EMH

The authors say that there exists lot of misconceptions against theefficient market hypothesis. They are all myths. Really these myths are alldue to the improper reading of the hypothesis. They examine theseapprehensions about the EMH and give clarity to ambiguity if any related tothe theory as below:

Myth 1: EMH claims that investors cannot outperform the market. Yetwe can see that some of the successful analysts (such as George Soros,Warren Buffett, or Peter Lynch) are able to do exactly that. Therefore, EMHmust be incorrect. EMH does not imply that investors are unable tooutperform the market. We know that the constant arrival of informationmakes prices fluctuate. It is possible for an investor to “make a killing” ifnewly released information causes the price of the security the investor ownsto substantially increase. What EMH does claim, though, is that one shouldnot be expected to outperform the market predictably or consistently. Itshould be noted, though, that some investors could outperform the marketfor a very long time by chance alone, even if markets are efficient. Imagine,for the sake of simplicity, that an investor who picks stocks “randomly” hasa 50% chance of “beating the market”. For such an investor, the chance ofoutperforming the market in each and every of the next ten years is then(0.5)10, or about one-tenth of one percent. However, the chance that therewill be at least one investor outperforming the market in each of the next 10years sharply increases as the number of investors trying to do exactly thatrises. In a group of 1,000 investors, the probability of finding one “ultimatewinner” with a perfect 10-year record is 63%. With a group of 10,000investors, the chance of seeing at least one who outperforms the market inevery of next ten years is 99.99%, a virtual certainty. Each individualinvestor may have dismal odds of beating the market for the next 10 years.Yet the likelihood of, after the ten years, finding one very successfulinvestor, even if he or she is investing purely randomly – is very high ifthere are a sufficiently large number of investors. This is the case with thestate lottery, in which the probability of a given individual winning isvirtually zero, but the probability that someone will win is very high. Theexistence of a handful of successful investors such as Messrs. Soros, Buffett,and Lynch is an expected outcome in a completely random distribution ofinvestors.

Myth 2: EMH claims that financial analysis is pointless and investorswho attempt to research security prices are wasting their time. “Throwingdarts at the financial page will produce a portfolio that can be expected to doas well as any managed by professional security analysts”. Yet we tend tosee that financial analysts are not “driven out of market”, which means thattheir services are valuable. Therefore, EMH must be incorrect.

Jonathan and others contradict this myth by saying that financialanalysis involve lot of work which is costly .Though by chance any onemakes profit more than the market with the help of the financial analyst thecost of such financial analysis will swallow the excess return therebyreducing the benefit par with the market. Therefore, any profits achieved bythe analysts while trading on "mis-priced" securities must be reduced by thecosts of financial analysis, as well as the transaction costs involved. Formutual funds and private investment managers these costs are passed on toinvestors as fees, loads, and reduced returns. In general, the advantagegained is not sufficient to outweigh the cost of their advice. In equilibrium,there will be only as many financial analysts in the market as optimal toinsure that, on average, the incurred costs are covered by the achieved grosstrading.

Myth 3: EMH claims that new information is always fully reflected inmarket prices. Yet one can observe prices fluctuating (sometimes verydramatically) every day, hour, and minute. Therefore, EMH must beincorrect.

The constant fluctuation of market prices can be viewed as anindication that markets are efficient. New information affecting the value ofsecurities arrives constantly, causing continuous adjustment of prices toinformation updates. In fact, observing that prices did not change would beinconsistent with market efficiency, since we know that relevant informationis arriving almost continuously.

Myth 4: EMH presumes that all investors have to be informed, skilled,and able to constantly analyze the flow of new information. Still, themajority of common investors are not trained financial experts. Therefore,EMH must be incorrect.

This is an incorrect statement of the underlying assumptions neededfor markets to be efficient. Not all investors have to be informed. In fact,

market efficiency can be achieved even if only a relatively small core ofinformed and skilled investors trade in the market, while the majority ofinvestors never follow the securities they trade.

Evidence in favor of the efficient markets hypothesis

The authors further offer adequate evidence for the validity of EMH

The weak form of market efficiency

The random walk hypothesis implies that successive price movementsshould be independent. A number of studies have attempted to test thishypothesis by examining the correlation between the current return on asecurity and the return on the same security over a previous period. Apositive serial correlation indicates that higher than average returns arelikely to be followed by higher than average returns (i.e., a tendency forcontinuation), while a negative serial correlation indicates that higher thanaverage returns are followed, on average, by lower than average returns(i.e.,a tendency toward reversal). If the random walk hypothesis were true, wewould expect zero correlation. Consistent with this theory, Fama (1965)found that the serial correlation coefficients for a sample of 30 Dow JonesIndustrial stocks, even though statistically significant, were too small tocover transaction costs of trading.10 Subsequent studies have mostly foundsimilar results, across other time periods and other countries.

The Semi-strong Form

The semi-strong form of the EMH is perhaps the most controversial,and thus, has attracted the most attention. If a market is semi-strong formefficient, all publicly available information is reflected in the stock price. Itimplies that investors should not be able to profit consistently by trading onpublicly available information.

Investment Managers

Many people suggest that mutual fund managers are skilled investorswho are able to beat the market consistently. Unfortunately, the empiricalevidence does not support this view. In one of the first studies of its kind,Michael Jensen found that over the period Multiple studies havedemonstrated that mutual funds, on average, do not exceed the return of the

market index. This has been demonstrated in both large markets and smaller,supposedly “less-efficient” markets. Equally important to investors iswhether or not they can identify some managers or mutual funds that canconsistently beat the index. The findings show that a mutual fund’sperformance over the past 1, 3, 5 or 10 years is not predictive of its futureperformance.

Event Studies

If markets are efficient and security prices reflect all currentlyavailable information, new information should rapidly be converted intoprice changes If the market is efficient, the stock price would quickly adjustto this new information. The price would jump instantaneously to $150 tofully reflect the effect of the new project announced by the company. Theefficient capital market theory implies that market participants will reactimmediately and in an unbiased manner. That is, one can expect that thestock price should not under-react and trade below $150 nor over-react tothe announcement and trade above $150 in a predictable manner.

The Strong Form

Empirical tests of the strong-form version of the efficient marketshypothesis have typically focused on the profitability of insider trading. Ifthe strong-form efficiency hypothesis is correct, then insiders should not beable to profit by trading on their private information.

Evidence against the Efficient Markets Hypothesis

Although most empirical evidence supports the weak-form and semi-strong forms of the EMH, they have not received uniform acceptance. Manyinvestment professionals still meet the EMH with a great deal of skepticism

Over-reaction and Under-reaction

The efficient markets hypothesis implies that investors react quicklyand in an unbiased manner to new information. In two widely publicizedstudies, DeBondt and Thaler present contradictory evidence. They find thatstocks with low long-term past returns tend to have higher future returns andvice versa - stocks with high long-term past returns tend to have lower futurereturns (long-term reversals).

Value versus Growth

A number of investment professionals and academics argue that socalled “value strategies” are able to outperform the market consistently.Typically, value strategies involve buying stocks that have low pricesrelative to their accounting “book” values, dividends, or historical prices.

Small Firm Effect

Rolf Banz uncovered another puzzling anomaly in 1981. He foundthat average returns on small stocks were too large to be justified by theCapital Asset Pricing Model, while the average returns on large stocks weretoo low. Subsequent research indicated that most of the difference in returnsbetween small and large stocks occurred in the month of January. The resultswere particularly surprising because for years financial economists hadaccepted that systematic risk or Beta was the single variable for predictingreturns. Current research indicates that this finding is not evidence of marketinefficiency, but rather indicates a failure of the Capital Asset PricingModel.

Conclusions

The goal of all investors is to achieve the highest returns possible.Indeed, each year investment professionals publish numerous books toutingways to beat the market and earn millions of dollars in the process.Unfortunately for these so-called “investment gurus”, these investmentstrategies fail to perform as predicted. The intense competition betweeninvestors creates an efficient market in which prices adjust rapidly to newinformation. Consequently, on average, investors receive a return thatcompensates them for the time value of money and the risks that they bear –nothing more and nothing less. In other words, after taking risk andtransaction costs into account, active security management is a losingproposition. Although no theory is perfect, the overwhelming majority ofempirical evidence supports the efficient market hypothesis. The vastmajority of students of the market agree that the markets are highly efficient.The opponents of the efficient markets hypothesis point to some recentevidence suggesting that there is under- and over-reaction in securitymarkets. However, it’s important to note that these studies are controversial

and generally have not survived the test of time. Ultimately, the efficientmarkets hypothesis continues to be the best description of price movementsin securities markets.

The authors have placed a lot of counter arguments against the contentof those who oppose the Efficient market Hypothesis in this paper. They areable to defend the validity of the efficient market hypothesis. But there arecertain anomalies which still remain unexplained. Jonathan and others aresilent about the Keynesian content of noise trade. Accordingly price changesnot solely based on the new information but due to the fear, anxiety,sentiment, and speculative surprises. Similarly, January effect, week-endeffect, Monday effect, weather effect etc are all found to have some impacton the price and there are investors who make use of these and make profitsmore than the market. Such phenomena still remain as enigma withoutexplanations. This paper also failed to give an explanation for such irrationalimpact on the price movement.

Capital market theory after the efficient market hypothesis6: A paperjointly authored by Dimitri Vayanos and Paul Woolley. The paper startwith the question that whether efficient market hypothesis still valid in thelight of recent capital market boom and crash. The authors share the viewthat EMH becomes blunt and new theory is necessary for capital assetpricing. They suggest a new model that explains asset pricing in terms of abattle between fair value and momentum driven by principal-agent issues.They diagnose the reason for the mispricing and volatility as the investmentagents’ desire for rational profit.

They agree that forty years have passed since the principles ofclassical economics were first applied formally to finance through thecontributions of Eugene Fama (1970) and his now-renowned fellowacademics. Over the intervening years, capital market theory and theefficient market hypothesis have been developed and modified to form anelegant and comprehensive framework for understanding asset pricing andrisk. But events have dealt a cruel blow to these theories. Capital marketbooms and crashes, culminating in the latest sorry and socially costly crisis,have discredited the idea that markets are efficient and that prices reflect fairvalue. a number of leading economists, have proclaimed the death of

mainstream finance theory and all that goes with it, especially the efficientmarket hypothesis, rational expectations, and mathematical modelling. Theway forward, they argue, is to understand finance based on behaviouralmodels on the grounds that psychological biases and irrational urges betterexplain the erratic performance of asset prices and capital markets.

In this predicament the authors suggest a new capital market assetpricing model scientifically based, unified theory of finance that is rigorousand tractable; one that retains as much as possible of the existing analyticalframework and simultaneously produces credible explanations andpredictions.

The litterateurs maintain that the efficient market hypothesis hasbeguiled policymakers into believing that market prices could be trusted andthat bubbles either did not exist, were positively beneficial for growth, orcould not be spotted. Intervention was therefore unnecessary, and regulationcould be light-touch.

Principal-agent investment problems: Mispricing with rationalityThe crucial flaw has been to assume that prices are set by the army of

private investors, the "representative household" as the jargon has it.Households are assumed to invest directly in equities and bonds and acrossthe spectrum of the derivatives markets. Theory has ignored the real worldcomplication that investors delegate virtually all their involvement infinancial matters to professional intermediaries – banks, fund managers,brokers – who dominate the pricing process. Delegation creates an agencyproblem. Agents have more and better information than the investors whoappoint them, and the interests of the two are rarely aligned. For their part,principals cannot be certain of the competence or diligence of theirappointed agents. The agency problem has been acknowledged in corporatefinance and banking but hardly at all in asset pricing. Introducing agentsbrings greater realism to asset-pricing models and can be shown to transformthe analysis and output.

Dimitri Vayanos and Paul Woolley, the authors in their recent paperintroduced a concept momentum, the commonly observed propensity fortrending in prices, which in extreme form causes bubbles and crashes.Momentum is incompatible with an efficient market and has proved difficultto explain in the traditional framework. Indeed, it has been described byFama and French (1993) as the “premier unexplained anomaly” in assetpricing. Central to the analysis is that investors have imperfect knowledge of

the ability of the fund managers they invest with. They are uncertain whetherunderperformance against the benchmark arises from the manager's prudentavoidance of over-priced stocks or is a sign of incompetence. As shortfallsgrow, investors conclude incompetence and react by transferring funds to theoutperforming managers, thereby amplifying the price changes that led tothe initial underperformance and generating momentum.

The Dot-Com Boom

The technology bubble ten years ago illustrates this well. Technologystocks received an initial boost from fanciful expectations of future profitsfrom scientific advance. Meanwhile, funds invested in theunglamorous, value sectors languished, prompting investors to loseconfidence in the ability of their underperforming value managers andswitch funds to the newly successful growth managers, a response whichgave a further boost to growth stocks. The same thing happened as valuemanagers themselves began switching from value to growth stocks to avoidbeing fired. Through this conceptually simple mechanism, the modelexplains asset pricing in terms of a battle between fair value and momentum.It shows how rational profit seeking by agents and the investors who appointthem gives rise to mispricing and volatility. Once momentum becomesembedded in markets, agents then logically respond by adopting strategiesthat are likely to reinforce the trends. Explaining the formation of assetpricing in this way seems to provide a clearer understanding of how and whyinvestors and prices behave as they do.

Of course, investors may not always behave in a perfectly rationalway. But that is beside the point. If this new approach meets the criteria ofrelevance, validity, and universality required of any new theory, then itprovides a valuable starting point in understanding markets. Models basedon irrational behaviour can always be helpful in offering supplementary ormore detailed insights.

The view of the authors that the EMH beguiles the market that nointervention is necessary is somewhat right. If the market is efficient thepresent financial crisis should have been ended. But things are not so. So itis clear that the market is not efficient. Moreover market cannot ignorehuman behavior. The investors, brokers and agents are all human beings.They are speculative in nature. They have feeling, fear, urges and irrational

motives also. They all will impact the mispricing and volatility. Therefore,the content of the authors is highly acceptable.

Company Fundamentals and Equity Returns in India7: It is a paper byVanitha Thripathi, Senior Lecturer Department of Commerce Delhi schoolof Economics, Delhi. The paper examines the impact of companyfundamentals on equity returns in India. The study is on the basis of fourfundamentals of the companies. The fundamentals selected are (1) Marketcapitalization, (2) book-equity to market-equity ratio, (3) Price earningsratio, and (4) debt-equity ratio. The study is made using monthly price dataof a sample of 455 companies over the period June 1997 to June 2007. Thepaper investigated whether the inclusion of one or more fundamentalvariables will better explain the cross sectional variations in equity returns inIndia than the single factor CAPM. The paper also intends to study whetherthere are any seasonal patterns in the equity returns in India. The study findsthat the market capitalization and price earnings ratio are statisticallysignificant. They have significant negative relationship with equity returns.Similarly, book equity to market equity ratio and debt equity ratiostatistically positive relationship with equity returns. It is found that theinvestment strategies based on these Variables produced extra risk adjustedreturns over the study period. The author further finds that Fama Frenchthree factor model (viz. market risk premium, size premium and valuepremium) explains cross sectional variations in equity returns in India in amuch better way than the single factor CAPM. However the author did notfind any seasonality patterns (April or January Effect) in equity returns inIndia. These results have important implications for market efficiency, assetpricing and market microstructure issues in Indian stock market. The studyfinds that the Indian market has strong size effect, Price earnings effect onequity returns and leverage impact on equity returns. The study also findsthat Indian stock market is still not strong efficient market because there areevidences for making use of publically available information for profit.Though the efficiency level of Indian stock market is increasing, it has notstill become semi-strong efficient market.

we have found that market risk premium is still ‘the’ most importantindependent factor in asset pricing framework although its relativeimportance has substantially declined since the decade of 1980’s or 1990’s.This implies that company fundamentals are gaining importance inexplaining cross sectional variations in equity returns in India.

Anomalies in CAPM: A Panel Data Analysis Under Indian Conditions8:This paper is jointly authored by Abilash S.Nair, Abhijith Sarkar, A.Ramanathan and A.Subramanyam. They in the paper blame Indian stockmarket country for not able to mobilize and allocate capital effectivelyinspite of a plethora of decade long reforms. While Indian corporate sectordepends predominantly on debt to raise finance, the Indian householddepends primarily on banks to invest its savings. A possible explanation forsuch a phenomenon in their opinion is inaccurate pricing of assets in thestock market. One of the widely used asset-pricing models is the CAPM.The authors in this paper try to analyze the relevance of factors other thanbeta that affect asset returns in the Indian stock markets. They improve uponthe methodology of Fama and MacBeth (FM) cross-sectional regression,generally followed in CAPM anomalies literature. According to the authorsone reason why the FM method is widely applied is because it allows fortime variation in estimated coefficients. However, due to the averaging ofcoefficients across time, the FM method will accept the null hypothesis of norelation between beta and stock returns even when the underlying model istrue. To overcome this limitation we use LSDV technique. The LSDVmethod, while incorporating time variation also helps overcome thelimitation of averaging of coefficients. They also have the opinion that sizeand value have effect on price movement. In the study they find that beta ofthe asset does not significantly explain the cross section of asset returns.Similarly they find the existence factors other than beta that affects equityreturns and stock price. As far as Sharpe’s theory is concerned beta is animportant factor that determines the return of a stock other than risk-freerate. The findings of the authors are path-breaking since Sharpe’s findings ofbeta; it is the only major factor that affects equity return. They also find thatbeta does not explain returns satisfactorily

Summary Findings of their study is given below:

(i) The informal test results indicated that beta of the asset does notsignificantly explain the cross section of asset returns. The test also indicatesexistence of factors other than beta that significantly explained asset returns.

(ii) In the FM cross-sectional regression analysis, beta does not significantlyexplain asset returns. Further, though the study finds evidence supportingexistence of anomalous factors, the causal relation of leverage and E/P withasset returns is not in congruence with theory. Hence, no conclusions withregard to existence of anomalous factors are drawn solely on the basis of FM

regression results. To confirm the findings a pooled regression analysis ofthe data is conducted.

(iii) The pooled regression results indicate that beta along with size andearning to price ratio significantly explains asset returns. Also, the causalrelation between E/P and asset returns as put forth by the pooled regressionanalysis is not supported by theory. Due to this incongruency as well as thelow explanatory power of the pooled regression model, a set of diagnostictests, to check for violation of OLS assumptions, are conducted. Thisanalysis revealed the presence of heteroskedasticity.

(iv) To capture the variation in asset returns across firm and time, a LSDV(robust) analysis of this model is conducted. The LSDV analysis reveals:

a) beta to be insignificant unlike pooled regression. As explained earlier, thisphenomenon is a consequence of the inclusion of firm and time dummies.Hence, this result is not taken to be a proof of the irrelevance of beta.

b) A negative size effect as well as a positive value effect, similar to FMcross-sectional regression analysis as well as the pooled regression analysis.However, unlike FM analysis, the LSDV results put forth a positive leverageeffect which is in congruence with theory. Though it is found that size, valueand leverage are significant anomalous factors, going by the law ofparsimony, it is conjectured that the value effect subsumes the leverageeffects since the value effect is essentially the ratio of market and bookleverages. Moreover, since value, leverage and size are all scaled versions ofprice, it is plausible that some of them turn out to be redundant. It is also tobe noted that the regression of asset returns on size and value has the highestexplanatory power.

The elasticity of the price of a stock and its beta9: This paper is co-authored by Cyriac Antony MPh, Lecturer (Selection Grade) inStatistics,Sacred Heart College Thevara, Kochi, India and E.S. JeevanandPhD, Reader in Statistics, Union Christian College, Aluva, Kerala, India. Inthe paper the authors study the role of beta in asset pricing. According tothem beta is used to estimate the expected return of a stock with respect toits market return. The paper finds some problems associated with theconcept of beta and its estimation. Estimating the expected return from thestock investment is considered to be a major task. The task is fulfilled withthe help of beta which is defined as the slope of the characteristic line that

shows the relationship between the rate of return on a security and themarket portfolio. The authors say that the concept of beta introduced byMarkowitz(1959) is being widely used to measure the systematic riskinvolved in an investment. Here authors express the importance of beta. Betais used in CAPM to calculate the required return on an asset which is part ofa portfolio. It is Markowitz in 1959 who introduced the concept of beta torepresent the relative sensitivity of an individual stock in relation to themarket portfolio. Later it was largely used by Sharpe in CAPM. As theauthors rightly put it now the measure beta is an inevitable tool in portfoliomanagement. Asset pricing without beta is unimaginable. The Capital AssetPricing Model (CAPM) is used to determine a theoretically appropriaterequired rate of return of an asset that is added to a portfolio. See Sharpe(1964), Linter (1965) and Mossin (1966). Beta plays a prominent role inCAPM. Therefore, the usefulness of CAPM mainly depends on theauthenticity of beta. In the paper the litterateurs attempt to elucidate the roleof beta, its potential to determine the price and return of the assets in relationto market. They have the view that beta is not the adequate measure of returnand risk. There is necessity to have an alternative measure of risk and return.The paper is devoted to find an alternative to beta. The authors propose theelasticity of security’s return to market return as an alternative to beta.Accordingly they offer a modified version of CAPM.

With this view they closely analyse the characteristic line and beta toexamine the efficacy of beta. Accordingly they state that the price ‘Y’ of astock depends on a number of factors some of which are internal to thecompany and others external. Empirical studies show that there is a linearrelation between the share price index ‘X’ and Y. Let (X1, Y1), (X2, Y2), …(Xn, Yn) be ‘n’ observations relating to X and Y made at ‘n’ consecutiveperiods of time. If ‘x’ denotes the percentage rate of return of the price indexand ‘y’ denotes that of the security, then the values of x and y are given by

x i = 100 ( Xi +1 - X i ) / X i, andy i = 100 (Y i+1 - Yi ) / Yi for i = 1,2,3,…,n-1

The equation of the characteristic line can be written as

y = α + β x (1)

Where,

α and β are constants.

The slope of characteristic line β is the security’s beta. Thecharacteristic line is estimated by the least squares method. At present, betais taken as the measure of the sensitivity of the security’s price Y withrespect to market changes. Beta shows how the price of a security respondsto market forces. It is an indispensable tool inasset pricing.

The authors intensively discuss the problems with the beta as ameasure of risk and return. The beta in the characteristic line is stable.Many researchers and scholars like Blume (1971), Hamada (1972) andAlexander & Chervani (1980) questioned the stability of beta. They arguedthat beta is time varying. Black (1976) linked beta to leverage whichchanges owing to changes in the stock price. Mandelker & Rhee (1984)related beta to decisions by the firm and thus a varying measure. Therelationship between macro-economic variables and the firm’s beta, asillustrated in the work of Rosenberg & Guy (1976) points to the varyingcharacter of beta. Since beta is evaluated as the covariance between the stockreturns and index returns, scaled down by the variance of the index returnsand the index volatility is time-varying (Bollerslev et al. 1992), beta is notconstant over a period of time. Roll et al (1994) point out the inefficiency ofthe CAPM for estimating the expected returns using beta. As doubted by theauthors of this paper, the beta everyone knows is a constant like alpha in thecharacteristic line. At the same time it is to show the relative volatility of thesecurity vis-à-vis market. The security’s risk perception varies along withthe changing situation of the market. As the market is highly dynamic thebeta cannot remain constant. But theoretically beta is constant is an anomaly.So the finding that beta is not an adequate measure is plausible. In the wordsof the authors “the constancy nature of beta raises doubts about thesuitability of using it as a measure of the sensitivity of the security’s returncorresponding to market returns. This led us to think of a suitable measurethat reflects instantaneous changes of the market.” In order to measuremarket sensitivity, authors suggest the use of the concept of elasticity ofprice stock.

Elasticity of Price of Stock.

Elasticity of price of stock ‘η’ is proposed to measure the relativesensitiveness of the stock’s price with market index. It is supposed tomeasure a percentage change in the price of the stock in question for a

percentage change in the market index. They develop this model on theassumption that there exists a functional relationship between the stock priceand market price as stated below:

Y = f (X)

Accordingly the alternative sensitivity index for beta is introduced bythe authors as below:

η = X . dYY dX

Here,η= ElastisticityX = Price of stockY = Price of market indexdY= Change in the price of YdX = change in the price of X

The model is finally concluded as

η = b Xa + b X

The elasticity as above is the ratio of beta change in X to a+ betachange in X on the assumption of a=0. Here both a and b are constant. Whenthe elasticity is unity a will become zero.

The notable thing is that, as the authors rightly claim, n is not constantlike beta. The above equation reveals that n is variable to the changes in theX. For every change in the X, there will be a change in the n also. But it isnot the case with the beta used in CAPM where beta and ‘a’ are constants.This constancy of beta makes it unsuitable to measure the highly dynamicprice and return of stock market.

As they put it rightly, thus n may be equal to 1, more than1 or lessthan 1. If n=1, the change in the price of stock X will be proportionate to thechange in market index. That is if the market index changes by 1% the priceof the stock X will also change by 1%. In this case the intercept value a willbe zero. The regression line (Characteristic line) will go through the point oforigin.

In case n>1, that is the change in the price of a stock is more than thechange in the price of market index, the intercept value a will be negativesince the stock and the market index are generally correlated positively.

In case n<1, that is for a 1% change in the market index the stock’sprice changes less than 1%, a will be positive.

On the basis of ‘a’ (alpha), the intercept of a regression line, it will beable to say whether the elasticity n is more than1 (n>1), or less than 1(n<1)or equal to 1(n=1).

If ‘a’ is negative, n>1; if ‘a’=0, n=1; if ‘a’ positive, n<1.

As elasticity ‘n’is derived to replace the beta as a measure of relativevolatility of price and return due to the latter’s inefficacy, the attempt of theauthors next is to modify the model of CAPM with ‘n”. They arrive to thefollowing modified CAPM:

Rs = Rf + η ( Rm – Rf )Where

Rs = the return required on investmentRf = the return that can be earned on a risk-free investmentRm = the return on the market index for a given indexη = the security’s sensitivity (elasticity) to market movement for a

given index

They further add that in the present form of the CAPM, beta remainsthe same irrespective of the sensitivity of the security with respect to marketindex. It depends only on the return on market and not on the index level.But, in the modified form, importance is given to η that varies as indexvaries. This is the striking advantage of the modified CAPM over the presentform.

The authors conclude their paper by stating that the elasticitycoefficient propounded by them is highly sensitive than beta coefficient.They say that the price and returns are highly sensitive and therefore theelasticity too will be sensitive. Elasticity can be estimated even from arandom sample. On the contrary according to them beta is only the slope ofthe regression line and it is constant. Beta varies only with X value and not

with the market index. Moreover, beta cannot be estimated from a randomsample.

The model suggested by the authors can be considered after making anintensive study and test of its efficacy. But the presentation is highly simpleand intuitive. The approach is plausible.

References:

1. Philip S Russel and Violet M Torbey, “The efficient market on trial-Survey.”

2. Fischer Black, “Noise,” Financial Journal, Vol.41, No.3., Papers andProceedings of the Forty-Fourth Annual Meeting of the AmericaFinance Association, New York, New York, December 28-30, 1985(Jul., 1986), pp. 529-543.

3. Francis X. Diebold and Kamil Yilmaz, “Macroeconomic volatilityand stock market volatility worldwide,” National Bureau ofEconomic Research, Cambridge, 2008.

4. G.William Schwert, “Stock market volatility: Ten years after thecrash:”, National Bureau of Economic Research, Cambridge, 1997.

5. Jonathan Clarke, Thomas Jandik, Gershon Mandelkar, “EfficientMarket Hypothesis.”

6. Dimitri Vayanos and Paul Woolley, “Capital market theory afterthe efficient market hypothesis.”

7. Vanitha Thripathi, “Company Fundamentals and Equity Returns inIndia,” International Research Journal of Finance and Economics,ISSN 1450-2887 Issue 29, 2009.

8. Abilash S.Nair, Abhijith Sarkar, A. Ramanathan andA.Subramanyam, “Anomalies in CAPM: A Panel Data AnalysisUnder Indian Conditions:” International Research Journal of Financeand Economics, ISSN 1450-2887 Issue 33, 2009.

9. Cyriac Antony and E.S. Jeevanand, “The elasticity of the price of astock and its beta,” Journal of applied quantitative Methods, 2003.