a dissertation report on capm--- final

A DISSERTATION REPORT ON

A Study on Capital Asset Pricing Model and Feasibility of its Anomalies in Indian Market

INPARTIAL FULFILLMENT OF

REQUIREMENT OF POST GRADUATE DIPLOMA IN MANAGEMENT PROGRAMME 2009-11

Submitted To:- Submitted By:-Vimal Babu Varun NarangFaculty (HR) PGDM 2009

Roll No. 29118

Northern Integrated Institute Of Learning ManagementCentre for Management Studies

Greater Noida,

ACKNOWLEDGEMENT

“When time, speed, skill, timing and diligence combine’s horizons become the ultimate site.”

No task however small could be accomplished without guidance, help, and assistance. I am

indebted to all persons who have contributed there worth in completion of my study.

The financial Dissertation project was successfully completed with the help, encouragement,

advice, inspiration and stimulus received from my faculty Vimal Babu, Sukumar Dutta and Hima

Bindu Kota

I feel deep sense of gratitude in thanking them as they sincerely helped me heaps to carry on this

project to its eventual friction. My Dissertation project would have been mobilized had they not

have given their invaluable guidance and consistent support at all hours.

EXECUTIVE SUMMARY

This study deals with the Effect Capital Asset Pricing Model and its anomalies in Indian Market.

The Capital Asset Pricing Model (CAPM) is the most popular model of the determination of

expected returns on securities and other financial assets. It is considered to be an “asset pricing”

model since, for a given exogenous expected payoff, the asset price can be backed out once the

expected return is determined. Additionally, the expected return derived within the CAPM or any

other asset pricing model may be used to discount future cash flows. These discounted cash

flows then are added to determine an asset’s price.

The first part of the study deals with the basic definition, advantages and application of CAPM.

CAPM is tool used for risk return analysis. It helps to know the Company’s Cost of capital. In

the first part of the study CAPM calculation, CML, SML etc has been explained. In the second

chapter the literary review about capital asset pricing model is stated. CAPM is based on

Markowitz Modern portfolio theory. Various other scholars gave their own analysis for CAPM

model. Like Fama modified CAPM and gave an alternative version called CCAPM which is

Conditional Capital Asset Pricing Model. The attraction of the CAPM is that it offers powerful

and intuitively pleasing predictions about how to measure risk and the relation between expected

return and risk. Unfortunately, the empirical record of the model is poor—poor enough to

invalidate the way it is used in applications. The CAPM’s empirical problems may reflect

theoretical failings, the result of many simplifying assumptions. But they may also be caused by

difficulties in implementing valid tests of the model

In the third chapter Alternative model is described and a comparison between Arbitrage Pricing

Model and Captial Asset Pricing Model. . The Capital Asset Pricing Model (CAPM) and the

Arbitrage Pricing Theory (APT) have emerged as two models that have tried to scientifically

measure the potential for assets to generate a return or a loss. They are similar in that they

attempt to measure an asset's propensity to follow the overall market however APT attempts to

divide market risk into smaller component risk.

The fourth chapter deals with testing CAPM in Indian market .a sample of 50 companies listed

on BSE are taken. The average return of all the 50 companies for 10 years is calculated. Sensex

yearly Market rate is taken for 10 years. Beta of each company with respect to the market return

is calculated. The result showed that higher beta firms always earn higher returns. Hence through

this finding Capital asset pricing model is tested.

In the fifth part of the study Efficient Market hypothesis is explained. All the types of Form:

Weak Form, Semi Strong form and Strong form is explained. In this part the CAPM Anomalies

like Small firm effect, P/E effect, D/e effect, and Seasonality effects are also explained.

TABLE OF CONTENTSubmitted To:- Submitted By:-.......1

INTRODUCTION TO CAPITAL ASSET PRICING MODEL..................................................6

RISK RETURN POSSIBILITIES WITH LEVERAGE........................................................11

THE DOMINANT PORTFOLIO ‘M’...................................................................................12

THE CAPITAL MARKET LINE..........................................................................................14

SECURITY MARKET LINE................................................................................................16

The Logic of the CAPM........................................................................................................19

Multifactor Models and Arbitrage Pricing Theory (APT).....................................................28

Testing Capital Asset Pricing Model in Indian Stock market..................................32

INTRODUCTION.................................................................................................................33

Methodology..........................................................................................................................38

CONCLUSION......................................................................................................................41

THREE VERSIONS OF THE EFFICIENT MARKETS HYPOTHESIS............................44

COMMON MISCONCEPTIONS ABOUT THE EMH........................................................46

CONCLUSIONS...................................................................................................................54

BIBLIOGRAPHY AND ANNEXURE........................................................................................55

Chapter - i

INTRODUCTION TO CAPITAL ASSET PRICING MODEL

EVOLUTION OF CAPM

The CAPM was developed in the mid 1960’s. The model has generally been attributed to

William Sharpe, but John Lintner and Jan Massin also made similar independent derivations.

Consequently, the model is often referred to as Sharpe-Lintner-Mossin (SLM) Capital Asset

Pricing Model. The CAPM explains the Relationship that should exist between the securities

expected returns and their risk in terms of the means and standard deviation about security

returns. Because of this focus of the mean and standard deviation, the CAPM is a direct

extension of the portfolio models developed by Sharpe and Markowitch. Although the model has

been extensively examined, modified and extended in the literature, the original SLM version of

CAPM still remains the central theme in the Capital Market theory as well as the current

practices of Investment management.

Using Simplified assumptions, the CAPM is an equation that expresses the equilibrium

relationship between the security’s or portfolio’s expected return and its systematic risk. Because

the CAPM is relatively a simple model, it has been applied in wide variety of academic and

institutional applications such as measuring the portfolio performance, testing of marketing

efficiency, identifying undervalued and overvalued securities, determining price of risk implicit

in the current market prices and capital budgeting.

The Capital Asset Pricing Model (CAPM) is the most popular model of the determination of

expected returns on securities and other financial assets. It is considered to be an “asset pricing”

model since, for a given exogenous expected payoff, the asset price can be backed out once the

expected return is determined. Additionally, the expected return derived within the CAPM or any

other asset pricing model may be used to discount future cash flows. These discounted cash

flows then are added to determine an asset’s price. So, even though the focus is on expected

return, we will continue to refer to the CAPM as an asset pricing model.

Assumption of CAPM

The capital market theory is based on the basis of Markowitz’s portfolio model. This theory is

based on certain assumptions as:

a) All the investors are considered to be efficient investors who like to position themselves

on the efficient frontier. Their exact location on the efficient frontier, however, depends

on their risk return utility function.

b) Investors are free to borrow or lend any amount of money at the Risk- Free Rate of

Return (RFR).

c) All investors are expected to have homogeneous expectations, i.e., their future rates of

return have identical probability distributions.

d) All investors have same investment time horizon.

e) All investors are assumed to be infinitely divisible making it possible to even buy or sell

fractional shares of any portfolio.

f) The process of buying or selling of assets does not involve any transaction costs. For

example, holders of pension funds and even religious groups do not have to pay taxes and

further it has been found out that the transaction cost of many financial instruments that

are traded by most of the financial institutions are less than one percent.

g) It is assumed that the inflation rates are fully anticipated or in other situations it may be

totally be absent thus resulting in no changes in the tax rate.

h) Another assumption of the theory is the equilibrium in the capital markets, that is, all

investments are correctly priced on par with their risk levels.

It might sound unrealistic; for instance, it may be possible to lend money at risk free rate by

buying the risk-free securities, say, treasury bills but it may not be possible to be possible to

borrow money at risk free rate while stating some of the assumptions and it should be borne

in mind that even by relaxing some of these assumptions, the model does not change much.

CAPM is the extension of the Markowitz portfolio theory. The assumptions, on which

Markowitz portfolio theory is based, are applicable to CAPM also.

CAPM is a model about the relationship between risk and required return of return on asset,

and embodies the two fundamental relationships: capital market line and security market line.

The risk that CAPM discussed consists of two components: systematic risk and fundamental

risk. The most important thought of CAPM is that beta; the systematic risk is a complete

measure of security risk. Therefore there is a need to distinguish the difference of systematic

and unsystematic risk in order to understand CAPM through security market line and capital

market line.

Figure 1: Systematic and Unsystematic Risk

The CAPM assumes that investors hold fully diversified portfolios. This means that investors are

assumed by the CAPM to want a return on an investment based on its systematic risk alone,

rather than on its total risk. The measure of risk used in the CAPM, which is called ‘beta’, is

therefore a measure of systematic risk. The minimum level of return required by investors occurs

when the actual return is the same as the expected return, so that there is no risk at all of the

return on the investment being different from the expected return. This minimum level of return

is called the ‘risk-free rate of return’.

Beta and Standard Deviation

Investments are risky because returns cannot be comprehended. The return that an investment is

expected to fetch probably will not be the same as that actually obtained. Therefore, variation

exists around the expected returns.

In investment analysis, it is necessary to quantify the risk usually employing the following two

measures:

The Standard deviation

The Beta

Now, a rational, risk-averse investor view variance as the appropriate risk measure if he holds

only one security. In that case, the only security he holds becomes his portfolio. So, the return on

his security becomes the return on his portfolio. Variance around the expected return statistically

can be measured by standard deviation or variance. On the other hand, for holding multiple

assets, the contribution of any one of the asset \s to the riskiness of the portfolio is its systematic

or non-diversifiable risk. Thus for a well diversified portfolio, the appropriate measure of risk

would be beta, for, in that case the return on assets move relative to the returns on the market

portfolio. Beta in fact absorbs the risk which cannot be diversified. By effective diversification,

asset specific risks are eliminated, and measure of risk in such a case is beta. It is an indication as

to how the individual asset is contributing to the total risk of the portfolio.

Risk Free Assets

Before going into detail of risk free assets one need to know about risky assets. In essence, a

risky asset is one which gives uncertain future returns. The uncertainty can be measured by the

variance or the standard deviation of the expected future returns. And the risk free assets are

those whose expected risk is fully certain and thus standard deviation of such expected returns

comes to zero i.e. σf = 0. Further it is to remember that the rate of return earned on such asset

should be the risk free rate of return (rf).

Covariance of Risk free Assets with Risky Assets

Let us consider the covariance of two sets of returns, A &B

CovAB = ∑[rA – E(rA)][rB – E(rB)] /n

The uncertainty for the risk free asset is known, so σ f = 0, which implies that rA = E(rA) for all the

periods. Thus, rA – E(rA) = 0, which further leads to the fact that the product of any other

expression with this expression will be zero. This will result in the covariance of the risk free

asset with any risky asset or portfolio to be also zero. Similarly, the covariance between any risk

free asset and risky asset will be zero as rf,a = Covf,a/ σf σa.

Combining a Risk free Asset with the Risky Portfolio

When risky assets are combined with risk free assets then the expected return of the portfolio is

written as :

E(ri) = Wf(rf) + (1-Wf) E(ra)

Where,

Wf = the proportion of the portfolio invested in the risk free assets

E(ra) = expected return on risky portfolio A

Further it is known that the expected variance for the two asset portfolio can be wriiten as:

E(σi2) = Wi

2σi2 + 2W1W2 r1,2σ1σ2

Now on substituting the risk free asset for security 1 and risky asset for security 2, the equation

would be:

E(σi2) = Wf

2σf2 + (1-Wf)2σA

2 + 2Wf(1-Wf)rf,A σfσa

It is further known that σf2 = 0 and rf,a is also zero, because of the correlation between the risk

free asset and any risky asset A is zero. So the above equation becomes:

E(σi2) = (1-Wf)2σA

2

Or E(σi) = (1-Wf) σA

So it can be said that for any portfolio that combines a risk free asset with any risky asset, the

standard deviation is the linear proportion of the standard deviation of the risky asset portfolio.

RISK RETURN POSSIBILITIES WITH LEVERAGE

As investor always want to increase his expected returns. Say, he is situated at a point ‘k’, on the

efficient frontier, he will want to go beyond that point i.e., increase his expected returns by

accepting higher degree of risk. One way of doing so may be by investing in risky portfolios on

the efficient frontier beyond the point ‘k’. Another way is to add leverage to the portfolio by

resorting to borrowing money at the risk free rate and use the proceeds to invest in risky asset

portfolio at point ‘k’.

LENDING AND BORROWING AT THE RISKLESS RATE

The consideration of riskless asset alters the efficient frontier considerably.

Figure 2: Borrowing and Lending at Riskless rate Rf and investing in risky portfolio M

The above figure displays the efficient frontier, in terms of expected return E(r ) and standard

deviation (σ), along with the riskless asset f and three risky portfolios M, A, B. Since the riskless

asset f has no risk (i.e., σf = 0), it’s E(r) and σ plot on the zero risk, vertical axis at the point r f,

represents the expected rate of return on the riskless asset f.

With the riskless asset f and the ability to borrow or lend (invest) at risk free rate r f, it is now

possible to form portfolio that have risky assets as well as the risk free assets within them.

Furthermore, all combinations of any portfolio and the riskless asset will lie along a straight line

connecting their E(r), σ plots. For example, portfolios containing f and the risky portfolio A will

lie along the line segment rfA as shown in above figure. Similarly, combination of f with either

portfolio B or portfolio M will lie along segment rfB and rfM respectively. Therefore, combining

any risky portfolio with riskless asset produces a linear relationship between their respective

E(r), σ points.

An important implication of introducing riskless rate of lending and borrowing is the

transformation of the efficient frontier. With the introduction of rf, the efficient frontier is

transformed into a linear form. Furthermore, as long as E(rM) > rf investor can continually

increase expected return and risk by borrowing increasing amount at rf, and investing the

borrowed amount in portfolio M. the below figure shows the efficient frontier with borrowing

and lending portfolio:

Figure 3: Borrowing and Lending at Riskless rate and Investing at Risky Portfolio M

THE DOMINANT PORTFOLIO ‘M’

By borrowing and lending at the riskless rate rf, investors can alter the risk/ expected return

profile of any efficient portfolio to meet personal preferences for risk and expected return. In the

below figure, regardless of whether investor want to borrow or lend, portfolio M is the best

efficient portfolio. This is because investors can invest in portfolio M and then borrow or lend at

Rf to suit their preference. That is, by borrowing and lending at Rf in conjunction with investing

in portfolio M, they can create portfolio combinations along the line RfM in such a way that for a

given level of risk it is possible to find a combination of M and risk free borrowing/ lending

which offers a return that is higher than the one available for a portfolio on the efficient frontier.

The figure is illustrated as follows:

Figure 4: Dominant Portfolio

Because of this dominance, all investors should choose efficient portfolio M in conjunction with

their preference for lending or borrowing at the risk free rate Rf. Graphically, portfolio M

represent the tangent between a ray drawn from the intercept Rf, to the efficient frontier. This

tangency drawn from Rf to m has the greatest slope for any line drawn from Rf to the efficiency

set of risky portfolio. That is, point M is the efficient portfolio that maximize the value of

[E(r)-rf]/σ , risk premium.

Thus portfolio along with the line will maximize E(r ) at their respective σ levels, when

compared to portfolios along lower rays drawn from Rf, to any other portfolio along the

efficient frontier.

Market Portfolio

Since every investor should choose to hold portfolio M, it follows that portfolio M must be a

portfolio containing all securities in the market. Such a portfolio that contains all available

securities is called Market Securities. Because all investors should choose market portfolio, it

should contain all available securities. If it does not, securities that are not included would not be

demanded by any investor and prices of these securities, therefore, would fall and their expected

returns would rise. At the same point the increased expected returns would be attractive to some

investors.

THE CAPITAL MARKET LINE

With the ability to borrow and lend at the risk free rate Rf, in conjunction with an investment in

the market portfolio M, the old curved efficient frontier is transformed into a new efficient

frontier, which is a line passing from Rf, through market portfolio M. this new linear efficient

frontier is called the Capital Market Line, or simply the CML. This CML, together with the old

efficient frontier, is illustrated in the below Figure. The inspection of the figure indicates that all

portfolios lying along the CML will dominate, in terms of E(r) and σ, the portfolio along the

previous curve efficient frontier.

Figure 6: The Capital Market Line(CML)

The CML not only represent the new efficient frontier, but it also expresses the equilibrium

pricing relationship between E( r) and σ for all efficient portfolios lying along the line. Since the

equation of any line can be expressed as y=a + bx, where a represents the vertical intercept and B

represents the slope of the line, the pricing relationship given by CML can be easily determined.

In the above figure a = Rf, and b = [E(Rm) – Rf]/σm.

Thus the CML, relationship for any efficient portfolio I is provided in equation:

E(ri) = Rf + {[E(Rm) – Rf]/σm}σi

In other words, the above equation states that the expected return on any efficient portfolio I,

E(ri), is the sum of two component: (1) the return on the risk free investment Rf, and (2) a risk

premium, {[E(Rm) – Rf]/σm}σi that is proportional to the portfolio’s σi. The slope of the CML

[E(Rm) – Rf]/σm is called the market price of the factor that distinguishes the expected return

among CML portfolios is the magnitude of the risk, σi. The greater is the σi, the greater would be

the risk premium and the expected return on the portfolio.

It is important to recognize that the CML pricing holds only for efficient portfolio that lies along

its line. That is, only the most efficient in term of risk-reducing potential, portfolio that are

constructed of combination of risk free asset f and market portfolio M lie along the CML. All

individual securities and inefficient portfolios lie under the curve. For the efficient set of

portfolios along the CML, their total risk, as measured by σi, represents their systematic risk,

since all unsystematic risk has been diversified. Thus the efficient frontier not only produce the

set of optimal portfolio in terms of risk and expected returns, but it also represents portfolios that

are efficient in a risk/expected return sense, but it also represent zero unsystematic risk

portfolios. Since total risk σi, is the sum of systematic risk, σi, can be thought of as either total

risk or systematic risk. Thus, the CML states that the appropriate measure of risk that is to be

priced for these efficient portfolios is the level of systematic risk present I these portfolios.

CML Vs CAPM

The CML set forth the relationship between expected return and risk for efficient well-

diversified portfolios, whereas the CAPM is a pricing relationship that is applicable to all

securities and portfolios, whether efficient or inefficient. In both the CML and CAPM, the

appropriate measure of risk is the systematic portion of total risk. However CML assumes well

diversified Portfolios, its systematic risk, since there is no unsystematic risk present in well

diversified portfolios. The CAPM utilizes the Beta, βi, or covariance σm, as its measure of

systematic risk.

Finally it is interesting to note that the CML relationship is a special case of CAPM.

E(Ri) = Rf + [E(Rm) – Rf]β

Recall that βi = σi,m/σ2m = [Coeff. Of Cov (i,m)* σi*σm]/σ2m

Inserting the result into the above equation, we get,

E(Ri) = Rf + {[E(Rm) – Rf]coeff. Of Cov (i,m)*σi}/σm

For portfolios, whose returns are perfectly, positively correlated with the market and thus lie

along the CML, Coeff. Of Cov (I,m) = 1. Therefore for these portfolios, the CAPM relationship

reduces to:

E(Ri) = Rf + {[E(Rm) – Rf]/σm}σi

This is the CAPM relationship. Thus, the CAPM is the general risk/ expected pricing relationship

for all assets, whereas the CML is a special case of the CAPM and represents an equilibrium

pricing relationship that holds only for widely diversified, efficient portfolios.

SECURITY MARKET LINE

Security market line or market Line is another way to perceive risk return equilibrium

relationship. With expected return on X axis and β on Y axis, if the market portfolio is drawn and

the line is extended to risk free rate of return, SML is obtained. It is a line which passes through

risk fre return and expected return of a market portfolio.

The CML specifies the equilibrium relationship between expected risk and return for efficient

portfolios. It cannot be used to evaluate the equilibrium relationship on single securities because

the standard deviation of the securities return is not the proper measure of security’s true risk,

since the risk of the security depend on the portfolio to which it is added and must reflect the co

variability of the security’s return with the other asset of the portfolio. Security Market Line

(SML) is broader and able to treat individual securities as well as portfolios. It expresses the

return that should be expected in terms of securities (or portfolios).

The SML expresses the expected return on any securities or portfolio in terms of the systematic

risk of the asset, beta.

E(Rp) = Rf + β(E(Rm) – Rf)

As with CML, there is a risk free and risk component, risk premium of an asset i, βi(E(Rm)-Rf).

But SML explains the risk of securities in relative terms through Beta whereas the CML treats

the total portfolio risk. In addition SML treats any security whereas CML treats efficient

portfolio only.

SML also describes whether a particular asset is defensive or aggressive. As the beta of the

markets is one, it serves as a reference to assess the assets. Assets for which the beta of the

market is less than 1 are called defensive assets and those whose beta is greater than one are

called aggressive assets.

Figure 7: Security Market Line

SML also describes whether a particular asset is underpriced, overpriced or correctly priced. In

the above figure, asset A is plotted above the SML line. It is expected to earn higher return,

corresponding to the risk level. It is undervalued because expected rate of return is higher than

the SML- based return. On the other hand, asset B is said to be overvalued as the expected rate of

return is lower than the SML based return.

Chapter - ii

CAPM: Literature

review by different

scholars

Capital Asset Pricing Model: Evidence and Theory

The Logic of the CAPMThe CAPM builds on the model of portfolio choice developed by Harry Markowitz (1959). In

Markowitz’s model, an investor selects a portfolio at time t _ 1 that produces a stochastic return

at t. The model assumes investors are risk averse and, when choosing among portfolios, they care

only about the mean and variance of their one-period investment return. As a result, investors

choose “mean variance- efficient” portfolios, in the sense that the portfolios 1) minimize the

variance of portfolio return, given expected return, and 2) maximize expected return, given

variance. Thus, the Markowitz approach is often called a “mean variance model.”

The portfolio model provides an algebraic condition on asset weights in mean variance- efficient

portfolios. The CAPM turns this algebraic statement into a testable prediction about the relation

between risk and expected return by identifying a portfolio that must be efficient if asset prices

are to clear the market of all assets.

Sharpe (1964) and Lintner (1965) add two key assumptions to the Markowitz model to identify a

portfolio that must be mean-variance-efficient. The first assumption is complete agreement:

given market clearing asset prices at t _ 1, investors agree on the joint distribution of asset

returns from t _ 1 to t. And this distribution is the true one—that is, it is the distribution from

which the returns we use to test the model are drawn. The second assumption is that there is

borrowing and lending at a risk-free rate, which is the same for all investors and does not

depend on the amount borrowed or lent.

Figure 1 describes portfolio opportunities and tells the CAPM story. The horizontal axis shows

portfolio risk, measured by the standard deviation of portfolio return; the vertical axis shows

expected return. The curve abc, which is called the minimum variance frontier, traces

combinations of expected return and risk for portfolios of risky assets that minimize return

variance at different levels of expected return. (These portfolios do not include risk-free

borrowing and lending.)

The tradeoff between risk and expected return for minimum variance portfolios is apparent. For

example, an investor who wants a high expected return, perhaps at point a, must accept high

volatility. At point T, the investor can have an intermediate expected return with lower volatility.

If there is no risk-free borrowing or lending, only portfolios above b along abc are mean-

variance-efficient, since these portfolios also maximize expected return, given their return

variances.

Adding risk-free borrowing and lending turns the efficient set into a straight line. Consider a

portfolio that invests the proportion x of portfolio funds in a risk-free security and 1 _ x in some

portfolio g. If all funds are invested in the risk-free security—that is, they are loaned at the risk-

free rate of interest—the result is the point Rf in Figure 1, a portfolio with zero variance and a

risk-free rate of return. Combinations of risk-free lending and positive investment in g plot on the

straight line between Rf and g. Points to the right of g on the line represent borrowing at the risk-

free rate, with the proceeds from the borrowing used to increase investment in portfolio g. In

short, portfolios that combine risk-free lending or borrowing with some risky portfolio g plot

along a straight line from Rf through g in Figure

Figure 8: Investment Oppurtunity

To obtain the mean-variance-efficient portfolios available with risk-free borrowing and lending,

one swings a line from Rf in Figure 1 up and to the left as far as possible, to the tangency

portfolio T. We can then see that all efficient portfolios are combinations of the risk-free asset

(either risk-free borrowing or lending) and a single risky tangency portfolio, T. This key result is

Tobin’s (1958) “separation theorem.”

The punch line of the CAPM is now straightforward. With complete agreement about

distributions of returns, all investors see the same opportunity set, and they combine the same

risky tangency portfolio T with risk-free lending or borrowing. Since all investors hold the same

portfolio T of risky assets, it must be the value-weight market portfolio of risky assets.

Specifically, each risky asset’s weight in the tangency portfolio, which we now call M (for the

“market”), must be the total market value of all outstanding units of the asset divided by the total

market value of all risky assets. In addition, the risk-free rate must be set (along with the prices

of risky assets) to clear the market for risk-free borrowing and lending.

In short, the CAPM assumptions imply that the market portfolio M must be on the minimum

variance frontier if the asset market is to clear. This means that the algebraic relation that holds

for any minimum variance portfolio must hold for the market portfolio. Specifically, if there are

N risky assets,

(Minimum Variance Condition for M) E(Ri) = E(Rzm) + [E(Rm) – E(Rzm)]βi,m, i= 1,……, N.

In this equation, E(Ri) is the expected return on asset i, and _iM, the market beta of asset i, is the

covariance of its return with the market return divided by the variance of the market return,

(Market Beta) βim = cov(Ri , RM)/σ2(Rm). The first term on the right-hand side of the minimum

variance condition, E(RZM), is the expected return on assets that have market betas equal to zero,

which means their returns are uncorrelated with the market return. The second term is a risk

premium—the market beta of asset i, _iM, times the premium per unit of beta, which is the

expected market return, E(RM), minus E(RZM).

Since the market beta of asset i is also the slope in the regression of its return on the market

return, a common (and correct) interpretation of beta is that it measures the sensitivity of the

asset’s return to variation in the market return. But there is another interpretation of beta more in

line with the spirit of the portfolio model that underlies the CAPM. The risk of the market

portfolio, as measured by the variance of its return (the denominator of β iM), is a weighted

average of the covariance risks of the assets in M (the numerators of βiM for different assets).

Thus, βiM is the covariance risk of asset i in M measured relative to the average covariance risk

of assets, which is just the variance of the market return. In economic terms, β iM is proportional

to the risk each dollar invested in asset I contributes to the market portfolio. The last step in the

development of the Sharpe-Lintner model is to use the assumption of risk-free borrowing and

lending to nail down E(RZM), the expected return on zero-beta assets. A risky asset’s return is

uncorrelated with the market return—its beta is zero—when the average of the asset’s

covariances with the returns on other assets just offsets the variance of the asset’s return. Such a

risky asset is riskless in the market portfolio in the sense that it contributes nothing to the

variance of the market return.

When there is risk-free borrowing and lending, the expected return on assets that are

uncorrelated with the market return, E(RZM), must equal the risk-free rate, Rf. The relation

between expected return and beta then becomes the familiar Sharpe-Lintner CAPM equation,

E(Ri) = Rf + [ E(Rm)- Rf)]βm, I = 1,….., N.

In words, the expected return on any asset i is the risk-free interest rate, Rf , plus a risk premium,

which is the asset’s market beta, _iM, times the premium per unit of beta risk, E(RM) _ Rf.

Unrestricted risk-free borrowing and lending is an unrealistic assumption. Fischer Black (1972)

develops a version of the CAPM without risk-free borrowing or lending. He shows that the

CAPM’s key result—that the market portfolio is meanvariance- efficient—can be obtained by

instead allowing unrestricted short sales of risky assets. In brief, back in Figure 8, if there is no

risk-free asset, investors select portfolios from along the mean-variance-efficient frontier from a

to b. Market clearing prices imply that when one weights the efficient portfolios chosen by

investors by their (positive) shares of aggregate invested wealth, the resulting portfolio is the

market portfolio. The market portfolio is thus a portfolio of the efficient portfolios chosen by

investors. With unrestricted short selling of risky assets, portfolios made up of efficient

portfolios are themselves efficient. Thus, the market portfolio is efficient, which means that the

minimum variance condition for M given above holds, and it is the expected return-risk relation

of the Black CAPM.

The relations between expected return and market beta of the Black and Sharpe-Lintner versions

of the CAPM differ only in terms of what each says about E(RZM), the expected return on assets

uncorrelated with the market. The Black version says only that E(RZM) must be less than the

expected market return, so the premium for beta is positive. In contrast, in the Sharpe-Lintner

version of the model, E(RZM) must be the risk-free interest rate, Rf , and the premium per unit of

beta risk is E(RM) _ Rf.

The assumption that short selling is unrestricted is as unrealistic as unrestricted risk-free

borrowing and lending. If there is no risk-free asset and short sales of risky assets are not

allowed, mean-variance investors still choose efficient portfolios—points above b on the abc

curve in Figure 1. But when there is no short selling of risky assets and no risk-free asset, the

algebra of portfolio efficiency says that portfolios made up of efficient portfolios are not

typically efficient. This means the market portfolio, which is a portfolio of the efficient portfolios

chosen by investors, is not typically efficient. And the CAPM relation between expected return

and market beta is lost. This does not rule out predictions about expected return and betas with

respect to other efficient portfolios—if theory can specify portfolios that must be efficient if the

market is to clear. But so far this has proven impossible.

In short, the familiar CAPM equation relating expected asset returns to their market betas is just

an application to the market portfolio of the relation between expected return and portfolio beta

that holds in any mean-variance-efficient portfolio.

The efficiency of the market portfolio is based on many unrealistic assumptions, including

complete agreement and either unrestricted risk-free borrowing and lending or unrestricted short

selling of risky assets. But all interesting models involve unrealistic simplifications, which is

why they must be tested against data.

Recent Tests

Starting in the late 1970s, empirical work appears that challenges even the Black version of the

CAPM. Specifically, evidence mounts that much of the variation in expected return is unrelated

to market beta. The first blow is Basu’s (1977) evidence that when common stocks are sorted on

earnings-price ratios, future returns on high E/P stocks are higher than predicted by the CAPM.

Banz (1981) documents a size effect: when stocks are sorted on market capitalization (price

times shares outstanding), average returns on small stocks are higher than predicted by the

CAPM. Bhandari (1988) finds that high debt-equity ratios (book value of debt over the market

value of equity, a measure of leverage) are associated with returns that are too high relative to

their market betas. Finally, Statman (1980) and Rosenberg, Reid and Lanstein (1985) document

that stocks with high book-to-market equity ratios (B/M, the ratio of the book value of a common

stock to its market value) have high average returns that are not captured by their betas.

There is a theme in the contradictions of the CAPM summarized above. Ratios involving stock

prices have information about expected returns missed by market betas. On reflection, this is not

surprising. A stock’s price depends not only on the expected cash flows it will provide, but also

on the expected returns that discount expected cash flows back to the present. Thus, in principle,

the cross-section of prices has information about the cross-section of expected returns. (A high

expected return implies a high discount rate and a low price.) The cross-section of stock prices is,

however, arbitrarily affected by differences in scale (or units). But with a judicious choice of

scaling variable X, the ratio X/P can reveal differences in the cross-section of expected stock

returns. Such ratios are thus prime candidates to expose shortcomings of asset pricing models.

Fama and French (1992) update and synthesize the evidence on the empirical failures of the

CAPM. Using the cross-section regression approach, they confirm that size, earnings-price, debt-

equity and book-to-market ratios add to the explanation of expected stock returns provided by

market beta. Fama and French (1996) reach the same conclusion using the time-series regression

approach applied to portfolios of stocks sorted on price ratios. They also find that different price

ratios have much the same information about expected returns. This is not surprising given that

price is the common driving force in the price ratios, and the numerators are just scaling

variables used to extract the information in price about expected returns.

Fama and French (1992) also confirm the evidence (Reinganum, 1981; Stambaugh, 1982;

Lakonishok and Shapiro, 1986) that the relation between average return and beta for common

stocks is even flatter after the sample periods used in the early empirical work on the CAPM.

The estimate of the beta premium is, however, clouded by statistical uncertainty (a large standard

error). Kothari, Shanken and Sloan (1995) try to resuscitate the Sharpe-Lintner CAPM by

arguing that the weak relation between average return and beta is just a chance result. But the

strong evidence that other variables capture variation in expected return missed by beta makes

this argument irrelevant. If betas do not suffice to explain expected returns, the market portfolio

is not efficient, and the CAPM is dead in its tracks.

Explanations: Irrational Pricing or Risk

Among those who conclude that the empirical failures of the CAPM are fatal, two stories

emerge. On one side are the behavioralists. Their view is based on evidence that stocks with high

ratios of book value to market price are typically firms that have fallen on bad times, while low

B/M is associated with growth firms (Lakonishok, Shleifer and Vishny, 1994; Fama and French,

1995). The behavioralists argue that sorting firms on book-to-market ratios exposes investor

overreaction to good and bad times. Investors overextrapolate past performance, resulting in

stock prices that are too high for growth (low B/M) firms and too low for distressed (high B/M,

so-called value) firms. When the overreaction is eventually corrected, the result is high returns

for value stocks and low returns for growth stocks. The second story for explaining the empirical

contradictions of the CAPM is that they point to the need for a more complicated asset pricing

model. The CAPM is based on many unrealistic assumptions. For example, the assumption that

investors care only about the mean and variance of one-period portfolio returns is extreme.

Merton’s (1973) intertemporal capital asset pricing model (ICAPM) is a natural extension of the

CAPM. The ICAPM begins with a different assumption about investor objectives. In the CAPM,

investors care only about the wealth their portfolio produces at the end of the current period. In

the ICAPM, investors are concerned not only with their end-of-period payoff, but also with the

opportunities they will have to consume or invest the payoff. Thus, when choosing a portfolio at

time t - 1, ICAPM investors consider how their wealth at t might vary with future state variables,

including labor income, the prices of consumption goods and the nature of portfolio

opportunities at t, and expectations about the labor income, consumption and investment

opportunities to be available after t.

Like CAPM investors, ICAPM investors prefer high expected return and low return variance.

But ICAPM investors are also concerned with the covariances of portfolio returns with state

variables. As a result, optimal portfolios are “multifactor efficient,” which means they have the

largest possible expected returns, given their return variances and the covariances of their returns

with the relevant state variables. Fama (1996) shows that the ICAPM generalizes the logic of the

CAPM. That is, if there is risk-free borrowing and lending or if short sales of risky assets are

allowed, market clearing prices imply that the market portfolio is multifactor efficient.

Moreover, multifactor efficiency implies a relation between expected return and beta risks, but it

requires additional betas, along with a market beta, to explain expected returns.

An ideal implementation of the ICAPM would specify the state variables that affect expected

returns. Fama and French (1993) take a more indirect approach, perhaps more in the spirit of

Ross’s (1976) arbitrage pricing theory. They argue that though size and book-to-market equity

are not themselves state variables, the higher average returns on small stocks and high book-to-

market stocks reflect unidentified state variables that produce undiversifiable risks (covariances)

in returns that are not captured by the market return and are priced separately from market betas.

In support of this claim, they show that the returns on the stocks of small firms covary more with

one another than with returns on the stocks of large firms, and returns on high book-to-market

(value) stocks covary more with one another than with returns on low book-to-market (growth)

stocks. Fama and French (1995) show that there are similar size and book-to-market patterns in

the covariation of fundamentals like earnings and sales. Based on this evidence, Fama and

French (1993, 1996) propose a three-factor model for expected returns,

Three Factor Model:- E(Rit) – Rf = β[E(Rmt) – Rf] + βisE(SMBt) + βih HMLt +eit

The three-factor model is now widely used in empirical research that requires a model of

expected returns The three-factor model is hardly a panacea. Its most serious problem is the

momentum effect of Jegadeesh and Titman (1993). Stocks that do well relative to the market

over the last three to twelve months tend to continue to do well for the next few months, and

stocks that do poorly continue to do poorly. This momentum effect is distinct from the value

effect captured by book-to-market equity and other price ratios. Moreover, the momentum effect

is left unexplained by the three-factor model, as well as by the CAPM. Following Carhart (1997),

one response is to add a momentum factor (the difference between the returns on diversified

portfolios of short-term winners and losers) to the three-factor model.

Chapter – iii

Capm and apt:

Comparisons and

interrelationship

Multifactor Models and Arbitrage Pricing Theory (APT)

All the multifactor asset pricing models try to explore the risk contribution of systematic factors

effective on expected returns by constructing linear multiple regression equations that are

expected to best represent the relationship between risk factors and asset returns. The most

important one of the multifactor prediction models is the Arbitrage

Pricing Theory which was developed by Stephen A. Ross (1976: 341-360). This theory has been

considered an alternative to the Capital Asset Pricing Model and does not presume the presence

of a fully efficient market. But, there are a few assumptions mentioned below on which the

theory is based:

a) The capital market fits the conditions of perfect competition,

b) Investors are rational under certainty conditions, which means that they prefer more wealth to

be less,

c) The stochastic process explaining how asset returns exist can be explained by a linear K-factor

model,

d) Market does not allow for arbitrage opportunities arising from the violation of the law of one

price. If any arbitrage opportunity existed, investors would immediately react in order to benefit

from that situation by buying the asset in the market where it has been undervalued and then

selling where the asset has been relatively overvalued. All these attempts would make the

existing arbitrage opportunity suddenly disappear.

Ross starts his model explanation with a single factor model resembling the

CAPM and formulates the risk-return relationship using the following single equation (Bolak,

2001: 270):

ri = α i + β iF +e I ------------------------------------ (6)

In the equation, the actual rate of return is abbreviated by ri, αi refers to the expected rate of

return on the asset i, F denotes systematic risk factor, and βi represents the sensitivity of the

asset’s returns to the risk factor. The prediction error arising from the effect of idiosyncratic

factors is symbolized with ei.

The theory assumes that all the firm-specific risk factors (ei) can be fully eliminated if a portfolio

has been sufficiently diversified and therefore systematic risk component becomes the only case

for portfolios. The return estimation equation turns out to be in a new form presented below.

rp = E(Rp) + βp F --------------------------------- (7)

It is a simplifying assumption to say that there is only one systematic risk factor affecting asset

returns. To get closer to the reality, the theory suggests the use of multiple variables as

determinants on systematic risk in order to cover all the effects of potential systematic risk

factors. In most of the relevant studies performed, major macroeconomic indicators such as

interest rate, inflation, gross domestic product (GDP), have been preferred as the representatives

of potential systematic risk factors.

A typical multifactor APT Model is similar to linear multiple regression models. Expected return

on any financial asset is finally formulated as in the Equation 8:

E(Rp) = rf + ∑βp,i.(E(Rfi) – rf) ------------------------ ( 8)

In the above equation, E (RP) is the expected rate of return on portfolio, E (RFi) is referred to as

the expected rate of return on ith factor portfolio, βp,i constitutes the sensitivity of portfolio’s

return to the factor portfolio i, and rf represents risk free rate of return. The difference term in

parenthesis is called the risk premium of the factor portfolio.

A factor portfolio is a portfolio whose return distribution has no correlation (zero correlation)

with those of other factor portfolios. This situation is seen as a bottleneck for the implementation

of the theory because examining separate factor portfolios not correlated to each other is so

difficult a business to succeed. The exploration of not correlated factor portfolios is a task similar

to searching for explanatory variables fulfilling the statistical requirement of absence of linear

multicollinearity (Maddala, 2004: 278).

The second remarkable theory in the relevant literature employing multifactor modeling

procedure is the Three-Factor Model proposed by Fama and French (1993: 3- 56). The Three-

Factor Model is another replication of the multifactor APT models. As different from the APT

models, three predetermined systematic risk factor are considered; market risk premium (the

return of market portfolio in excess of risk free return), the difference between the mean rates of

return of small and big-scaled companies, and the difference between the average return of the

companies with high book to market ratios and the average return of those with low book to

market ratios (Hu, 2007: 113).

The presence of two main theories, APT and CAPM, in the field of asset pricing has cast strong

concern in investigating the superiority of these models to each other. Following the introduction

of these theories to the literature, a huge number empirical studies were carried out aiming to

compare their performance. Most of the findings reported in these studies have provided results

favoring the APT models against the CAPM even in the emerging markets. There are few studies

that suggest the superiority of CAPM over APT.

The multifactor APT models could provide better results than the CAPM in the Indian Stock

Market on monthly and weekly returns data. In another research carried out by Sun and Zhang

(2001: 617) in America using the data of eight forestry-related companies’ financial

performance, some empirical results were reported favoring the better performance of the APT

models as compared to CAPM. As a unique study arguing the applicability of the APT models,

Altay (2005: 217 – 237) pointed out that unexpected interest rate and inflation changes proved to

be statistically significant determinants on stock returns in Germany. However, he also stated

that the same judgment couldn’t be made for the stock market in Turkey.

Comparison of CAPM and APT

CAPM requires something more than APT to support its prediction that sensitivity to one

economic force- the force reflected in the returns to the market portfolio- is the only determinant

of expected or required return on an asset. On the other hand, APT views several economic

forces as the systematic determinants of actual returns on an asset.

The development of the CAPM risk-return relationship is more involved than the APT

relationship. But the relationship itself is the same as APT would have if there was only one

pervasive economic force influencing the return generation process.

CAPM’s assumption that sensitivity to the market is the only required indicator of risk, and thus

the only determinant of expected or required return, may perhaps be good enough even if APT

provides a better description of how markets generate returns. This is because, different

sensitivities of each asset to the collection of economic forces could ‘net out’, so that sensitivity

to a single market index would do as good a job as any multi-factor model in explaining the

expected return differences among assets. Further, the factors other than the market index

considered under APT would also have influences that market index as it would have affected

the security. If it were so, the market index would capture that effect too. Hence, if the

unanticipated changes in economic factor were highly correlated, then stock sensitivity to any

one factor like market index could well represent sensitivity to all factors. In either case, the

CAPM model would be a satisfactory proxy for the multi-index model. But, it seems more than

likely those stocks have different sensitivity to various economic factors and that unanticipated

changes in economic factors do not have much correlation.

Apart from these, the CAPM is characterized by simplicity as it expresses, the pricing

relationship in terns if just two elements – the riskless asset ( or the minimum- variance zero beta

security) and the market portfolio. However, empirical tests of the basic CAPM have not been

fully supportive of the theory. While most tests indicate a relatively linear relationship between

realized returns and their systematic risks, the empirical results, in general indicate an intercept

that exceeds the returns on the riskless asset and a market risk premium that is lower than its

theoretical value, Furthermore, even though the zero beta version of the basic CAPM provides a

theoretical model that is consistent with the empirical anomalies of the basic CAPM, empirical

tests of any form of the CAPM is questioned by many researchers owing to the reliance of the

theory on an unobservable market portfolio.

To sum up, APT does not overcome all of the objectives, and it has some shortcomings of its

own. Nevertheless, it is the first model to challenge CAPM and has a real chance of replacing it.

The feature that makes APT of greater potential value to decision makers lies in its attempt to

explain the risk-return relationship using several factors instead of a single market index.

Chapter - IV

Testing Capital Asset Pricing Model in Indian

Stock market

TESTING THE CAPITAL ASSET PRICING MODEL

INTRODUCTIONInvestors and financial researchers have paid considerable attention during the last few years to

the new equity markets that have emerged around the world. This new interest has undoubtedly

been spurred by the large, and in some cases extraordinary, returns offered by these markets.

Practitioners all over the world use a plethora of models in their portfolio selection process and

in their attempt to assess the risk exposure to different assets.

One of the most important developments in modern capital theory is the capital asset pricing

model (CAPM) as developed by Sharpe [1964], Lintner [1965] and Mossin [1966]. CAPM

suggests that high expected returns are associated with high levels of risk. Simply stated, CAPM

postulates that the expected return on an asset above the risk-free rate is linearly related to the

non-diversifiable risk as measured by the asset’s beta. Although the CAPM has been

predominant in empirical work over the past 30 years and is the basis of modern portfolio theory,

accumulating research has increasingly cast doubt on its ability to explain the actual movements

of asset returns.

The purpose is to examine thoroughly if the CAPM holds true in the capital market of India.

Tests are conducted for a period of ten years (2001-2011), which is characterized by intense

return volatility (covering historically high returns for the Indian Stock market as well as

significant decrease in asset returns over the examined period). These market return

characteristics make it possible to have an empirical investigation of the pricing model on

differing financial conditions thus obtaining conclusions under varying stock return volatility.

Existing financial literature on the Athens stock exchange is rather scanty and it is the goal of

this study to widen the theoretical analysis of this market by using modern finance theory and to

provide useful insights for future analyses of this market.

Empirical appraisal of the model and competing studies of the model’s validity

Empirical appraisal of CAPM

Since its introduction in early 1960s, CAPM has been one of the most challenging topics in

financial economics. Almost any manager who wants to undertake a project must justify his

decision partly based on CAPM. The reason is that the model provides the means for a firm to

calculate the return that its investors demand. This model was the first successful attempt to

show how to assess the risk of the cash flows of a potential investment project, to estimate the

project’s cost of capital and the expected rate of return that investors will demand if they are to

invest in the project.

The model was developed to explain the differences in the risk premium across assets.

According to the theory these differences are due to differences in the riskiness of the returns on

the assets. The model states that the correct measure of the riskiness of an asset is its beta and

that the risk premium per unit of riskiness is the same across all assets. Given the risk free rate

and the beta of an asset, the CAPM predicts the expected risk premium for an asset.

The theory itself has been criticized for more than 30 years and has created a great academic

debate about its usefulness and validity. In general, the empirical testing of CAPM has two broad

purposes

(Baily et al, [1998]): (i) to test whether or not the theories should be rejected (ii) to provide

information that can aid financial decisions. To accomplish (i) tests are conducted which could

potentially at least reject the model. The model passes the test if it is not possible to reject the

hypothesis that it is true.

Methods of statistical analysis need to be applied in order to draw reliable conclusions on

whether the model is supported by the data. To accomplish (ii) the empirical work uses the

theory as a vehicle for organizing and interpreting the data without seeking ways of rejecting the

theory. This kind of approach is found in the area of portfolio decision-making, in particular with

regards to the selection of assets to the bought or sold. For example, investors are advised to buy

or sell assets that according to

CAPM are underpriced or overpriced. In this case empirical analysis is needed to evaluate the

assets, assess their riskiness, analyze them, and place them into their respective categories. A

second illustration of the latter methodology appears in corporate finance where the estimated

beta coefficients are used in assessing the riskiness of different investment projects. It is then

possible to calculate “hurdle rates” that projects must satisfy if they are to be undertaken.

This part of the paper focuses on tests of the CAPM since its introduction in the mid 1960’s, and

describes the results of competing studies that attempt to evaluate the usefulness of the capital

asset pricing model (Jagannathan and McGrattan [1995]).

The classic support of the theory

The model was developed in the early 1960’s by Sharpe [1964], Lintner [1965] and Mossin

[1966]. In its simple form, the CAPM predicts that the expected return on an asset above the

risk-free rate is linearly related to the non-diversifiable risk, which is measured by the asset’s

beta.

One of the earliest empirical studies that found supportive evidence for CAPM is that of Black,

Jensen and Scholes [1972]. Using monthly return data and portfolios rather than individual

stocks, Black et al tested whether the cross-section of expected returns is linear in beta. By

combining securities into portfolios one can diversify away most of the firm-specific component

of the returns, thereby enhancing the precision of the beta estimates and the expected rate of

return of the portfolio securities. This approach mitigates the statistical problems that arise from

measurement errors in beta estimates. The authors found that the data are consistent with the

predictions of the CAPM i.e. the relation between the average return and beta is very close to

linear and that portfolios with high (low) betas have high (low) average returns.

Another classic empirical study that supports the theory is that of Fama and McBeth [1973]; they

examined whether there is a positive linear relation between average returns and beta. Moreover,

the authors investigated whether the squared value of beta and the volatility of asset returns can

explain the residual variation in average returns across assets that are not explained by beta alone

Challenges to the validity of the theory

In the early 1980s several studies suggested that there were deviations from the linear CAPM

risk return trade-off due to other variables that affect this tradeoff. The purpose of the above

studies was to find the components that CAPM was missing in explaining the risk-return trade-

off and to identify the variables that created those deviations.

Banz [1981] tested the CAPM by checking whether the size of firms can explain the residual

variation in average returns across assets that remain unexplained by the CAPM’s beta. He

challenged the CAPM by demonstrating that firm size does explain the cross sectional-variation

in average returns on a particular collection of assets better than beta. The author concluded that

the average returns on stocks of small firms (those with low market values of equity) were higher

than the average returns on stocks of large firms (those with high market values of equity). This

finding has become known as the size effect.

The research has been expanded by examining different sets of variables that might affect the

risk return tradeoff. In particular, the earnings yield (Basu [1977]), leverage, and the ratio of a

firm’s book value of equity to its market value (e.g. Stattman [1980], Rosenberg, Reid and

Lanstein [1983] and Chan, Hamao, Lakonishok [1991]) have all been utilized in testing the

validity of CAPM. was to support the view that although the data may suggest deviations from

CAPM, these deviations are not so important as to reject the theory.

However, this idea has been challenged by Fama and French [1992]. They showed that Banz’s

findings might be economically so important that it raises serious questions about the validity of

the CAPM. Fama and French [1992] used the same procedure as Fama and McBeth [1973] but

arrived at very different conclusions. Fama and McBeth find a positive relation between return

and risk while Fama and French find no relation at all.

The Academic Debate Continues

The Fama and French [1992] study has itself been criticized. In general the studies responding to

the Fama and French challenge by and large take a closer look at the data used in the study.

Kothari,

Shaken and Sloan [1995] argue that Fama and French’s [1992] findings depend essentially on

how the statistical findings are interpreted.

Amihudm, Christensen and Mendelson [1992] and Black [1993] support the view that the data

are too noisy to invalidate the CAPM. In fact, they show that when a more efficient statistical

method is used, the estimated relation between average return and beta is positive and

significant. Black [1993] suggests that the size effect noted by Banz [1981] could simply be a

sample period effect i.e. the size effect is observed in some periods and not in others.

Despite the above criticisms, the general reaction to the Fama and French [1992] findings has

been to focus on alternative asset pricing models. Jagannathan and Wang [1993] argue that this

may not be necessary. Instead they show that the lack of empirical support for the CAPM may be

due to the inappropriateness of basic assumptions made to facilitate the empirical analysis. For

example, most empirical tests of the CAPM assume that the return on broad stock market indices

is a good proxy for the return on the market portfolio of all assets in the economy. However,

these types of market indexes do not capture all assets in the economy such as human capital.

Other empirical evidence on stock returns is based on the argument that the volatility of stock

returns is constantly changing. When one considers a time-varying return distribution, one must

refer to the conditional mean, variance, and covariance that change depending on currently

available information. In contrast, the usual estimates of return, variance, and average squared

deviations over a sample period, provide an unconditional estimate because they treat variance as

constant over time.

The most widely used model to estimate the conditional (hence time- varying) variance of stocks

and stock index returns is the generalized autoregressive conditional heteroscedacity (GARCH)

model pioneered by Robert.F.Engle.

To summarize, all the models above aim to improve the empirical testing of CAPM. There have

also been numerous modifications to the models and whether the earliest or the subsequent

alternative models validate or not the CAPM is yet to be determined.

Sample Selection and Data

Sample Selection

The study covers the period from January 2001 to January 2011. This time period was chosen

because it is characterized by intense return volatility with historically high and low returns for

the Indian Stock Market.

The selected sample consists of 100 stocks that are included in a sampling frame to make the

portfolio. The stock varies in size, P/E ratio, financial Leverage. All the securities included in the

portfolio are traded on the Bombay Stock Exchange on the continuous basis throughout the full

BSE trading day.

For the Purpose of the study, 50 stocks were selected from the pool of securities of 100 stocks. I

have selected only 50 stocks out of 100 securities because of various constraints like Data

unavailability, De-listing of the stocks from the Index etc. Each series of stocks consists of 10

observations of yearly closing prices. The selection of stocks varies on the basis of market

capitalization, P/E, leverage etc.

Data Selection

The study uses weekly stock returns from 50 companies listed on the Bombay Stock Exchange

for the period of January 2001 to January 2011. The data are obtained from BSE Stock Data

Base and from PROWESS Database

In order to obtain better estimates of the value of the beta coefficient, the study utilizes yearly

stock returns. Returns calculated using a longer time period (e.g. yearly) might result in changes

of beta over the examined period introducing biases in beta estimates. On the other hand, high

frequency data such as daily observations covering a relatively short and stable time span can

result in the use of very noisy data and thus yield inefficient estimates.

All stock returns used in the study are adjusted for dividends as required by the CAPM.

The BSE Composite Share index is used as a proxy for the market portfolio. This index is a

market value weighted index, is comprised of the 60 most highly capitalized shares of the main

market, and reflects general trends of the Indian stock market.

Furthermore, the Indian Government Bonds is used as the proxy for the risk-free asset. The

yields were obtained from the Reserve Bank of India website. The yield on the Indian

Government Bonds is specifically chosen as the benchmark that better reflects the short-term

changes in the Indian financial markets.

MethodologyThe first step was to estimate a beta coefficient for each stock using weekly returns during the

period of January 1998 to December 2002. The beta was estimated by regressing each stock’s

yearly return against the market index according to the following equation:

Rit -R ft = αi +βi (Rmt - Rft) + eit

Where,

R it is the return on stock i (i=1…100),

R ft is the rate of return on a risk-free asset,

R mt is the rate of return on the market index,

βi is the estimate of beta for the stock i , and

eit is the corresponding random disturbance term in the regression equation.

[Equation 1 could also be expressed using excess return notation, where ( - )= it ft it R R r and

ft mt ( - )=r mt R R ]

Here the beta is calculated for all the individual stocks for 10 years using Regression tool in MS

Excel. The returns of individual stock are calculated on the basis of:

Rit = [Pit – Pi (t-1)]/ P(t-1)i

Where,

Rit = Return of individual stock i for time period (t = 1 …… 10)

Pit = closing price of the stocks of current year

Pi(t-1) = closing price of the stock of previous year.

Empirical results and Interpretation of the findings

The first part of the methodology required the estimation of betas for individual stocks by using

observations on rates of return for a sequence of dates. Useful remarks can be derived from the

results of this procedure, for the assets used in this study. The beta of the individual security is

given below:

Table 1: Beta of individual securities (Companies)Company Beta Company BetaABB 3.989952 PNB 1.121698ACC 0.91 Ranbaxy 0.8134Ambuja Cements 0.68 HCL 1.435777BHEL 1.28 Reliance industries 0.875433BPCL 0.62 Satyam 0.336327Bharti Airtel 1.21 Bajaj Electricals 2.08628Cipla 0.51 Wipro 2.08628Glaxosmith 0.46 Zee 1.073134Grasim 1.53 Unitech 1.466225HDFC 0.87 Tata Comm 0.466867Hero Honda 0.70 Tata Power 1.34148Hindalco Industry 1.48625 Tata Motors 2.300307GAIL 1.660744 Sun Pharma 0.365135Dr. Reddy Lab 0.83813 Sterlite 4.039579HP 0.348161 SAIL 2.687173HUL 0.090135 SBI 0.928988Housing Dev Fin Cor 1.01929 Siemen 1.705396ITC 0.417147 Abbot 0.641285ICICI Bank 1.202891 Adani Enterprise 2.264626Infosys Tech 0.775173 Raymonds 1.126948Larsen & Turbo 1.583617 Novartis 0.744671Mahanagar Telephone 0.550618 Aditya Birla 1.44455M&M 2.298222 Titan 1.040378ONGC 0.761257 Kotak 1.535358Maruti Suzuki 1.127525 Berger Paints 1.101799

The range of the estimated stock betas is between 0.090 the minimum and 4.039 the maximum

with a standard deviation of 0.2240. Most of the beta coefficients for individual stocks are

statistically significant at a 95% level and all estimated beta coefficients are statistical significant

at a 90% level.

HYPOTHESIS

According to the CAPM theory, the theory indicates higher risk (beta) is associated with higher

returns which is the basic hypothesis of the study.

Higher β = Higher Rit The beta and return of individual securities are:

Table 2: Average Return and Beta of all SecuritiesCompany Beta Return Company Beta ReturnSterlite 4.039579 89.23636 Zee 1.073134 4.354545ABB 3.989952 31.73727 Titan 1.040378 70.20909

SAIL 2.687173 78.88545 Housing Dev Fin Cor 1.01929 17.43364

Tata Motors 2.300307 67.56727 SBI 0.928988 33.60636

M&M 2.298222 48.07727 ACC 0.91 26.65273

Adani Enterprise 2.264626 60.55818 Reliance industries 0.875433 20.62818

Bajaj Electricals 2.08628 52.48091 HDFC 0.87 28.08

Wipro 2.08628 -0.57455 Dr. Reddy Lab 0.83813 12.5

Siemen 1.705396 47.84455 Ranbaxy 0.8134 9.950909

GAIL 1.660744 40.55909 Infosys Tech 0.775173 5.729091

Larsen & Turbo 1.583617 48.48182 ONGC 0.761257 34.27909

Kotak 1.535358 49.68364 Novartis 0.744671 11.29909

Grasim 1.53 38.42727 Hero Honda 0.70 22.60364

Hindalco Industry 1.48625 23.95455 Ambuja Cements 0.68 13.06727

Unitech 1.466225 43.53273 Abbot 0.641285 18.54091

Aditya Birla 1.44455 42.79364 BPCL 0.62 21.96818

HCL 1.435777 19.25364 Mahanagar Telephone 0.550618 -6.72

Tata Power 1.34148 40.65091 Cipla 0.51 0.482727

BHEL 1.28 38.89273 Tata Comm 0.466867 9.089091

Bharti Airtel 1.21 56.46 Glaxosmith 0.46 20.02182

ICICI Bank 1.202891 30.13 ITC 0.417147 -0.27636

Maruti Suzuki 1.127525 24.91 Sun Pharma 0.365135 3.819091

Raymonds 1.126948 24.12455 HP 0.348161 12.87545

PNB 1.121698 39.17364 Satyam 0.336327 -6.84

Berger Paints 1.101799 18.42909 HUL 0.090135 4.231818

The result of the study supports the hypothesis. The beta coefficient o the 50 securities indicate

that higher beta portfolio are related with higher return. For example: the highest beta in the

indices is of Sterlite (i.e β = 4.039) and Sterlite also provide highest return among all other

security (i.e Rit = 89.23).

CONCLUSION The article examined the validity of the CAPM for the Greek stock market. The study used

monthly stock returns from 100 companies listed on the Athens stock exchange from January

2001 to January 2011.

The findings of the article are not supportive of the theory’s basic hypothesis that higher risk

(beta) is associated with a higher level of return. In order to diversify away most of the firm-

specific part of returns thereby enhancing the precision of the beta estimates, the securities where

combined into portfolios to mitigate the statistical problems that arise from measurement errors

in individual beta estimates.

The model does explain, however, excess returns. The results obtained lend support to the linear

structure of the CAPM equation being a good explanation of security returns. The high value of

the estimated correlation coefficient between the intercept and the slope indicates that the model

used, explains excess returns. However, the fact that the intercept has a value around zero

weakens the above explanation.

CHAPTER- v

EFFICIENT MARKET HYPOTHESIS ANF ITS

ANAMOLIES

EFFICIENT MARKET HYPOTHESIS AND ITS ANAMOLIES

INTRODUCTION

Many investors try to identify securities that are undervalued, and are expected to increase in

value in the future, and particularly those that will increase more than others.

Many investors, including investment managers, believe that they can select securities that will

outperform the market. They use a variety of forecasting and valuation techniques to aid them in

their investment decisions. Obviously, any edge that an investor possesses can be translated into

substantial profits. If a manager of a mutual fund with $10 billion in assets can increase the

fund’s return, after transaction costs, by 1/10th of 1 percent, this would result in a $10 million

gain. The EMH asserts that none of these techniques are effective (i.e., the advantage gained

does not exceed the transaction and research costs incurred), and therefore no one can

predictably outperform the market.

Arguably, no other theory in economics or finance generates more passionate discussion between

its challengers and proponents. For example, noted Harvard financial economist Michael Jensen

writes “there is no other proposition in economics which has more solid empirical evidence

supporting it than the Efficient Market Hypothesis,” while investment maven Peter Lynch claims

“Efficient markets? That’s a bunch of junk, crazy stuff” (Fortune, April 1995).

The efficient markets hypothesis (EMH) suggests that profiting from predicting price movements

is very difficult and unlikely. The main engine behind price changes is the arrival of new

information. A market is said to be “efficient” if prices adjust quickly and, on average, without

bias, to new information. As a result, the current prices of securities reflect all available

information at any given point in time. Consequently, there is no reason to believe that prices

are too high or too low. Security prices adjust before an investor has time to trade on and profit

from a new a piece of information.

The key reason for the existence of an efficient market is the intense competition among

investors to profit from any new information. The ability to identify over- and underpriced

stocks is very valuable (it would allow investors to buy some stocks for less than their “true”

value and sell others for more than they were worth). Consequently, many people spend a

significant amount of time and resources in an effort to detect "mispriced" stocks. Naturally, as

more and more analysts compete against each other in their effort to take advantage of over- and

under-valued securities, the likelihood of being able to find and exploit such mis-priced

securities becomes smaller and smaller. In equilibrium, only a relatively small number of

analysts will be able to profit from the detection of mispriced securities, mostly by chance. For

the vast majority of investors, the information analysis payoff would likely not outweigh the

transaction costs.

The most crucial implication of the EMH can be put in the form of a slogan: Trust market prices!

At any point in time, prices of securities in efficient markets reflect all known information

available to investors. There is no room for fooling investors, and as a result, all investments in

efficient markets are fairly priced, i.e. on average investors get exactly what they pay for. Fair

pricing of all securities does not mean that they will all perform similarly, or that even the

likelihood of rising or falling in price is the same for all securities. According to capital markets

theory, the expected return from a security is primarily a function of its risk. The price of the

security reflects the present value of its expected future cash flows, which incorporates many

factors such as volatility, liquidity, and risk of bankruptcy.

However, while prices are rationally based, changes in prices are expected to be random and

unpredictable, because new information, by its very nature, is unpredictable.

Therefore stock prices are said to follow a random walk.

THREE VERSIONS OF THE EFFICIENT MARKETS HYPOTHESISThe efficient markets hypothesis predicts that market prices should incorporate all available

information at any point in time. There are, however, different kinds of information that

influence security values. Consequently, financial researchers distinguish among three versions

of the Efficient Markets Hypothesis, depending on what is meant by the term “all available

information”.

Weak Form Efficiency

The weak form of the efficienct markets hypothesis asserts that the current price fully

incorporates information contained in the past history of prices only. That is, nobody can detect

mispriced securities and “beat” the market by analyzing past prices. The weak form of the

hypothesis got its name for a reason – security prices are arguably the most public as well as the

most easily available pieces of information. Thus, one should not be able to profit from using

something that “everybody else knows”. On the other hand, many financial analysts attempt to

generate profits by studying exactly what this hypothesis asserts is of no value - past stock price

series and trading volume data. This technique is called technical analysis.

The empirical evidence for this form of market efficiency, and therefore against the value of

technical analysis, is pretty strong and quite consistent. After taking into account transaction

costs of analyzing and of trading securities it is very difficult to make money on publicly

available information such as the past sequence of stock prices.

Semi-strong Form Efficiency

The semi-strong-form of market efficiency hypothesis suggests that the current price fully

incorporates all publicly available information. Public information includes not only past prices,

but also data reported in a company’s financial statements (annual reports, income statements,

filings for the Security and Exchange Commission, etc.), earnings and dividend announcements,

announced merger plans, the financial situation of company’s competitors, expectations

regarding macroeconomic factors (such as inflation, unemployment), etc. In fact, the public

information does not even have to be of a strictly financial nature. For example, for the analysis

of pharmaceutical companies, the relevant public information may include the current

(published) state of research in pain-relieving drugs. The assertion behind semi-strong market

efficiency is still that one should not be able to profit using something that “everybody else

knows” (the information is public).

Nevertheless, this assumption is far stronger than that of weak-form efficiency. Semi-strong

efficiency of markets requires the existence of market analysts who are not only financial

economists able to comprehend implications of vast financial information, but also

macroeconomists, experts adept at understanding processes in product and input markets.

Arguably, acquisition of such skills must take a lot of time and effort. In addition, the “public”

information may be relatively difficult to gather and costly to process. It may not be sufficient to

gain the information from, say, major newspapers and company-produced publications. One

may have to follow wire reports, professional publications and databases, local papers, research

journals etc. in order to gather all information necessary to effectively analyze securities.

As we will see later, financial researchers have found empirical evidence that is overwhelming

consistent with the semi-strong form of the EMH.

Strong Form Efficiency

The strong form of market efficiency hypothesis states that the current price fully incorporates all

existing information, both public and private (sometimes called inside information). The main

difference between the semi-strong and strong efficiency hypotheses is that in the latter case,

nobody should be able to systematically generate profits even if trading on information not

publicly known at the time. In other words, the strong form of EMH states that a company’s

management (insiders) are not be able to systematically gain from inside information by buying

company’s shares ten minutes after they decided (but did not publicly announce) to pursue what

they perceive to be a very profitable acquisition. Similarly, the members of the company’s

research department are not able to profit from the information about the new revolutionary

discovery they completed half an hour ago. The rationale for strong-form market efficiency is

that the market anticipates, in an unbiased manner, future developments and therefore the stock

price may have incorporated the information and evaluated in a much more objective and

informative way than the insiders. Not surprisingly, though, empirical research in finance has

found evidence that is inconsistent with the strong formof the EMH.

COMMON MISCONCEPTIONS ABOUT THE EMHAs was suggested in the introduction to this chapter, EMH has received a lot of attention since its

inception. Despite its relative simplicity, this hypothesis has also generated a lot of controversy.

After all, the EMH questions the ability of investors to consistently detect mispriced securities.

Not surprisingly, this implication does not sit very well with many financial analysts and active

portfolio managers.

Arguably, in liquid markets with many participants, such as stock markets, prices should adjust

quickly to new information in an unbiased manner. However, much of the criticism leveled at the

EMH is based on numerous misconceptions, incorrect interpretations, and myths about the

theory of efficient markets. We present some of the most persistent “myths” about the EMH

below.

Myth 1: EMH claims that investors cannot outperform the market. Yet we can see that some of

the successful analysts (such as George Soros, Warren Buffett, or Peter Lynch) are able to do

exactly that. Therefore, EMH must be incorrect.

EMH does not imply that investors are unable to outperform the market. We know that the

constant arrival of information makes prices fluctuate. It is possible for an investor to “make a

killing” if newly released information causes the price of the security the investor owns to

substantially increase. What EMH does claim, though, is that one should not be expected to

outperform the market predictably or consistently. It should be noted, though, that some

investors could outperform the market for 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” has a

50% chance of “beating the market”. For such an investor, the chance of outperforming the

market in each and every of the next ten years is then 0.5. However, the chance that there will be

at least one investor outperforming the market in each of the next 10 years sharply increases as

the number of investors trying to do exactly that rises.

In a group of 1,000 investors, the probability of finding one “ultimate winner” with a perfect 10-

year record is 63%. With a group of 10,000 investors, the chance of seeing at least one who

outperforms the market in every of next ten years is 99.99%, a virtual certainty. Each individual

investor may have dismal odds of beating the market for the next 10 years. Yet the likelihood of,

after the ten years, finding one very successful investor, even if he or she is investing purely

randomly – is very high if there are a sufficiently large number of investors. This is the case

with the state lottery, in which the probability of a given individual winning is virtually zero, but

the probability that someone will win is very high. The existence of a handful of successful

investors such as Messrs. Soros, Buffett, and Lynch is an expected outcome in a completely

random distribution of investors. The theory would only be threatened if you could identify who

those successful investors would be prior to their performance, rather than after the fact.

Myth 2: EMH claims that financial analysis is pointless and investors who attempt to research

security prices are wasting their time. “Throwing darts at the financial page will produce a

portfolio that can be expected to do as well as any managed by professional security analysts”.

Yet we tend to see that financial analysts are not “driven out of market”, which means that their

services are valuable. Therefore, EMH must be incorrect

There are two principal counter-arguments against the equivalency of “dart-throwing” and

professional analysis strategies. First, investors generally have different “tastes” –some may, for

example, prefer to put their money in high-risk “hi-tech” firm portfolios, while others may like

less risky investment strategies. Optimal portfolios should provide the investor with the

combination of return and risk that the investor finds desirable. A randomly chosen portfolio

may not accomplish this goal. Second, and more importantly, financial analysis is far from

pointless in efficient capital markets. The competition among investors who actively seek and

analyze new information with the goal to identify and take advantage of mis-priced stocks is

truly essential for the existence of efficient capital markets. In fact, one can say that financial

analysis is actually the engine that enables incoming information to get quickly reflected into

security prices. So why don’t all investors find it optimal to search for profits by performing

financial analysis? The answer is simple – financial research is very costly. As we have already

discussed, financial analysts have to be able to gather, process, and evaluate vast amounts of

information about firms, industries, scientific achievements, the economy, etc. They have to

invest a lot of time and effort in sophisticated analysis, as well as many resources into data

gathering, purchases of computers, software.

In addition, analysts who frequently trade securities incur various transaction costs, including

brokerage costs, bid-ask spread, and market impact costs. Therefore, any profits achieved by the

analysts while trading on "mispriced" securities must be reduced by the costs of financial

analysis, as well as the transaction costs involved. For mutual funds and private investment

managers these costs are passed on to investors as fees, loads, and reduced returns. There is

some evidence that some professional investment managers are able to improve performance

through their analyses. However, this may be by pure chance. In general, the advantage gained

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 to insure

that, on average, the incurred costs are covered by the achieved gross trading profits. For the

majority of other investors, the chasing of "mispriced" stocks would indeed be pointless and they

should stick with passive investment, such as with index mutual funds.

Myth 3: EMH claims that new information is always fully reflected in market prices. Yet one can

observe prices fluctuating (sometimes very dramatically) every day, hour, and minute.

Therefore, EMH must be incorrect.

The constant fluctuation of market prices can be viewed as an indication that markets are

efficient. New information affecting the value of securities arrives constantly, causing

continuous adjustment of prices to information updates. In fact, observing that prices did not

change would be inconsistent with market efficiency, since we know that relevant information is

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, the majority of common investors are not trained

financial experts. Therefore, EMH must be incorrect.

This is an incorrect statement of the underlying assumptions needed for 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 of informed and skilled investors trade in the market, while the majority of

investors never follow the securities they trade.

EVIDENCE IN FAVOR OF THE EFFICIENT MARKETS HYPOTHESIS

Since its introduction into the financial economics literature over almost 40 years ago, the

efficient markets hypothesis has been examined extensively in numerous studies. The vast

majority of this research indicates that stock markets are indeed efficient.

The weak form of market efficiency:

The random walk hypothesis implies that successive price movements should be independent.

A number of studies have attempted to test this hypothesis by examining the correlation between

the current return on a security and the return on the same security over a previous period. A

positive serial correlation indicates that higher than average returns are likely to be followed by

higher than average returns (i.e., a tendency for continuation), while a negative serial correlation

indicates that higher than average returns are followed, on average, by lower than average returns

(i.e., a tendency toward reversal). If the random walk hypothesis were true, we would expect

zero correlation. Consistent with this theory, Fama (1965) found that the serial correlation

coefficients for a sample of 30 Dow Jones Industrial stocks, even though statistically significant,

were too small to cover transaction costs of trading. Subsequent studies have mostly found

similar results, across other time periods and other countries.

Another strand of literature tests the weak form of market efficiency by examining the gains

from technical analysis. While many early studies found technical analysis to be useless, recent

evidence (e.g., by Brock, Lakonishok, and LeBaron (1992) finds evidence to the contrary.

They find that relatively simple technical trading rules would have been successful in predicting

changes in the Dow Jones Industrial Average. However, subsequent research has found that the

gains from these strategies are insufficient to cover their transaction costs. Consequently, the

findings are consistent with weak-form market efficiency.

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 form efficient, all publicly available information is

reflected in the stock price. It implies that investors should not be able to profit consistently by

trading on publicly available information.

The Strong Form

Empirical tests of the strong-form version of the efficient markets hypothesis have typically

focused on the profitability of insider trading. If the strong-form efficiency hypothesis is

correct, then insiders should not be able to profit by trading on their private information. Jaffe

(1974) finds considerable evidence that insider trades are profitable. A more recent paper by

Rozeff and Zaman (1988) finds that insider profits, after deducting an assumed 2 percent

transactions cost, are 3% per year. Thus, it does not appear to be consistent with the strong-form

of the EMH

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. Many investment professionals still meet the

EMH with a great deal of skepticism. For example, legendary portfolio manager Michael Price

does not leave anybody guessing which side he is on: “…markets are not perfectly efficient.

The academics are all wrong. 100% wrong. It’s black and white.” (taken from Investment

Gurus by Peter Tanous) We will discuss some of the recent evidence against efficient markets.

Over-reaction and Under-reaction

The efficient markets hypothesis implies that investors react quickly and in an unbiased manner

to new information. In two widely publicized studies, DeBondt and Thaler present contradictory

evidence. They find that stocks with low long-term past returns tend to have higher future returns

and vice versa - stocks with high long-term past returns tend to have lower future returns (long-

term reversals). These findings received significant publicity in the popular press, which ran

numerous headlines touting the benefits of these so-called contrarian strategies. The results

appear to be inconsistent with the EMH. However, they have not survived the test of time.

Although the issues are complex, recent research indicates that the findings might be the result of

methodological problems arising from the measurement of risk. Once risk is measured correctly,

the findings tend to disappear.

One of the most enduring anomalies documented in the finance literature is the empirical

observation that stock prices appear to respond to earnings for about a year after they are

announced. Prices of companies experiencing positive earnings surprises tend to drift upward,

while prices of stocks experiencing negative earnings surprises tend to drift downward. This

“post-earnings-announcement drift” was first noted by Ball and Brown in 1968 and has since

been replicated by numerous studies over different time periods and in different countries. After

more than thirty years of research, this anomaly has yet to be explained.

Another study reported that stocks with high returns over the past year tended to have high

returns over the following three to six months (short-term momentum in stock prices). This

“momentum” effect is a fairly new anomaly and consequently significantly more research is

needed on the topic. However, the effect is present in other countries and has persisted

throughout the 1900s.

A variety of other anomalies have been reported. Some indicate market over-reaction to

information, and others under-reaction. Some of these findings are simply related to chance: if

you analyze the data enough, you will find some patterns. Dredging for anomalies is a rewarding

occupation. Some apparent anomalies, such as the long-term reversals of DeBondt and Thaler,

may be a by-product of rational (efficient) pricing. This is not evident until alternative

explanations are examined by appropriate analysis.

Value versus growth

A number of investment professionals and academics argue that so called “value strategies” are

able to outperform the market consistently. Typically, value strategies involve buying stocks

that have low prices relative to their accounting “book” values, dividends, or historical prices.

In a provocative study, Lakonishok, Schleifer, and Vishny find evidence that the difference in

average returns between stocks with low price-to-book ratios (“value stocks”) and stocks with

high price-to-book ratios (“glamour stocks”) was as high as 10 percent year. Surprisingly, this

return differential cannot be attributed to higher risk (as measured by volatility) - value stocks

are typically no riskier than glamour stocks. Rather, the authors argue , market participants

consistently overestimate the future growth rates of glamour stocks relative to value stocks.

Consequently, these results may represent strong evidence against the EMH. It was also

interesting that nearly the entire advantage of the value stocks occurred in January each year.

However, current research indicates that the anomalous returns may be caused by a selection bias

in a popular commercial database used by financial economists.

Small Firm Effect

Rolf Banz uncovered another puzzling anomaly in 1981. He found that average returns on small

stocks were too large to be justified by the Capital Asset Pricing Model, while the average

returns on large stocks were too low. Subsequent research indicated that most of the difference

in returns between small and large stocks occurred in the month of January. The results were

particularly suprising because for years financial economists had accepted that systematic risk or

Beta was the single variable for predicting returns. Current research indicates that this finding is

not evidence of market inefficiency, but rather indicates a failure of the Capital Asset Pricing

Model

Seasonality Effect

Even in efficient markets, where security prices accurately reflect all relevant and recent

information, many well-documented seasonal effects continue to exist in many markets. In this

article, we'll take you through some of these existing seasonal anomalies and show you how to

take advantage of stock market seasonality by timing your buying and selling decisions

according to daily, weekly and monthly trends.

Monthy Effect

The markets tend to have strong returns around the turn of the year as well as during the

summer months, while September is traditionally a down month.

January Effect

The month of January in the stock market has strong significance in predicting

the trend of the stock market for the rest of the calendar year. This phenomenon occurs

between the last trading day in December of the previous year and the fifth trading day of

the new year in January. The January Effect is a result of tax-loss selling which causes

investors to sell their losing positions at the end of December. The January Effect is

predicated on the idea that these stocks, which have been sold off to realize the tax losses,

will be at a discount to their market value. Bargain hunters step in and load up on these

laggards and this creates buying pressure in the market. At the beginning of January,

investors return to equity markets with a vengeance, pushing up prices of mostly small

cap and value stocks, according to "Stocks for the Long Run.

Weekend Effect

The weekend effect (also known as the Monday effect, the day-of-the-week effect or

the Monday seasonal) refers to the tendency of stocks to exhibit relatively large returns

on Fridays compared to those on Mondays. This is a particularly puzzling anomaly

because, as Monday returns span three days, if anything, one would expect returns on a

Monday to be higher than returns for other days of the week due to the longer period and

the greater risk.

IMPLICATIONS OF MARKET EFFICIENCY FOR INVESTORS

Much of the existing evidence indicates that the stock market is highly efficient, and

consequently, investors have little to gain from active management strategies. Such attempts to

beat the market are not only fruitless, but they can reduce returns due to the costs incurred

(management, transaction, tax, etc). Investors should follow a passive investment strategy,

which makes no attempt to beat the market. This does not mean that there is no role for portfolio

management. Returns can be optimized through diversification and asset allocation, and by

minimization of investment costs and taxes. In addition, the portfolio manager must choose a

portfolio that is geared toward the time horizon and risk profile of the investor. The appropriate

mixture of securities may vary according to the age, goals, tax bracket, employment, and risk

aversion of the investor.

CONCLUSIONSThe goal of all investors is to achieve the highest returns possible. Indeed, each year investment

professionals publish numerous books touting ways to beat the market and earn millions of

dollars in the process. Unfortunately for these so-called “investment gurus”, these investment

strategies fail to perform as predicted. The intense competition between investors creates an

efficient market in which prices adjust rapidly to new information. Consequently, on average,

investors receive a return that compensates them for the time value of money and the risks that

they bear – nothing more and nothing less. In other words, after taking risk and transaction

costs into account, active security management is a losing proposition. Although no theory is

perfect, the overwhelming majority of empirical evidence supports the efficient market

hypothesis. The vast majority of students of the market agree that the markets are highly

efficient. The opponents of the efficient markets hypothesis point to some recent evidence

suggesting that there is under- and over-reaction in security markets. However, it’s important to

note that these studies are controversial and generally have not survived the test of time.

Ultimately, the efficient markets hypothesis continues to be the best description of price

movements in securities markets

CHAPTER – VII

BIBLIOGRAPHY AND ANNEXURE

BIBLIOGRAPHY

Agrawal, A., and Tandon, K., "Anomalies or Illusions", Evidence from Stock Markets in

Eighteen Countries", Journal of International Money and Finacne, 1994, 14, pp.83-106.

Ariel, R., "A Monthly Effect in Stock Returns", Journal of Financial Economics, 1987,

18, pp.161 174.

Ariel, R., "High Stock Returns before Holidays: Existence and Evidence on Possible

Causes", Journal of Finance, 1990, 45, pp.1611-1626.

Banz, Rolf W. “The Relationship between Return and Market Value of Common Stock”,

Journal of Financial Economics, March 1981, pp.3-18.

Barbee, W., S. Mukherjee and G. Raines (1996), 'Do Sales-Price and Debt-Equity

Explain Stock Returns Better than Book-Market and Firm Size", Financial Analyses

Journal, 52, pp.56- 60.

Bhandari, L.C., "Debt-Equity Ratio and Expected Common Stock Returns: Empirical

Evidence", Journal of Finance, 1988, 43, pp.507-528.

Black, F., Jensen, M., and Scholes, M., "The Capital Asset Pricing Model: Some

Empirical Tests", in Studies in the Theory of Capital Markets, M. Jensen (ed.), Praeger,

New York, 1972, pp.79-121.

Carvera, A., and Keim D.B., "High Stock Returns before Holidays: International

Evidence and Additional Tests", this volume, 1999, pp.512-531.

Chan, K.C. and Chen, Nai-Fu, “Structural and Return Characteristics of Small and Large

Firms”, Journal of Finance, September 1991, 56, 4, pp.1467-1484.

Chan, L., Hamao, Y., and Lakonishok, J., "Fundamentals and Stock Returns in Japan",

Journal of Finance, 1991, 46, pp.1739-1764.

Chan, L., Jegadeesh, N., and Lakoishok, J., "Momentum Strategies', Working Paper,

University of Illinois at Urbana-Champaign, 1995.

Chan, M., Khanthavit, W.L.A., and Thomas, H., "Seasonality and Cultural Influences on

Four Asian Stock Markets", Asia Pacific Journal of Management, 1996, 13, pp.1-24.

Connor G. and Sehgal S. "Tests of Fama and French Model in India" (2003). Decision,

Vol.30, No.2, July-Dec., 2003, pp.1-30.

Cook, Thomas J. and Rozeff Michael, “Size and Earnings/Price Ratio Anomalies: One

Effect or Two?” Journal of Financial and Quantitative Analysis, December 1984,

pp.449-466.

DeBondt, W., and Thaler, R., "Does the Stock Market Overreact?", Journal of Finance,

1985, 40, pp.793-805.

Fama Eugene F, and Frnech Kenneth R., 2004. The Capital Asset Pricing Model : Theory

and Evidence, Journal of Economic Perspectives 18 (2004) 3, pp.25-46

Fama, E. and K. French (1992), 'The Cross Section of Expected Stock Returns', Journal

of Finance, Vol.47, pp.427-466.

Fama, E. and K. French (1993), 'Common Risk Factors in the Returns of Stock and

Bonds', Journal of Financial Economics, Vol.33, pp.3-56.

Fama, E. and K. French (1996), 'Multifactor Explanations of Asset Pricing Anomalies',

Journal of Finance, Vol.51, pp.55-84.

Fama, E., and MacBeth, J., "Risk, Return and Equilibrium: Empirical Tests", Journal of

Political Economy, 1973, 71, pp.607-636.

Fama, E., K. French, D. Booth and R. Sinquefield (1993), 'Differences in the Risks and

Returns of NYSE and NASD Stocks, 'Financial Analysts Journal, Vol.49, pp.37-41.

French, K.R., "Stock Returns and the Weekend Effect", Journal of Financial Economics,

1980, 8, pp.55-70.

Gibbons, M.R., and Hess, P., "Day of the Week Effects and Asset Returns", Journal of

Business, 1981, 54, pp.579-596.

Graham, B., and Dodd, D., Security Analysis: Principles and Techniques, McGraw-Hill

Book Company, Inc., New York, 1940.

Keim, Donald B., “Size-related Anomalies and Stock Return Seasonality: Further

Empirical Evidence”, Journal of Financial Economics, June 1983, 12, pp.13-32.

Lakonishok, J., A. Schleifer and R.W. Vishny (1994), 'Contrarian Investment,

Extrapolation and

Risk', Journal of Finance, Vol.49, pp.1541-1578.

Lakonishok, Josef and Smidt Seymour, “Trading Bargains in Small Firms at Year-end”,

Journal of Portfolio Management, Spring 1986, pp.24-29.

M. Kumar, Sehgal S, “Company Characteristics and Common Returns : The Indian

Experience”, Vision, July-December, 2004, pp.34-45.

Markowitz, Harry M., "Portfolio Selection: Efficient Diversification of Investments",

New York John Wiley and Sons Inc., 1959.

Mohanty P., "Efficiency of the market for small stocks, NSE research initiative, April

2001.

Rathinasany R.S., and Mantripragada, K.G., "The January Size Effect Revisited: Is it a

case of Risk Mismeasurement?". Journal of Financial and Strategic Decisions, 1996,

Vol.9, No.3, pp.9-14.

Reilly and Brown, Investment Analysis and Portfolio Management, 8th ed. Thomson.

Reinganum, Marc R., “Misspecifications of Capital Market Pricing: Empirical Anomalies

based on Earnings Yields and Market Values”, Journal of Financial Economics, 1981, 9,

pp.19-46.

Roll, Richard, “Vas ist das? The Turn-of-the Year Effect and the Return Premium of

Small Firms”, Journal of Portfolio Management, Winter 1983a, 9, pp.18-28.

Sehgal S and Tripathi V., "Value Effect in Indian Stock Market", in Advances in

Research in Business and Finance 2005, Vol.1 Capital Markets (ed.), Mishra C.S. and

Malhotra D.K., The ICFAI University Press, First Published 2006, pp.202-221.

Sehgal S. and Tripathi V., 2005, "Size Effect in Indian Stock Market", Vision, Oct.-

Dec.2005, Vol.9, No.4, p.27-42.

Sehgal S. Munnesh Kumar, “The Relationship Between Company Size, Relative Distress

and Returns in Indian Stock Market”, The ICFAI Journal of Applied Finance, Vol.8,

No.2, March (2002), pp.41-50.

Sharpe, W., "Capital Asset Prices: A Theory of Market Equilibrium under Conditions of

Risk",Journal of Finance, 1964, 19, pp.425-442.

Treynor, J., "Toward a Theory of Market Value of Risky Assets", unpublished

manuscript,1961.

Tripathi V, 2007, “Size Effect in Indian Stock Market”, Serials Publications, New Delhi.

Wong, K.A., "Is There an Intra-month Effect on Stock Returns in Developing Stock

Markets?", Applied Financial Economics, 1995, 5, pp.285-289.

a dissertation report on capm--- final

Documents