The Validity Of Capital Asset Pricing Model

Published Date: 02 Nov 2017

Faisal – Bin - Umar

Introduction

Capital Asset Pricing Model (CAPM) is one of the most widely used Asset pricing model. It is used by many professional investors and portfolio managers, because every investors needs to calculate risk and return before purchasing or investing in a stock. The CAP model states that every investor needs to be compensated for two types of risk, first the time value of money, i.e The risk free rate, and secondly for investing in risky ventures. CAP Model was introduced by William Sharpe in early 1960’s (Sharpe, 1963) and later on it was further refined.

The early findings from CAP Model supported its validity but later on during late 80’s it was found that there are so many other factors that affect the risk and return of a stock such as Market value, Firm size, financial ratios economic conditions, price earnings ratio. Karachi stock exchange is highly volatile stock market, because of some political instability and some other factors so investor’s finds big fluctuations in the stock market, during December 2008 the KSE went down by 3300 points from 9187 to 5865 points, and then within two months it went up by 2638 points from 5707 to 8345 points.

Thus CAP Model is tested by many people, some accepted it (Lau & quay, 1974) and some rejected it (Eatzaz & Attiya, 2008), (Hanif, 2009). It is still part of text books in leading business schools. The purpose of this study is to find out whether CAP model is suitable for investors to predict stock prices in Pakistani institutional framework. Our study covers five years period (2008 to 2012). The methodology used is the Beta calculation through variance/covariance method, and then calculating required rate of return through CAPM equation consequently the price of underlying security. The information about dividend is paid is unavailable, that’s why we used only capital gain to find the return on a security.

Literature Review

It is generally agreed that if a security is more risky it will pay higher returns because the investors expect higher returns for investing in riskier securities. There are many ways of predicting the risk of underlying security, CAP model is one of them, and it is mostly used by the investors and finance managers to predict the risk and return of a particular security (Jagannathan & Wang, 1993). Usually all the models are based on assumptions and thus CAP model is also based on certain assumptions (Van Horne, 2006). There are two types of risk, one is unsystematic risk which can be diversified by investing in a portfolio but the systematic cannot be diversified, therefore the investors should be compensated for it, and Beta is the measure of systematic risk, and it has a positive correlation with return. Higher the systematic risk higher will be the return (Lau & Quay, 1974).

CAPM advocates; investors need to be rewarded in two ways: firstly for time value of money

and secondly risk associated with the security. First half of formula represents risk free return (RF) that compensates the investors for placing money in any investment over a period of time.The other half of the formula represents [β (Rm-Rf)] risk premium for bearing additional risk. CAPM is the most widely used model for finding the investors return. However results have not always supported the model. Since the development of the CAPM, number of studies conducted for testing the validity of the model.

CAMP is based on certain assumptions like any other model which provided ground for criticism. The assumptions of CAPM are Investors hold diversified portfolios, Single period transaction horizon, Investors can borrow and lend at the risk-free rate of return, perfect capital market (Tony Head, 2008). These assumptions are the weakness of this model. The CAPM consist of science and art (Adeyemi, 2006). The science is decision making relates to the construction of market portfolio. But the art relates to realistic considerations that are relevant at the margin of these decisions. The CAP-Model is tested in various countries by different authors to find out the return of the stock.

In 1974, Lau & quay applied CAPM on Tokyo stock market and concluded that the Model is applicable to the Tokyo stock market and gives the accurate results; the investors in stock market were compensated for bearing systematic risk. The study covered the period of five years (Oct 1964 to Sep 1969) with sample size of 100 companies. Bjorn and hordahl, (1998) in their paper examine the relation between expected return and time varying risk on the Swedish stock market covering fourteen years period (1977 to 1990) with the sample size of 80 firms. Results of CAPM were also compared with the results of traditional (GARCH) model. They concluded that their results are very different from international evidence of CAPM, where the traditional CAPM very often is rejected in favour of asset pricing models that rely on more general measures of risk.

The study conducted on the validity of CAPM by Huang, (2000) covers period of eight years (1986 to 1993) with sample size of 93 firms. It was applied on the two different sets a high risk and the other was low risk set. He found that the high risk sets are conflicting with CAPM whereas data from the low-risk set is consistent with CAPM. He concluded that the results of CAPM are not valid; the return calculated by the model does not interpret the actual position and could not be relied upon. There are some findings which support the argument that the return was not just based on the single risk factor (Scheicher, 2000). The study of Scheicher, (2000) covers period of twenty three years on a sample of twelve companies with 276 observations. The result of the study documents that the result of the GARCH or other multi risk factor models simply out performs the CAPM results.

The research conducted by Gomez and zapatro, (2003) covering 26 years period (1973 -1998) with sample size of 220 US securities from S&P 500 index. They use two risk factors one was standardized market systematic risk factor and other was active management risk. The interpretation of these results as evidence is in favour of the two Beta model. The same research applied on the UK stock market with sample of 64 securities gave the results in favour of this model because of the similarities in the market structure of UK and US.

Fraser and Hamelink, (2004) documented that in early researches the findings conclude that the

results of CAPM are accurate and correct but as the time pass the more accurate tools like APT

outperforms the CAPM result. The study covers twenty two years period (1975 to 1996) and the

sample size was 7 sectors. The research conducted on the London stock exchange and results of CAPM were compared with the conditional GARCH model. The risk and return calculated by the GARCH model are correct that are negative in nature but when calculated through CAPM the finding didn’t match the actual situation which is correctly measured by the GARCH model. The same study conducted in the Australian stock market covering six years period (1988 to 1993) with sample size of 8 sectors, gave the same results. They concluded that the results of GARCH model and Arbitrage Pricing Theory (APT) model are same but the findings of the CAPM are different, hence, decisions taken on the basis of CAPM might be misleading (Groenewold and Fraser, 1997).

The asymmetric approach says that it focus on the single equation specification or single Beta

which was corrected and explained in the research of the Quo and Perron, (2005). They conducted research covering period of twenty seven years (1978 to 2004) with the sample size of 50 securities on US stock market and concluded that the CAPM only identify single equation factor which leads to the wrong estimation of the results.

The literature also contains some of the researchs that shows CAPM takes into account two

important features found in most time series, namely, nonlinearity and structural instability

(asymmetry). The research conducted by hung and Wu, (2005) covering 81 years (1924-2004) sample consist of 926 companies, takes into account the two above mentioned features. They concluded that the CAPM is the model that leads to inappropriate Betas, if not incorrect.

Another study conducted by Grigoris and Stavros, (2006) on Greek stock market covering five

years period (1998-2000) with sample size was 100 securities listed on Athens Stock Exchange. The main finding of this study does not support basic statement like high risk and high level of return. The finding from the CAP-Model provides better results for some years but overall it did not support the model.

Hui and Christopher, (2008) conducted a study covering eleven years (1996 to 2006) with sample size of 95 companies in United States and Japan institutional frame work, shows that Capital Asset Pricing Model fails to explain the exact return when applied to Japan and US stock markets. It significantly gives negative return which occurs as a result of the volatility. Volatility does influence stock returns. However, the volatility of the Japan and US stock prices predicts the time series of stock returns and is priced in the cross-section of stock returns. The return calculated using the rates eventually give return which do not show the accurate results on a particular time series.

In Pakistan a study conducted by Eatzaz and Attiya, (2008) on Karachi stock market with the

sample size of 49 stocks covering period of twelve years (1993-2004). They applied CAPM and

matched their results with the conditional multi risk factors model taking macroeconomic factors as an evidence of the risk. They concluded that the traditional CAPM performs well in explaining the risk and return relationship but the results are only convincing for few stocks and only for few years. They supported conditional multifactor model over the traditional single factor model for decision making.

Another study in local institutional setting was conducted by Hanif, (2009) covering four years period (2004 to 2007) sample covering the tobacco sector only documented that CAP Model is not applicable in pricing the assets as required returns calculated through Beta is not accurate.

The exploration power of the CAPM is low because it is using market return for the calculation

of returns and only single Beta for the decision making and compensation for the risk.

According to Raei and Mohammadi, (2008), covering twelve years period (1994-2005) with sample size of seventy companies from NASDAQ 100 concluded that CAPM is used for pricing, calculating cost of capital. However, estimation methods frequently have been changed. CAPM model always give way to very low return values. The low clarifying power of the CAPM is due to the economical specification, which uses market returns as the only independent variable it neglects other variables that are used in different estimation models (e.g. APT) for giving accurate results.

The results generated by Shafer and Vovk’s (2008), covering six years period (2000-2005) with a sample size of fifty companies, also gives the same results when applied in actual practice the conclusion of their research also say that the CAPM is using single independent variable which could not be used for estimating return. To conclude results are although mix but favoring inapplicability of CAPM in its original farm and demands modification. CAPM relies on single measure of risk (Beta) and ignores the other factors contributing in risk of a security. The basic risk return relationship is not rejected hence model retains its place in literature and can be a helping hand to investors with certain modifications especially inclusion of more risk factors as suggested in APT.

The risk is divided into two parts; unsystematic risk and systematic risk. The unsystematic risk is related to specific company, industry or security it also known as diversifiable risk or specific risk. The unsystematic risk can be eliminated by diversification. The systematic risk is related to entire market or entire financial system it also known as un-diversifiable risk or market risk (Interest rate, Recession, wars). The systematic risk is directly affecting the entire market and it cannot be eliminated by diversification.5 Diversification can be defined by a proverb "do not put your all eggs in one basket". In diversification the investors reduced the risk by investing in a variety of securities.

In capital assets pricing model the (CAPM) unsystematic risk is eliminated through efficient diversification. The capital assets pricing model (CAPM) is mainly discussing about the systematic risk; the risk related to entire market. The measure of a systematic risk in CAPM is the beta. The capital assets pricing models argues that the expected return on investment or on security will be positively related to its market beta that’s mean higher or lower the security’s beta the higher or lower the expected return on investment.

The basic belief of capital assets pricing model is that the investor needs to be compensated more than the risk free return. According to CAP model the investor needs to be compensated in two ways, for time value of money (risk free rate) and for taking risk (beta of security). The capital assets pricing model was developed in hypothetical world with certain assumptions.6 (i) There are many investors in market. (ii) All investments are for the same period of time. (iii) There are no taxes on trading. (iv) There is no transaction cost on trading. (v) All investors can lend and borrow unlimited amounts at the risk free rate. (vi) The investors are rational and risk averse. (vii) Investors have all and equal information. (viii) The investors have same expectations about expected returns. (ix) The investors deal with securities that are all highly divisible into small parts.

Black, Jensen and Scholes (1972) analyzed the impact of CAP model on New York stock exchange covering the period of 41 years from 1926 to 1966. They found that CAPM is applicable on New York stock exchange and they found positive relationship between beta and average return that’s mean the investor will get high return on high risk securities. They also concluded that the CAPM model accurately predicts the expected return on securities.

Lau and Quay (1974) analyzed the validity of CAP model on Tokyo stock exchange. They used the data of period of 100 companies listed on Tokyo stock exchange for the period of five years (1964 – 1969). They found that the CAP model is accurately predicts the expected return of stocks and they concluded that CAP model is perfectly applicable on Tokyo stock exchange.

Dowen (1988) aruged in the favor of CAPM he concluded that investors may use beta as a tool but not as their only tool. He also concluded that that there is no sufficiently large portfolio guarntee the elimination of non systematic risk. Cheung and Wong (1992) analyzed the Hong Kong equity market from the period of 1980 to 1989 to study the relationship between risk and return in Hong Kong equity market. They concluded that applicability and validity of CAP model is very weak in Hong Kong stock market. Cheung, Wong and Ho (1993) analyzed the Korean and Taiwan stock exchanges to study the relationship between risk and return in emerging Asian markets. They concluded that the applicability and validity of CAP model is very weak in both markets, especially in Taiwan stock exchange.

Jagannath and Wang (1993) argued that the Capital Assets Pricing Model (CAPM) is widely used model to predict the risk of investment and expected return of the stocks among theinvestors and portfolio managers. Groenewold and Fraser (1997) used data of eight sectors of Australian stock exchange for the period from 1983 to 1993 to make comparison between CAP model, GARCH model and Arbitrage Pricing Theory (APT) model. They found that the GARCH model and APT model provides almost same results and both models accurately predict the expected return of securities. They also concluded that the results of CAP model do not match with actual situations and provide misleading results to investors.

Huang (2000) analyzed the validity of CAP model on two different sets of securities. He used the data of 93 companies from the period of (1986-1993). He used two different sets of securities in first set he selected high risk securities and in second set he selected low risk securities. He found in their research that CAPM is accurately predicting the expected returns of low risk securities and give consistent results. On the other side the high risk securities give inconsistent results with CAPM that’s mean CAPM is not accurately predicting the expected return of high risk securities. He concluded that the on the high risk securities the CAPM does not validate their results and CAPM does not accurately predicts the expected return on investments and investors could not relied upon CAP model.

Scheicher (2000) researched on 12 companies listed on German stock exchange for the period of 23 years. He found that the expected return was not just predicted by a single risk factor. There are some other factors also affecting the returns of investments. He concluded that the results of other models like multi risk factor model and GARCH model more accurately predicts the expected return of investments on stock than CAPM model. Gomez and Zapatro (2003) analyzed the data of 220 US securities covering period of twenty six year from 1973 to 1978. They used two betas model considering the systematic market risk factor and active

management risk factor. They concluded that the result of their two betas model is better than CAPM.

Fraser and Hamelink (2004) made comparison between the results of CAP model and GARCH model. They researched on seven sectors of London stock exchange covers period of twenty two years (1975-1976). They found that the results of GARCH model more accurately predicts the expect return in compare to CAP model. Quo and Perron (2005) analyzed the data of 50 companies listed on New York stock exchange from the period of 1978 to 2004. They concluded that the capital asset pricing model only identify single risk factor and investor get wrong estimation of the expected return on their investments.

Grigoris and Stavros (2006) found that the basic statement or assumption of high return on high risk does not fulfill on Greek stock market. They used data of 100 companies of Athens stock exchange covering the period of five years from 1998 to 2002. They also conclude that the results of CAPM are consistent for shorter period but overall the CAPM does not provide accurate and consistent results. Hui and Christoper (2008) used the data of 95 companies of United States and Japan stock markets for the period of 11 years from 1996 to 2006. They found that CAP model does not provide accurate and consistent results when applied to stock markets of Japan and United States.

Eatzaz and Attiya (2008) made comparison between the CAP model and conditional multi risk factor model. They used data of 49 companies of Karachi stock exchange from the period of 1993 to 2004. They concluded that the results of CAPM model are consistent and accurate with only few securities and only for few years. They also found that multi risk factor model predicts more accurately results as compare to CAP model. Raei and Mohammadi (2008) analyzed the data of 70 companies listed on NASDAQ stock market for the period of twelve years from 1994 to 2005. They concluded that methods of estimating expected return have been changed; CAPM is just useful for calculating cost of capital. They also found that the returns from CAPM models are always lower than compare to multi factor model (APT). They suggested that APT provide more accurate result compare to CAPM.

Tony Head (2008) argued that although the CAPM is widely used for predicting the expected return on stocks but the results of previous research have not always positively supported this model. Like many other models the main reason of criticism is a certain assumptions of CAP model. Hanif (2010) analyzed the validity of CAP model on Tobacco sector of Karachi stock exchange covering the period from 2004 to 2007. He found that CAP model is not applicable on Tobacco sector of Karachi stock exchange and the results of CAP model do not match with actual results.

Hanif and Bhatti (2010) analyzed the validity of CAP model on 60 firms listed on Karachi stock exchange covering the period from 2003 to 2008. They found that CAP model is not applicable on of Karachi stock exchange and the results of CAP model do not match with actual results. They concluded that the results of only 28 observations out of 360 observations are supporting CAPM.

The capital asset pricing model (CAPM) of Sharpe (1964), Lintner (1966),

and Black (1972) is the major analytical tool for explaining the relationship between

expected return and risk used in financial economics. The CAPM model measures

the risk of an asset by covariance of asset’s return with the return of all invested

wealth, known as market return. The main implications of the model are that

expected return should be linearly related to an asset covariance with the return on

market portfolio, called the beta risk. The principle of risk compensation is that

higher beta risk is associated with higher return. However, empirical evidence has

found weak or no statistical relationship to support this relationship [Banz (1981);

Basu (1983); Fama and French (1992) and others].

The well documented poor empirical performance of Sharpe (1964) and

Lintner (1966) static version of CAPM has motivated much research on

conditional test of this asset pricing model [Gibbons and Ferson (1985); Ferson,

Kandel, and Stambaugh (1987); Bollerslev, Engle, and Woodridge (1988);

Harvey (1989); Ng (1991) and Jagannathan and Wang (1996), among others].

These tests incorporate conditioning information to allow risk and prices of risk

to vary through time. This suggests while empirical examining CAPM by using

the data from the real world, it is appropriate to make certain assumption, which

are more close to real world. The unconditional CAPM is derived by examining

the behaviour of the investor in only one period, where in real world investment

decision are made over many periods. The assumption of betas of assets and the

risk premium remain constant is also not reasonable because the betas and

expected return generally depends on nature of information available at any

point of time, and they vary over time as information set varies. The relative risk

of a firm cash flow is likely to vary over the business cycles as Jagannathan and

Wang (1996) have argued that to the extent that the business cycle is induced by

technology and taste shocks, the relative share of different sectors in the

economy fluctuates, inducing fluctuations in the betas of the firms in these

sectors. In addition, during recession, for example the financial leverage of

poorly performing firms may increase relative to other firms causing their stock

betas to rise. In bad times the risk premium is high because investors want to

smooth their out their consumption, therefore to make sure that investors hold

their portfolio of stocks, the risk premium must be high in equilibrium. This line

of argument implies that the instrument variables that are used for conditioning

information must be related to current and/or future macroeconomic

environment.

Another response is that empirical inadequacy of standard CAPM may be

due to a number of seemingly unexplained patterns in asset returns that has

resulted to use attribute sorted portfolios of stocks to represent the additional

risk factor in the standard model. The most prominent work in this regard is

series of papers by Fama and French (1992, 1993, 1995, 1996, 1998 and 2004).1

The three-factor model of Fama and French (1996) says that the expected return

in excess of risk-free rate is explained by the excess market return, the

difference between the return on portfolio of small stocks and return on portfolio

of large stocks (SMB) and the difference between the return on portfolio of high

book-to-market stocks and return on a portfolio of low book-to-market stocks

(HML). The three factor model of Fama and French (1993) is now widely used

in empirical research that requires a model of expected return [Iqbal, et al.

(2008); Ferson and Harvey (1999) and numerous other studies]. Given the

prominence of Fama-French (1992) three-factor model it is interesting to test its

empirical performance as an asset pricing model in an emerging market Pakistan

The main focus of this study is to examine empirically how well the

market equilibrium model of Sharpe (1964) and Lintner (1966) can explain the

risk return relationship in case of Pakistani market. This study extends the

standard CAPM of Sharpe (1965) and Lintner (1966) by including Fama-French

(1993) variables The conditional version of Sharpe-Lintner CAPM and Fama-

French three factor CAPM is empirically investigated by estimating CAPM by

allowing time variability in line that is suggested by Ferson and Harvey (1993,

1999) and others. These extended CAPM are dynamic, in which investors

update their estimates of means, variances and covariance of asset returns each

period to new information set. This implies that expected excess returns vary

with time to reflect time variations in systematic risk and price of risk. The

present study adds to the existing literature, first, by testing the conditional

standard and the three-factor model for the firm-level data both daily as well as

monthly, where book-to–market value is used as a variable instead of portfolio

sorted on these two attributes of the firms. Second, for more insight, the

investigation is done for different time intervals as the market has a different

sentiment in different periods, and, third, the information sets used for

conditioning the models are different.2 This study contributes to exiting

literature for emerging markets by testing consumption CAPM for Pakistani

market in static and dynamic context

The study is organised as follows. The previous empirical evidence on

standard CAPM and its various extensions are discussed briefly in Section 2.

Section 3 provides the empirical methodology followed in this study. The

empirical results of unconditional and conditional standard CAPM and threefactor

are presented and discussed in Section 4, while Section 5 concludes the

study.

The Sharpe-Lintner CAPM has been subjected to extensive empirical

testing in the past and various researchers have come up with mixed findings.

Lintner (1966) and Douglas (1969) are the earliest studies to conduct tests of

CAPM on individual stocks in the excess-return form. They have found that

the intercept has values much larger than the risk-free rate of return, while the

coefficient of beta is statistically has a lower value, though it is statistically

significant and the residual risk affects asset returns. According to Miller and

Scholes (1972) these results, which contradict the CAPM, arise due to

measurement error. As regards the test of CAPM on portfolios, Fama and

McBeth (1973) have performed the classical test. The study estimated beta

from time series regression over the monthly data for the period 1935-1968

and then performed a cross-sectional regression for each month to compute

risk premium. Fama and McBeth (1973) have formed twenty portfolios of

assets. Their results show that the coefficient of beta is statistically significant

and its value has remained small for many sub-periods. Fama and McBeth

(1973) have validated the CAPM on all stocks listed on NYSE during 1935-

1968, while Tinic and West (1984) who has used same NYSE data for the

period 1935-1982 have found contrary evidence. Their study finds that

residual risk has no effect on asset returns, however, their intercept is much

greater than risk-free rate and the results indicate that CAPM might not hold.

Black, et al. (1972) have tested CAPM by using time series regression

analysis. The results show that the intercept term is different from zero and in

fact is time varying. The study also finds that when 1 the intercept is

negative and when 1 then intercept is positive. Thus the findings of Black,

et al. (1972) violate the standard CAPM. Sharpe and Cooper (1972) have

examined the risk return relationship on the stocks traded on NYSE for the

period 1931–1967 and found contrary evidence.

As regards the findings about other markets, Greene (1990) investigated

the CAPM on UK private sector data and has shown that CAPM does not hold.

Sauer and Murphy (1992) have confirmed that CAPM is the best model for

describing the German Stock Market data. In a more detailed study Hawawini

(1993) could not confirm the validity of CAPM in equity markets in Belgium,

Canada, France, Japan, Spain, UK and USA. The other studies which have

tested CAPM for different countries include Lau, et al. (1975), for Tokyo Stock

Exchange, Sareewiwathana and Molone (1985) for Thailand Stock Exchange

and Bark (1991) for Korean Stock Market.

The mixed empirical findings on the risk return relationship have

proposed different responses and as a result CAPM has extended in different

ways. One response is that the lack of empirical support for standard CAPM is

due to time-varying market risk and risk premium [Bollerslev, Engle, and

Wooldridge (1988); Ferson and Harvey and others]. In an early works on

conditional CAPM Fama and McBeth (1974) extended CAPM to multi-period

analysis but empirical tests indicate poor performance of the model. Merton

(1980) analysed three equilibrium expected market return for the period 1926-

1978 for US market. The main conclusion he derives from his exploratory

investigation are, first in estimating models of expected market return, the nonnegativity

restriction of the expected excess return should be explicitly included

as the part of specification. Second estimators which use realised returns should

be adjusted for hetroskedasticity.

Since the introduction of ARCH type processes by Engle (1982) and others,

testing for time-varying volatility of stock market returns (and hence the timevarying

beta) has been given considerable attention in the literature [Bollerslev,

Engle, and Wooldridge (1988); Ng (1991); Bollerslev, Engle, and Nelson (1994)].

The ARCH-based empirical models appear to provide stronger evidence, of the

risk-return relationship than do the unconditional models. Gibbons and Ferson

(1985), Ferson, Kandel and Stambaugh (1987) and Ferson (1988) are some early

work that test the asset pricing models at the conditional level and allow expected

return to vary through time. However, all of these studies assume that that the

conditional covariances are constant. Time variation in conditional covariances

that has been modeled with the autoregressive conditional hetroskedasticity in the

mean model ARCH-M of Engle, Lillen and Robbins (1987), Bollerslev, Engle and

Wooldridge (1988), Bodurtha and Mark (1988) and Ng (1991) carry out tests of

Sharpe (1964) and Lintner (1966) specification by modeling the conditional

covariances as a function of past conditional covariances. Following the

instrumental approach of Campbell (1987), Harvey (1989) undertakes test of

conditional CAPM that allow for both time varying expected returns and

conditional covariances and they use Generalised Method of Moments (GMM) as

estimation technique.

Ferson and Harvey (1991, 1993, 1999)) in their studies of US stocks and

bond returns, reveal that the time variation in the premium for beta-risk is more

important than the changes in the betas themselves. This is because equity risk

premiums are found to vary with market conditions and business cycles.

Schwert (1989) attributes differential risk premium between up and down

markets due to varying systematic risk over the business cycle. Jagannathan and

Wang (1996) have shown that about 50 percent cross-sectional variation in

average return is explained by conditional CAPM. The study by Jagannathan

and Wang (1996) also finds empirical support for conditional CAPM when betas

and expected return are allowed to vary over time assuming that CAPM hold

period by period. When a proxy for return on human capital is also included in

measuring aggregate wealth, the pricing errors are found to be statistically

insignificant.

The well-documented failure of standard CAPM has motivated much

research in to testing multifactor asset pricing models. Due to a number of

seemingly unexplained patterns in asset returns that has led researchers to use

attribute sorted portfolios of stocks to represent the factors in multifactor model.

Some of such puzzling anomalies are small firm effect, January effect, earningto-

price ratio, book to market value and leverage etc. Reiganum (1981) has

found that small capitalisation firms have risk adjusted returns that significantly

exceeds those of large market value firm. Keim (1983) finds more than 50

percent of the excess return for small is concentrated in the first week of

January; this effect is called January effect. Bhandari (1988) finds that leverage

is positively related to expected stock returns. The studies of Banz (1981),

Rosenberg, Reid, and Lanstein (1985) and Lakonshok, Shleifer, and Vishney

(1994) show that firm’s average stock return is related to size (stock price times

number of shares), book-to-market equity (the ratio of book value of common

equity to its market value), earning-price ratio, cash flow-price ratio, past sales

growth. The most influential work of Fama-French three factor model in which

they add two variables besides the market return, the return on small minus big

shocks (SMB) and the return of high book/value minus low book/market value

stocks (HML). Fama and French (1992) show that there is virtually no

detectable cross-sectional beta mean return relationship. They show that

variation on average return of 25 size and book/market sorted portfolio can be

explained by betas on the latter two factors. Fama and French (1993) find that

higher book-to-market ratios are associated with higher expected return, in their

tests that also include market. Fama and French (1995) explain the real

macroeconomic aggregate non-diversifiable risks that are provided by the return

of HML and SMB portfolios. Fama and French (1996) extend their analysis and

find that HML and SMB portfolios comfortably explain strategies based on

alternative price multiplier (price-to-earning, book-to-market), strategies based

on five year sale growth and tendency of five year return to reverse. All these

strategies are not explained by CAPM betas. Fama and French (1996) conclude

that many of CAPM average return anomalies are related and they are captured

by their three factor model. Latter they show in their work Fama and French

(2004) its usefulness for practitioners as an alternate model to CAPM. The study

by Faff (2001) tests the Fama-French model using the daily Australian data and

finds less support of three-factor model in explaining the cross-section variation

in expected return. He comes up with negative size effect. The contradictory

evidence is found by Drew and Veeraraghavan (2003) study, who report that

size and book-to-market value explain the variation in expected return and reject

the claim that these factors are due to seasonal phenomena or due to data

snooping for Australia.

Chang, Johnson and Schill (2001) observe that as higher-order systematic

co-moments are included in the cross-sectional regressions for portfolio returns,

the SMB and HML generally become insignificant. In contrast to Fama-French

Findings Clare Priestley and Thomas (1998) find a significant and prominent

role of beta in explaining expected return. The find some role of size variable

however, stock prices have no role in explain the expected return. Kathari,

Shanken and Sloan (1995) conclude a significant role of beta and economically

small role of size variable in their findings. Therefore, they argue that SMB and

HML are good proxies for higher-order co-moments. Ferson and Harvey (1999)

claim that many multifactor model specifications are rejected because they

ignore conditioning information. They show that identified predetermined

conditional variables (market return, per capita growth in durable consumption,

spread between Moody’s Baa corporate bonds and long term US corporate bond,

change in difference between 10-years treasury bond return and three-month

treasury bill return, unanticipated inflation and one month treasury bill return

less the rate of inflation) have significant explanatory power for cross-sectional

variation in portfolio returns. They reject the three factor model advocated by

Fama and French (1993). They come to the conclusion that these loadings are

important over and above Fama and French three factors and also the four

factors of Elton, Gruber and Blake (1995).

In case of Pakistani market Iqbal and Brook (2007) find evidence of nonlinearity

in the risk return relationship and come to the conclusion that for

Pakistanis Stock market the unconditional version of the CAPM is rejected.

Iqbal, et al (2008) have tested CAPM and Fama and French (1993) three-factor

model for Pakistani market and conclude that the unconditional Fama-French

model augmented with a cubic market factor perform the best among the

competing models. Latter in their study Iqbal, et al. (2008) they find that the

pricing model with higher co movements does not appear to be superior to the

model with Fama-French variables. Ahmed and Zaman (1999) attempt to

investigate the risk-return relationship for Pakistani market and the results of

GARCH-M model show the presence of strong volatility clusters implying that

the time path of stock returns follows a cyclical trend. Ahmad and Qasim

(2004) find asymmetric asset pricing behaviour and show that the positive

shocks have more pronounced effect on the expected volatility than the negative

shocks in case of Pakistani market.

The above review of literature indicates an increasing interest in

analysing the activities of the stock market in Pakistan but many issues in this

area still remain uncovered. In addition most of the studies are based on the

sector indices and overall market index. In particular, risk return relationship,

which is the central issue of financial economics, needs an in-depth research. It

is in this perspective this study aims to make contribution in the literature on

stock market by testing the unconditional and conditional CAPM using the firm

level data.