Relative Merits of the Capital Asset Pricing Model and Empirical Approaches to Asset Pricing - Case Study Example

Relative Merits of the Capital Asset Pricing Model and empirical approaches to Asset Pricing CAPM framework: The CAPM model was construed to figure out the pricing of risky securities in the market.It was the brain child of experts like Sharpe and Lintner. Various researchers have explained the CAPM model. This model is an important tool for critiquing and determing the association between the risk and the average rate of return.The Capital Asset Pricing Model gives way to the following conclusion “The relative risk of an individual asset to its contribution to a well.” For instance, take the example of a doctor . The doctor has a portfolio of 40 stocks. Some of these stocks are of General Electrics. The risk of General Electric’s is the contribution of General Electric’s to the overall riskiness of the stock. The stock may be quiet risky when held on its own but its riskiness is mellowed down when it’s made a part of the overall portfolio. (Kothari, 1995) It determines the contribution of riskiness by individual stocks in a well diversified portfolio. Not all stocks can contribute equal amount of riskiness to a well diversified portfolio. Different stocks will affect the portfolio different, so the amount of their riskiness will vary. Therefore the relevant riskiness of a stock to its portfolio is called the beta-coefficient. In light of the CAPM terminology, it is a measure of the risk that the stock contributes to the total risk of the portfolio in general. According to the CAPM definition, the formula for Beta is as following: Beta = (standard deviation of the ith stock’s return/ standard deviation of the ith stock’s market return) (co-relation between the ith stock’s return and the return in market) (Craig, 1995) This explains us why stocks that have higher standard deviation will have higher beta and those that have smaller standard deviation will have smaller beta. It should also be noted that those stocks that have a higher co-relation with the market will also have a higher beta. Assumptions of the Capital Asset Pricing Model: The cost of capital is the minimal return which is needed by the financial markets to provide capital to the firm. In CAPM, the markets are assumed to be efficient. The returns on the other hand are expected to be conditional. This explains why the cost-of-capital is equated with expected return when the Cash flows are discounted back for multi-period discounting forumula. It becomes complicating to associate discounting rate to a multi-period discounting formulae when conditional expected returns are variable with respect to time. The approach is slightly different when there is auto-correlated variation in the risk and the prices of risk. When there is auto-correlated variation in risks, then one period expected returns follow. The sum of expected cash flows discounted for one period are not equivalent to market value any more. However, despite this, concepts such as cost of capital and the present value concepts are used widely and more particularly when returns vary with time. (Mackinlay, 1995) Cost-of capital models generally assume that markets are efficient. Under the efficient market hypothesis, in an efficient market, the current price exhibits all the relevant information. Thus any information which is used for pricing can be used as a sign to allocate capital. In this respect, the capital asset pricing model (CAPM) happens to be the most popular and the oldest cost of capital model in use. It is not a just a theoretical approach to stock valuation. It is a more pragmatic approach to stock valuation. It throws light on the behavioral pattern of investors when making up portfolios. (Sehgal, 2001) In CAPM model it is assumed that the investor perceives the risk and the return to be the same. Unless this consensus standard is not followed, the estimates of mean and variance will vary. As a result, the forecast portfolio of each will be different. There will be no consistency. There will be in-numeral efficient frontiers. Each of these frontiers will have some dependency on their preferred amount of risk and return. If the expectations of the investors don’t resemble, the homogeneity in the overall conception dies. A single efficient frontier line will no longer be applicable. Going from the aforementioned inferences, it can be concluded that the asset price is the best and the most apt estimate of the net present value of the future cash flows that the asset will provide. This will be different for different investors.In the practical world, this assumption tends to be a little un-realistic. 3 investors make investment decision on the basis of 2 factors; the risk and the return. Risk is determined by mean and variance. In the Capital Asset Pricing Model, it is determined that rational investors diversify their portfolio’s risk. Systematic risk is the name of this risk. It is dependent on the Beta of the security and changes only when the Beta of the security changes. Both beta and variance or only beta can be used to measure overall risk. In the CAPM world of asset pricing and risk,the perception of risk and reward is different for every individual out there. (Keim, 1983) The Main Advantages of CAPM: Capital Asset Pricing Model has its own set of advantages too. The biggest advantage of CAPM is the fact, that it is an approach which revolves around industry standards. It is primarily to set the most apt and accurate benchmarks for most industries. . It is easily understandable, implementable and interpretable. The estimations of beta are very accurate in the CAPM model. Most of the beta estimates for regulated industries are not too far from one. Thus, when implemented, it resembles the use of market return as an appropriate equity benchmark. The axioms followed by this model are reasonable and standard behavioral. (Muneesh, 2000) Dis-advantages of CAPM: On the major drawbacks of the CAPM approach is the fact that its foundation rests on un-observable variables. This led to some problems related to conceptualization. Empirically speaking, CAPM method has not been very outstanding in its performance. However, there is no proof to claim whether this is a result of the absence of proxies for variables or is it because the model itself failed. Yet another problem, is related to the high volatility of the data which is incorporated to make estimations in the CAPM based model. (Sanjay, 2001) Fama and French Model: Fama and French (1992) were of the view that CAPM model cannot be applied in the US. Markets. They claimed that the linear relationship that the model promises between return and risk will cease to function in the U.S stock market. There were two other factors that were equally important. They were: size based factor book and the book-to-market factor. Both factors, when juxtaposed together are given the name - “value factor”. An overall difference in risk is the primary reason behind the systematic differences in average returns. This assumption holds true only if the pricing of the stock is logical and rational.(Sehgal, May 2001) Thus, the proxy for sensitivity where pervasive risk factors are concerned should be all three market, size and value. According to Fama and French (1993), most portfolios imitated risk factors. These risk factors are related to market size and value. They explain why most well-diversified stock portfolios give random returns. They relayed their idea through their and comprehensive elaboration on the three-factor pricing model. They explained how random return factors are related to earning shocks. From their point of view, the stock behavior r However Fama and French also claim that their findings and results are weak. This is particularly true when it comes to relating the value factor. They feel that measurement errors are because of flaws in earnings data. Research and Literature related to asset pricing critiques and criticizes the FAMA and French Model. Empirical Demonstration of the FAMA-French Model: This paper throws light on the FAMA-French three-factor model in the Indian stock market. It examines it empirically. It tests the two relationships i.e. the one factor linear pricing relationship of the CAPM against the Fama-French model which is three-factor linear pricing relationship. It will be seen how the market and size. The empirical evidence in the end is in favor of the Fama and French Model. The research proves that all three Fama-French factors i.e the market, the size and the value, impact random returns for the Indian stock market. This implies that the three-factor model cannot be denied or ignored completely. Data collected: In Connor and Sehgal’s study on the Fama-French Model, the data has been obtained from 367 companies between the time periods from June 1989 to March 1999. The risk free proxy is the implied yield that is declared at the end of month auction of 91 day Treasury bills. (Sehgal, May 2001) Return Series and its Descriptive Statistics: Connor and Sehgal demonstrate the negative relation between the two i.e the size of the stock and the average return on the stock. However it should be noted that there is a direct relation between value and average return on small stocks. The relation is inverse and negative for larger stocks. Common variations in the Returns with Factor Portfolios: In the tests conducted by Connor and Sehgal, the Fama and French model applied standard multi-variate regression. The following denominations were used 1. Return(j in t) – the return to portfolio w in month t; 2. Market return – this is the market return to the portfolio 3. Size factor- this denomination marks the return to the size factor 4. Value factor- the one is for the return to the value factor portfolio. The multivariate regression system used the following equation: Return(w in t) = aw + (bw)(market return) + (sw)(size factor) +hw(value factor)+ew, w=1,...,N ; t=1,…,T (Fama, 1996) Where: bw, sw, and hw denote market, size and value factor components for portfolio w aw denotes the abnormal mean return of portfolio j; However in a hypothesized portfolio this return is zero The test conducted by Connor and Sehgal proved that in the asset pricing model, all these factors contribute significantly in the risk of a well-diversified portfolio. When used as a single entity R2, was 70-80% but the adjusted R2 declined to 25% when used with the other 2 factors, without the market factor. Thus, the market factors ranks before the other two factors in its influence on the diversified risk portfolio. Tests of the Cross-sectional Restriction on Mean Return: Connor and Sehgal examined the intercepts of the multi-variate regression system to explain the cross-sections in mean returns. When the CAPM was used, it was found that that all 3 stock portfolios gave positive intercepts. They were significant and lied in the 95% confidence level.In the three factor model, the intercept values from all sample models could not recognized at the 95% level. The results demonstrated how the cross-section factors were captured by the three-factor model but they were not captured by the CAPM model. (Fama, 1995) Conclusion: The Fama and French Model concluded three findings when they root for the three-factor asset pricing model. 1. Market, size and value factors in the US stock market are impactful. 2. US stocks are linear and this explains the cross-sectional dispersion of the mean returns. 3. The linear exposures of the US equities to these factors elaborate on the cross-sectional dispersion of their mean return (Fama, 1995) As a concluding thought, CAPM asset pricing model continues to be the most widely used pricing model today. It has been criticized on various grounds, especially for its one-factor assumption. The Fama and French model countered, by its three-factor modeling assumption, the results of which have been demonstrated in the aforementioned factors. References: Sehgal, G. C. (May 2001). Tests of the Fama and French Model in India*. Fama, Eugene F., and Kenneth R. French, (1995), Size and book-to-market factors in Earnings and returns, Journal of Finance 50, 131-155. Fama, Eugene F., and Kenneth R. French, (1996), Multifactor explanations of asset Pricing anomalies, Journal of Finance 51, 55-84. Gibbons, Michael R., Stephen A. Ross and Jay Shaken, (1989), a test of the efficiency Of a given portfolio, Econometrical 57, 1121-1152. Keim, Donald, (1983), Size-related anomalies and stock return seasonality: further empirical evidence, Journal of Financial Economics 12, 13-32. Kothari, S.P., Jay Shaken and Richard G. Sloan, (1995), another look at the cross-section Of expected stock returns, Journal of Finance 50, 185-224. Lakonishok, Josef, Andrei Schleifer and Robert W. Vishny, (1994), Contrarian investment, extrapolation and risk, Journal of Finance 49, 1541-1578. MacKinlay, A. Craig, (1995), Multifactor models do not explain deviations from the CAPM, Journal of Financial Economics 38, 3-28. Muneesh, Kumar and Sanjay Sehgal, (2000), Company characteristics and common stock return: the India experience, working paper, University of Dehli. Sehgal, Sanjay,( 2001), Investor behaviour in Indian capital markets, working paper, University of Dehli. Read More