Journal of Exclusive Management Science May Vol 4 Issue 5  ISSN


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1 Journal of Exclusive Management Science May 2015 Vol 4 Issue 5 ISSN A Study on the Emprical Testing Of Capital Asset Pricing Model on Selected Energy Sector Companies Listed In NSE Abstract *S.A. Nasrin Hussaina *Assistant professor, Department of Commerce, Bishop Heber College, Trichy17 In finance, one of the most important concepts is that return is a function of risk. This means the more the risk the higher potential return. One tool that finance professionals use to calculate the return on investment is the Capital Asset Pricing Model. Capital Asset Pricing Model explains the relationship between risk and return. The CAPM is a model for pricing an individual security or portfolio. Keywords: Investment, Risk, Return, Capital Asset Pricing Model, Portfolio. Introduction In finance, the capital asset pricing model is the price decision theory about financial assets including shares, bond, futures and option. American economists Markowitz first created the modern portfolio theory and put forward the method of diversification when he researched the relationship between risk and required rate of return. Sharpe, on the basis of portfolio theory, assumed that all investors used the utility function with average yields and risk as the independent variable to make decision. When analyzing the relationship between system risks and benefits, he put forward Capital Asset Pricing Model, namely CAPM. The primary significance of the model is to establish the relationship between risk and return of capital, clearly indicating the expected return of securities is the sum of riskfree rate of return and risk compensation, which reveals the internal structure of securities compensation. Another important significance of CAPM is that it divided risk into unsystematic risk and systemic risk. Unsystematic risk is the risk that belongs to some particular companies or specific industry; it can be dispersive through asset diversification. Systematic risk refers to the inherent risk factors that affect the whole market. It intrinsic exists in the stock market and this risk cannot be eliminated through diversification. The function of CAPM is to use the assets portfolio to eliminate unsystematic risk; the systematic risk is the only one remains. β coefficient has been introduced in the model to characterize the systematic risk. Objectives of the Study 1. To measure the return on securities of selected energy sectors listed in NSE. 2. To measure the systematic risk of the security by using beta as a measure of risk. 3. To calculate expected rate of return expected by investors on securities of any level of risk. 4. To examine whether a higher/lower risk stocks yield higher/lower expected rate of return. 5. To examine whether the expected rate of return is linearly related with the stock beta, i.e. its systematic risk. Scope of the Study The study is confined to the statistics of the three leading companies in energy sectors industries. The Beta is calculated based on the returns for the limited period as share prices for the five years. Only secondary sources data is used for the study, which limits the scope of the research work. Statement of the Problem Many investors are risk averse and they will choose to hold a portfolio of securities to take advantage of the benefits of diversification. The standard deviation of an individual stock does not indicate how that stock will contribute to the risk and return of a diversified portfolio. So the Capital Asset Pricing Model (CAPM) which relates the expected return on an asset to its 1
2 Journal of Exclusive Management Science May 2015 Vol 4 Issue 5 ISSN systematic risk has been applied by the researcher to measure the risk return relationship of securities. Significance of the Study This study helps to examine the relationship between the risk and return of the selected energy sectors companies listed in CNX NIFTY. It serves as a benchmark to the investors for evaluating the securities. The investors can reduce their portfolio risk through diversification which helps them to revise their portfolio periodically to earn maximum return. Research Methodology Research design Descriptive method of research is conducted on the construction of portfolio risk and return. Sources of Data The data required for the study has been collected from secondary source. The data has been collected from the official website of National Stock Exchange ( Sample size The sample size of the study is limited to daily stock prices of energy sectors namely Reliance, ONGC, Power Grid and these stocks are also a part of CNX Nifty. The sampling technique adopted is convenience sampling. Period of the study The study is conducted with the financial data for the past five years from (1 st Jan st Dec2014). Analytical Tools The following tools are applied to analyze and interpret the data. 1. Capital asset pricing model. 2. Jensen s alpha. Limitations of the study 1. The result may not reveal accuracy since the data was collected through secondary sources. 2. Due to time constraints, only fiveyear data were analyzed. 3. Jensen s Alpha measures the excess performance relative to the beta, so any limitations to the beta also apply to the Jensen Alpha. Literature Review William F. Sharpe in 1964 and John Lintner in 1965 developed the CAPM model (Capital Asset Pricing Model), which, using the same values as Markowitz model, i.e. the expected rate of return and risk, provided a simpler way of determining efficient portfolio of securities. The model establishes the existence of a positive linear relationship between the required rate of return on securities and the related risks in a portfolio context. The expected rate of return equals the sum of returns without risk and the risk premium that reflects diversification. The model is based on a set of basic assumptions and establishes that for higher more inevitable corporate risk investors expect a higher return, and that there is market equilibrium. Assumptions of the CAPM model are: 1) Investors evaluate portfolios taking into account the expected rate of return and standard deviation over oneperiod horizon. 2) Investors prefer a portfolio with higher returns. 2
3 Journal of Exclusive Management Science May 2015 Vol 4 Issue 5 ISSN ) Investors are averse to risk. 4) There is a riskfree rate of return at which it is possible to lend and borrow. 5) The property is indefinitely divisible. 6) All investors have oneperiod holding horizon. 7) Information is currently free and available to all investors. Investors have homogeneous expectations regarding the expected rate of return, standard deviation and covariance of securities. The CAPM relates the expected rate of return of an individual security to a measure of its systematic risk. Systematic risk is represented by the CAPM formula E(ri) = Rf + βi(e(rm) Rf) E(ri) = return required on financial asset i Rf = riskfree rate of return βi = beta value for financial asset i E(rm) = average return on the capital market The CAPM is an important area of financial management. In fact, it has even been suggested that finance only became a fullyfledged, scientific discipline when William Sharpe published his derivation of the CAPM in The CAPM was introduced by Jack Treynor (1961, 1962), William Sharpe (1964), John Lintner (1965) and Jan Mossin (1966) independently, building on the earlier work of Harry Markowitz on diversification and modern portfolio theory. Despite its empirical flaws and the existence of more modern approaches to asset pricing and portfolio selection, the CAPM still remains popular due to its simplicity and utility in a variety of situations. Jensen s Alpha: Jensen's alpha (or Jensen's Performance Index) is used to determine the excess return of a stock, other security, or portfolio over the security's required rate of return as determined by the Capital Asset Pricing Model. This model is used to adjust for the level of beta risk, so that riskier securities are expected to have higher returns. Jensen's alpha = Portfolio Return [Risk Free Rate + Portfolio Beta * (Market Return Risk Free Rate)] Empirical Reviews Kapil Choudhary and Sakshi Choudhary (2013) in their article Testing Capital Asset Pricing Model: Empirical Evidences from Indian Equity Market have examined the Capital Asset Pricing Model (CAPM) for the Indian stock market using monthly stock returns from 278 companies of BSE 500 Index listed on the Bombay stock exchange for the period of January 1996 to December The findings of this study are not substantiating the theory s basic result that higher risk (beta) is associated with higher levels of return. The model does explain, however, excess returns and thus lends support to the linear structure of the CAPM equation. The theory s prediction for the intercept is that it should equal zero and the slope should equal the excess returns on the market portfolio. The results of the study lead to negate the above hypotheses and offer evidence against the CAPM. The tests conducted to examine the nonlinearity of the relationship between return and betas bolster the hypothesis that the expected returnbeta relationship is linear. Additionally, this study investigates whether the CAPM adequately captures allimportant determinants of returns including the residual variance of stocks. The results exhibit that residual risk has no effect on the expected returns of portfolios. 3
4 Journal of Exclusive Management Science May 2015 Vol 4 Issue 5 ISSN Diwani (2010) examined the validity of the CAPM for the Bombay stock exchange. The study has used weekly stock returns from 28 companies listed on the Bombay stock exchange from November 2004 to October Dividing the data in to 5 subsamples and arrived a better results but still not supportive in favor of the CAPM in the BSE. The Lazar and Yaseer (2009) investigated the validity of CAPM in Indian Market. The study used the data of 70 companies of BSE100 and tested the validity of CAPM, test of SML and test of Nonlinearity. Further the study compared the relationship between beta and portfolio return. The analysis gives mixed result and we could not find conclusive evidence in support of CAPM in the selected study periods. Abbilash et al (2009) analysed the relevance of factors other than beta that affect asset returns in the Indian stock market. Only nonfinancial firms included in the BSE100 index were considered for the analysis. BSE 100 index comprises of 100 scripts representing different industries. As compared to BSE Sensex which has only 30 scripts, and NSE Nifty which comprises of only 50 scripts, BSE 100 is a much broader based index. The improved version of Fama and MacBeth (FM) crosssectional regression was performed in the study. To overcome the limitations of the general CAPM model, LSDV technique was used and found that the existence of size effect in Indian market. Mohamed and Abirami (2004) investigated the applicability of CAPM in Indian market using 200 stocks from BSE for the period of 12 years between The sensex and 91 days Treasury bill were used as market proxy and risk free return respectively. The application of second pass regression statistically proved that the SharpeLinter CAPM is not relevant to Indian market. Obaidullah (1994) examined the risk return relationship using CAPM in Indian market. The study used monthly price of 30 stocks ranging from Multiple regressions was performed and found that the description of CAPM was not valid in Indian market during the period for the sample stocks. TABLE 4.1 Standard Deviation of the Companies Company RELIANCE ONGC POWERGRID From the table 4.1 it is inferred that in 2010 the share prices of Reliance varies greatly since it has the highest standard deviation. During 2011, ONGC has a highest deviation from the mean, so its share prices vary greatly. During 2012, the share prices of RELIANCE varies greatly as it has the highest standard deviation and the share prices of POWERGRID has a less variation from the mean as it has the lowest standard deviation. During 2013, the share 4
5 Journal of Exclusive Management Science May 2015 Vol 4 Issue 5 ISSN prices of ONGC varies greatly as it has the highest standard deviation and the share prices of POWERGRID has a less variation from the mean as it has the lowest standard deviation. During 2014, the share prices of ONGC varies greatly as it has the highest standard deviation and the share prices of RELIANCE has a less variation from the mean as it has the lowest standard deviation. TABLE 4.2 Beta of the Companies COMPANY RELIANCE ONGC POWERGRID The table 4.2 shows that during 2010, Reliance is the most volatile company since its beta is greater than one. The other two companies are less volatile than the market as their beta values are less than one. During 2011, RELIANCE is more volatile, since its beta is greater than one. The other two companies are less volatile than the market as their beta values are less than one. During 2012, RELIANCE is more volatile, since its beta is greater than one. The other two companies are less volatile than the market as their beta values are less than one. During 2013, ONGC is more volatile, since its beta is greater than one. The other two companies are less volatile than the market as their beta values are less than one. During 2014, ONGC is more volatile, since its beta is greater than one. The other two companies are less volatile than the market as their beta values are less than one. TABLE 4.3 Calculation of Expected Return (Reliance) Year Rf Beta (RmRf) CAPM ( ) ( ) ( ) ( ) ( ) From the table 4.3 it can be inferred that during the entire study period reliance has negative returns. During 2012, the company has the highest return and it has the lowest return in During 2011, the company has the highest return and it has the lowest return in
6 Journal of Exclusive Management Science May 2015 Vol 4 Issue 5 ISSN TABLE 4.4 Calculation of Expected Return of ONGC YEAR RF BETA (RMRF) CAPM ( ) ( ) ( ) ( ) ( ) From the table 4.4 it can be interpreted that ONGC has the highest expected return during 2010 and During the other years, it has a negative return. TABLE 4.5 Calculation of Expected Return of Power Grid YEAR RF BETA (RMRF) CAPM ( ) ( ) ( ) ( ) ( ) From the table 4.5 it can be interpreted that Power Grid has the highest expected return during 2010 and it has a lowest return during TABLE 4.6 CAPM (Capital Asset Pricing Model) COMPANY RELIANCE ONGC POWERGRID
7 Journal of Exclusive Management Science May 2015 Vol 4 Issue 5 ISSN From the table 4.6 it can be inferred that POWER GRID has the highest expected return during During , RELIANCE has negative returns.ongc has positive returns during 2010 and 2012 and has negative returns during 2011, 2013 and TABLE 4.7 Jensen Alpha of the Companies COMPANY YEAR 2010 YEAR 2011 YEAR 2012 YEAR 2013 YEAR 2014 RELIANCE ONGC POWERGRID From the table 4.7 during , RELIANCE has the highest alpha value which means it has outperformed the market index (CNX NIFTY). The other two companies having less Jensen s alpha value indicates that the market index has outperformed the company. Table 4.8 Comparison of the Expected return and the Actual return during 2010 Securities Beta Expected return (Using CAPM) Actual return Overvalued/ Undervalued Reliance Undervalued ONGC Overvalued Power Grid Overvalued From table 4.8 It can be interpreted that Reliance which has a high beta which gives high risk produces a less expected return while the other two securities which has a less beta value gives a higher expected return. So there is an inverse relation between the beta and the expected return of the securities. During 2010, Reliance stocks were found undervalued and ONGC and Power Grid were the overvalued stocks. 7
8 Journal of Exclusive Management Science May 2015 Vol 4 Issue 5 ISSN TABLE 4.9 Comparison of the Expected Return and the Actual Return During 2011 Securities Beta Expected return (Using CAPM) Actual return Overvalued/ Undervalued Reliance Undervalued ONGC Overvalued Power Grid Undervalued From table 4.9 it can be interpreted that Reliance which has a high beta which gives high risk produces a less expected return while ONGC inspite of its high risk produces a high expected return. The stocks of Power Grid produce a negative expected return even though it has a less risk. During 2011, the stocks of Reliance and Power Grid were found undervalued and stocks of ONGC were overvalued. TABLE 4.10 Comparison of the Expected Return and the Actual Return During 2012 Securities Beta Expected return (Using CAPM) Actual return Overvalued/ Undervalued Reliance Undervalued ONGC Overvalued Power Grid Overvalued From table 4.10 it can be interpreted that Reliance with high beta which gives high risk while the other two securities which have a less beta value gives a higher expected return. So there is an inverse relation between the beta and the expected return of the securities. During 2012, Reliance stocks were found undervalued and ONGC and Power Grid were the overvalued stocks. 8
9 Journal of Exclusive Management Science May 2015 Vol 4 Issue 5 ISSN TABLE 4.11 Comparison of the Expected Return and the Actual Return During 2013 Securities Beta Expected return (Using CAPM) Actual return Overvalued/ Undervalued Reliance Undervalued ONGC Overvalued Power Grid Undervalued From table 4.11 it can be interpreted that Reliance which has a high beta which gives high risk produces a less expected return while ONGC inspite of its high risk produces a high expected return. The stocks of Power grid produce a negative expected return even though it has a less risk. During 2013, the stocks of Reliance and Power Grid were found undervalued and stocks of ONGC were overvalued. TABLE 4.12 Comparison of the Expected Return and the Actual Return During 2014 Securities Beta Expected return (Using CAPM) Actual return Overvalued/ Undervalued Reliance Undervalued ONGC Overvalued Power Grid Undervalued From table 4.12 it can be interpreted that Reliance which has a high beta which gives high risk produces a less expected return while ONGC inspite of its high risk produces a high expected return. The stocks of Power Grid produce a negative expected return even though it has a less risk. During 2014, the stocks of Reliance and Power Grid were found undervalued and stocks of ONGC were overvalued. Suggestions 1. Beta is a measure of a securities future risk. The investors can only estimate beta based on historical data. Investors can use historical beta as the measure of future risk only if it is stable over time. 9
10 Journal of Exclusive Management Science May 2015 Vol 4 Issue 5 ISSN To get a high average longrun rate of return in a portfolio or for a stock, the investor needs to increase the risk level of the portfolio that cannot be diversified away. 3. Beta reflects funds volatility relative to a particular benchmark index. But if a fund of the investor has little correlation with that index, the resulting beta and Jensen alpha will have little use as risk measures. Conclusion The result obtained from our analysis implied controversially to the predictions that investors who bear higher risk in the Indian stock market should not necessarily expect a higher return from his investment as well as the risk averse investor for whom the probability to yield a low return by bearing a low risk is not certain. The basic logic behind the capitalasset pricing model is that there is no premium for bearing risks that can be diversified away. But since our result shows that high risk don t necessarily yield high return; this logic is no longer applicable in the NSE market. The analysis can be concluded that each of the investigation conducted is a confirmation of the other that the empirical investigations carried out during this study do not fully hold up with CAPM. The data did not provide evidence that higher beta yields higher return.the data also provide a difference between average risk free rate, risk premium and their estimated values. However, a linear relationship between beta and return is established. To an extent, the consequence of the tests conducted on the data with period 1 Jan 2010 to 31 Dec 2014 obtained from the National Stock Exchange do not appear to absolutely reject CAPM. On the other hand, it may be mentioned that the data do not support CAPM since there are other factors available and capable of affecting the results. References 1. Kapil Choudhary and Sakshi Choudhary: Eurasian Journal of Business and Economics 2010, 3(6), Mazen Diwani' A study that investigates the validity of the CAPM in Bombay Stock Exchange SENSEX Lund University 3. Dr D. LAZAR* and YASEER K.M' "Testing the Empirical Validity of CAPM" in Shorter Periods Evidence from Indian Capital Market" Pondichery university 4. Abhilash S. Nair, Abhijit Sarkar, A. Ramanathan and A. Subramanyam "Anomalies in CAPM: A Panel Data Analysis under Indian Conditions" International Research Journal of Finance and Economics Issue 33 (2009). 5. Peer Mohamed and Abirami," Risk,Retum and Equilibrium in Indian Market Under CAPM" SCMS Journal of Indian Management ' Obaidulllah, M, 1994, Indian Stock Market: Theories and Evidence, Hyderabad: ICFAL Palaha, Sa tinder, 1991, Cost of Capital and Corporate Policy, Anmol Publications. 10
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