Capital Asset Pricing Model Assessment - Assignment Example

Running Head: CAPM ASSIGNMENT CAPM Assignment Name of Student Name of Course Course Instructor Date CAPM Assignment Part 1 Ho: αj = 0 for the Microsoft Stock H1: αj =/ 0 for the Microsoft Stock If we look at the estimation model of the Microsoft stock in the appendix, then we can see that αj for the Microsoft stock is 0.005 thus the null hypothesis would be rejected. Part 2 The estimation model has been generated in Eviews by taking Microsoft stock as the dependent variable and the Market index returns as the independent variable. The coefficient of the Msft stock is 1.32 which is the beta of the stock. Since, this beta is greater than 1 hence, we conclude that the stock of Microsoft is an aggressive stock. Part 3 The estimation model has been generated in Eviews by taking Mobil-Exxon stock as the dependent variable and the Market index returns as the independent variable. The coefficient of the Xom stock is 0.41 which is the beta of the stock. Since, this beta is less than 1 hence, we conclude that the stock of Mobil-Exxon is a conservative stock. Part 4 The confidence intervals for all the stock betas are shown in the table below:   Beta Lower Bound Upper Bound Mean of Stock Returns Standard Deviation Confidence at 95% Microsoft 1.320 1.3013 1.3387 0.008557 0.11 0.02 GE 0.940 0.9281 0.9519 0.001361 0.07 0.01 GM 1.260 1.2383 1.2817 -0.009082 0.13 0.02 IBM 1.190 1.1746 1.2054 0.008332 0.09 0.02 Disney 0.890 0.8762 0.9038 0.001379 0.08 0.01 XOM 0.410 0.4009 0.4191 0.010488 0.05 0.01 Part 5 If we look at the confidence interval of GE stock, then we can see that this stock would remain conservative as the lower and upper bounds are less than 1. If we look at the GM stock’s confidence interval then the lower and the upper bound both are above 1 hence, this stock would be considered as an aggressive stock. Lastly, if we look at the stock of Disney, then its lower and upper bounds are also both less than 1 hence, this stock would also be a conservative stock. Among all the 3 stocks, the stock of GM has the wider confidence interval hence this shows that the riskiness of this stock is highest among all. Part 6 The expected returns and the standard deviations of the Microsoft and the Mobil Exxon stocks are shown in the table below: Market Return Probability Mean Return of Microsoft Mean Return of XOM 5% 0.5 4% 2.07% -5% 0.5 -2% 0.02% Risk free rate 0     Alpha 0       Expected Return 1.71% 2.10%   Stdev 0.42% 0.18%   CV 24.52% 8.61% If we wish to minimize our risk as measured by standard deviation then we would choose the Microsoft stock which has a higher coefficient of variation or units of risk per unit of return. Part 7 A well diversified portfolio can be made by investing in a neutral, defensive and aggressive stock. The investor should invest in Microsoft, IBM and XOM stocks. These are aggressive, neutral and conservative stocks among the six stocks given. If we assume that the investment in each stock would be same then the mean portfolio return can also be calculated as shown below: PORTFOLIO RETURN   MSFT XOM IBM Mean Return 0.86% 1.05% 0.83% Investment Weights 0.333333333 0.333333333 0.333333333 Portfolio Return 0.91%     Beta 1.32 0.41 1.19 Portfolio Beta 0.973     The portfolio will have a return of 0.91% and the beta of the portfolio would be 0.97 which will bring the portfolio in a conservative category and minimize the risk also. Part 8 If we look at the R2 values of all the regression models shown in the appendix, then it can be seen that the IBM stock has the higher goodness of fit (0.40) among all the models and its returns are more predictable as compared to the returns of all the other stocks. This is the reason we have considered this stock as a neutral stock and included in our portfolio. The XOM stock has the lowest R2 value of 0.14 but since, its beta is lower we have included it in the portfolio as well. However, the goodness of fit for this stock is weak. Appendices Microsoft Regression Dependent Variable: MSFT Method: Least Squares Date: 04/28/17 Time: 16:59 Sample: 1998M01 2008M12 Included observations: 132 Variable Coefficient Std. Error t-Statistic Prob.   C 0.005238 0.007755 0.675495 0.5006 MKT 1.321295 0.160344 8.240380 0.0000 R-squared 0.343115     Mean dependent var 0.008557 Adjusted R-squared 0.338062     S.D. dependent var 0.109361 S.E. of regression 0.088976     Akaike info criterion -1.985865 Sum squared resid 1.029174     Schwarz criterion -1.942187 Log likelihood 133.0671     Hannan-Quinn criter. -1.968116 F-statistic 67.90387     Durbin-Watson stat 2.345462 Prob(F-statistic) 0.000000 GE Regression Dependent Variable: GE Method: Least Squares Date: 04/28/17 Time: 17:01 Sample: 1998M01 2008M12 Included observations: 132 Variable Coefficient Std. Error t-Statistic Prob.   C -0.000910 0.004767 -0.190830 0.8490 MKT 0.904170 0.098573 9.172613 0.0000 R-squared 0.392912     Mean dependent var 0.001361 Adjusted R-squared 0.388242     S.D. dependent var 0.069934 S.E. of regression 0.054699     Akaike info criterion -2.958917 Sum squared resid 0.388954     Schwarz criterion -2.915238 Log likelihood 197.2885     Hannan-Quinn criter. -2.941168 F-statistic 84.13684     Durbin-Watson stat 2.238534 Prob(F-statistic) 0.000000 GM Regression Dependent Variable: GM Method: Least Squares Date: 04/28/17 Time: 17:02 Sample: 1998M01 2008M12 Included observations: 132 Variable Coefficient Std. Error t-Statistic Prob.   C -0.012270 0.009752 -1.258258 0.2106 MKT 1.269683 0.201632 6.297016 0.0000 R-squared 0.233727     Mean dependent var -0.009082 Adjusted R-squared 0.227833     S.D. dependent var 0.127328 S.E. of regression 0.111887     Akaike info criterion -1.527614 Sum squared resid 1.627439     Schwarz criterion -1.483935 Log likelihood 102.8225     Hannan-Quinn criter. -1.509865 F-statistic 39.65241     Durbin-Watson stat 2.065964 Prob(F-statistic) 0.000000 IBM Regression Dependent Variable: IBM Method: Least Squares Date: 04/28/17 Time: 17:02 Sample: 1998M01 2008M12 Included observations: 132 Variable Coefficient Std. Error t-Statistic Prob.   C 0.005343 0.006098 0.876149 0.3826 MKT 1.190086 0.126093 9.438182 0.0000 R-squared 0.406607     Mean dependent var 0.008332 Adjusted R-squared 0.402043     S.D. dependent var 0.090485 S.E. of regression 0.069970     Akaike info criterion -2.466473 Sum squared resid 0.636449     Schwarz criterion -2.422794 Log likelihood 164.7872     Hannan-Quinn criter. -2.448724 F-statistic 89.07928     Durbin-Watson stat 2.172358 Prob(F-statistic) 0.000000 Disney Regression Dependent Variable: DIS Method: Least Squares Date: 04/28/17 Time: 17:03 Sample: 1998M01 2008M12 Included observations: 132 Variable Coefficient Std. Error t-Statistic Prob.   C -0.000875 0.005964 -0.146667 0.8836 MKT 0.897293 0.123316 7.276385 0.0000 R-squared 0.289407     Mean dependent var 0.001379 Adjusted R-squared 0.283941     S.D. dependent var 0.080866 S.E. of regression 0.068429     Akaike info criterion -2.511010 Sum squared resid 0.608725     Schwarz criterion -2.467332 Log likelihood 167.7267     Hannan-Quinn criter. -2.493261 F-statistic 52.94577     Durbin-Watson stat 2.426524 Prob(F-statistic) 0.000000 Mobil-Exxon Regression Dependent Variable: XOM Method: Least Squares Date: 04/28/17 Time: 17:03 Sample: 1998M01 2008M12 Included observations: 132 Variable Coefficient Std. Error t-Statistic Prob.   C 0.009444 0.004328 2.181901 0.0309 MKT 0.415520 0.089496 4.642884 0.0000 R-squared 0.142233     Mean dependent var 0.010488 Adjusted R-squared 0.135635     S.D. dependent var 0.053417 S.E. of regression 0.049662     Akaike info criterion -3.152116 Sum squared resid 0.320621     Schwarz criterion -3.108437 Log likelihood 210.0397     Hannan-Quinn criter. -3.134367 F-statistic 21.55637     Durbin-Watson stat 2.347979 Prob(F-statistic) 0.000008 Output from Eviews   GE GM IBM DIS MSFT XOM Mean 0.00136 -0.00908 0.00833 0.00138 0.00856 0.01049 Median -0.00472 -0.01302 0.00648 0.00586 0.004 0.00331 Maximum 0.19239 0.27662 0.3538 0.24145 0.40778 0.23217 Minimum -0.2349 -0.38931 -0.22645 -0.26779 -0.34353 -0.11646 Std. Dev. 0.06993 0.12733 0.09049 0.08087 0.10936 0.05342 Skewness -0.00688 -0.22218 0.47933 -0.07581 0.5408 0.68119 Kurtosis 3.96754 3.43104 4.88668 4.23186 4.66517 4.87855     Jarque-Bera 5.14979 2.10787 24.6322 8.47259 21.6846 29.6176 Probability 0.07616 0.34856 4E-06 0.01446 0.00002 0     Sum 0.17964 -1.19876 1.09979 0.18199 1.12947 1.38436 Sum Sq. Dev. 0.64069 2.12384 1.07256 0.85664 1.56675 0.37379     Observations 132 132 132 132 132 132 Read More