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Posted on Sep 04, 2024

1. Use Appendix A for this question. Appendix A shows the regression results of one, three and four factor models of IBM, and the average

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1. Use Appendix A for this question. Appendix A shows the regression results of one, three and four factor models of IBM, and the average risk premiums of the market, size, growth and momentum factors a. Among the four factors, which factors are significantly related to the returns of IBM? Explain the relation between the risk premium of IBM and the significant factors. (Review your statistics textbook for help.) b. Which model provides the highest explanatory power for the returns of IBM? C. Based on your answer in b., estimate expected return of IBM. Appendix A: IBM A.1: Average "monthly risk premium (%) over a sample period: Risk-free rate = 0.32% Market risk premium = 0.72% M/B risk premium (HML) = 0.49% Size risk premium (SMB) = 0.28% Momentum risk premium (UMD) = 0.77% A.2: Regression results of one, three and four factor models of IBM over a sample period. The models are estimated using monthly data. One Factor Model SUMMARY OUTPUT Regression Statistics Multiple R 0.52222 R Square 0.2727 Adjusted R Square 0.2689 Standard Error 0.0775 Observations 192 ANOVA Senticence F 71.2487 0.0000 Regression Residual Total 1 190 191 SS 0.4275 1.1 404 1.5680 MS 0.4276 0.0060 Intercept mktrf oefficients tenderd Eno 0.0002 0.0057 1.1710 0.1387 2 SI 0.0317 8.4403 P-veke 0.97 48 0.0000 Lover 9096 Upper 9536 Lower -0.0110 0.0114 -0.0110 0.8373 1.4446 0.8973 per 90.096 0.0114 1.4446 Three Factor Model SUMMARY OUTPUT Regression Statistics Multiple R 0.5507 R Square 0.3033 Adjusted R Square 0.2322 Standard Error 0.0762 Observations 192 ANOVA Regression Residual Total SS 0 4756 10924 1 SFAN Significancer 0.0000 3 188 191 US O. 1585 0 0058 Intercept mktif hml smb Coefficients Standard Emo 0.0044 0.0009 1.099 0.159 0.4563 0.2087 0.4399 0.1678 + Star 0.750 6.6295 2. 1863 2.6213 P-value Lower 8.596 Upper 9596 Lower 95 096 Upper 95 096 0.4501 -0.0071 0.0160 -0.0071 O 0160 0.000 0.7417 1.3700 0.7417 1.3700 0.0300 0.000 0.0446 O.OEDO 0.0445 0.0095 0.7710 0.1009 0.7710 0.1009 Four Factor Model SUMMARY OUTPUT Regression Statistics Multiple R 0.6315 R Square 0.3900 Adjusted R Square 0.3050 Standard Error UU/ 10 Ohservations 192 ANOVA MS Significance 31.0137 OOOOD Regression Residual Total SS 0.5253 0942 1 SAN 4 18/ 191 O. 1953 D 5D Intercept moktit hml ent und Coefficients Standard Emo 0.0105 0.003s DY 0.1523 - 5693 n 1953 -O 3217 0 1578 -0.6065 0.1113 + Stat 1.9007 5.byus - -20582 -5.4510 P-vaks 0.0570 . n47 00420 0.0000 Lower 9596 Usper 9596 Lower 95.0%6UBoer 95.0% 0.0004 0.0217 0.0004 0.0217 US4 1.1674 U.SI 1.1674 - 9445 - 1741 - 9445 -0 1741 -0.6330 -0.0103 -0.6330 -00103 -0.8260 -0.3870 -0.8260 -0.3870 1. Use Appendix A for this question. Appendix A shows the regression results of one, three and four factor models of IBM, and the average risk premiums of the market, size, growth and momentum factors a. Among the four factors, which factors are significantly related to the returns of IBM? Explain the relation between the risk premium of IBM and the significant factors. (Review your statistics textbook for help.) b. Which model provides the highest explanatory power for the returns of IBM? C. Based on your answer in b., estimate expected return of IBM. Appendix A: IBM A.1: Average "monthly risk premium (%) over a sample period: Risk-free rate = 0.32% Market risk premium = 0.72% M/B risk premium (HML) = 0.49% Size risk premium (SMB) = 0.28% Momentum risk premium (UMD) = 0.77% A.2: Regression results of one, three and four factor models of IBM over a sample period. The models are estimated using monthly data. One Factor Model SUMMARY OUTPUT Regression Statistics Multiple R 0.52222 R Square 0.2727 Adjusted R Square 0.2689 Standard Error 0.0775 Observations 192 ANOVA Senticence F 71.2487 0.0000 Regression Residual Total 1 190 191 SS 0.4275 1.1 404 1.5680 MS 0.4276 0.0060 Intercept mktrf oefficients tenderd Eno 0.0002 0.0057 1.1710 0.1387 2 SI 0.0317 8.4403 P-veke 0.97 48 0.0000 Lover 9096 Upper 9536 Lower -0.0110 0.0114 -0.0110 0.8373 1.4446 0.8973 per 90.096 0.0114 1.4446 Three Factor Model SUMMARY OUTPUT Regression Statistics Multiple R 0.5507 R Square 0.3033 Adjusted R Square 0.2322 Standard Error 0.0762 Observations 192 ANOVA Regression Residual Total SS 0 4756 10924 1 SFAN Significancer 0.0000 3 188 191 US O. 1585 0 0058 Intercept mktif hml smb Coefficients Standard Emo 0.0044 0.0009 1.099 0.159 0.4563 0.2087 0.4399 0.1678 + Star 0.750 6.6295 2. 1863 2.6213 P-value Lower 8.596 Upper 9596 Lower 95 096 Upper 95 096 0.4501 -0.0071 0.0160 -0.0071 O 0160 0.000 0.7417 1.3700 0.7417 1.3700 0.0300 0.000 0.0446 O.OEDO 0.0445 0.0095 0.7710 0.1009 0.7710 0.1009 Four Factor Model SUMMARY OUTPUT Regression Statistics Multiple R 0.6315 R Square 0.3900 Adjusted R Square 0.3050 Standard Error UU/ 10 Ohservations 192 ANOVA MS Significance 31.0137 OOOOD Regression Residual Total SS 0.5253 0942 1 SAN 4 18/ 191 O. 1953 D 5D Intercept moktit hml ent und Coefficients Standard Emo 0.0105 0.003s DY 0.1523 - 5693 n 1953 -O 3217 0 1578 -0.6065 0.1113 + Stat 1.9007 5.byus - -20582 -5.4510 P-vaks 0.0570 . n47 00420 0.0000 Lower 9596 Usper 9596 Lower 95.0%6UBoer 95.0% 0.0004 0.0217 0.0004 0.0217 US4 1.1674 U.SI 1.1674 - 9445 - 1741 - 9445 -0 1741 -0.6330 -0.0103 -0.6330 -00103 -0.8260 -0.3870 -0.8260 -0.3870