Exhibit demonstrated how the Fama French three factor and four factor models could be used to estimate the expected excess returns for three stocks (MSFT, CSX, and XRX) Specifically, using return data from 20052009, the following equations were estimated Three Factor Model MSFT E(R) RFR (0 966) ( M ) (0 018) ( SMB ) ( 0 388) ( HML ) CSX E(R) RFR (1 042) ( M ) (0 043) ( SMB ) (0 370) ( HML ) XRX E(R) RFR (1 178) ( M ) (0 526) ( SMB ) (0 517) ( HML ) Four Factor Model MSFT E(R) RFR (1 001)( M ) (0 012)( SMB ) (0 341)( HML ) (0 073)( MOM ) CSX E(R) RFR (1 122)( M ) (0 031)( SMB ) (0 478)( HML ) (0 166)( MOM ) XRX E(R) RFR (1 041)( M ) (0 505)( SMB ) (0 335)( HML ) ( 0 283)( MOM ) Using the estimated factor risk premia of M 7 23 , SMB 2 00 , HML 4 41 and MOM 4 91 , it was then shown that the expected excess returns for the three stocks were 5 24 , 9 08 , and 11 86 (three factor model) or 6 07 , 10 98 , and 8 63 (four factor model), respectively a Exhibit 9 8 also lists historical factor risk prices from two different time frames (1) 19802009 ( M 7 11 , SMB 1 50 , and HML 5 28 ), and (2) 19272009 ( M 7 92 , SMB 3 61 , and HML 5 02 ) Calculate the expected excess returns for MSFT, CSX, and XRX using both of these alternative sets of factor risk premia in conjunction with the three factor risk model b Exhibit also lists historical estimates for the MOM risk factor (i) MOM 7 99 (19802009), and (2) MOM 9 79 (19272009) Using this additional information, calculate the expected excess returns for MSFT, CSX, and XRX in conjunction with the four factor risk model c Do all of the expected excess returns you calculated in Part a and Part b make sense If not, identify which ones seem inconsistent with asset pricing theory and discuss why d Would you expect the factor betas to remain constant over time Discuss how and why these coefficients might change in response to changing market conditions

Question: Exhibit demonstrated how the Fama-French three-factor and four-factor models could be used to estimate the expected excess returns for three stocks (MSFT, CSX, and XRX).

Exhibit demonstrated how the Fama-French three-factor and four-factor models could be used to estimate the expected excess returns for three stocks (MSFT, CSX, and XRX). Specifically, using return data from 2005–2009, the following equations were estimated:

Three-Factor Model:

MSFT: [E(R) – RFR] = (0:966) (λ_M) + (−0:018) (λ_SMB) + (− 0:388) (λ_HML)

CSX: [E(R) – RFR] = (1:042) (λ_M) + (−0:043) (λ_SMB) + (0:370) (λ_HML)

XRX: [E(R) – RFR] = (1:178) (λ_M) + (0:526) (λ_SMB) + (0:517) (λ_HML)

Four-Factor Model:

MSFT: [E(R) – RFR] = (1:001)(λ_M)+ (−0:012)(λ_SMB) + (−0:341)(λ_HML) + (0:073)(λ_MOM)

CSX: [E(R) – RFR] = (1:122)(λ_M) + (−0:031)(λ_SMB) + (0:478)(λ_HML) + (0:166)(λ_MOM)

XRX: [E(R) – RFR] = (1:041)(λ_M) + (0:505)(λ_SMB) + (0:335)(λ_HML) + (− 0:283)(λ_MOM)

Using the estimated factor risk premia of λ_M = 7.23%, λ_SMB = 2.00%, λ_HML = 4.41% and λ_MOM = 4.91%, it was then shown that the expected excess returns for the three stocks were 5.24%, 9.08%, and 11.86% (three-factor model) or 6.07%, 10.98%, and 8.63% (four-factor model), respectively.

a. Exhibit 9.8 also lists historical factor risk prices from two different time frames: (1) 1980–2009 (λ_M = 7.11%, λ_SMB = 1.50%, and λ_HML = 5.28%), and (2) 1927–2009 (λ_M = 7.92%, λ_SMB = 3.61%, and λ_HML = 5.02%). Calculate the expected excess returns for MSFT, CSX, and XRX using both of these alternative sets of factor risk premia in conjunction with the three-factor risk model.

b. Exhibit also lists historical estimates for the MOM risk factor: (i) λ_MOM = 7.99% (1980–2009), and (2) λ_MOM = 9.79% (1927–2009). Using this additional information, calculate the expected excess returns for MSFT, CSX, and XRX in conjunction with the four-factor risk model.

c. Do all of the expected excess returns you calculated in Part a and Part b make sense? If not, identify which ones seem inconsistent with asset pricing theory and discuss why.

d. Would you expect the factor betas to remain constant over time? Discuss how and why these coefficients might change in response to changing market conditions.

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