Equity risk factors and the Intertemporal CAPM
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1 Equity risk factors and the Intertemporal CAPM Ilan Cooper 1 Paulo Maio 2 1 Norwegian Business School (BI) 2 Hanken School of Economics BEROC Conference, Minsk
2 Outline 1 Motivation 2 Cross-sectional tests Models Data Factor risk premia 3 Future investment opportunities State variables Forecasting the equity premium Forecasting stock market volatility 4 Economic activity 5 ICAPM state variables 6 Conclusion
3 New CAPM anomalies in recent years: momentum-, investmentand profitability-based anomalies Emergence of new multifactor models containing (different versions of) investment and profitability factors (e.g., Novy-Marx (2013), Fama and French (2014a), and Hou, Xue, and Zhang (2014a)) seeking to explain the extended cross-section of average stock returns Although these models perform relatively well in explaining the new patterns in cross-sectional risk premia, there is still some controversy about the theoretical background of such models (e.g., Hou, Xue, and Zhang (2014a) critique on Fama and French (2014a)) We evaluate whether several equity factor models are consistent with the Merton s Intertemporal CAPM (Merton (1973), ICAPM)
4 Models Three-factor model from Fama and French (1993, 1996, FF3): E(R i,t+1 R f,t+1 ) = γcov(r i,t+1, RM t+1 ) + γ SMB Cov(R i,t+1, SMB t+1 ) +γ HML Cov(R i,t+1, HML t+1 ) Four-factor model from Carhart (1997, C4): E(R i,t+1 R f,t+1 ) = γcov(r i,t+1, RM t+1 ) + γ SMB Cov(R i,t+1, SMB t+1 ) +γ HML Cov(R i,t+1, HML t+1 ) + γ UMD Cov(R i,t+1, UMD t+1 ) Four-factor model employed by Pastor and Stambaugh (2003, PS4): E(R i,t+1 R f,t+1 ) = γcov(r i,t+1, RM t+1 ) + γ SMB Cov(R i,t+1, SMB t+1 ) +γ HML Cov(R i,t+1, HML t+1 ) + γ LIQ Cov(R i,t+1, LIQ t+1 )
5 Models Four-factor model from Novy-Marx (2013, NM4): E(R i,t+1 R f,t+1 ) = γcov(r i,t+1, RM t+1 ) + γ HML Cov(R i,t+1, HML t+1 +γ UMD Cov(R i,t+1, UMD t+1) + γ PMU Cov(R i,t+1, PMU t+1) Four-factor model from Hou, Xue, and Zhang (2014a, 2014b, HXZ4): E(R i,t+1 R f,t+1 ) = γcov(r i,t+1, RM t+1 ) + γ ME Cov(R i,t+1, ME t+1 ) +γ IA Cov(R i,t+1, IA t+1 ) + γ ROE Cov(R i,t+1, ROE t+1 ) Five-factor model by Fama and French (2014a, 2014b, FF5): E(R i,t+1 R f,t+1 ) = γcov(r i,t+1, RM t+1 ) + γ SMB Cov(R i,t+1, SMB t+1 ) +γ HML Cov(R i,t+1, HML t+1 ) + γ RMW Cov(R i,t+1, RMW t+1 ) +γ CMA Cov(R i,t+1, CMA t+1 )
6 Data Correlations of equity factors UMD LIQ HML UMD PMU ME IA ROE RMW CMA RM SMB HML UMD LIQ HML UMD PMU ME IA ROE RMW CMA 1.00
7 Factor risk premia Testing portfolios: deciles sorted on size, book-to-market, momentum, investment-to-assets, return on equity, operating profitability, and asset growth Sample: 1972:01 to 2012:12 Estimation is by first-stage GMM Maio and Santa-Clara (2012): If a state variable forecasts an increase in future aggregate returns, the risk price associated with the corresponding risk factor in the asset pricing equation should be positive If a state variable forecasts an increase in future aggregate stock volatility, the risk price associated with the corresponding factor should be negative
8 Factor risk premia Factor risk premiums for equity risk factors γ SMB γ HML γ UMD γ LIQ γ HML γ UMD γ PMU γ ME γ IA γ ROE γ RMW ( 0.62) (2.25) ( 0.17) (4.15) (3.51) ( 0.68) (2.07) ( 2.77) (4.70) (2.67) (2.07) (2.57) (3.71) (3.60) (2.75) ( 2.08) (5.18)
9 State variables A candidate ICAPM state variable should forecast future investment opportunities The state variables correspond to the cumulative sums on the corresponding factors: CIA t = t s=t 59 IA s
10 Forecasting the equity premium Single long-horizon predictive regressions: r t+1,t+q = a q + b q z t + u t+1,t+q where r t+1,t+q r t r t+q is the continuously compounded excess return over q periods into the future (from t + 1 to t + q) Multivariate regressions: r t+1,t+q = a q + b q CSMB t + c q CHML t + u t+1,t+q, r t+1,t+q = a q + b q CSMB t + c q CHML t + d q CUMD t + u t+1,t+q, r t+1,t+q = a q + b q CSMB t + c q CHML t + d q CLIQ t + u t+1,t+q, r t+1,t+q = a q + b q CHML t + c q CUMD t + d q CPMU t + u t+1,t+q, r t+1,t+q = a q + b q CME t + c q CIA t + d q CROE t + u t+1,t+q, r t+1,t+q = a q + b q CSMB t + c q CHML t + d q CRMW t + e q CCMA t + u
11 Forecasting the equity premium Single predictive regressions: equity premium q = 1 q = 3 q = 12 q = 24 q = 36 q = 48 q = 60 CSMB ( 0.15) ( 0.39) ( 0.16) (0.25) (0.06) (0.07) ( 0.17) R CHML (0.07) (0.28) (1.03) (0.70) (0.41) ( 0.25) ( 0.54) R CUMD ( 1.37) ( 1.33) ( 0.47) (0.06) (0.76) (1.38) (1.21) R CLIQ ( 0.59) ( 0.60) ( 0.38) ( 0.58) ( 1.29) ( 1.90) ( 1.90) R CHML (0.37) (0.50) (0.86) (0.74) (0.39) ( 0.25) ( 0.73) R CUMD ( 1.55) ( 1.55) ( 0.58) ( 0.04) (0.45) (1.21) (1.35) R
12 Forecasting the equity premium Single predictive regressions: equity premium (contd) q = 1 q = 3 q = 12 q = 24 q = 36 q = 48 q = 60 CPMU (1.44) (1.89) (2.11 ) (2.19 ) (1.23) (0.19) ( 0.03) R CME ( 0.42) ( 0.72) ( 0.36) (0.17) (0.01) ( 0.17) ( 0.48) R CIA (0.81) (1.00) (1.23) (1.16) (0.93) (0.38) ( 0.14) R CROE (0.22) (0.61) (1.47) (1.91) (2.45 ) (2.39 ) (2.96 ) R CRMW (1.09) (1.81) (2.18 ) (2.06 ) (1.31) (0.03) (0.25) R CCMA (0.40) (0.53) (0.71) (0.85) (0.49) ( 0.26) ( 0.79) R
13 Forecasting stock market volatility Single regressions: svar t+1,t+q = a q + b q z t + u t+1,t+q where svar t+1,t+q svar t svar t+q and svar t+1 ln(svar t+1 ) is the log of the realized market volatility Multiple regressions: svar t+1,t+q = a q + b q CSMB t + c q CHML t + u t+1,t+q, svar t+1,t+q = a q + b q CSMB t + c q CHML t + d q CUMD t + u t+1,t+q, svar t+1,t+q = a q + b q CSMB t + c q CHML t + d q CLIQ t + u t+1,t+q, svar t+1,t+q = a q + b q CHML t + c q CUMD t + d q CPMU t + u t+1,t+q, svar t+1,t+q = a q + b q CME t + c q CIA t + d q CROE t + u t+1,t+q, svar t+1,t+q = a q + b q CSMB t + c q CHML t + d q CRMW t + e q CCMA t
14 Forecasting stock market volatility Single predictive regressions: stock market volatility q = 1 q = 3 q = 12 q = 24 q = 36 q = 48 q = 60 CSMB ( 3.10 ) ( 2.37 ) ( 1.37) ( 0.70) ( 0.25) (0.10) (0.49) R CHML ( 5.19 ) ( 4.36 ) ( 2.41 ) ( 0.97) ( 0.25) (0.31) (0.58) R CUMD (0.50) (0.27) ( 0.46) ( 0.78) ( 1.03) ( 1.10) ( 0.63) R CLIQ (3.26 ) (2.47 ) (1.60) (1.37) (1.42) (1.47) (1.46) R CHML ( 3.76 ) ( 3.14 ) ( 2.42 ) ( 1.41) ( 0.74) ( 0.11) (0.36) R CUMD (0.77) (0.48) ( 0.44) ( 0.86) ( 1.23) ( 1.70) ( 1.86) R
15 Forecasting stock market volatility Single predictive regressions: stock market volatility (contd) q = 1 q = 3 q = 12 q = 24 q = 36 q = 48 q = 60 CPMU (1.06) (0.47) ( 0.47) ( 0.74) ( 0.71) ( 0.40) (0.27) R CME ( 3.23 ) ( 2.49 ) ( 1.42) ( 0.76) ( 0.32) (0.08) (0.51) R CIA ( 4.41 ) ( 3.71 ) ( 3.23 ) ( 3.52 ) ( 3.54 ) ( 2.43 ) ( 1.30) R CROE (0.44) (0.09) ( 0.60) ( 0.78) ( 0.90) ( 0.93) ( 0.88) R CRMW (1.46) (0.77) ( 0.42) ( 0.79) ( 0.92) ( 0.76) ( 0.49) R CCMA ( 3.12 ) ( 2.63 ) ( 2.25 ) ( 2.56 ) ( 2.56 ) ( 1.66) ( 0.77) R
16 Forecasting stock market volatility Consistency of factors with the ICAPM: single regressions CSMB CHML CUMD CLIQ CHML CUMD CPMU CME CIA CROE CRMW CCMA Panel A (r) Panel B (svar)
17 Forecasting stock market volatility Consistency of factors with the ICAPM: multiple regressions CSMB CHML CUMD CLIQ CHML CUMD CPMU CME CIA CROE CRMW CCMA Panel A (r) Panel B (svar)
18 Economic activity as a proxy for investment opportunities (Roll s critique) Two measures of economic activity: log growth in the industrial production index (IPG) and the Chicago FED National Activity Index (CFED) Single regressions: y t+1,t+q = a q + b q z t + u t+1,t+q where y IPG, CFED and y t+1,t+q y t y t+q denotes the forward cumulative sum in either IPG or CFED
19 Single predictive regressions: industrial production growth q = 1 q = 3 q = 12 q = 24 q = 36 q = 48 q = 60 CSMB ( 0.33) ( 0.48) ( 0.88) ( 0.99) ( 0.95) ( 1.12) ( 1.17) R CHML (0.70) (0.72) (0.75) (0.44) ( 0.81) ( 1.43) ( 1.94) R CUMD ( 1.24) ( 1.10) (0.30) (0.82) (1.67) (2.50 ) (2.25 ) R CLIQ ( 1.50) ( 1.50) ( 1.21) ( 1.06) ( 1.49) ( 2.33 ) ( 2.74 ) R CHML (1.39) (1.36) (1.04) (0.81) ( 0.56) ( 1.36) ( 2.03 ) R CUMD ( 2.02 ) ( 1.93) ( 0.02) (0.66) (1.42) (2.26 ) (2.11 ) R
20 Single predictive regressions: industrial production growth (contd) q = 1 q = 3 q = 12 q = 24 q = 36 q = 48 q = 60 CPMU (0.14) (0.37) (1.33) (1.76) (1.87) (0.82) (0.42) R CME ( 0.46) ( 0.60) ( 0.98) ( 1.04) ( 1.12) ( 1.44) ( 1.59) R CIA (2.16 ) (1.96 ) (1.59) (1.50) (1.14) (0.01) ( 1.17) R CROE ( 0.36) ( 0.08) (1.20) (2.04 ) (3.73 ) (4.83 ) (5.11 ) R CRMW ( 0.46) ( 0.19) (1.08) (1.55) (1.82) (0.49) (0.31) R CCMA (1.59) (1.47) (1.17) (0.81) ( 0.25) ( 1.22) ( 2.30 ) R
21 Single predictive regressions: Chicago FED Index q = 1 q = 3 q = 12 q = 24 q = 36 q = 48 q = 60 CSMB (0.36) (0.13) ( 0.17) ( 0.20) ( 0.16) ( 0.30) ( 0.41) R CHML (4.64 ) (3.98 ) (2.81 ) (2.06 ) (0.18) ( 0.99) ( 1.79) R CUMD (0.12) (0.19) (0.88) (1.30) (2.35 ) (3.10 ) (3.03 ) R CLIQ ( 1.12) ( 0.97) ( 0.71) ( 0.58) ( 0.92) ( 1.53) ( 2.05 ) R CHML (4.53 ) (3.67 ) (2.75 ) (2.39 ) (0.70) ( 0.85) ( 2.11 ) R CUMD ( 0.49) ( 0.36) (0.60) (1.14) (2.01 ) (2.64 ) (2.66 ) R
22 Single predictive regressions: Chicago FED Index (contd) q = 1 q = 3 q = 12 q = 24 q = 36 q = 48 q = 60 CPMU ( 0.83) ( 0.43) (0.49) (0.91) (1.15) (0.42) ( 0.27) R CME (0.43) (0.20) ( 0.12) ( 0.16) ( 0.25) ( 0.57) ( 0.75) R CIA (5.29 ) (4.25 ) (3.30 ) (3.34 ) (3.49 ) (1.32) ( 0.50) R CROE (0.37) (0.54) (1.26) (1.93) (3.05 ) (3.65 ) (4.23 ) R CRMW ( 1.18) ( 0.70) (0.46) (0.95) (1.15) (0.30) ( 0.31) R CCMA (4.31 ) (3.45 ) (2.56 ) (2.45 ) (1.67) ( 0.06) ( 2.02 ) R
23 Consistency of factors with the ICAPM: economic activity CSMB CHML CUMD CLIQ CHML CUMD CPMU CME CIA CROE CRMW CCMA Panel A (IPG)? Panel B (CFED)??
24 Multiple forecasting regressions for the equity premium: r t+1,t+q = a q +b q z t +c q TERM t +d q DEF t +e q dp t +f q TB t +g q vs t +h q pe t +u Multiple forecasting regressions for market volatility: svar t+1,t+q = a q +b q z t +c q TERM t +d q DEF t +e q dp t +f q TB t +g q vs t +h q pe t
25 Predictive regressions for equity premium: controls q = 1 q = 3 q = 12 q = 24 q = 36 q = 48 q = 60 CSMB (0.16) ( 0.30) (0.36) (1.46) (1.20) (1.24) (0.60) R CHML ( 0.62) ( 0.78) ( 0.48) (0.39) (0.82) ( 0.43) ( 0.70) R CUMD ( 0.74) ( 0.59) (0.23) (0.75) (2.21 ) (2.39 ) (1.55) R CLIQ ( 0.11) ( 0.21) ( 0.32) (0.11) ( 1.18) ( 0.79) (1.56) R CHML (0.38) (0.36) (0.26) (0.86) (1.39) (0.52) (0.16) R CUMD ( 0.98) ( 0.89) ( 0.07) (0.43) (1.57) (2.68 ) (2.25 ) R
26 Predictive regressions for equity premium: controls (contd) q = 1 q = 3 q = 12 q = 24 q = 36 q = 48 q = 60 CPMU (2.83 ) (3.70 ) (3.15 ) (5.74 ) (3.54 ) (2.01 ) (3.26 ) R CME ( 0.33) ( 0.79) (0.18) (1.58) (1.46) (1.07) (0.39) R CIA (1.10) (1.39) (1.00) (1.07) (1.27) ( 0.36) ( 0.96) R CROE (0.62) (1.32) (1.76) (2.61 ) (2.80 ) (0.99) (1.42) R CRMW (1.63) (2.67 ) (2.47 ) (3.24 ) (2.21 ) (0.68) (1.86) R CCMA (0.48) (0.62) (0.44) (0.74) (1.24) ( 0.22) ( 0.74) R
27 Predictive regressions for stock market volatility: controls q = 1 q = 3 q = 12 q = 24 q = 36 q = 48 q = 60 CSMB ( 5.85 ) ( 4.85 ) ( 3.61 ) ( 3.00 ) ( 1.80) ( 0.33) (1.56) R CHML ( 0.66) ( 0.42) ( 0.66) ( 0.31) (0.42) (1.46) (1.15) R CUMD (0.36) ( 0.17) ( 1.98 ) ( 3.17 ) ( 3.08 ) ( 0.69) (0.75) R CLIQ (0.36) (0.34) (0.46) (0.74) (1.61) (1.14) (0.08) R CHML ( 0.19) ( 0.34) ( 1.19) ( 1.13) ( 1.05) ( 1.33) ( 1.54) R CUMD (1.06) (0.33) ( 1.76) ( 3.01 ) ( 6.89 ) ( 3.95 ) ( 2.30 ) R
28 Predictive regressions for stock market volatility: controls (contd) q = 1 q = 3 q = 12 q = 24 q = 36 q = 48 q = 60 CPMU ( 2.08 ) ( 2.62 ) ( 2.95 ) ( 3.02 ) ( 3.62 ) ( 3.69 ) ( 2.15 ) R CME ( 5.19 ) ( 4.22 ) ( 3.74 ) ( 3.22 ) ( 2.12 ) ( 0.66) (1.21) R CIA ( 0.91) ( 1.25) ( 1.70) ( 2.55 ) ( 4.05 ) ( 4.45 ) ( 2.63 ) R CROE (2.37 ) (1.26) ( 0.26) ( 0.61) ( 0.52) (0.39) (0.97) R CRMW ( 0.66) ( 1.33) ( 1.94) ( 2.06 ) ( 2.92 ) ( 4.62 ) ( 3.11 ) R CCMA ( 0.66) ( 0.99) ( 1.76) ( 2.73 ) ( 4.70 ) ( 7.67 ) ( 5.11 ) R
29 The state variables associated with (alternative) profitability factors help to forecast the equity premium in a way consistent with the ICAPM Several state variables (those associated with investment factors) forecast a significant decline in stock volatility, being consistent with the ICAPM There is strong evidence of predictability for future economic activity, especially from investment and profitability factors The new factors are generally consistent with the ICAPM The four-factor model of Hou, Xue, and Zhang (2014a) presents the best convergence with the ICAPM The predictive ability of most equity state variables does not seem to be subsumed by traditional ICAPM state variables
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