• R Program

• > setwd("~/Dropbox/Teaching/EF4822_Spring2020")

• > da=read.csv("PredictorData2018part.csv") > head(da)

 yyyy Index D12 E12 

1. 1 1927 17.66 0.77 1.11 

2. 2 1928 24.35 0.85 1.38 

3. 3 1929 21.45 0.97 1.61 

4. 4 1930 15.34 0.98 0.97 

5. 5 1931 8.12 0.82 0.61 

6. 6 1932 6.89 0.50 0.41 

 b.m 0.3746886 0.2596667 0.3384578 0.5547454 1.1707317 1.4420843 

 tbl AAA 0.0317 0.0446 0.0426 0.0461 0.0303 0.0467 0.0148 0.0452 0.0241 0.0532 0.0004 0.0459 

 BAA lty cay 0.0532 0.0316 NaN 0.0560 0.0340 NaN 0.0595 0.0340 NaN 0.0671 0.0330 NaN 0.1042 0.0407 NaN 0.0842 0.0315 NaN 

ntis Rfree

1. 1 0.076474752 0.0317 

2. 2 0.063068738 0.0426 

3. 3 0.163522172 0.0303 

4. 4 0.113885891 0.0148 

5. 5 -0.012944196 0.0241 

6. 6 -0.005031571 0.0004 svar csp ik 

1. 1 0.009419065 NaN NaN 

2. 2 0.019799325 NaN NaN 

3. 3 0.124614012 NaN NaN 

4. 4 0.066648919 NaN NaN 

5. 5 0.159402740 NaN NaN 

6. 6 0.307451657 NaN NaN 

 infl eqis -0.022598870 0.26551235 -0.011560694 0.49742929 0.005847953 0.72059294 -0.063953488 0.30784749 -0.093167702 0.14466470 -0.102739726 0.03726708 

 CRSP_SPvw CRSP_SPvwx 0.35879164 0.2945602 0.38844041 0.3331307 

-0.08834698 -0.1213454 -0.26302852 -0.2958606 -0.45525321 -0.4892035 -0.08890738 -0.1483694 

 ltr 0.089448628 0.000827246 0.034099467 0.046429195 -0.053157349 0.168452113 

 corpr 0.07443637 0.02841156 0.03273004 0.07975053 -0.01850982 0.10820224 

> CRSP_SPvw=da[,20] > Rfree=da[,12] > exret=CRSP_SPvw-Rfree # stock market excess return > D12=da[,3] > Index=da[,2] > dp=log(D12/Index) # log dividend-to-price ratio > bm=da[,5] # book-to-market ratio

> T=length(exret) 

# use log dividend-to-price ratio to predict market excess return > lmdp=lm(exret[2:T]~dp[1:T-1]) > View(lmdp) > summary(lmdp)

Call: lm(formula = exret[2:T] ~ dp[1:T - 1]) 

Residuals: Min 1Q Median 3Q Max 

-0.60678 -0.13020 0.02396 0.14358 0.39421 

Coefficients: Estimate Std. Error t value Pr(>|t|) 

(Intercept) 0.33301 0.15107 2.204 0.0301 * dp[1:T - 1] 0.07474 0.04429 1.688 0.0950 . --- Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1   1 

Residual standard error: 0.1973 on 89 degrees of freedom Multiple R-squared: 0.03101, Adjusted R-squared: 0.02012 F-statistic: 2.848 on 1 and 89 DF, p-value: 0.09498

> anova(lmdp) Analysis of Variance Table 

Response: exret[2:T] Df Sum Sq Mean Sq F value Pr(>F) 

dp[1:T - 1] 1 0.1109 0.110873 2.8481 0.09498 . Residuals 89 3.4646 0.038928 --- Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1   1 

> plot(x=dp[1:T-1],y=exret[2:T],main="exret~dp") > abline(lm(exret[2:T]~dp[1:T-1]))

# use book-to-market ratio to predict market excess return > lmbm=lm(exret[2:T]~bm[1:T-1])

> summary(lmbm) 

Call: lm(formula = exret[2:T] ~ bm[1:T - 1]) 

Residuals: Min 1Q Median 3Q Max 

-0.5587 -0.1417 0.0096 0.1400 0.4011 

Coefficients: Estimate Std. Error t value Pr(>|t|) 

(Intercept) -0.01411 0.04853 -0.291 0.7719 bm[1:T - 1] 0.16851 0.07839 2.150 0.0343 * --- Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1   1 

Residual standard error: 0.1954 on 89 degrees of freedom Multiple R-squared: 0.04936, Adjusted R-squared: 0.03868

As we discussed in class, this problem set asks you to use log dividend-price ratio and book-market ratio to predict the long-horizon 5 year stock market excess returns for United States. Please use the data set we used in Week 11 class, i.e., PredictorData2018part.csv, to answer the following questions. 4. For 5-year simple excess return, which variable predicts better, log dividend-price ratio or book-market ratio? For 5-year log excess return, which variable predicts better, log dividend-price ratio or book-market ratio? As we discussed in class, this problem set asks you to use log dividend-price ratio and book-market ratio to predict the long-horizon 5 year stock market excess returns for United States. Please use the data set we used in Week 11 class, i.e., PredictorData2018part.csv, to answer the following questions. 4. For 5-year simple excess return, which variable predicts better, log dividend-price ratio or book-market ratio? For 5-year log excess return, which variable predicts better, log dividend-price ratio or book-market ratio