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Table 4 In-sample predictability of Swiss stock market and bond market excess returns using GVD or its components

From: What Goliaths and Davids among Swiss firms tell us about expected returns on Swiss asset markets

Panel A: Predictability of stock market excess returns
Predictor\(\hat {\beta }\)R2(%)\(\hat {\beta }\)R2(%)\(\hat {\beta }\)R2(%)\(\hat {\beta }\)R2(%)
(p value)(0.03) (0.02) (0.02) (0.02) 
(p value)(0.03) (0.02) (0.00) (0.01) 
GVDold– 0.340.72– 0.180.49–
(p value)(0.84) (0.73) (0.53) (0.37) 
Panel B: Predictability of bond market excess returns
Predictor\(\hat {\beta }\)R2(%)\(\hat {\beta }\)R2(%)\(\hat {\beta }\)R2(%)\(\hat {\beta }\)R2(%)
(p value)(0.39) (0.33) (0.32) (0.35) 
GVDnew– 0.020.07–
(p value)(0.67) (0.57) (0.38) (0.36) 
(p value)(0.01) (0.14) (0.35) (0.41) 
  1. Notes: This table presents OLS estimates from univariate regressions of h-month ahead Swiss stock (panel A) and bond market (panel B) returns on GVD or one of its components (see Table 1 for a description of the variables). All variables are z-standardized. The sample period runs from January 1999 to December 2017. We compute heteroskedasticity and autocorrelation robust p values (in parentheses below the estimates) from a wild bootstrap procedure that tests the null hypothesis of \(\hat {\beta }^{h}=0\) against the alternative that \(\hat {\beta }^{h}>0\) because the regressors are defined in such a way that high values predict high excess returns. *, **, and *** indicate significance at the 10%, 5%, and 1% level, respectively