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

Swiss Journal of Economics and Statistics

Table 2 Pairwise correlations of CH GVD components with the US asset return predictors

	dp	dy	ep	de	svar	ntis	ts	ds	\(GVD_{250}^{US}\)	GVD^new	GVD^old
dp	1
dy	0.98	1
ep	– 0.05	– 0.05	1
de	0.51	0.50	– 0.89	1
svar	0.30	0.22	– 0.28	0.38	1
ntis	– 0.56	– 0.55	0.07	– 0.32	– 0.26	1
ts	0.36	0.36	– 0.22	0.46	0.16	0.20	1
ds	0.68	0.60	– 0.52	0.74	0.62	– 0.45	0.32	1
\(GVD_{250}^{US}\)	0.43	0.43	– 0.07	0.25	0.10	– 0.51	– 0.13	0.31	1
GVD^new	0.46	0.48	0.16	0.08	– 0.05	– 0.12	0.32	0.08	0.07	1
GVD^old	0.31	0.28	– 0.13	0.26	0.18	– 0.07	0.35	0.36	0.14	– 0.03	1

Notes: This table gives the pairwise correlations between the US predictors under study and the two components of GVD. The US variables are the dividend-price ratio (dp), the dividend yield (dy), the earnings-price ratio (ep), the dividend payout ratio (de), the stock market variance (svar), the net equity expansion (ntis), and two interest rate variables, i.e., the term spread (ts) and the default spread (ds). Moreover, we computed a US version of GVD (\(\mathrm {GVD_{250}^{US}}\)). The sample period runs from January 1999 to December 2017