This study investigates the predictability of 11 industrialised stock returns with emphasis on the role of US returns. Using monthly data spanning 1980:2 to 2014:12, we show that there exist multiple structural breaks and non-linearities in the data. Therefore, we employ methods that are capable of accounting for these and at the same time date stamping the periods of causal relationship between the US returns and those of the other countries. First we implement a subsample analysis which relies on the set of models, data set and sample range as in Rapach et al. (J Finance LXVIII(4):1633-1662, 2013). Our results show that while the US returns played a strong predictive role based on the OLS pairwise Granger causality predictive regression and news-diffusion models, its role based on the adaptive elastic net model is weak. Second, we implement our preferred model: a bootstrap rolling window approach using our newly updated data on stock returns for each countries, and find that US stock return has significant predictive ability for all the countries at certain sub-periods. Given these results, it would be misleading to rely on results based on constant-parameter linear models that assume that the relationship between the US returns and those of other industrialised countries are permanent, since the relationship is, in fact, time-varying, and holds only at specific periods.