Forecasting stock returns: Do less powerful predictors help?

This paper proposes a simple but efficient way to improve the predictability of stock returns. Instead of torturously constructing new powerful predictors, we readily select existing predictors that have low correlations and thus provide complementary information. Our forecasting strategy is to use the selected predictors based on a multivariate regression model. In our forecasting strategy, less powerful predictors are also useful for forecasting stock returns if they could provide complementary information. The empirical results show that our forecasting strategy outperforms not only the univariate regression models that use each predictor's information separately but also combination approaches that use all predictors jointly. We also document that our strategy extracts significantly more useful information from the complementary predictors than the competing models. In addition, from an asset allocation perspective, a mean-variance investor realizes substantial economic gains. Furthermore, the evidence based on Monte Carlo simulations supports the feasibility of our forecasting strategy.