PREDICTING STOCK RETURNS — THE INFORMATION CONTENT OF PREDICTORS ACROSS HORIZONS

Abstract

We evaluate and compare the information contents of dividend-price ratio and consumption-wealth ratio ([Formula: see text] for predicting stock returns at different horizons. To do this, we conduct a canonical correlation analysis of wavelet-decomposed stock returns and a selected group of predictors. We show that predictive information is often wasted due to a weak signal problem: The highly predictive component is met with very low variation. Nevertheless, we find that cay contains valuable information about the long run and that, after allowing for structural breaks, dividend-price ratio becomes very informative about short-to-medium-horizon returns and outperforms [Formula: see text] in terms of in-sample [Formula: see text].