Stock Return Predictability: Using the Cyclical Component of the Price Ratio

Abstract

We examine whether the cyclical component of the log dividend-price and price-earnings ratios contain forecast power for stock returns. While the levels of these series contain slow moving information for predicting long horizon returns, they typically provide poor short horizon forecasts. Using three approaches to extract the ratios cyclical component, we conduct several in- and out-of-sample tests. In-sample estimation using the cyclical component leads to economically sensible values, as well as an improved fit compared to the ratio level results. Out-of-sample evidence reveals forecast improvement over a historical mean model, although this varies according to the metric used. The historical mean model is preferred using mean absolute and squared error measures, but the predictive models perform better using Mincer-Zarnowitz and encompassing regressions. Using economic based forecast evaluation, the predictive models are clearly preferred, with a stronger ability to predict the future direction of return movements and with higher trading returns. A further examination of the results reveal that this greater performance largely arises from a superior ability to predict future negative returns.