Cross-Predictability of Earnings and Stock Returns: A Latent Factor Approach

Abstract

I propose a new method, three-pass regression filter developed by Kelly and Pruitt (2015), to predict non-announcing firms’ earnings news using the cross-section of all available early announcers’ earnings news, the number of which can be as large as thousands. The method assumes a set of common latent factors driving the earnings news of non-announcing firms and early announcers and thus efficiently reduces the dimension of announced earnings. Empirical tests show that the extracted measure strongly predicts the earnings surprise and the earnings announcement return with both statistical and economic significance. A long-short trading strategy based on the extracted information realizes a 10% value-weighted alpha annually, indicating a delayed reaction of investors. The return predictability is stronger for small firms or firms with less investor attention. Controlling a series of documented information channels has little impact on the predictive power of this extracted measure.