DO ARBITRAGE PRICING-MODELS EXPLAIN THE PREDICTABILITY OF STOCK RETURNS

This article studies predictability in U.S. stock returns for multiple investment horizons. We measure to what extent predictability is driven by premiums for economywide risk factors, comparing two standard methods for factor selection. We study single-beta models and multiple-beta models. We show how to estimate the fraction of the predictability in returns captured by the model, simultaneously with the other parameters. Our analysis indicates that the models capture a large fraction of the predictability for all of the investment horizons. The performance of the principal components and the prespecified-factor approaches are broadly similar.