Tactical Industry Allocation and Model Uncertainty

Abstract

We use Bayesian model averaging to analyze the sample evidence on industry return predictability within the U.S. stock market in the presence of model uncertainty. The posterior analysis shows the importance of inflation and earnings yield in predicting industry returns. The out-of-sample performance of the Bayesian approach is, in general, superior to that of other statistical model selection criteria. However, the out-of-sample forecasting power of a naive iid forecast is similar to the Bayesian forecast. A variance decomposition into model risk, estimation risk, and forecast error shows that model risk is less important than estimation risk.

Keywords: Bayesian Model Averaging, Tactical Asset Allocation

JEL Classification: G11, G12, G14