Estimating Beta: Forecast Adjustments and the Impact of Stock Characteristics for a Broad Cross-Section

Abstract

Researchers and practitioners face many choices when estimating an asset's sensitivities toward risk factors, i.e., betas. Using the entire U.S. stock universe and a sample period of more than 50 years, we find that a historical estimator based on daily return data with an exponential weighting scheme as well as a shrinkage toward the industry average yield the best predictions for future beta. Adjustments for asynchronous trading, macroeconomic conditions, or regression-based combinations, on the other hand, typically yield very high prediction errors. Finally, we document a robust link between stock characteristics and beta predictability.

Keywords: Beta Estimation, Forecast Combinations, Forecast Adjustments

JEL Classification: G12, G11, G17