Abstract/Summary

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 simple shrinkage adjustments 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 and fail to create market-neutral anomaly portfolios. Finally, we document a robust link between stock characteristics and beta predictability.