We propose a hybrid approach for estimating beta that shrinks rolling window estimates towards firm-specific priors motivated by economic theory. Our method yields superior forecasts of beta that have important practical implications. First, hybrid betas carry a significant price of risk in the cross-section even after controlling for characteristics, unlike standard rolling window betas. Second, the hybrid approach offers statistically and economically significant out-of-sample benefits for investors who use factor models to construct optimal portfolios. We show that the hybrid estimator outperforms existing estimators because shrinkage towards a fundamentals-based prior is effective in reducing measurement noise in extreme beta estimates.