Rethinking Performance Evaluation

Abstract

The standard equation-by-equation OLS that is routinely used in performance evaluation ignores information in the alpha population and leads to severely biased estimates for the alpha population. Recent research has proposed a new approach -- essentially rethinking performance evaluation. Our contribution is a framework that treats fund alphas as random effects. This allows us to make inference on the alpha population while controlling for various sources of estimation risk. At the individual fund level, our method pools information from the entire alpha distribution to make density forecasts for each fund's alpha. In simulations, we show that our method generates parameter estimates that universally dominate the OLS estimates, both at the population and at the individual fund level. We also show the advantage of our approach compared to recently proposed alternative methods. An out-of-sample forecasting exercise also shows that our method generates superior alpha forecasts.