Detecting Repeatable Performance

Abstract

Past fund performance does a poor job of predicting future outcomes. The reason is noise. Using a random effects framework, we reduce the noise by pooling information from the cross-sectional alpha distribution to make density forecasts for each individual fund's alpha. In simulations, we show that our method generates parameter estimates that outperform alternative methods, both at the population and at the individual fund level. An out-of-sample forecasting exercise also shows that our method generates improved alpha forecasts.

Keywords: Performance evaluation, Mutual funds, Hedge funds, EM algorithm, Fixed effects, Random effects, Regularization, Multiple testing, Bayesian, Rethinking Performance Evaluation

JEL Classification: G10, G11, G12, G14, G23, G24