Detecting Repeatable Performance

107 Pages Posted: 19 Nov 2015 Last revised: 22 Jan 2018

See all articles by Campbell R. Harvey

Campbell R. Harvey

Duke University - Fuqua School of Business; National Bureau of Economic Research (NBER)

Yan Liu

Purdue University; Texas A&M University, Department of Finance

Multiple version iconThere are 2 versions of this paper

Date Written: January 21, 2018


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

Suggested Citation

Harvey, Campbell R. and Liu, Yan, Detecting Repeatable Performance (January 21, 2018). Available at SSRN: or

Campbell R. Harvey (Contact Author)

Duke University - Fuqua School of Business ( email )

Box 90120
Durham, NC 27708-0120
United States
919-660-7768 (Phone)


National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Yan Liu

Purdue University ( email )

West Lafayette, IN 47907-1310
United States


Texas A&M University, Department of Finance ( email )

Wehner 401Q, MS 4353
College Station, TX 77843-4218
United States

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