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Detecting Repeatable Performance

106 Pages Posted: 19 Nov 2015 Last revised: 8 Nov 2017

Campbell R. Harvey

Duke University - Fuqua School of Business; National Bureau of Economic Research (NBER); Duke Innovation & Entrepreneurship Initiative

Yan Liu

Texas A&M University, Department of Finance

Multiple version iconThere are 2 versions of this paper

Date Written: November 3, 2017

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

Suggested Citation

Harvey, Campbell R. and Liu, Yan, Detecting Repeatable Performance (November 3, 2017). Available at SSRN: https://ssrn.com/abstract=2691658 or http://dx.doi.org/10.2139/ssrn.2691658

Campbell Harvey (Contact Author)

Duke University - Fuqua School of Business ( email )

Box 90120
Durham, NC 27708-0120
United States
919-660-7768 (Phone)
919-660-8030 (Fax)

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Duke Innovation & Entrepreneurship Initiative ( email )

215 Morris St., Suite 300
Durham, NC 27701
United States

Yan Liu

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

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

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