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Hedge Funds: The Good, the Bad, and the Lucky

Published online by Cambridge University Press:  18 May 2017

Abstract

We develop an estimation approach based on a modified expectation-maximization (EM) algorithm and a mixture of normal distributions associated with skill groups to assess performance in hedge funds. By allowing luck to affect both skilled and unskilled funds, we estimate the number of skill groups, the fraction of funds from each group, and the mean and variability of skill within each group. For each individual fund, we propose a performance measure combining the fund’s estimated alpha with the cross-sectional distribution of fund skill. In out-of-sample tests, an investment strategy using our performance measure outperforms those using estimated alpha and t-statistic.

Type
Research Article
Copyright
Copyright © Michael G. Foster School of Business, University of Washington 2017 

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Footnotes

1

We are grateful to Stephen Brown (the editor) and Olivier Scaillet (the referee) for constructive suggestions that substantially improved the paper. For helpful comments and discussions, we thank Vikas Agarwal, Charles Cao, Heber Farnsworth, Wayne Ferson, Will Goetzmann, Feng Guo, Michael Halling, Petri Jylha, Greg Kadlec, Andrew Karolyi, Robert Kieschnick, Bing Liang, Andrew Lo, Hugues Pirotte, Jeffrey Pontiff, Zheng Sun, Josef Zechner, Harold Zhang, and seminar and conference participants at Cornerstone Research, the Institute for Quantitative Asset Management (IQAM), Pennsylvania State University, Shanghai University of Finance and Economics, Texas A&M University, the University of North Carolina at Chapel Hill, the University of Texas at Dallas, the University of Virginia, Vienna University of Economics and Business, Virginia Tech, VU University Amsterdam, FBE 654 Asset Pricing class at the University of Southern California, the 2012 NYSE/Euronext Hedge Fund Conference in Paris, and the 2015 Financial Intermediation Research Society Conference. The paper was previously circulated under the title “Hedge Funds: The Good, the (Not-So) Bad, and the Ugly.” All remaining errors are ours alone. The views expressed in this article do not necessarily represent those of Analysis Group, Inc.

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