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Multi-Period Performance Persistence Analysis of Hedge Funds

Published online by Cambridge University Press:  06 April 2009

Abstract

Since hedge funds specify significant lock-up periods, we investigate persistence in the performance of hedge funds using a multi-period framework in which the likelihood of observing persistence by chance is lower than in the traditional two-period framework. Under the null hypothesis of no manager skill (no persistence), the theoretical distribution of observing wins or losses follows a binormial distribution. We test this hypothesis using the traditional two-period framework and compare the findings with the results obtained using our multi-period framework. We examine whether persistence is sensitive to the length of return measurement intervals by using quarterly, half-yearly and yearly returns. We find maximum persistence at the quarterly horizon indicating that presistence among hedge fund managers is short term in nature.

Type
Research Article
Copyright
Copyright © School of Business Administration, University of Washington 2000

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Footnotes

*

Both authors, London Business School, Sussex Place, Regent's Park, London NWI 4SA, U.K. We thank Ravi Bansal, Stephen Brown (the editor), Jennifer Carpenter, Elroy Dimson, William Goet-zmann (associate editor and referee), David Hsieh, Frans de Roon, Henri Servaes, Fauchier Partners Ltd., and participants at the hedge fund conference at Duke University and Issues in Performance Measurement workshop at the European Institute for Advanced Studies in Management, Brussels for many helpful comments and constructive suggestions. Naik is grateful for funding from Inquire U.K. and the European Commission's TMR program (network ref. ERBFMRXCT 960054). Agarwal is grateful for financial support from the British Council's Chevening scholorship, and the Edward Jones, Frank Russell, and Fauchier Partners scholarships during the past three years in the Ph.D. pro-gram. We are gratful to Bob Potsic of Hedge Fund Research Inc., Chicago for providing the data. We are responsible for all errors.

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