Original Article
Journal of Derivatives & Hedge Funds (2009) 15, 223–240. doi:10.1057/jdhf.2009.11
Chasing performance persistence of hedge funds – Comparative analysis of evaluation techniques
Eero J Pätäri1 and Jussi Tolvanen2
Correspondence: Eero J. Pätäri, Lappeenranta School of Business, Lappeenranta University of Technology, PO Box 20, FI-53851 LAPPEENRANTA, Finland. E-mail: Eero.Patari@lut.fi
1is Professor of Finance at the School of Business at Lappeenranta University of Technology (Finland) and a Partner of Confido Capital. He has published a large number of papers in journals such as the Journal of Risk, the International Research Journal of Finance and Economics, the Journal of Performance Measurement, and the Journal of Money, Investment and Banking. His research interests include performance of equity investment strategies, measurement techniques of portfolio risk and performance measurement, particularly performance persistence of mutual funds and hedge funds. In addition to his academic duties, he has consulted for management of several institutional investors in the field of risk and performance measurement.
2is Investment Specialist for a large Scandinavian financial institution based in Sweden.
Received 5 November 2008; Revised 5 November 2008.
Abstract
We apply two complement methodologies (that is, stacked cross-sectional regression and quartile portfolio approach) in detecting the performance persistence of five different hedge fund styles. In addition, we compare the results obtained by using model-free performance metrics to those obtained by using both standard alphas of multifactor models and their empirical Bayesian counterparts. The results show that both the degree and existence of performance persistence vary among hedge fund styles and, in addition, depend on performance metric employed. Based on the combination of 3-year selection period and the subsequent 1-year holding period, model-free performance metrics (such as the Sharpe ratio and its downside risk-based variants) are more sensitive to detecting performance persistence than are factor-based performance metrics. Correspondingly, the prediction power of empirical Bayesian alphas is better than that of standard OLS alphas. The strongest evidence of performance persistence within the sample is among event-driven funds, for which 10 out of 12 persistence tests performed indicate significant results.
Keywords:
hedge funds, performance persistence, Bayesian estimation, Bayesian alphas, performance measures, factor models
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