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The Peer Performance of Hedge Funds

Author Info

Listed author(s):
  • David Ardia
  • Kris Boudt

Abstract

An essential component in the analysis of (hedge) fund returns is to measure its performance with respect to the group of peer funds. Through the analysis of risk-adjusted return percentiles an answer is given to the question how many funds are out-performed by the focal fund. In case all funds perform equally well, this approach will lead a random number between zero and one, depending on how lucky the fund is. We use the false discovery rate approach to construct relative performance ratios that account for the uncertainty in estimating the performance differential of two funds. Our application is on hedge funds, which leads us to develop a test for equality of the modified Sharpe ratio of two funds. The effectiveness of the method is illustrated with a Monte Carlo study and an empirical study is performed on the Hedge Fund Research database.

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Paper provided by CIRPEE in its series Cahiers de recherche with number 1329.

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Date of creation: 2013
Handle: RePEc:lvl:lacicr:1329
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Keywords: equal-performance ratio; false discovery rate; hedge fund; modified Sharpe ratio; out-performance ratio; peer group; performance measurement;

Find related papers by JEL classification:

  • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
  • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
  • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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  1. Bajgrowicz, Pierre & Scaillet, Olivier, 2012. "Technical trading revisited: False discoveries, persistence tests, and transaction costs," Journal of Financial Economics, Elsevier, vol. 106(3), pages 473-491.
  2. Ledoit, Oliver & Wolf, Michael, 2008. "Robust performance hypothesis testing with the Sharpe ratio," Journal of Empirical Finance, Elsevier, vol. 15(5), pages 850-859, December.
  3. Laurent Barras & Olivier Scaillet & Russ Wermers, 2010. "False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas," Journal of Finance, American Finance Association, vol. 65(1), pages 179-216, 02.
  4. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
  5. Carhart, Mark M, 1997. " On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
  6. Scott, Robert C & Horvath, Philip A, 1980. " On the Direction of Preference for Moments of Higher Order Than the Variance," Journal of Finance, American Finance Association, vol. 35(4), pages 915-919, September.
  7. Dimitris Politis & Halbert White, 2004. "Automatic Block-Length Selection for the Dependent Bootstrap," Econometric Reviews, Taylor & Francis Journals, vol. 23(1), pages 53-70.
  8. Treynor, Jack L & Black, Fischer, 1973. "How to Use Security Analysis to Improve Portfolio Selection," The Journal of Business, University of Chicago Press, vol. 46(1), pages 66-86, January.
  9. John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498.
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