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Estimation Risk-Adjusted Sharpe Ratio and Fund Performance Ranking under a General Return Distribution

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  • Yong Bao

Abstract

We study the sample estimation risk of the traditional Sharpe ratio without the restrictive assumption of normality for return series. We derive analytical results for the approximate bias and variance of the sample Sharpe ratio in terms of the underlying distribution parameters. The results clarify several misinterpretations existing in the literature. A Monte Carlo study shows that our bias and variance formulae approximate the true moments of the sample Sharpe ratio remarkably well. We propose using the analytical results to design an estimation risk-adjusted Sharpe ratio. An empirical study of mutual fund performance shows that using the adjusted Sharpe ratio gives a quite different performance ranking of those traditionally top-ranked funds. Copyright The Author 2008. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org., Oxford University Press.

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  • Yong Bao, 2009. "Estimation Risk-Adjusted Sharpe Ratio and Fund Performance Ranking under a General Return Distribution," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 152-173, Spring.
    • Handle: RePEc:oup:jfinec:v:7:y:2009:i:2:p:152-173
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbn022
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    Cited by:

    1. Michael Pinelis & David Ruppert, 2020. "Machine Learning Portfolio Allocation," Papers 2003.00656, arXiv.org, revised Nov 2021.
    2. Schuster, Martin & Auer, Benjamin R., 2012. "A note on empirical Sharpe ratio dynamics," Economics Letters, Elsevier, vol. 116(1), pages 124-128.
    3. Homm, Ulrich & Pigorsch, Christian, 2012. "Beyond the Sharpe ratio: An application of the Aumann–Serrano index to performance measurement," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2274-2284.
    4. Fischer, Thomas & Lundtofte, Frederik, 2020. "Unequal returns: Using the Atkinson index to measure financial risk," Journal of Banking & Finance, Elsevier, vol. 116(C).
    5. Taylor, Mark & Filippou, Ilias & Rapach, David & Zhou, Guofu, 2020. "Exchange Rate Prediction with Machine Learning and a Smart Carry Trade Portfolio," CEPR Discussion Papers 15305, C.E.P.R. Discussion Papers.
    6. Ken Johnston & John Hatem & Elton Scott, 2013. "A note on the evaluation of long-run investment decisions using the sharpe ratio," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 37(1), pages 150-157, January.
    7. Vedolin, Andrea, 2012. "Uncertainty and leveraged Lucas Trees: the cross section of equilibrium volatility risk premia," LSE Research Online Documents on Economics 43091, London School of Economics and Political Science, LSE Library.
    8. Lipton, Amy F. & Kish, Richard J., 2010. "Robust performance measures for high yield bond funds," The Quarterly Review of Economics and Finance, Elsevier, vol. 50(3), pages 332-340, August.

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