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

Author

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  • Chen, Yong
  • Cliff, Michael
  • Zhao, Haibei

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.

Suggested Citation

  • Chen, Yong & Cliff, Michael & Zhao, Haibei, 2017. "Hedge Funds: The Good, the Bad, and the Lucky," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(3), pages 1081-1109, June.
    • Handle: RePEc:cup:jfinqa:v:52:y:2017:i:03:p:1081-1109_00
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    Citations

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    Cited by:

    1. Keith Cuthbertson & Dirk Nitzsche & Niall O’Sullivan, 2023. "UK mutual funds: performance persistence and portfolio size," Journal of Asset Management, Palgrave Macmillan, vol. 24(4), pages 284-298, July.
    2. Laurent Barras & Patrick Gagliardini & O. Scaillet, 2018. "The Cross-Sectional Distribution of Fund Skill Measures," Swiss Finance Institute Research Paper Series 18-66, Swiss Finance Institute.
    3. Yang, Fan & Havranek, Tomas & Irsova, Zuzana & Novak, Jiri, 2022. "Hedge Fund Performance: A Quantitative Survey," EconStor Preprints 260612, ZBW - Leibniz Information Centre for Economics.
    4. Alan Crane & Kevin Crotty, 2020. "How Skilled Are Security Analysts?," Journal of Finance, American Finance Association, vol. 75(3), pages 1629-1675, June.
    5. Fisher, Mark & Jensen, Mark J., 2022. "Bayesian nonparametric learning of how skill is distributed across the mutual fund industry," Journal of Econometrics, Elsevier, vol. 230(1), pages 131-153.
    6. Yang, Fan & Havranek, Tomas & Irsova, Zuzana & Novak, Jiri, 2024. "Where Have All the Alphas Gone? A Meta-Analysis of Hedge Fund Performance," EconStor Preprints 289497, ZBW - Leibniz Information Centre for Economics.
    7. Ardia, David & Barras, Laurent & Gagliardini, Patrick & Scaillet, Olivier, 2024. "Is it alpha or beta? Decomposing hedge fund returns when models are misspecified," Journal of Financial Economics, Elsevier, vol. 154(C).
    8. Ardia, David & Boudt, Kris, 2018. "The peer performance ratios of hedge funds," Journal of Banking & Finance, Elsevier, vol. 87(C), pages 351-368.
    9. Cuthbertson, Keith & Nitzsche, Dirk & O'Sullivan, Niall, 2022. "Mutual fund performance persistence: Factor models and portfolio size," International Review of Financial Analysis, Elsevier, vol. 81(C).
    10. Alan Crane & Kevin Crotty & Tarik Umar, 2023. "Hedge Funds and Public Information Acquisition," Management Science, INFORMS, vol. 69(6), pages 3241-3262, June.
    11. Yan, Cheng & Cheng, Tingting, 2019. "In search of the optimal number of fund subgroups," Journal of Empirical Finance, Elsevier, vol. 50(C), pages 78-92.
    12. Chen, Yong & Kelly, Bryan & Wu, Wei, 2020. "Sophisticated investors and market efficiency: Evidence from a natural experiment," Journal of Financial Economics, Elsevier, vol. 138(2), pages 316-341.
    13. Meng, Yifan & Yang, Mo & Li, Weiping, 2024. "Skilled analysts and earnings management in Chinese listed companies," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 227-243.
    14. Yong Chen & Bing Han & Jing Pan, 2021. "Sentiment Trading and Hedge Fund Returns," Journal of Finance, American Finance Association, vol. 76(4), pages 2001-2033, August.
    15. Zhang, Junsheng & Peng, Zezhi & Zeng, Yamin & Yang, Haisheng, 2023. "Do big data mutual funds outperform?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    16. Ling, Yun & Satchell, Stephen & Yao, Juan, 2023. "Decreasing returns to scale and skill in hedge funds," Journal of Banking & Finance, Elsevier, vol. 156(C).
    17. Moraes, Fernando & Cavalcante-Filho, Elias & De-Losso, Rodrigo, 2021. "Unskilled fund managers: Replicating active fund performance with few ETFs," International Review of Financial Analysis, Elsevier, vol. 78(C).
    18. Laurent Barras & Patrick Gagliardini & Olivier Scaillet, 2022. "Skill, Scale, and Value Creation in the Mutual Fund Industry," Journal of Finance, American Finance Association, vol. 77(1), pages 601-638, February.
    19. Nezafat, Mahdi & Shen, Tao & Wang, Qinghai & Wu, Julie, 2022. "Longs, shorts, and the cross-section of stock returns," Journal of Banking & Finance, Elsevier, vol. 138(C).
    20. Wenbo Wu & Jiaqi Chen & Zhibin (Ben) Yang & Michael L. Tindall, 2021. "A Cross-Sectional Machine Learning Approach for Hedge Fund Return Prediction and Selection," Management Science, INFORMS, vol. 67(7), pages 4577-4601, July.
    21. Kolari, James W. & Huang, Jianhua Z. & Butt, Hilal Anwar & Liao, Huiling, 2022. "International tests of the ZCAPM asset pricing model," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    22. Cheng, Tingting & Yan, Cheng, 2017. "Evaluating the size of the bootstrap method for fund performance evaluation," Economics Letters, Elsevier, vol. 156(C), pages 36-41.
    23. Ma, Tianyi & Li, Baibing & Tee, Kai-Hong, 2022. "Mispricing chasing and hedge fund returns," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 34-49.
    24. Huazhu Zhang & Cheng Yan, 2018. "A skeptical appraisal of the bootstrap approach in fund performance evaluation," Financial Markets, Institutions & Instruments, John Wiley & Sons, vol. 27(2), pages 49-86, May.
    25. Fan Yang & Tomas Havranek & Zuzana Irsova & Jiri Novak, 2024. "Is research on hedge fund performance published selectively? A quantitative survey," Journal of Economic Surveys, Wiley Blackwell, vol. 38(4), pages 1085-1131, September.

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