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Detecting Repeatable Performance

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  • Campbell R Harvey
  • Yan Liu

Abstract

Past fund performance does a poor job of predicting future outcomes. The reason is noise. Using a random effects framework, we reduce the noise by pooling information from the cross-sectional alpha distribution to make density forecasts for each individual fund’s alpha. In simulations, we show that our method generates parameter estimates that outperform alternative methods, both at the population and at the individual fund level. An out-of-sample forecasting exercise also shows that our method generates improved alpha forecasts. Received November 23, 2016; editorial decision November 1, 2017 by Editor Andrew Karolyi. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web Site next to the link to the final published paper online.

Suggested Citation

  • Campbell R Harvey & Yan Liu, 2018. "Detecting Repeatable Performance," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2499-2552.
    • Handle: RePEc:oup:rfinst:v:31:y:2018:i:7:p:2499-2552.
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    File URL: http://hdl.handle.net/10.1093/rfs/hhy014
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    Citations

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    Cited by:

    1. Alan Crane & Kevin Crotty, 2020. "How Skilled Are Security Analysts?," Journal of Finance, American Finance Association, vol. 75(3), pages 1629-1675, June.
    2. 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.
    3. 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).
    4. 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).
    5. Timothy B. Riley, 2021. "Portfolios of actively managed mutual funds," The Financial Review, Eastern Finance Association, vol. 56(2), pages 205-230, May.
    6. Sermpinis, Georgios & Hassanniakalager, Arman & Stasinakis, Charalampos & Psaradellis, Ioannis, 2021. "Technical analysis profitability and Persistence: A discrete false discovery approach on MSCI indices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 73(C).
    7. Campbell R. Harvey & Yan Liu, 2022. "Luck versus Skill in the Cross Section of Mutual Fund Returns: Reexamining the Evidence," Journal of Finance, American Finance Association, vol. 77(3), pages 1921-1966, June.
    8. Christiansen, Charlotte & Grønborg, Niels S. & Nielsen, Ole L., 2020. "Mutual fund selection for realistically short samples," Journal of Empirical Finance, Elsevier, vol. 55(C), pages 218-240.
    9. Barras, Laurent & Gagliardini, Patrick & Scaillet, Olivier, 2018. "The Cross-Sectional Distribution of Fund Skill Measures," Working Papers unige:110006, University of Geneva, Geneva School of Economics and Management.
    10. 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.
    11. 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.
    12. Cai, Biqing & Cheng, Tingting & Yan, Cheng, 2018. "Time-varying skills (versus luck) in U.S. active mutual funds and hedge funds," Journal of Empirical Finance, Elsevier, vol. 49(C), pages 81-106.
    13. Chinco, Alex & Neuhierl, Andreas & Weber, Michael, 2021. "Estimating the anomaly base rate," Journal of Financial Economics, Elsevier, vol. 140(1), pages 101-126.
    14. Campbell R. Harvey & Yan Liu, 2020. "False (and Missed) Discoveries in Financial Economics," Journal of Finance, American Finance Association, vol. 75(5), pages 2503-2553, October.
    15. 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.
    16. Warwick Smith & Anca M. Hanea & Mark A. Burgman, 2022. "Can Groups Improve Expert Economic and Financial Forecasts?," Forecasting, MDPI, vol. 4(3), pages 1-18, August.
    17. 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.
    18. Cheng, Tingting & Yan, Cheng & Yan, Yayi, 2021. "Improved inference for fund alphas using high-dimensional cross-sectional tests," Journal of Empirical Finance, Elsevier, vol. 61(C), pages 57-81.
    19. Campbell R. Harvey & Yan Liu, 2020. "False (and Missed) Discoveries in Financial Economics," Papers 2006.04269, arXiv.org.

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