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New Evidence on Mutual Fund Performance: A Comparison of Alternative Bootstrap Methods

Author

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  • Blake, David
  • Caulfield, Tristan
  • Ioannidis, Christos
  • Tonks, Ian

Abstract

We compare two bootstrap methods for assessing mutual fund performance. The first produces narrow confidence intervals due to pooling over time, whereas the second produces wider confidence intervals because it preserves the cross correlation of fund returns. We then show that the average U.K. equity mutual fund manager is unable to deliver outperformance net of fees under either bootstrap. Gross of fees, 95% of fund managers on the basis of the first bootstrap and all fund managers on the basis of the second bootstrap fail to outperform the luck distribution of gross returns.

Suggested Citation

  • Blake, David & Caulfield, Tristan & Ioannidis, Christos & Tonks, Ian, 2017. "New Evidence on Mutual Fund Performance: A Comparison of Alternative Bootstrap Methods," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(3), pages 1279-1299, June.
    • Handle: RePEc:cup:jfinqa:v:52:y:2017:i:03:p:1279-1299_00
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    Citations

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    Cited by:

    1. Imran Hussain Shah & Hans Matthias Wanovits & Richard Hatfield, 2021. "Uncovering investment management performance using SPIVA data," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3676-3695, July.
    2. 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.
    3. Emmanuel Mamatzakis & Mike G. Tsionas, 2021. "Testing for persistence in US mutual funds’ performance: a Bayesian dynamic panel model," Annals of Operations Research, Springer, vol. 299(1), pages 1203-1233, April.
    4. 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).
    5. 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.
    6. Hanke, Bernd & Keswani, Aneel & Quigley, Garrett & Zagonov, Maxim, 2018. "Survivorship bias and comparability of UK open-ended fund databases," Economics Letters, Elsevier, vol. 172(C), pages 110-114.
    7. Wolfgang Bessler & David Blake & Peter Lückoff & Ian Tonks, 2018. "Fund Flows, Manager Changes, and Performance Persistence [Does motivation matter when assessing trade performance? An analysis of mutual funds]," Review of Finance, European Finance Association, vol. 22(5), pages 1911-1947.
    8. 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.
    9. Ayadi, Mohamed A. & Lazrak, Skander & Liao, Yusui & Welch, Robert, 2018. "Performance of fixed-income mutual funds with regime-switching models," The Quarterly Review of Economics and Finance, Elsevier, vol. 69(C), pages 217-231.
    10. Huang, Haitao & Jiang, Lei & Leng, Xuan & Peng, Liang, 2023. "Bootstrap analysis of mutual fund performance," Journal of Econometrics, Elsevier, vol. 235(1), pages 239-255.
    11. 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.
    12. Buschong, René, 2022. "Financial Literacy is associated with Stock Market Expectations but not with Forecast Accuracy: Evidence from Germany," EconStor Preprints 266404, ZBW - Leibniz Information Centre for Economics.

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