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Improved inference in the evaluation of mutual fund performance using panel bootstrap methods

Author

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  • Blake, David
  • Caulfield, Tristan
  • Ioannidis, Christos
  • Tonks, Ian
Abstract
Two new methodologies are introduced to improve inference in the evaluation of mutual fund performance against benchmarks. First, the benchmark models are estimated using panel methods with both fund and time effects. Second, the non-normality of individual mutual fund returns is accounted for by using panel bootstrap methods. We also augment the standard benchmark factors with fund-specific characteristics, such as fund size. Using a dataset of UK equity mutual fund returns, we find that fund size has a negative effect on the average fund manager’s benchmark-adjusted performance. Further, when we allow for time effects and the non-normality of fund returns, we find that there is no evidence that even the best performing fund managers can significantly out-perform the augmented benchmarks after fund management charges are taken into account.

Suggested Citation

  • Blake, David & Caulfield, Tristan & Ioannidis, Christos & Tonks, Ian, 2014. "Improved inference in the evaluation of mutual fund performance using panel bootstrap methods," Journal of Econometrics, Elsevier, vol. 183(2), pages 202-210.
    • Handle: RePEc:eee:econom:v:183:y:2014:i:2:p:202-210
    DOI: 10.1016/j.jeconom.2014.05.010
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    2. 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.
    3. Lazzarino, Marco & Berrill, Jenny & Šević, Aleksandar, 2022. "The importance of distinguishing between precious and industrial metals when investing in mining stocks," Resources Policy, Elsevier, vol. 78(C).
    4. Jiti Gao & Bin Peng & Yayi Yan, 2022. "Higher-order Expansions and Inference for Panel Data Models," Papers 2205.00577, arXiv.org, revised Jun 2023.
    5. 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.
    6. 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.
    7. 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.
    8. Emmanuel Mamatzakis & Mike Tsionas, 2018. "A Bayesian dynamic model to test persistence in funds' performance," Working Paper series 18-23, Rimini Centre for Economic Analysis.
    9. Jiti Gao & Bin Peng & Yayi Yan, 2022. "A Simple Bootstrap Method for Panel Data Inferences," Monash Econometrics and Business Statistics Working Papers 7/22, Monash University, Department of Econometrics and Business Statistics.
    10. 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.
    11. 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.
    12. Abramov, Alexander (Абрамов, Александр) & Akshentseva, Kseniya (Акшенцева, Ксения) & Radygin, Alexander (Радыгин, Александр), 2015. "The effectiveness of mutual funds: theoretical approaches and the experience of Russia [Эффективность Паевых Инвестиционных Фондов: Теоретические Подходы И Опыт России]," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 4, pages 60-86.

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    More about this item

    Keywords

    Mutual funds; Unit trusts; Open-ended investment companies; Performance measurement; Factor benchmark models; Panel methods; Bootstrap methods;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

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