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Machine-Learning the Skill of Mutual Fund Managers

Author

Listed:
  • Ron Kaniel
  • Zihan Lin
  • Markus Pelger
  • Stijn Van Nieuwerburgh

Abstract

We show, using machine learning, that fund characteristics can consistently differentiate high from low-performing mutual funds, as well as identify funds with net-of-fees abnormal returns. Fund momentum and fund flow are the most important predictors of future risk-adjusted fund performance, while characteristics of the stocks that funds hold are not predictive. Returns of predictive long-short portfolios are higher following a period of high sentiment or a good state of the macro-economy. Our estimation with neural networks enables us to uncover novel and substantial interaction effects between sentiment and both fund flow and fund momentum.

Suggested Citation

  • Ron Kaniel & Zihan Lin & Markus Pelger & Stijn Van Nieuwerburgh, 2022. "Machine-Learning the Skill of Mutual Fund Managers," NBER Working Papers 29723, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:29723
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    Cited by:

    1. Fragkiskos, Apollon & Krasotkina, Olga & Spilker, Harold D. & Wermers, Russ, 2025. "Private Equity Fund Performance: A Time-Series Approach," Journal of Banking & Finance, Elsevier, vol. 177(C).
    2. Jiang, Hao & Li, Sophia Zhengzi & Yuan, Peixuan, 2025. "Granular information and sectoral movements," Journal of Economic Dynamics and Control, Elsevier, vol. 171(C).
    3. Cong Wang, 2024. "Stock return prediction with multiple measures using neural network models," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-34, December.
    4. Ha, Yeonjeong & Oh, Haejune, 2024. "Choice for smart investment in mutual funds: Single- or multi-period performance ranks," Finance Research Letters, Elsevier, vol. 59(C).
    5. Alexandre Momparler & Pedro Carmona & Francisco Climent, 2025. "Catalyzing Sustainable Investment: Revealing ESG Power in Predicting Fund Performance with Machine Learning," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1617-1642, March.
    6. Hanauer, Matthias X. & Kalsbach, Tobias, 2023. "Machine learning and the cross-section of emerging market stock returns," Emerging Markets Review, Elsevier, vol. 55(C).
    7. Yizhan Shu & Chenyu Yu & John M. Mulvey, 2024. "Dynamic Asset Allocation with Asset-Specific Regime Forecasts," Papers 2406.09578, arXiv.org, revised Aug 2024.
    8. Li, Zhiyong & Rao, Xiao, 2023. "Exploring the zoo of predictors for mutual fund performance in China," Pacific-Basin Finance Journal, Elsevier, vol. 77(C).
    9. Evangelos Liaras & Michail Nerantzidis & Antonios Alexandridis, 2024. "Machine learning in accounting and finance research: a literature review," Review of Quantitative Finance and Accounting, Springer, vol. 63(4), pages 1431-1471, November.
    10. Gang Kou & Yang Lu, 2025. "FinTech: a literature review of emerging financial technologies and applications," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-34, December.
    11. Maarten P. Scholl & Mahmoud Mahfouz & Anisoara Calinescu & J. Doyne Farmer, 2025. "Learning to Manage Investment Portfolios beyond Simple Utility Functions," Papers 2510.26165, arXiv.org.
    12. Ma, Tian & Wang, Wanwan & Jiang, Fuwei, 2025. "Machine learning the performance of hedge fund," Journal of International Money and Finance, Elsevier, vol. 155(C).
    13. Jozef Barunik & Martin Hronec & Ondrej Tobek, 2024. "Forecasting stock return distributions around the globe with quantile neural networks," Papers 2408.07497, arXiv.org, revised Aug 2025.
    14. DeMiguel, Victor & Gil-Bazo, Javier & Nogales, Francisco J. & Santos, André A.P., 2023. "Machine learning and fund characteristics help to select mutual funds with positive alpha," Journal of Financial Economics, Elsevier, vol. 150(3).
    15. Damir Filipovi'c & Puneet Pasricha, 2022. "Empirical Asset Pricing via Ensemble Gaussian Process Regression," Papers 2212.01048, arXiv.org, revised Jan 2025.
    16. Yizhan Shu & Chenyu Yu & John M. Mulvey, 2025. "Dynamic asset allocation with asset-specific regime forecasts," Annals of Operations Research, Springer, vol. 346(1), pages 285-318, March.
    17. Amit Pandey & Anil Kumar Sharma, 2023. "Indian institutional investor's portfolio concentration decision: skill and performance," Journal of Advances in Management Research, Emerald Group Publishing Limited, vol. 21(1), pages 66-95, December.
    18. Inigo Martin-Melero & Raul Gomez-Martinez & Maria Luisa Medrano-Garcia & Felipe Hernandez-Perlines, 2025. "Comparison of sectorial and financial data for ESG scoring of mutual funds with machine learning," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-31, December.
    19. Li, Bin & Rossi, Alberto G. & Yan, Xuemin (Sterling) & Zheng, Lingling, 2025. "Machine learning from a “Universe” of signals: The role of feature engineering," Journal of Financial Economics, Elsevier, vol. 172(C).
    20. Guilherme V. Moura & Andr'e P. Santos & Hudson S. Torrent, 2025. "Variable selection for minimum-variance portfolios," Papers 2508.14986, arXiv.org.

    More about this item

    JEL classification:

    • G0 - Financial Economics - - General
    • 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
    • G5 - Financial Economics - - Household Finance

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