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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
    Note: AP
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    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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