Machine-learning the skill of mutual fund managers

Ron Kaniel, Zihan Lin, Markus Pelger and Stijn Van Nieuwerburgh

Journal of Financial Economics, 2023, vol. 150, issue 1, 94-138

Abstract: We show, using machine learning, that fund characteristics can consistently differentiate high from low-performing mutual funds, before and after fees. The outperformance persists for more than three years. 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. Our estimation with neural networks enables us to uncover novel and substantial interaction effects between sentiment and both fund flow and fund momentum.

Keywords: Mutual fund performance; Fund flow; Momentum; Machine learning; Sentiment; Big data; Neural networks (search for similar items in EconPapers)
JEL-codes: C45 G11 G12 G17 G23 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (4)

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Related works:
Working Paper: Machine-Learning the Skill of Mutual Fund Managers (2023)
Working Paper: Machine-Learning the Skill of Mutual Fund Managers (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jfinec:v:150:y:2023:i:1:p:94-138

DOI: 10.1016/j.jfineco.2023.07.004

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