Machine-Learning the Skill of Mutual Fund Managers

71 Pages Posted: 7 Dec 2021 Last revised: 6 Jul 2022

See all articles by Ron Kaniel

Ron Kaniel

University of Rochester - Simon Business School; CEPR

Zihan Lin

Stanford University

Markus Pelger

Stanford University - Department of Management Science & Engineering

Stijn Van Nieuwerburgh

Columbia University Graduate School of Business; National Bureau of Economic Research (NBER); Centre for Economic Policy Research (CEPR); ABFER

Multiple version iconThere are 2 versions of this paper

Date Written: July 4, 2022

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.

Keywords: Mutual fund performance, fund flow, momentum, machine learning, sentiment, big data, neural networks

JEL Classification: G11, G12, G17, G23, C45

Suggested Citation

Kaniel, Ron and Lin, Zihan and Pelger, Markus and Van Nieuwerburgh, Stijn, Machine-Learning the Skill of Mutual Fund Managers (July 4, 2022). Available at SSRN: https://ssrn.com/abstract=3977883 or http://dx.doi.org/10.2139/ssrn.3977883

Ron Kaniel

University of Rochester - Simon Business School ( email )

Rochester, NY 14627
United States

HOME PAGE: http://rkaniel.simon.rochester.edu

CEPR ( email )

London
United Kingdom

Zihan Lin

Stanford University ( email )

Stanford, CA 94305
United States

Markus Pelger

Stanford University - Department of Management Science & Engineering ( email )

473 Via Ortega
Stanford, CA 94305-9025
United States

Stijn Van Nieuwerburgh (Contact Author)

Columbia University Graduate School of Business ( email )

3022 Broadway
Uris Hall 809
New York, NY New York 10027
United States

HOME PAGE: http://https://www0.gsb.columbia.edu/faculty/svannieuwerburgh/

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Centre for Economic Policy Research (CEPR)

London
United Kingdom

ABFER ( email )

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1 Business Link
Singapore, 117592
Singapore

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