Man vs. Machine: Quantitative and Discretionary Equity Management

61 Pages Posted: 21 Dec 2020 Last revised: 15 Sep 2022

See all articles by Simona Abis

Simona Abis

Columbia University - Columbia Business School, Finance

Date Written: October 23, 2020

Abstract

I use machine learning to categorize US active equity mutual funds as quantitative (reliant on computer models and fixed-rules) or discretionary (reliant on human judgment). I then formulate hypotheses of how their holdings and performance might differ, based on the conjecture that quantitative funds might have more learning capacity but less flexibility to adapt to changing market conditions than discretionary funds. Consistent with those hypotheses, I find that quantitative funds hold more stocks, specialize in stock picking, and engage in more overcrowded trades. Discretionary funds hold lesser-known stocks, switch between picking and timing and outperform quantitative funds in recessions.

Keywords: Investment Management, Quantitative Mutual Funds, Machine Learning, Rational Inattention

JEL Classification: G11, G23, G14

Suggested Citation

Abis, Simona, Man vs. Machine: Quantitative and Discretionary Equity Management (October 23, 2020). Available at SSRN: https://ssrn.com/abstract=3717371 or http://dx.doi.org/10.2139/ssrn.3717371

Simona Abis (Contact Author)

Columbia University - Columbia Business School, Finance ( email )

3022 Broadway
New York, NY 10027
United States

HOME PAGE: http://https://www8.gsb.columbia.edu/cbs-directory/detail/sa3518

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