Machine learning and fund characteristics help to select mutual funds with positive alpha

Victor DeMiguel, Javier Gil-Bazo, Francisco J. Nogales and Andre Santos ()

Journal of Financial Economics, 2023, vol. 150, issue 3

Abstract: Machine-learning methods exploit fund characteristics to select tradable long-only portfolios of mutual funds that earn significant out-of-sample annual alphas of 2.4% net of all costs. The methods unveil interactions in the relation between fund characteristics and future performance. For instance, past performance is a particularly strong predictor of future performance for more active funds. Machine learning identifies managers whose skill is not sufficiently offset by diseconomies of scale, consistent with informational frictions preventing investors from identifying the outperforming funds. Our findings demonstrate that investors can benefit from active management, but only if they have access to sophisticated prediction methods.

Keywords: Active asset management; Mutual-fund performance; Mutual-fund misallocation; Machine learning; Tradable strategies; Nonlinearities and interactions (search for similar items in EconPapers)
JEL-codes: G11 G17 G23 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jfinec:v:150:y:2023:i:3:s0304405x23001770

DOI: 10.1016/j.jfineco.2023.103737

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