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

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• dc.contributor.author DeMiguel, Victor
• dc.contributor.author Gil-Bazo, Javier
• dc.contributor.author Nogales, Francisco J.
• dc.contributor.author Santos, André A.P.
• dc.date.accessioned 2025-02-04T07:39:07Z
• dc.date.available 2025-02-04T07:39:07Z
• dc.date.issued 2023
• dc.description.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.
• dc.description.sponsorship Javier Gil-Bazo acknowledges financial support from the Spanish Government, Ministry of Science and Innovation grant PID2020-118541GB-I00, and Spanish Agencia Estatal de Investigación (AEI), through the Severo Ochoa Programme for Centres of Excellence in R&D (Barcelona School of Economics CEX2019-000915-S). Francisco J. Nogales acknowledges the financial support from the Spanish Government through project PID2020-116694GB-I00. André A. P. Santos acknowledges the financial support from the Comunidad de Madrid Government through project 2022-T1/SOC-24167.
• dc.format.mimetype application/pdf
• dc.identifier.citation DeMiguel V, Gil-Bazo J, Nogales FJ, Santos AAP. Machine learning and fund characteristics help to select mutual funds with positive alpha. Journal of Financial Economics. 2023;150(3):103737. DOI: 10.1016/j.jfineco.2023.103737
• dc.identifier.doi http://dx.doi.org/10.1016/j.jfineco.2023.103737
• dc.identifier.issn 0304-405X
• dc.identifier.uri http://hdl.handle.net/10230/69473
• dc.language.iso eng
• dc.publisher Elsevier
• dc.relation.ispartof Journal of Financial Economics. 2023;150(3):103737
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2020-118541GB-I00
• dc.rights © 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Active asset management
• dc.subject.keyword Mutual-fund performance
• dc.subject.keyword Mutual-fund misallocation
• dc.subject.keyword Machine learning
• dc.subject.keyword Tradable strategies
• dc.subject.keyword Nonlinearities and interactions
• dc.title Machine learning and fund characteristics help to select mutual funds with positive alpha
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion