Can machine learning help to select portfolios of mutual funds?
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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.contributor.other Universitat Pompeu Fabra. Departament d'Economia i Empresa
- dc.date.accessioned 2024-11-14T10:09:39Z
- dc.date.available 2024-11-14T10:09:39Z
- dc.date.issued 2021-03-22
- dc.date.modified 2024-11-14T10:07:47Z
- dc.description.abstract Identifying outperforming mutual funds ex-ante is a notoriously difficult task. We use machine learning methods to exploit the predictive ability of a large set of mutual fund characteristics that are readily available to investors. Using data on US equity funds in the 1980-2018 period, the methods allow us to construct portfolios of funds that earn positive and significant out-of-sample risk-adjusted after-fee returns as high as 4.2% per year. We further show that such outstanding performance is the joint outcome of both exploiting the information contained in multiple fund characteristics and allowing for flexibility in the relationship between predictors and fund performance. Our results confirm that even retail investors can benefit from investing in actively managed funds. However, we also find that the performance of all our portfolios has declined over time, consistent with increased competition in the asset market and diseconomies of scale at the industry level.
- dc.format.mimetype application/pdf*
- dc.identifier https://econ-papers.upf.edu/ca/paper.php?id=1772
- dc.identifier.citation
- dc.identifier.uri http://hdl.handle.net/10230/68568
- dc.language.iso eng
- dc.relation.ispartofseries Economics and Business Working Papers Series; 1772
- dc.rights L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
- dc.subject.keyword mutual fund performance
- dc.subject.keyword performance predictability
- dc.subject.keyword active management
- dc.subject.keyword machine learning
- dc.subject.keyword elastic net
- dc.subject.keyword random forests
- dc.subject.keyword gradient boosting
- dc.subject.keyword Finance and Accounting
- dc.title Can machine learning help to select portfolios of mutual funds?
- dc.title.alternative
- dc.type info:eu-repo/semantics/workingPaper