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The approach reveals nonlinear interactions between fund characteristics and future performance and shows that past performance is a especially strong predictor for more active funds. Results indicate informational frictions and manager-skill effects that reduce misallocation, and suggest investors benefit from active management only with access to sophisticated predictive models.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-and-fund-characteristics-help-to-select-mutual-funds-with-positive-alpha-article-abstract/290590/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-and-fund-characteristics-help-to-select-mutual-funds-with-positive-alpha-article-abstract/290590.png","ImageObject",300,407,{"name":92,"@type":93},"Oliver","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What do machine-learning methods target in selecting mutual funds?","Question",{"text":112,"@type":113},"They exploit mutual-fund characteristics to build tradable long-only portfolios that can earn significant out-of-sample alphas net of costs.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How do fund characteristics relate to future performance in the study?",{"text":117,"@type":113},"The methods uncover nonlinearities and interactions, and they show past performance is a particularly strong predictor for more active funds.",{"name":119,"@type":110,"acceptedAnswer":120},"Why might investors still struggle to identify outperforming funds?",{"text":121,"@type":113},"The findings are consistent with informational frictions that prevent investors from properly identifying the funds that will outperform.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290590,1789642140,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144,"preview_page_count":4},8796095461610,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Journal of Financial Economics 150 (2023) 103737  \nContents lists available at ScienceDirect Journal of Financial Economics  \njournal [homepage:](homepage: www.elsevier.com/locate/jfec)[ www.elsevier.com/locate/jfec](homepage: www.elsevier.com/locate/jfec)  \n| Machine learning and fund characteristics help to select mutual funds with   positive alpha ✩\u003Cbr>Victor DeMiguel a,∗ , Javier Gil-Bazo b, Francisco J. Nogales c, André A.P. Santos d\u003Cbr>a Management Science and Operations, London Business School, United Kingdom\u003Cbr>b Department of Economics and Business, Universitat Pompeu Fabra, Barcelona School of Economics, and UPF Barcelona School of Management, Spain c Department of Statistics, Universidad Carlos III de Madrid, Spain\u003Cbr>d CUNEF Universidad, Spain |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Dataset link: [https://](https://)\u003Cbr>[doi.org/10.17632/rpgb99m5zy.3](doi.org/10.17632/rpgb99m5zy.3) |  | 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. |\n| JEL classification: G11\u003Cbr>G17\u003Cbr>G23\u003Cbr>Keywords:\u003Cbr>Active asset management Mutual-fund performance Mutual-fund misallocation Machine learning\u003Cbr>Tradable strategies Nonlinearities and interactions |  |  |\n\n1. Introduction  \nMutual-fund research consistently shows that the average active fund earns negative risk-adjusted returns (alpha) after transaction costs,  \nfees, and other expenses (Sharpe, 1966; Jensen, 1968; Gruber, 1996; Ferreira et al., 2013). Moreover, although several studies document the existence of a subset of managers that outperform their benchmarks (Wermers, 2000; Barras et al., 2010; Fama and French, 2010; Kacper-  \n✩ Nikolai Roussanov was the editor for this article. A previous version of this manuscript was circulated under the title “Can Machine Learning Help to Select Portfolios of Mutual Funds?” We are grateful for detailed and constructive feedback from an anonymous referee and the editor. We are also grateful for comments from Eddie Anderson, Fahiz Baba-Yara, Paul Borochin, Andrea Buraschi, Joao Cocco, Pasquale Della Corte, Francisco Gomes, Jin Guo, Martin Haugh, Juan Imbet, Marcin Kacperczyk, Howard Kung, Narayan Naik, Jean Pauphilet, Anna Pavlova, Markus Pelger, Zhen Qi, Alexandre Rubesam, Stephen Schaefer, Henri Servaes, Raman Uppal, Michael Young, and Paolo Zaffaroni, as well as seminar participants at CUNEF, Imperial College Business School, London Business School, Universidad Autónoma de Madrid, Universidad de Zaragoza, University of Bath, University of Bristol and Université Paris Dauphine and conference participants at the 2021 conference of the French Finance Association, 2021 Finance Forum (Spanish Finance Association), 2021 EFMA annual meeting, 2021 conference of the Brazilian Finance Society, 2021 FMA annual meeting, 2021 INFORMS Annual Meeting, 2021 Paris December Finance Meeting, 2022 Funcas (Jornadas de Análisis Financieroy Big Data), 2022 Global Finance Conference, 2022 UF Research Conference on Machine Learning in Finance, 2023 Oxford-Man Institute Machine Learning in Quantitative Finance Conference, and 2023 SoFiE conference. 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 S","cbCaiqbgVCVmDvt2","https://ap.wps.com/l/cbCaiqbgVCVmDvt2","pdf",3993907,22,"English","# Abstract\n# Keywords\n## 1. Introduction","[{\"question\":\"What do machine-learning methods target in selecting mutual funds?\",\"answer\":\"They exploit mutual-fund characteristics to build tradable long-only portfolios that can earn significant out-of-sample alphas net of costs.\"},{\"question\":\"How do fund characteristics relate to future performance in the study?\",\"answer\":\"The methods uncover nonlinearities and interactions, and they show past performance is a particularly strong predictor for more active funds.\"},{\"question\":\"Why might investors still struggle to identify outperforming funds?\",\"answer\":\"The findings are consistent with informational frictions that prevent investors from properly identifying the funds that will outperform.\"}]","Machine learning and fund characteristics help to select mutual funds with positive alpha - Article Abstract | PDF",55]