[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119824-en":3,"doc-seo-119824-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119824,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine Learning and Fund Characteristics Help to Select Mutual Funds with Positive Alpha","Machine-learning methods use mutual-fund characteristics to construct tradable long-only portfolios and identify significant out-of-sample annual alphas of 2.4% net of all costs. The approach uncovers interactions between characteristics and future performance, showing that past performance is an especially powerful predictor for more active funds. Results indicate informational frictions: managers’ skill is not fully recognized because scale diseconomies are not offset. Investors can benefit from active management when equipped with sophisticated prediction methods.","LBS Research Online  \nV DeMiguel, J Gil-Bazo, F J Nogales and A A P Santos  \nMachine Learning and Fund Characteristics Help to Select Mutual Funds with Positive Alpha Article  \nThis version is available in the LBS Research Online repository: [https://lbsresearch.london.edu/](https://lbsresearch.london.edu/)[ ](https://lbsresearch.london.edu/)[id/eprint/3548/](id/eprint/3548/)  \nDeMiguel, V, Gil-Bazo, J, Nogales, F J and Santos, A A P (2023)  \nMachine Learning and Fund Characteristics Help to Select Mutual Funds with Positive Alpha. Journal of Financial Economics, 150 (3) . p. 103737. ISSN 0304-405X  \nDOI: [https://doi.org/10.1016/j.jfineco.2023.103737](https://doi.org/10.1016/j.jfineco.2023.103737)  \nElsevier  \n[https://www.sciencedirect.com/science/article/pii/..](https://www.sciencedirect.com/science/article/pii/..) .  \nUsers may download and/or print one copy of any article(s) in LBS Research Online for purposes of research and/or private study. Further distribution of the material, or use for any commercial gain, isnot permitted.  \nJournal of Financial Economics 150 (2023) 103737  \nContents lists available at ScienceDirect Journal of Financial Economics  \njournal [homepage: 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, ","cbCaieflYvETJnOF","https://ap.wps.com/l/cbCaieflYvETJnOF","pdf",3970532,1,23,"English","en",105,"# Introduction\n## Related literature and motivation\n# Methodology and approach\n## Machine-learning selection of mutual funds\n# Empirical results\n## Out-of-sample alpha and predictive interactions\n# Interpretation and implications\n## Informational frictions and investor usefulness","[{\"question\":\"How do machine-learning methods help select mutual funds with positive alpha?\",\"answer\":\"They exploit mutual-fund characteristics to build tradable long-only portfolios and estimate out-of-sample alphas net of all costs. The study also reveals which characteristics interact to predict future performance.\"},{\"question\":\"What level of out-of-sample alpha do the methods achieve?\",\"answer\":\"The reported out-of-sample annual alpha is 2.4% net of all costs. This is described as significant in the article’s abstract.\"},{\"question\":\"Why might active managers’ skill be difficult for investors to identify?\",\"answer\":\"The findings suggest informational frictions: managers’ skill may be insufficiently offset by diseconomies of scale, preventing investors from recognizing the outperforming funds without advanced prediction tools.\"}]","Machine Learning and Fund Characteristics Help to Select Mutual Funds with Positive Alpha | PDF",1785726508,58,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-and-fund-characteristics-help-to-select-mutual-funds-with-positive-alpha","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-and-fund-characteristics-help-to-select-mutual-funds-with-positive-alpha/119824/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How do machine-learning methods help select mutual funds with positive alpha?","Question",{"text":75,"@type":76},"They exploit mutual-fund characteristics to build tradable long-only portfolios and estimate out-of-sample alphas net of all costs. The study also reveals which characteristics interact to predict future performance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What level of out-of-sample alpha do the methods achieve?",{"text":80,"@type":76},"The reported out-of-sample annual alpha is 2.4% net of all costs. This is described as significant in the article’s abstract.",{"name":82,"@type":73,"acceptedAnswer":83},"Why might active managers’ skill be difficult for investors to identify?",{"text":84,"@type":76},"The findings suggest informational frictions: managers’ skill may be insufficiently offset by diseconomies of scale, preventing investors from recognizing the outperforming funds without advanced prediction tools.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]