Machine learning and fund characteristics help to select mutual funds with positive alpha
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,...
| Autores: | ; ; ; |
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| Tipo de documento: | Artigo |
| Estado: | Published version |
| Data de publicação: | 2023 |
| País: | España |
| Recursos: | Universitat Pompeu Fabra |
| Repositório: | Repositorio Digital de la UPF |
| OAI Identifier: | oai:repositori.upf.edu:10230/69473 |
| Acesso em linha: | http://hdl.handle.net/10230/69473 http://dx.doi.org/10.1016/j.jfineco.2023.103737 |
| Nível de acesso: | Acesso aberto |
| Palavra-chave: | Active asset management Mutual-fund performance Mutual-fund misallocation Machine learning Tradable strategies Nonlinearities and interactions |
| Resumo: | 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. |
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