2026-04-18T00:28:20Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/694732025-12-24T08:48:26Zcom_2072_6col_2072_452952

00925njm 22002777a 4500 dc DeMiguel, Victor author Gil-Bazo, Javier author Nogales, Francisco J. author Santos, André A.P. author 2025-02-04T07:39:07Z 2025-02-04T07:39:07Z 2023 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. 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. Active asset management Mutual-fund performance Mutual-fund misallocation Machine learning Tradable strategies Nonlinearities and interactions Machine learning and fund characteristics help to select mutual funds with positive alpha