Machine Learning and Fund Characteristics Help to Select Mutual Funds with Positive Alpha

Published: 9 October 2023| Version 3 | DOI: 10.17632/rpgb99m5zy.3
Andre Santos, Victor DeMiguel, Javier Gil-Bazo, Javier Nogales


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Replication files to ``Machine Learning and Fund Characteristics Help to Select Mutual Funds with Positive Alpha'' %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Each folder contains files to replicate one table or figure of the paper. The folders also contain a readme file to help users to run the codes. The replication_file.tex file generates a PDF file with the replicated tables and figures. Notice that the replicated tables and figures are not always identical to those reported in the paper. The reason is that in order to protect the proprietary nature of the data, in some cases we have added noise to mutual fund characteristics and returns; see the README file for details. The data files that are required to run the codes are stored in the /data_sets/ folder. Some tables and figures require the output from the codes stored in folders /code_for_ML_methods/ , /code_for_AW_method/ and /code_for_EW_method/. It is recommended to run the codes in those folders before running the codes that generate tables and figures. We include a file in both tex and pdf formats containing the full set of results.



Universitat Pompeu Fabra, Universidad Carlos III de Madrid, London Business School, Colegio Universitario de Estudios Financieros


Investment, Financial Econometrics, Machine Learning


Agencia Estatal de Investigación


Comunidad de Madrid