Abstract
In this study, we evaluate the performance of five Machine Learning (ML) portfolios—logistic regression, random forest, decision tree, gradient boosting, and adaptive boosting—against that of the Dow Industrial Average -passive approach. We consider an active portfolio management approach, employing out-of-sample backtesting to simulate the strategy performance as a categorical approach. We employ as predictors the opening price, the highest price, the lowest price, the closing price, the Williams %R and a the 13-week T-bills. During the whole period, before COVID-19, and during the pandemic, in all cases, at least one ML portfolio beats the index. These results suggest that overall, investors obtain positive outcomes if they use ML portfolios instead of investing passively in the index, obtaining the most benefits in times of greater uncertainty, such as the peak of the pandemic.
© 2018, Facultad de Contaduría y Administración, Universidad Nacional Autónoma de México. All rights reserved. Publication of the article implies full assignment of property rights (copyright) in Journal of Contaduría y Administración. The publication mreserves the right to total or partial reproduction of the work in other print, electronic or any other alternative means, but always recognizing its responsibility.
License for Published Content
Unless otherwise stated, all contents of the electronic edition of the journal are distributed under a license and distribution "Creative Commons Attribution-Noncommercial 4.0 International" (CC-by). You can see from here the version of the license information. This circumstance must be expressly stated in this way when necessary.
Metadata License
The metadata of papers published by Contaduría y Administración are in the public domain, through the publisher's waiver of all rights to the work under copyright law worldwide, including all rights and related rights, to the extent permitted by law. You may copy, modify, and distribute the metadata, even for commercial purposes, without requesting permission.
