@inproceedings{76b5515c05c140e880d417c86e6f7b28,
title = "Comparing Deep RL and Traditional Financial Portfolio Methods",
abstract = "Portfolio allocation aims to optimize the risk/return ratio in investment management. Traditional methods based on modern portfolio theory have been widely used for this purpose. However, the emergence of deep reinforcement learning (DRL) offers an alternative approach. This article conducts a comprehensive comparative analysis of traditional portfolio allocation methods and DRL, examining their principles, methodologies, and performance in maximizing risk-return profiles. It demonstrates that a basic version of DRL converges to traditional methods, while a myopic agent driven by immediate rewards represents the dynamic version of traditional methods. Experimental results indicate some improvement of DRL over traditional methods.",
keywords = "Deep RL, Portfolio allocation",
author = "Eric Benhamou and Ohana, \{Jean Jacques\} and Beatrice Guez and David Saltiel and Rida Laraki and Jamal Atif",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 23rd Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023 ; Conference date: 18-09-2023 Through 22-09-2023",
year = "2025",
month = jan,
day = "1",
doi = "10.1007/978-3-031-74643-7\_24",
language = "English",
isbn = "9783031746420",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "323--338",
editor = "Rosa Meo and Fabrizio Silvestri",
booktitle = "Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2023, Revised Selected Papers",
}