Comparing Deep RL and Traditional Financial Portfolio Methods (ECML PKDD 2023 - MIDAS Slides)

13 Pages Posted: 21 Oct 2023

See all articles by Eric Benhamou

Eric Benhamou

Université Paris Dauphine; AI For Alpha; EB AI Advisory; Université Paris-Dauphine, PSL Research University

Beatrice Guez

AI For Alpha

Jean-Jacques Ohana

AI For Alpha

David Saltiel

Université Paris Dauphine; A.I. Square Connect; AI For Alpha

Rida Laraki

Université Paris-Dauphine, PSL Research University

Jamal Atif

Université Paris Dauphine

Date Written: September 24, 2023

Abstract

Traditional portfolio allocation methods are based on the assumption of known risk tolerance and parametric models of market returns. Deep reinforcement learning (DRL) is a non-parametric machine learning technique that can learn optimal policies for complex sequential decision-making problems. This paper conducts a comprehensive comparative analysis of traditional portfolio allocation methods and DRL. The paper finds that DRL outperforms traditional methods in terms of both risk-adjusted return and Sharpe ratio in a simulation study. The authors argue that DRL is a promising new approach to portfolio management with the potential to outperform traditional methods in a variety of market conditions.

Keywords: deep reinforcement learning, portfolio allocation, risk-adjusted return, Sharpe ratio, financial markets

JEL Classification: G11, C61, C45

Suggested Citation

Benhamou, Eric and Guez, Beatrice and Ohana, Jean-Jacques and Saltiel, David and Laraki, Rida and Atif, Jamal, Comparing Deep RL and Traditional Financial Portfolio Methods (ECML PKDD 2023 - MIDAS Slides) (September 24, 2023). Available at SSRN: https://ssrn.com/abstract=4581947 or http://dx.doi.org/10.2139/ssrn.4581947

Eric Benhamou (Contact Author)

Université Paris Dauphine ( email )

Place du Maréchal de Tassigny
Paris, Cedex 16 75775
France

AI For Alpha ( email )

35 boulevard d'Inkermann
Neuilly sur Seine, 92200
France

EB AI Advisory ( email )

35 Boulevard d'Inkermann
Neuilly sur Seine, 92200
France

Université Paris-Dauphine, PSL Research University ( email )

Place du Maréchal de Lattre de Tassigny
Paris, 75016
France

Beatrice Guez

AI For Alpha ( email )

35 boulevard d'Inkermann
Neuilly sur Seine, 92200
France

Jean-Jacques Ohana

AI For Alpha ( email )

35 boulevard d'Inkermann
Neuilly sur Seine, 92200
France

David Saltiel

Université Paris Dauphine ( email )

Place du Maréchal de Tassigny
Paris, Cedex 16 75775
France

A.I. Square Connect ( email )

35 Boulevard d'Inkermann
Neuilly sur Seine, 92200
France

AI For Alpha ( email )

35 boulevard d'Inkermann
Neuilly sur Seine, 92200
France

Rida Laraki

Université Paris-Dauphine, PSL Research University ( email )

Place du Maréchal de Lattre de Tassigny
Paris, 75016
France

Jamal Atif

Université Paris Dauphine ( email )

Place du Maréchal de Tassigny
Paris, Cedex 16 75775
France

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