Comparing Deep RL and Traditional Financial Portfolio Methods

22 Pages Posted: 1 Sep 2023

See all articles by Eric Benhamou

Eric Benhamou

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

Jean-Jacques Ohana

AI For Alpha

Beatrice Guez

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: August 31, 2023

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 \and Portfolio allocation.

Keywords: Deep Reinforcement Learning, Portfolio Allocation

JEL Classification: G11

Suggested Citation

Benhamou, Eric and Ohana, Jean-Jacques and Guez, Beatrice and Saltiel, David and Laraki, Rida and Atif, Jamal, Comparing Deep RL and Traditional Financial Portfolio Methods (August 31, 2023). Université Paris-Dauphine Research Paper No. 4557792, Available at SSRN: https://ssrn.com/abstract=4557792 or http://dx.doi.org/10.2139/ssrn.4557792

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

Jean-Jacques Ohana

AI For Alpha ( email )

35 boulevard d'Inkermann
Neuilly sur Seine, 92200
France

Beatrice Guez

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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