Bridging the Gap Between Markowitz Planning and Deep Reinforcement Learning

9 Pages Posted: 28 Jan 2021

See all articles by Eric Benhamou

Eric Benhamou

Université Paris Dauphine; EB AI Advisory; AI For Alpha

David Saltiel

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

Sandrine Ungari

Societe Generale

Abhishek Mukhopadhyay

SGCIB

Date Written: September 30, 2020

Abstract

While researchers in the asset management industry have mostly focused on techniques based on financial and risk planning techniques like Markowitz efficient frontier, minimum variance, maximum diversification or equal risk parity, in parallel, another community in machine learning has started working on reinforcement learning and more particularly deep reinforcement learning to solve other decision making problems for challenging task like autonomous driving, robot learning, and on a more conceptual side games solving like Go.

This paper aims to bridge the gap between these two approaches by showing Deep Reinforcement Learning (DRL) techniques can shed new lights on portfolio allocation thanks to a more general optimization setting that casts portfolio allocation as an optimal control problem that is not just a one-step optimization, but rather a continuous control optimization with a delayed reward. The advantages are numerous: (i) DRL maps directly market conditions to actions by design and hence should adapt to changing environment, (ii) DRL does not rely on any traditional financial risk assumptions like that risk is represented by variance, (iii) DRL can incorporate additional data and be a multi inputs method as opposed to more traditional optimization methods. We present on an experiment some encouraging results using convolution networks.

Keywords: Deep Reinforcement Learning, Portfolio selection

JEL Classification: G11

Suggested Citation

Benhamou, Eric and Saltiel, David and Ungari, Sandrine and Mukhopadhyay, Abhishek, Bridging the Gap Between Markowitz Planning and Deep Reinforcement Learning (September 30, 2020). Université Paris-Dauphine Research Paper No. 3702112, Available at SSRN: https://ssrn.com/abstract=3702112 or http://dx.doi.org/10.2139/ssrn.3702112

Eric Benhamou (Contact Author)

Université Paris Dauphine ( email )

Place du Maréchal de Tassigny
Paris, Cedex 16 75775
France

EB AI Advisory ( email )

35 Boulevard d'Inkermann
Neuilly sur Seine, 92200
France

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

Sandrine Ungari

Societe Generale ( email )

52 Place de l'Ellipse
La Défense, 92000
France

Abhishek Mukhopadhyay

SGCIB ( email )

52 Place de l'Ellipse
La Défense, 92000
France

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
6,959
Abstract Views
80,095
rank
1,400
PlumX Metrics