AAMDRL: Augmented Asset Management With Deep Reinforcement Learning

15 Pages Posted: 28 Jan 2021

See all articles by Eric Benhamou

Eric Benhamou

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

David Saltiel

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

Sandrine Ungari

Société Générale

Jamal Atif

Université Paris Dauphine

Abhishek Mukhopadhyay

SGCIB

Date Written: September 30, 2020

Abstract

Can an agent learn efficiently in a noisy and self adapting environment with sequential, non-stationary and non-homogeneous observations? Through trading bots, we illustrate how Deep Reinforcement Learning (DRL) can tackle this challenge. Our contributions are threefold: (i) the use of contextual information also referred to as augmented state in DRL, (ii) the impact of a one period lag between observations and actions that is more realistic for an asset management environment, (iii) the implementation of a new repetitive train test method called walk forward analysis, similar in spirit to cross validation for time series. Although our experiment is on trading bots, it can easily be translated to other bot environments that operate in sequential environment with regime changes and noisy data. Our experiment for an augmented asset manager interested in finding the best portfolio for hedging strategies shows that AAMDRL achieves superior returns and lower risk.

Keywords: Deep Reinforcement Learning, Portfolio selection

JEL Classification: G11

Suggested Citation

Benhamou, Eric and Saltiel, David and Ungari, Sandrine and Atif, Jamal and Mukhopadhyay, Abhishek, AAMDRL: Augmented Asset Management With Deep Reinforcement Learning (September 30, 2020). Université Paris-Dauphine Research Paper No. 3702113, Available at SSRN: https://ssrn.com/abstract=3702113 or http://dx.doi.org/10.2139/ssrn.3702113

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

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

Société Générale ( email )

52 Place de l'Ellipse
La Défense, 92000
France

Jamal Atif

Université Paris Dauphine ( email )

Place du Maréchal de Tassigny
Paris, Cedex 16 75775
France

Abhishek Mukhopadhyay

SGCIB ( email )

52 Place de l'Ellipse
La Défense, 92000
France

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