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Communication Dans Un Congrès Année : 2020

Deep Reinforcement Learning (DRL) for portfolio allocation

Eric Benhamou
  • Fonction : Auteur
Jean Jacques Ohana
  • Fonction : Auteur
Jamal Atif
Rida Laraki

Résumé

Deep reinforcement learning (DRL) has reached an unprecedent level on complex tasks like game solving (Go [6], StarCraft II [7]), and autonomous driving. However, applications to real financial assets are still largely unexplored and it remains an open question whether DRL can reach super human level. In this demo, we showcase state-of-the-art DRL methods for selecting portfolios according to financial environment, with a final network concatenating three individual networks using layers of convolutions to reduce network's complexity. The multi entries of our network enables capturing dependencies from common financial indicators features like risk aversion, citigroup index surprise, portfolio specific features and previous portfolio allocations. Results on test set show this approach can overperform traditional portfolio optimization methods with results available at our demo website.
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Dates et versions

hal-03815055 , version 1 (02-09-2022)
hal-03815055 , version 2 (14-10-2022)

Identifiants

Citer

Eric Benhamou, David Saltiel, Jean Jacques Ohana, Jamal Atif, Rida Laraki. Deep Reinforcement Learning (DRL) for portfolio allocation. The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database - ECML PKKDD, Sep 2020, Ghent ( on line), Belgium. pp.527-531, ⟨10.1007/978-3-030-67670-4_32⟩. ⟨hal-03815055v2⟩
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