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Benchmarking Robustness of Deep Reinforcement Learning approaches to Online Portfolio Management

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  • Marc Velay
  • Bich-Li^en Doan
  • Arpad Rimmel
  • Fabrice Popineau
  • Fabrice Daniel

Abstract

Deep Reinforcement Learning approaches to Online Portfolio Selection have grown in popularity in recent years. The sensitive nature of training Reinforcement Learning agents implies a need for extensive efforts in market representation, behavior objectives, and training processes, which have often been lacking in previous works. We propose a training and evaluation process to assess the performance of classical DRL algorithms for portfolio management. We found that most Deep Reinforcement Learning algorithms were not robust, with strategies generalizing poorly and degrading quickly during backtesting.

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  • Marc Velay & Bich-Li^en Doan & Arpad Rimmel & Fabrice Popineau & Fabrice Daniel, 2023. "Benchmarking Robustness of Deep Reinforcement Learning approaches to Online Portfolio Management," Papers 2306.10950, arXiv.org.
    • Handle: RePEc:arx:papers:2306.10950
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    File URL: http://arxiv.org/pdf/2306.10950
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    1. Ricard Durall, 2022. "Asset Allocation: From Markowitz to Deep Reinforcement Learning," Papers 2208.07158, arXiv.org.
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