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Deep Reinforcement Learning for ESG financial portfolio management

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  • Eduardo C. Garrido-Merch'an
  • Sol Mora-Figueroa-Cruz-Guzm'an
  • Mar'ia Coronado-Vaca

Abstract

This paper investigates the application of Deep Reinforcement Learning (DRL) for Environment, Social, and Governance (ESG) financial portfolio management, with a specific focus on the potential benefits of ESG score-based market regulation. We leveraged an Advantage Actor-Critic (A2C) agent and conducted our experiments using environments encoded within the OpenAI Gym, adapted from the FinRL platform. The study includes a comparative analysis of DRL agent performance under standard Dow Jones Industrial Average (DJIA) market conditions and a scenario where returns are regulated in line with company ESG scores. In the ESG-regulated market, grants were proportionally allotted to portfolios based on their returns and ESG scores, while taxes were assigned to portfolios below the mean ESG score of the index. The results intriguingly reveal that the DRL agent within the ESG-regulated market outperforms the standard DJIA market setup. Furthermore, we considered the inclusion of ESG variables in the agent state space, and compared this with scenarios where such data were excluded. This comparison adds to the understanding of the role of ESG factors in portfolio management decision-making. We also analyze the behaviour of the DRL agent in IBEX 35 and NASDAQ-100 indexes. Both the A2C and Proximal Policy Optimization (PPO) algorithms were applied to these additional markets, providing a broader perspective on the generalization of our findings. This work contributes to the evolving field of ESG investing, suggesting that market regulation based on ESG scoring can potentially improve DRL-based portfolio management, with significant implications for sustainable investing strategies.

Suggested Citation

  • Eduardo C. Garrido-Merch'an & Sol Mora-Figueroa-Cruz-Guzm'an & Mar'ia Coronado-Vaca, 2023. "Deep Reinforcement Learning for ESG financial portfolio management," Papers 2307.09631, arXiv.org.
    • Handle: RePEc:arx:papers:2307.09631
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    References listed on IDEAS

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    1. Mark Rubinstein, 2002. "Markowitz's “Portfolio Selection”: A Fifty‐Year Retrospective," Journal of Finance, American Finance Association, vol. 57(3), pages 1041-1045, June.
    2. Florian Berg & Julian F Kölbel & Roberto Rigobon, 2022. "Aggregate Confusion: The Divergence of ESG Ratings [Corporate social responsibility and firm risk: theory and empirical evidence]," Review of Finance, European Finance Association, vol. 26(6), pages 1315-1344.
    3. Lars Kaiser & Jan Welters, 2019. "Risk-mitigating effect of ESG on momentum portfolios," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 20(5), pages 542-555, October.
    4. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
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