Deep Reinforcement Learning for ESG financial portfolio management
Eduardo C. Garrido-Merchán Sol Mora-Figueroa-Cruz-Guzmán María Coronado-Vaca
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.