Integration of investor behavioral perspective and climate change in reinforcement learning for portfolio optimization

Addressing environmental impact is increasingly imperative for individual investors and large financial institutions, making it a key objective of socially responsible investing. However, there is a noticeable gap in research on integrating sustainability and low-carbon considerations into machine learning-based portfolio optimization. To meet this challenge, this study introduces a Portfolio Emissions Sentiment Attention Aware Reinforcement Learning (PESAARL) model based on the Proximal Policy Optimization (PPO) algorithm to optimize a portfolio of Dow Jones Industrial Average (DJIA) stocks. PESAARL uniquely integrates environmental impact considerations, specifically carbon footprint using the firm level scope 1 and scope 2 emissions data, alongside firm-level investor sentiment and attention, into the investment decision-making process. Through multiple experiments, PESAARL demonstrates significant advantages, in terms of financial and environmental performance, over the benchmarks. [Display omitted] •We propose a new Portfolio Emissions Sentiment Attention Aware Reinforcement Learning model (PESAARL).•PESAARL balances portfolio profitability with environmental performance considering investor behavioral perspectives.•PESAARL allows to achieve competitive returns while minimizing the carbon footprint.•PESAARL outperforms the benchmark models.