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. [ABSTRACT FROM AUTHOR]