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

Youssef Bouyaddou and Ikram Jebabli

Research in International Business and Finance, 2025, vol. 73, issue PB

Abstract: 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.

Keywords: Socially responsible investing; Investor behavior; Carbon footprint; Deep reinforcement learning; Portfolio optimization (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:riibaf:v:73:y:2025:i:pb:s027553192400432x

DOI: 10.1016/j.ribaf.2024.102639

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