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

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  • Bouyaddou, Youssef
  • Jebabli, Ikram

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.

Suggested Citation

  • Bouyaddou, Youssef & Jebabli, Ikram, 2025. "Integration of investor behavioral perspective and climate change in reinforcement learning for portfolio optimization," Research in International Business and Finance, Elsevier, vol. 73(PB).
    • Handle: RePEc:eee:riibaf:v:73:y:2025:i:pb:s027553192400432x
    DOI: 10.1016/j.ribaf.2024.102639
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