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

被引:0
|
作者
Bouyaddou, Youssef [1 ]
Jebabli, Ikram [1 ]
机构
[1] Univ Int Rabat, Rabat Business Sch, Rabat, Morocco
关键词
Socially responsible investing; Investor behavior; Carbon footprint; Deep reinforcement learning; Portfolio optimization; ATTENTION; SENTIMENT; SEARCH; MEDIA; NEWS;
D O I
10.1016/j.ribaf.2024.102639
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
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.
引用
收藏
页数:13
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