Artificial intelligence for socially responsible investment: a multi-objective decision-making framework
提出一种将机器学习与Black-Litterman模型结合的多目标投资方法，使用CVaR作为风险度量并融入ESG标准，在实现较高回报的同时平衡风险，促进可持续投资。
Abstract This paper presents a novel multi-objetive methodology for optimizing financial investment decisions. To do this, we integrate machine learning (ML) techniques into a Black–Litterman (BL) model, using conditional value at risk (CVaR) as a risk measure within a socially responsible investment framework. The proposed approach employs ML models, including long short-term memory, random forest, artificial neural networks, gated recurrent unit, and autoregressive integrated moving average (ARIMA), to forecast asset returns. These predictions are aggregated through a model based on historical performance, and they are incorporated into a Black–Litterman–CVaR model (BL–ML–CVaR). Environmental, social, and governance (ESG) criteria are used to construct portfolios that align financial returns with sustainable investment practices. Results demonstrate that the BL–ML–CVaR portfolio offers higher returns and balanced risk compared to traditional models, with the ESG-integrated version achieving competitive performance while adhering to sustainability goals. This study provides valuable insights into how ML-driven models can enhance investment strategies in some emerging sectors facing environmental challenges, offering a path toward more sustainable and responsible financial practices.