Optimizing Large Language Models for Sustainable Investors

Vol. 23, No. 3, 2025
By Andrew Chin, Che Guan, Promod Rajaguru, Qifeng Sun and Yuning Wu

We use large language models (LLMs) and natural language processing (NLP) to extract environmental, social and governance (ESG) insights from real-time news, creating an expert-annotated dataset to evaluate ESG classification, firm relevance, and sentiment. Our fine-tuned models outperform pre-trained ones in ESG detection, firm impact, and sentiment analysis. Furthermore, in-context learning does not improve performance, indicating optimal tuning. Event studies and back tests show that our sentiment signals predict underperforming stocks, with higher model confidence in negative sentiment correlating to worse outcomes. These findings emphasize the value of fine-tuning models with expert annotated data, and leveraging ESG sentiment signals to generate investment insights from qualitative data to enhance alpha generation.