Revolutionizing ESG risk assessment through machine learning: Insights from U.S. corporations

• Main Authors: NGUYEN, Huynh Long Hung, MEGARGEL, Alan @ Ali MADJELISI
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: ESG; machine learning; sustainability; Artificial Intelligence and Robotics
• Online Access: https://ink.library.smu.edu.sg/sis_research/9686; https://ink.library.smu.edu.sg/context/sis_research/article/10686/viewcontent/Predicting_Corporate_ESG_Risk_Score___Paper_Submission.pdf
• Institution: Singapore Management University

Environmental, Social, and Governance (ESG) factors are increasingly essential in evaluating corporate performance, driving demand for accurate ESG risk assessments. However, smaller companies often face challenges in obtaining validated ESG scores due to resource constraints. This study explores the use of Machine Learning (ML) to predict ESG risk scores for U.S. companies, leveraging data from Wharton Research Data Services (WRDS), Yahoo Finance, and Sustainalytics. The XGBoost model demonstrated the best performance, significantly improving the accuracy of ESG risk predictions. These findings suggest that ML can enhance ESG risk assessments, offering valuable insights for investors, regulatory bodies, and corporate management.