Publication Type
Conference Paper
Version
submittedVersion
Publication Date
12-2024
Abstract
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.
Keywords
ESG, machine learning, sustainability
Discipline
Artificial Intelligence and Robotics
Research Areas
Data Science and Engineering
Publication
Annual International Open Innovation Conference 2024
Publisher
Taylor and Francis Group
City or Country
Vietnam
Citation
NGUYEN, Huynh Long Hung and MEGARGEL, Alan @ Ali MADJELISI.
Revolutionizing ESG risk assessment through machine learning: Insights from U.S. corporations. (2024). Annual International Open Innovation Conference 2024.
Available at: https://ink.library.smu.edu.sg/sis_research/9686
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.