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

Year: 2024

Type: conference-paper

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.

Source: Singapore Management University Institutional Knowledge (InK) (Singapore Management University)

Authors: NGUYEN, Huynh Long Hung, MEGARGEL, Alan @ Ali MADJELISI

Language: English

Topic: Corporate Social Responsibility Reporting

Subfield: Strategy and Management

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W6996309433

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