Predicting ESG Ratings by Machine Learning and Analyzing Influencing Factors by XAI
Pages 120 - 125
Abstract
This study examines the role of Environmental, Social, and Governance (ESG) management in corporate strategy, particularly focusing on predicting ESG ratings with machine learning. Given the diverse ESG evaluation criteria employed by global rating agencies, there's a need for clear guidelines to facilitate effective ESG management. The research aims to develop an ESG rating prediction model utilizing a triennial compendium of Korean corporate financial data. This process involves a comparative analysis of linear models, tree-based models, and neural network-based models. Additionally, this study explains the importance of various variables by applying SHAP, one of the XAI techniques. The results indicate that XGB is the most effective, achieving an 85.1% F1 score in ESG rating predictions. By understanding how financial factors impact ESG ratings, companies can develop more effective ESG strategies, forming an essential foundation for sustainable growth.
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Published: 17 April 2024
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ICSIM 2024
ICSIM 2024: 2024 the 7th International Conference on Software Engineering and Information Management
January 23 - 25, 2024
Suva, Fiji
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