Machine Learning in Financial Performance Prediction: The Role of ESG
Dublin Core
Title
Machine Learning in Financial Performance Prediction: The Role of ESG
Creator
Hien Thi Thu Hoang
Proceedings Item Type Metadata
meta_title
Machine Learning in Financial Performance Prediction: The Role of ESG
Abstract/Description
This study explores the application of machine learning techniques to predict corporate Environmental, Social, and Governance (ESG) scores, with a particular focus on identifying the most influential factors derived from company reports. Three predictive models - linear regression, random forests, and gradient boosting - were employed to estimate ESG risk scores. The experimental results demonstrate that the gradient boosting model outperforms the other approaches in predictive accuracy. Analysis using Shapley Additive Explanations (SHAP) reveals that industry classification is the most significant determinant of ESG scores, followed by key financial indicators such as Price/Sales ratio, Price/Book ratio, and Market Capitalization. The proposed predictive framework offers valuable insights for investors and corporations, facilitating informed investment decisions and strategic enhancements in ESG performance.
publication_date
2025/06/20
pdf_url
https://insyma.org/proceedings/files/articles/8. Vietnam_Hien Thi Thu Hoang.pdf
abstract_html_url
https://insyma.org/proceedings/items/show/502
keywords
ESG, Random Forest, Decision Tree, Logistic Regression, financial performance, machine learning
firstpage
1171
lastpage
1174
issn
3047-857X
conference
Proceedings of the International Symposium on Management (Volume 21, 2024)
Volume
22
publisher_name
Fakultas Bisnis dan Ekonomika, Universitas Surabaya
no article
190
Citation
Hien Thi Thu Hoang, “Machine Learning in Financial Performance Prediction: The Role of ESG,” Proceedings of the International Symposium on Management (INSYMA), accessed July 1, 2025, https://insyma.org/proceedings/items/show/502.