Bridging financial disclosures and ESG ratings: A data-driven predictive framework

Kahyun Lee; Kahyun Lee

doi:10.3934/QFE.2026005

Quantitative Finance and Economics

2026, Volume 10, Issue 1: 86-107. doi: 10.3934/QFE.2026005

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Research article

Bridging financial disclosures and ESG ratings: A data-driven predictive framework

Kahyun Lee ^,

College of Business Administration, Hongik University, 94 Wausan-ro, Mapo-gu, Seoul, 04066, Republic of Korea

Received: 24 August 2025 Revised: 03 December 2025 Accepted: 12 January 2026 Published: 05 February 2026
JEL Codes: G32, Q56, C38, C43, C55

This paper examines the extent to which standardized financial statement disclosures can anticipate both overall environmental, social, and governance (ESG) ratings and their evolution over time for listed firms. Utilizing a longitudinal dataset of 851 companies spanning 11 years, we systematically extract 279 International Financial Reporting Standards (IFRS)-compliant accounts and generate an extensive set of financial ratios to capture the static and dynamic features of corporate performance. Through an empirical comparison of traditional machine learning, gradient boosting (LightGBM, XGBoost), and neural network methods (multilayer perceptrons, convolutional neural networks, and TabNet), the analysis finds that boosting algorithms deliver consistently superior accuracy in ESG prediction tasks involving high-dimensional tabular data. Notably, the overall ESG composite score exhibits the highest level of predictability from financial information, while environmental ratings remain more elusive. Further investigation reveals that balance sheet variables most strongly explain absolute ESG levels, whereas cash flow metrics are pivotal in predicting annual changes in the scores. These findings indicate that both corporate financial structures and resource flows encode substantial amounts of information that is relevant to sustainability evaluations. By linking financial accounting theory with ESG analytics, this study provides a rigorous, data-driven framework that offers practical insights for researchers, policy-makers, and market participants seeking to enhance the reliability and timeliness of ESG evaluations using objective financial data.
- ESG,
- ESG prediction,
- financial disclosure,
- machine learning,
- deep learning
Citation: Kahyun Lee. Bridging financial disclosures and ESG ratings: A data-driven predictive framework[J]. Quantitative Finance and Economics, 2026, 10(1): 86-107. doi: 10.3934/QFE.2026005

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Abstract

This paper examines the extent to which standardized financial statement disclosures can anticipate both overall environmental, social, and governance (ESG) ratings and their evolution over time for listed firms. Utilizing a longitudinal dataset of 851 companies spanning 11 years, we systematically extract 279 International Financial Reporting Standards (IFRS)-compliant accounts and generate an extensive set of financial ratios to capture the static and dynamic features of corporate performance. Through an empirical comparison of traditional machine learning, gradient boosting (LightGBM, XGBoost), and neural network methods (multilayer perceptrons, convolutional neural networks, and TabNet), the analysis finds that boosting algorithms deliver consistently superior accuracy in ESG prediction tasks involving high-dimensional tabular data. Notably, the overall ESG composite score exhibits the highest level of predictability from financial information, while environmental ratings remain more elusive. Further investigation reveals that balance sheet variables most strongly explain absolute ESG levels, whereas cash flow metrics are pivotal in predicting annual changes in the scores. These findings indicate that both corporate financial structures and resource flows encode substantial amounts of information that is relevant to sustainability evaluations. By linking financial accounting theory with ESG analytics, this study provides a rigorous, data-driven framework that offers practical insights for researchers, policy-makers, and market participants seeking to enhance the reliability and timeliness of ESG evaluations using objective financial data.

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