Empirical Study of ESG Score Prediction through Machine Learning—A Case of Non-Financial Companies in Taiwan

Hsio-Yi Lin and Bin-Wei Hsu ()
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Hsio-Yi Lin: Department of Finance, Chien Hsin University of Science and Technology, Taoyuan City 320678, Taiwan
Bin-Wei Hsu: Department of Business Administration, Chien Hsin University of Science and Technology, Taoyuan City 320678, Taiwan

Sustainability, 2023, vol. 15, issue 19, 1-19

Abstract: In recent years, ESG (Environmental, Social, and Governance) has become a critical indicator for evaluating sustainable companies. However, the actual logic used for ESG score calculation remains exclusive to rating agencies. Therefore, with the advancement of AI, using machine learning to establish a reliable ESG score prediction model is a topic worth exploring. This study aims to build ESG score prediction models for the non-financial industry in Taiwan using random forest (RF), Extreme Learning Machines (ELM), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) and investigates whether the COVID-19 pandemic has affected the accuracy of these models. The dependent variable is the Taiwan ESG Sustainable Development Index, while the independent variables are 27 financial metrics and corporate governance indicators with three parts: pre-pandemic, pandemic, and the entire period (2018–2021). RMSE, MAE, MAPE, and r 2 are conducted to evaluate these models. The results demonstrate the four supervised models perform well during all three periods. ELM, XGBoost, and SVM exhibit excellent performance, while RF demonstrates good accuracy but relatively lower than the others. XGBoost’s r 2 shows inconsistency with RMSE, MAPE, and MAE. This study concludes the predictive performance of RF and XGBoost is inferior to that of other models.

Keywords: ESG score prediction; machine learning; SVM; random forest; XGBoost; ELM (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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