A Novel Stacked Generalization Ensemble-Based Hybrid SGM-BRR Model for ESG Score Prediction

Zhie Wang, Xiaoyong Wang (), Xuexin Liu, Jun Zhang, Jingde Xu and Jun Ma
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Zhie Wang: Management Engineering School, Capital University of Economics and Business, Beijing 100070, China
Xiaoyong Wang: Management Engineering School, Capital University of Economics and Business, Beijing 100070, China
Xuexin Liu: College of Business Administration, Capital University of Economics and Business, Beijing 100070, China
Jun Zhang: Management Engineering School, Capital University of Economics and Business, Beijing 100070, China
Jingde Xu: Institute of Higher Education, North China Institute of Science and Technology, Langfang 065201, China
Jun Ma: Management Engineering School, Capital University of Economics and Business, Beijing 100070, China

Sustainability, 2024, vol. 16, issue 16, 1-25

Abstract: Recently, financial institutions and investors have placed an increasing emphasis on ESG (environmental, social, and governance) as a principal indicator for the evaluation of companies. However, the current ESG scoring systems lack uniformity and are often subjective. It is of great importance to be able to make accurate predictions regarding the ESG scores of corporations. A Stacked Generalization Model that employs Random Forest (RF), Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) as base learners, with Bayesian Ridge Regression (BRR) as the meta-model for integrating the predictions of these diverse models is proposed. The goal is to develop an ESG score prediction model for Chinese companies. The experimental data set encompasses Chinese A-share listed companies from 2012 to 2020. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R 2 ) are employed for model evaluation and are compared with seven benchmark models. The results demonstrate that SGM-BRR reduces the RMSE by 18.4%, 17.3%, 13.7%, and 76.1%, the MAE by 15.4%, 18.4%, 15.8%, and 68.4%, and increases the R 2 by 2%, 1.4%, 2%, and 6% for ESG, E, S, and G scores, respectively. Furthermore, the model’s performance is validated across different industries, with SGM-BRR exhibiting the most optimal performance of RMSE, MAE, and R 2 in 27, 25, and 27 groups, respectively. Consequently, the model demonstrates broad applicability and stability performance in ESG score prediction.

Keywords: ESG score; prediction; ensemble learning algorithms; Stacked Generalization Model; Bayesian Ridge Regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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