Corporate ESG rating prediction based on XGBoost-SHAP interpretable machine learning model
Jianfeng Zhang, Zexin Zhao · Expert Systems with Applications · 2025
The prediction of corporate ESG ratings is of paramount importance in augmenting the scientific rigor and precision of ESG investment decisions and steering corporate management of ESG-related risks. While machine learning methodologies have been extensively utilized in forecasting corporate behavior, their deployment in corporate ESG ratings remains relatively nascent and is often criticized for a lack of interpretability. This study develops a predictive model for corporate ESG ratings using an XGBoost algorithm enhanced with SHAP interpretability. The methodological framework incorporates SMOTE-ENN for handling class imbalance and a comprehensive optimization approach utilizing 3-fold cross-validation and randomized hyperparameter search. The model incorporates a comprehensive set of 15 indicators spanning four critical dimensions—financial performance, environmental impact, social responsibility, and corporate governance, using a dataset of Chinese A-share listed companies from 2013 to 2022. The model’s predictive efficacy is subsequently elucidated, revealing that the XGBoost-SHAP framework achieves an accuracy and precision rate of 91.0% and 90.7%, respectively, with an F1-score and AUC value of 90.1% and 0.977, outperforming comparative models. The analysis underscores the significant influence of financial and non-financial factors on ESG rating predictions, with financial attributes exerting a relatively more pronounced impact than individual non-financial metrics. Tailored to the diverse objectives of ESG investors, this research further delineates a level definition model and a risk identification model, achieving predictive accuracies of 92.4% and 97.7%, respectively. The insights from this study furnish ESG investors with a robust foundation for enhancing investment outcomes and offer strategic guidance for corporations aiming to elevate their ESG performance.