Research on Enterprise Environmental, Social, and Governance Rating Prediction Model Integrating Financial Features and Random Forest

Abstract

This paper briefly introduces the enterprise environmental, social, and governance (ESG) rating and the random forest (RF) model for ESG rating prediction. The China Securities Index (CSI) ESG rating dataset is selected for simulation experiments. During the experiments, the effects of the number of decision trees, maximum tree depth, maximum number of features, and minimum number of samples per node on the RF model are first examined. The RF model is then compared with the linear logistic regression (LLR), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost) algorithms. Finally, the importance of the feature indicators for predicting the ESG rating is analyzed. Furthermore, ablation experiments and cross-data generalization control experiments are conducted. The RF model exhibits the best predictive performance, with a precision of 0.8414, a recall of 0.8463, an F-score of 0.8442, and an area under the curve (AUC) of 0.8971, when the number of decision trees is 300, the maximum tree depth is 10, the maximum number of features is 6, and the minimum number of samples per node is 3. The RF model has better predictive performance than the LLR, SVM, and XGBoost algorithms. The proportion of independent directors is the most important (218.74), followed by the number of green patents (196.32), information disclosure violations (182.59), and return on net assets (167.81). The results of the ablation experiment show that the RF model incorporating financial features performs significantly superior to the RF model that only uses non-financial features; the cross-data generalization control experiment verifies the generalization performance of the RF model.