Prediction of corporate default risk considering ESG performance and unbalanced samples

Abstract

This paper constructs a corporate default risk prediction model taking ESG scores into account in an unbalanced sample state. Four indicators are introduced—the ESG composite score, environmental dimension score, social dimension score, and governance dimension score—to assess an enterprise's capacity for sustainable development as well as its level of greenness and low carbon emissions. These indicators improve and supplement the current corporate default prediction indicator system. The Focal Loss function and the cost-sensitive decision threshold are used to improve the traditional Stacking model at the algorithmic aspect. The CS-FL-Stacking model is then built to address the problem of sample class imbalance. After conducting an empirical analysis with data from 3006 Chinese A-share listed companies during 2021–2023, the following conclusions are drawn: (1) The inclusion of ESG indicators can somewhat enhance the model's prediction ability and reduce the misclassification loss. (2) The CS-FL-Stacking model generally outperforms the benchmark model in terms of accuracy and other indicators. It also considerably improves its capacity to identify minority samples, which can effectively address the problem of unbalanced sample classification. (3) Relevant recommendations are provided for the improvement of the CS-FL-Stacking model and the application of ESG indicators in corporate risk management in light of the analysis just mentioned.