Financial risk forewarning with an interpretable ensemble learning approach: An empirical analysis based on Chinese listed companies

Abstract

An efficient and intelligent forewarning model for financial risk is essential to assist company managers, investors, and market regulators in risk management. This research aims to establish an accurate and understandable financial distress forewarning model based on advanced ensemble learning techniques, including Bootstrap Aggregation (Bagging) and Light Gradient Boosting Machine (LightGBM). Meanwhile, the Borderline-Synthetic Minority Over-sampling Technique (Borderline-SMOTE) is employed to address the imbalance problem between financial distress and non-distressed samples. The Multi-Objective Particle Swarm Optimization (MOPSO) is applied to perform multi-objective optimization of the hyperparameters of the proposed model. In addition, the SHapley Additive exPlanations (SHAP) is adopted to conduct an interpretability analysis of the forewarning results. The Chinese listed companies from 2006 to 2022 are considered as the research samples, and the financial ratios of the sample companies in the first, second, and third years before the financial distress is utilized as forewarning features. The experimental findings reveal that the proposed model has the best forewarning performance by using the features from first year before the financial distress. The model proposed in this study also outperforms other comparative models in terms of Accuracy (88.45%), Negative Predictive Value (74.44%), Recall (91.10%), and Specificity (80.26%). Finally, this research has discovered that the Return on Total Assets is the most influential indicator in forewarning financial distress. Therefore, the proposed model can provide listed company managers, investors, and market regulators with an intelligent and effective tool in decision-making for financial distress forewarning.