Financial distress prediction using an improved particle swarm optimization wrapper feature selection method and tree boosting ensemble
提出一种改进粒子群优化算法(IPSO)嵌入树提升集成模型进行特征选择，构建财务困境预测模型，在中国上市公司样本上验证了其提升预测性能和鲁棒性的效果。
Financial distress prediction (FDP) is a complex task involving both feature selection and model construction. While many studies have addressed these challenges individually, there is a lack of FDP models that integrate feature selection into the overall model building process. To address the issues of fast convergence and local optimization in particle swarm optimization (PSO), this study proposes a modified PSO algorithm with three improvement strategies. This improved PSO, known as IPSO, is embedded within state-of-the-art tree boosting ensemble models for feature selection. IPSO continuously evolves toward the optimal feature subset, preventing premature convergence and avoiding local optima. To construct the FDP model, four state-of-the-art tree boosting models are combined using an improved majority voting ensemble strategy. The integration of IPSO enhances the prediction performance of the model. Comparative experiments are conducted on samples of Chinese listed companies under short-term and long-term prediction scenarios to evaluate the proposed model. The results demonstrate that IPSO significantly improves the prediction performance, and the ensemble strategy enhances the robustness of the predictions.