Multi-class financial distress prediction based on hybrid feature selection and improved stacking ensemble model

Abstract

Multi-class financial distress prediction (FDP) can accurately assess the corporate financial status. Improving its prediction performance is the academic focus. Feature selection and classifier models play a crucial role in the multi-class FDP model. Therefore, this paper proposes a new hybrid feature selection and an improved stacking ensemble model. The hybrid feature selection uses information gain and an improved particle swarm optimization to filter the indicators. The hyperopt hyperparameter optimization method is used to optimize the base learners of stacking ensemble model; The F1-score weighted optimization method is designed for dealing with the discrepancies of the base learners; To objectively solve the combination configuration problem of stacking ensemble model, a constrained genetic algorithm is proposed. The Chinese listed companies are used as research objects for empirical research. The results show that the hybrid feature selection outperforms other feature selection. The F1-score weighted optimized model has 8.97% higher accuracy than the unweighted optimized model. The proposed model performs better in terms of accuracy, robustness, and sensitivity compared to the baseline models and the classifier models in existing multi-class FDP studies. The proposed hybrid feature selection and the improved stacking ensemble model provide new and reliable research ideas for multi-class FDP.