A Boosting Framework for Financial Distress Prediction Based on Imbalanced Data

This study introduces a boosting framework for financial distress prediction, specifically designed for imbalanced data, and incorporates robust business logic to enhance interpretability. The framework employs a clustering algorithm to group data samples based on corporate governance features, and then determines the optimal number of clusters using a unique validation measure. An oversampling method is applied post-clustering, followed by a base prediction algorithm to predict financial distress for each cluster. The empirical analysis is conducted using imbalanced sample data from Chinese listed companies, with feature data at time $t-m$ where $m=1,2,3$ and the Special Treatment ST status at time $t$ used to train the model. The aim is to predict the occurrence of financial distress $m$ years into the future. The results demonstrate that the proposed boosting framework outperforms the base model in terms of prediction accuracy on imbalanced data.