Financial Distress Multi-Classification Prediction: A Case Study in Vietnam

Stock investment remains one of the most attractive and profitable activities in financial markets. However, assessing a company’s financial health is a complex task due to the vast amount of financial data involved. This study classifies companies into three financial status categories of safe, risky, and distressed by employing three key financial distress measures: Distance to Default (DD), Emerging Market Score (EMS), and Altman Z-score. A set of 68 financial ratios is utilized to predict the financial status of a company. We employ the Adaptive Synthetic Sampling (ADASYN) technique alongside advanced machine learning algorithms, including Random Forest, CatBoost, XGBoost, and Support Vector Machine to further improve model performance. Our results show that Random Forest yields highly accurate predictions in multi-class classification by integrating machine learning with the EMS method. The best-performing model achieves an exceptional ROC-AUC score of 99.26%. These findings provide a powerful decision-making tool for investors, traders, practitioners, and policymakers, enabling more precise assessments of corporate financial stability.