Corporate Financial Distress Prediction in Vietnam Using Calibrated and Explainable Machine Learning

Corporate financial distress imposes sizable and persistent costs on shareholders, creditors, employees, and the broader economy. However, practical risk governance in emerging markets requires not only accurate risk ranking but also reliable probabilities that can be translated into monitoring thresholds and escalation actions. This study develops an early-warning framework for Vietnamese listed non-financial firms that targets decision-useful one-year-ahead distress probabilities. It evaluates whether calibrated and explainable machine-learning models remain operationally usable under temporal change. The analysis uses a firm-year panel of companies listed on the Ho Chi Minh City Stock Exchange and the Hanoi Stock Exchange over 2014–2024, comprising 7305 observations. A strict chronological design is implemented to emulate forward deployment: model estimation uses 2015–2019, tuning and probability calibration use 2020–2021, and final evaluation is conducted once on an out-of-time hold-out period of 2022–2024. During the test period, distress prevalence increases to 15.42%, compared with approximately 12% in earlier windows. A conventional probabilistic benchmark is compared with multiple machine-learning classifiers under an identical feature space and temporal protocol. An explanation layer is also applied to support governance-oriented interpretation. Out-of-time evidence indicates that tree-ensemble methods provide the strongest combination of ranking performance and probability accuracy, with the random forest providing the best out-of-time performance among the evaluated models, with reasonable discrimination and the lowest probability error, although the magnitude of the AUC improvement should be interpreted as moderate rather than exceptional. The random forest’s calibrated probabilities support monotonic risk stratification and transparent, capacity-constrained watchlists. Selecting the top 10% of firm-years by predicted risk captures 48.3% of distress events with 44.3% precision, corresponding to a 2.87-fold lift over the base rate. Incremental gains from the structural, market-implied distance-to-default proxy are limited once standard accounting and market measures are included. This suggests that routinely available accounting and market variables already capture most of the relevant distress information in this setting. Overall, the results support an out-of-time calibrated and explainable pipeline as a practical foundation for auditable monitoring and tiered escalation in Vietnam’s listed corporate sector.