Financial ratios as indicators in bankruptcy prediction: A comparative analysis of statistical and machine learning models

This paper investigates the optimal approach for predicting corporate bankruptcy risk in Vietnam, utilizing a unique dataset of listed firms from 2010 to 2021 based on financial ratios. The results confirm that machine learning models significantly outperform traditional logistic regression, with XGBoost and Random Forest demonstrating superior predictive power compared to K-Nearest Neighbor and logistic regression across both one-year and two-year forecast horizons. The study also contributes methodologically by incorporating additional evaluation metrics including F1 Score, AUC-ROC, Brier Score, and Log-loss to assess classification and probability prediction performance more comprehensively. Model performance is further enhanced through hyperparameter tuning, class imbalance adjustment, and grid search optimization. Empirical findings highlight the importance of all six financial indicator groups, with specific ratios such as Total Assets Turnover, Current Assets to Sales, Current Assets to Total Assets, Equity to Total Assets, and Total Equity Growth playing a critical role in predicting corporate failure. These indicators emphasize the importance of liquidity management, asset efficiency, and equity growth in determining a firm’s financial resilience. Overall, this study not only enhances forecasting accuracy through advanced modeling but also provides valuable insights for stakeholders, particularly financial institutions, investors, and corporate managers supporting more informed decision-making and proactive risk management in Vietnam’s dynamic and evolving business environment. JEL Codes: G33, G34, M10.