Financial Distress Multi-Classification Prediction: A Case Study in Vietnam
DOI:
https://doi.org/10.54536/ajfti.v3i1.4570Keywords:
Adaptive Synthetic Sampling, Financial Distress, Multi-ClassificationAbstract
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.
References
Alibrahim, H., & Ludwig, S. A. (2021, 28 June-1 July 2021). Hyperparameter Optimization: Comparing Genetic Algorithm against Grid Search and Bayesian Optimization. 2021 IEEE Congress on Evolutionary Computation (CEC), pp. 1551-1559. IEEE
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The journal of finance, 23(4), 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
Altman, E. I. (1983). Corporate financial distress: A complete guide to predicting, avoiding, and dealing with bankruptcy. Wiley.
Altman, E. I. (2005). An emerging market credit scoring system for corporate bonds. Emerging Markets Review, 6(4), 311-323. https://doi.org/10.1016/j.ememar.2005.09.007
Altman, E. I., Eom, Y. H., & Kim, D. W. (1995). Failure Prediction: Evidence from Korea. Journal of International Financial Management & Accounting, 6(3), 230-249. https://doi.org/10.1111/j.1467-646X.1995.tb00058.x
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
Byström, H. (2006). Merton Unraveled: A Flexible Way of Modeling Default Risk. The Journal of Alternative Investments, 8, 39-47. https://doi.org/10.3905/jai.2006.627849
Carmona, P., Dwekat, A., & Mardawi, Z. (2022). No more black boxes! Explaining the predictions of a machine learning XGBoost classifier algorithm in business failure. Research in International Business and Finance, 61, 101649. https://doi.org/10.1016/j.ribaf.2022.101649
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). Association for Computing Machinery.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297. https://doi.org/10.1007/BF00994018
Đạt, T. M. T. (2021). Kaggle’s state of machine learning and data science 2021. Scribd. https://www.scribd.com/document/554216647/Kaggle-s-State-of-Machine-Learning-and-Data-Science-2021
Dinh, D. V., Powell, R. J., & Vo, D. H. (2021). Forecasting corporate financial distress in the Southeast Asian countries: A market-based approach. Journal of Asian Economics, 74, 101293. https://doi.org/10.1016/j.asieco.2021.101293
Doğan, S., Koçak, D., & Atan, M. (2022). Financial Distress Prediction Using Support Vector Machines and Logistic Regression. In M. K. Terzioğlu (Ed.), Advances in Econometrics, Operational Research, Data Science and Actuarial Studies: Techniques and Theories (pp. 429-452). Springer International Publishing. https://doi.org/10.1007/978-3-030-85254-2_26
Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: Gradient boosting with categorical features support. arXiv. https://doi.org/10.48550/arXiv.1810.11363
Dorogush, A. V., Gulin, A., Gusev, G., Kazeev, N., Ostroumova, L., & Vorobev, A. (2017). Fighting biases with dynamic boosting. arXiv. https://doi.org/10.48550/arXiv.1706.09516
Haibo, H., Yang, B., Garcia, E. A., & Shutao, L. (2008, June 1–8). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) (pp. 1322–1328). IEEE.
Hanafy, M., & Ming, R. (2021). Comparing SMOTE Family Techniques in Predicting Insurance Premium Defaulting using Machine Learning Models. International Journal of Advanced Computer Science and Applications, 12. https://doi.org/10.14569/IJACSA.2021.0120970
Hou, G. D., Tong, D. L., Liew, S. Y., & Choo, P. Y. (2024, September 3–4). Exploring random forest regression for financial distress detection. In 2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS) (pp. 7–12). Aconf.
Lopez, J. A. (2004). The empirical relationship between average asset correlation, firm probability of default, and asset size. Journal of Financial Intermediation, 13(2), 265-283. https://doi.org/10.1016/S1042-9573(03)00045-7
Lu, H., & Hu, X. (2024). Enhancing Financial Risk Prediction for Listed Companies: A Catboost-Based Ensemble Learning Approach. Journal of the Knowledge Economy, 15(2), 9824-9840. https://doi.org/10.1007/s13132-023-01601-5
Merton, R. C. (1974). On the pricing of corporate debt: The risk structure of interest rates. The journal of finance, 29(2), 449-470. https://doi.org/10.1111/j.1540-6261.1974.tb03058.x
Mooney, P. (2020). 2020 Kaggle machine learning & data science survey. Kaggle. https://www.kaggle.com/competitions/kaggle-survey-2020
Nguyen, M., Nguyen, B., & Liêu, M.-L. (2024). Corporate financial distress prediction in a transition economy. Journal of Forecasting, 43(8), 3128-3160. https://doi.org/10.1002/for.3177
Pham Vo Ninh, B., Do Thanh, T., & Vo Hong, D. (2018). Financial distress and bankruptcy prediction: An appropriate model for listed firms in Vietnam. Economic Systems, 42(4), 616-624. https://doi.org/10.1016/j.ecosys.2018.05.002
Tran, K. L., Le, H. A., Nguyen, T. H., & Nguyen, D. T. (2022). Explainable Machine Learning for Financial Distress Prediction: Evidence from Vietnam. Data, 7(11), 160. https://doi.org/10.3390/data7110160
Tran, T., Nguyen, N. H., Le, B. T., Thanh Vu, N., & Vo, D. H. (2023). Examining financial distress of the Vietnamese listed firms using accounting-based models. PloS one, 18(5), e0284451. https://doi.org/10.1371/journal.pone.0284451
Triều, T. (2025). There are still many reasons to be optimistic. Vietnam.vn. https://www.vietnam.vn/en/van-co-nhieu-ly-do-de-lac-quan
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