From Variable Importance to Economic Scenarios: Interpreting Machine Learning Models for Firm Failure Risk Management
By: Vlad TEODORESCU and Catalina-Ioana TOADER
References
- Alanis, E., Chava, S., & Shah, A. (2023). Benchmarking machine learning models to predict corporate bankruptcy. Journal of Credit Risk, 19(2), 77–110.
- Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589–609.
- Alonso, A., & Carbó, J. M. (2021). Understanding the predictors of corporate bankruptcy: A machine learning approach. European Central Bank Working Paper Series
- Barboza, F., Kimura, H., & Altman, E. (2017). “Machine learning models and bankruptcy prediction”. Expert Systems with Applications.
- Beaver, W.H. (1966) Financial Ratios as Predictors of Failure, Empirical Research in accounting, Selected Studies. Journal of Accounting Research, 4, 71-111. https://doi.org/10.2307/2490171
- Campbell, J. Y., Hilscher, J., & Szilagyi, J. (2008). “In Search of Distress Risk”. Journal of Finance.
- 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.
- Grinsztajn, L., Oyallon, E., & Varoquaux, G. (2022). Why do tree-based models still outperform deep learning on typical tabular data? In Advances in Neural Information Processing Systems (Vol. 35). Curran Associates, Inc.
- Jones, S., Johnstone, D., & Wilson, R. (2017). Predicting corporate bankruptcy: An evaluation of alternative statistical frameworks. Journal of Business Finance & Accounting, 44(1-2), 3-34.
- Lessmann, S., Baesens, B., Seow, H. V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. European Journal of Operational Research, 247(1), 124-136.
- Lin, J. (2024). Research on loan default prediction based on logistic regression, random forest, XGBoost, and AdaBoost. SHS Web of Conferences, 181, 02008.
- Merton, R. C. (1974). On the pricing of corporate debt. Journal of Finance, 29(2), 449–470.
- Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 109-131.
- Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model. The Journal of Business, 74(1), 101-124.
- Teodorescu, V., & Obreja Brașoveanu, L. (2025). Assessing the Validity of k-Fold Cross-Validation for Model Selection: Evidence from Bankruptcy Prediction Using Random Forest and XGBoost. Computation, 13(5), 127.
- Teodorescu, V., & Toader, C. I. (2024). Using machine learning to model bankruptcy risk in listed companies. In Proceedings of the International Conference on Economics and Social Sciences (pp. 610–619). Bucharest University of Economic Studies.
- Tian, S., Yu, Y., & Guo, H. (2015). Variable selection and corporate bankruptcy forecasts: Evidence from China. International Review of Financial Analysis, 42, 439-446.
- Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 59-82.
DOI: https://doi.org/10.2478/picbe-2026-0055 | Journal eISSN: 2558-9652
Language: English
Page range: 699 - 717
Published on: Jul 16, 2026
Published by: Bucharest University of Economic Studies
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
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© 2026 Vlad TEODORESCU, Catalina-Ioana TOADER, published by Bucharest University of Economic Studies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.