Benchmarking Financial Distress Prediction Models through Outcome Based Validation in Indian Companies
DOI:
https://doi.org/10.62569/iijb.v3i3.295Keywords:
Altman Z-Score, Financial distress prediction, Indian companies, Springate Model, Zmijewski ModelAbstract
Although many studies have compared financial distress prediction models, most evaluate performance based on agreement among models rather than validation against actual financial outcomes. This limitation makes it difficult to identify which models provide the most reliable early-warning signals in practice. Addressing this gap, this study benchmarks five widely used financial distress prediction models namely the Altman Z-Score, Ohlson O-Score, Zmijewski, Springate, and Grover models through an outcome-based validation framework using real-world financial outcomes of selected Indian companies. Secondary financial data from six companies in the telecommunications, infrastructure, and automobile sectors covering the 2015–2025 period were analyzed. Model predictions were validated against independently verified evidence, including credit ratings, insolvency proceedings, financial restructuring events, and company-specific distress indicators. Predictive performance was evaluated using confusion matrix analysis and multiple classification metrics, including accuracy, sensitivity, specificity, precision, F1 score, and Type II error. The findings reveal substantial variation in model performance. The Springate model achieved perfect sensitivity and identified all distressed observations, whereas the Zmijewski model provided the most balanced overall performance with the highest F1 score. In contrast, the Altman, Ohlson, and Grover models showed higher overall accuracy but missed several actual distress cases. By introducing an outcome-based validation framework, this study advances the evaluation of financial distress prediction models beyond conventional model comparison and provides more reliable guidance for investors, lenders, regulators, and corporate managers in selecting effective early-warning tools.
Downloads
References
Abdullah, M., Gulzar, I., Chaudhary, A., Tabash, M. I., Rashid, U., Naaz, I., & Ali, A. (2023). Dynamics of speed of leverage adjustment and financial distress in the Indian steel industry. Journal of Open Innovation: Technology, Market, and Complexity, 9(4). https://doi.org/10.1016/j.joitmc.2023.100152
Ahemed, A. M., Iqbal, U., & Atif, M. M. (2025). Asymmetric cost behavior and financial distress. Economics Letters, 247. https://doi.org/10.1016/j.econlet.2024.112121
Barboza, F., & Altman, E. (2024). Predicting financial distress in Latin American companies: A comparative analysis of logistic regression and random forest models. North American Journal of Economics and Finance, 72. https://doi.org/10.1016/j.najef.2024.102158
Baxi, A. (2023). Interim Finance in Creditor-Oriented Bankruptcy Codes: A Study in the Context of Insolvency & Bankruptcy Code, India. Vikalpa, 48(3). https://doi.org/10.1177/02560909221150689
Cameron, R. (2009). A sequential mixed model research design: Design, analytical and display issues. International Journal of Multiple Research Approaches, 3(2). https://doi.org/10.5172/mra.3.2.140
Chandrasekar, V., Ashraf, S., Naresh, N. R., Piradeep, S., Muralidhar, S., & Poornima, J. (2025). A Study on the Plausibility of Financial Distress Due to Covid 19: Evidence from Nifty Fifty Companies. In Studies in Systems, Decision and Control (Vol. 555). https://doi.org/10.1007/978-3-031-67890-5_91
Chen, S. (2025). Quantitative Trading (pp. 161–167). https://doi.org/10.1007/978-981-95-3064-9_16
Chen, Y., Xu, J., Wang, S., & Xu, S. (2024). Economic environment uncertainty and financialization of real estate firms. International Review of Economics and Finance, 93. https://doi.org/10.1016/j.iref.2024.05.011
Chhabra, B. (2018). Impact of core-self evaluation and job satisfaction on turnover intentions: A study of Indian retail sector. Organizations and Markets in Emerging Economies, 9(2). https://doi.org/10.15388/omee.2018.10.00015
Elangovan, R., Irudayasamy, F. G., & Parayitam, S. (2022). Testing the market efficiency in Indian stock market: evidence from Bombay Stock Exchange broad market indices. Journal of Economics, Finance and Administrative Science, 27(54). https://doi.org/10.1108/JEFAS-04-2021-0040
Elhoseny, M., Metawa, N., Sztano, G., & El-hasnony, I. M. (2025). Deep Learning-Based Model for Financial Distress Prediction. Annals of Operations Research, 345(2–3). https://doi.org/10.1007/s10479-022-04766-5
Farooq, M., Hunjra, A. I., Ullah, S., & Al-Faryan, M. A. S. (2023). The determinants of financial distress cost: A case of emerging market. Cogent Economics and Finance, 11(1). https://doi.org/10.1080/23322039.2023.2186038
Ghosh, T. (2019). Corporate Governance and Audit Fees Evidence from Bangladeshi Listed Banks and NBFIs. Journal of Corporate Governance Research, 3(1). https://doi.org/10.5296/jcgr.v3i1.15638
Halawi, L., Clarke, A., & George, K. (2022). Evaluating Predictive Performance. In Harnessing the Power of Analytics (pp. 51–59). Springer International Publishing. https://doi.org/10.1007/978-3-030-89712-3_4
Khan, M. A., Farooqi, M. R., Ahmad, M. F., Haque, S., & Alkhuraydili, A. (2024). Influence of Compensation, Performance Feedback on Employee Retention in Indian Retail Sector. SAGE Open, 14(2). https://doi.org/10.1177/21582440241236615
Li, D., & Azman, N. H. N. (2025). Digital Financial Inclusion Promotes Economic Environment-Inclusive Growth-in Underdeveloped Regions: Evidence from Gansu, China. Chinese Journal of Urban and Environmental Studies, 13(1). https://doi.org/10.1142/S2345748125500071
Lisin, A., Kushnir, A., Koryakov, A. G., Fomenko, N., & Shchukina, T. (2022). Financial Stability in Companies with High ESG Scores: Evidence from North America Using the Ohlson O-Score. Sustainability (Switzerland), 14(1). https://doi.org/10.3390/su14010479
Marsenne, M., Ismail, T., Taqi, M., & Hanifah, I. A. (2024). Financial distress predictions with Altman, Springate, Zmijewski, Taffler and Grover models. Decision Science Letters, 13(1). https://doi.org/10.5267/j.dsl.2023.10.002
Nurul CH, F., Sulistyowati, S., Rusli, D., Rohmah, A., & Suprati, D. (2024). Accuracy of the Financial Distress Model in Transportation Firms: Altman, Springate and Grover Model. Taxation and Public Finance, 2(1). https://doi.org/10.58777/tpf.v2i1.362
Ohlson, J. A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1). https://doi.org/10.2307/2490395
Oware, K. M., & Appiah, K. (2022). CSR assurance practice and financial distress likelihood: evidence from India. Meditari Accountancy Research, 30(6). https://doi.org/10.1108/MEDAR-10-2020-1055
Pandey, C. S., Mishra, P., Pandey, S. R., Singh, R. D., & Pandey, S. (2025). Research paradigms: a systematic literature review of methodological shifts and interdisciplinary approaches in research. Quality & Quantity, 60(1), 2297–2325. https://doi.org/10.1007/s11135-025-02359-5
Ravichandra Rao, N., & Kasture, J. (2026). Sectoral insights into corporate insolvency: a comprehensive analysis of Corporate Insolvency Resolution Process (CIRP) outcomes in India. International Journal of Law and Management, 68(2). https://doi.org/10.1108/IJLMA-08-2024-0291
Sehgal, S., Mishra, R. K., Deisting, F., & Vashisht, R. (2021). On the determinants and prediction of corporate financial distress in India. Managerial Finance, 47(10). https://doi.org/10.1108/MF-06-2020-0332
Shome, S., & Verma, S. (2020). Financial Distress in Indian Aviation Industry: Investigation Using Bankruptcy Prediction Models. Eurasian Journal of Business and Economics, 13(25). https://doi.org/10.17015/ejbe.2020.025.06
Singh Chauhan, S., Suri, P., Alam, F., Hani, U., Johri, A., & Ali, F. (2025). A causality investigation into stock prices and macroeconomic indicators in the Indian stock market. F1000Research, 13. https://doi.org/10.12688/f1000research.157041.3
Singh, G., & Singla, R. (2024). Logit model for predicting financial distress in Indian corporate sector. International Journal of Business and Globalisation, 37(4). https://doi.org/10.1504/IJBG.2024.140052
Sun, J., Zhou, M., Ai, W., & Li, H. (2019). Dynamic prediction of relative financial distress based on imbalanced data stream: from the view of one industry. Risk Management, 21(4). https://doi.org/10.1057/s41283-018-0047-y
Swalih, M. M., Adarsh, K. B., & Sulphey, M. M. (2021). A study on the financial soundness of indian automobile industries using altman z-score. Accounting, 7(2). https://doi.org/10.5267/j.ac.2020.12.001
Thakur, Dr. S. (2019). A Study on Financial Performance Analysis of Dabur India Limited. International Journal of Scientific & Engineering Research, 10(9). https://doi.org/10.14299/ijser.2019.09.01
Xu, Y., & Pan, H. (2025). The Effects of Enterprises’ E-Business Adoptions on Cross-Border Firm Internationalization. Systems, 13(2). https://doi.org/10.3390/systems13020084
Yendrawati, R., & Adiwafi, N. (2020). Comparative analysis of Z-score, Springate, and Zmijewski models in predicting financial distress conditions. Journal of Contemporary Accounting, 2(2). https://doi.org/10.20885/jca.vol2.iss2.art2
Zhang, Z., Wu, C., Qu, S., & Chen, X. (2022). An explainable artificial intelligence approach for financial distress prediction. Information Processing and Management, 59(4). https://doi.org/10.1016/j.ipm.2022.102988
Zmijewski, M. E. (1984). Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research, 22, 59. https://doi.org/10.2307/2490859
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Mohd Jameel (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
