Financial Fragility and Sectoral Risk in MSMEs: A Longitudinal Validation of The Altman Z-Score Model in India

  • Authors

    • Dr. Vijay Agrawal Associate Professor, Department of Management, Birla Institute of Technology, Mesra Patna Campus
    https://doi.org/10.14419/zzvvh125

    Received date: September 20, 2025

    Accepted date: October 22, 2025

    Published date: October 26, 2025

  • MSMEs; Altman Z-Score; Financial Distress; Profitability Modelling; Sectoral Analysis

  • Abstract

    The post-pandemic economic landscape has amplified financial vulnerabilities in Micro, Small, and Medium Enterprises (MSMEs), necessitating granular and sectoral-level diagnostics. This study validates the application of the Altman Z-Score model for five Indian MSME sectors — Auto Components, Chemicals & Petrochemicals, Industrial Products, Pharmaceuticals & Biotechnology, and Textiles & Apparel — analyzing data from 2016-2024. Based on the firm-level dataset of 100 companies, the study classifies the firms into Distress, Grey, and Safe zones and examines the drivers of profitability using regression models where Net Profit, ROA, and ROE serve as dependent variables. The results indicate that although sales always have a positive impact on profitability, the effects of depreciation and raw material costs are negative across all indicators of performance. The sectoral interaction terms are also noteworthy, although they are statistically insignificant. The study as a whole captures a sector-wide financial performance comparison before and after COVID. This research incorporates risk analysis across time and sector disaggregation, and profitability modeling within the MSME distress context, which augments existing literature. Furthermore, it provides critical data for pre-emptively guiding managers, lenders, and policymakers towards understanding signs of impending financial distress and strengthening financial resilience. This study employs cross-sectional OLS regression with robust standard errors on a dataset of 100 MSMEs across five manufacturing sectors from 2016–2024, complemented by longitudinal Z-score tracking to capture pre- and post-COVID performance differentials.

  • References

    1. Ali, F., Ahmed, N., & Tariq, Y. (2023). Predicting financial misconduct using hybrid deep learning and forensic ratios. Annals of Operations Re-search, 321(2), 945–970. https://doi.org/10.1007/s10479-023-05199-6
    2. 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/10.2307/2978933
    3. Altman, E. I., Iwanicz-Drozdowska, M., & Suvas, A. (2022). Financial flexibility and performance during stress periods: A sectoral comparison. Journal of Corporate Finance, 73, 102186. https://doi.org/10.1016/j.jcorpfin.2021.102186
    4. Basu, S., & Mohanty, A. (2022). Role of liquidity and solvency indicators in MSME distress forecasting: An Indian perspective. Journal of Small Business Strategy, 32(1), 105–120. https://jsbs.org/vol32/issue1/basu
    5. Bouri, E., Jain, A., Jain, M., Roubaud, D., & Kristoufek, L. (2021). Risk spillovers among financial and non-financial firms during COVID-19: Ev-idence from a tail-event approach. Finance Research Letters, 39, 101861. https://doi.org/10.1016/j.frl.2020.101861
    6. Chatterjee, S., & Ray, A. (2022). Structural transformation of India’s pharmaceutical sector post-COVID: A policy and market perspective. Ap-plied Economics Letters, 29(16), 1472–1478. https://doi.org/10.1080/13504851.2021.1966673
    7. Chatterjee, S., & Ray, A. (2022). Structural transformation of India’s industrial sector post-COVID: A policy and market perspective. Applied Economics Letters, 29(16), 1472–1478. https://doi.org/10.1080/13504851.2021.1976149
    8. Chen, L., Yu, K., & Zhang, T. (2023). AI capability and firm productivity: Evidence from manufacturing sectors. Journal of Business Research, 161, 113836. https://doi.org/10.1016/j.jbusres.2023.113836
    9. Dwivedi, Y. K., Hughes, D. L., Ismagilova, E., Aarts, G., & Buhalis, D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
    10. Farooq, S., & Shaikh, F. M. (2022). Hybrid indicators for detecting accounting irregularities in publicly listed firms. International Journal of Emerg-ing Markets, 17(8), 1889–1910. https://doi.org/10.1108/IJOEM-08-2020-0885
    11. Ghosh, S., & Bose, I. (2023). Predicting financial distress in Indian MSMEs using Altman Z-score: A sectoral validation. Asia-Pacific Journal of Business Administration, 15(2), 239–256. https://doi.org/10.1108/APJBA-09-2022-0426
    12. Goyal, S., & Rajput, N. (2022). Financial distress prediction in emerging markets: An Altman-based hybrid machine learning approach. Journal of Indian Business Research, 14(1), 33–49. https://doi.org/10.1108/JIBR-04-2021-0113
    13. Jain, V., Chatterjee, T., & Majumdar, S. (2023). Financial distress and manipulation detection in emerging markets: A hybrid approach. Review of Accounting and Finance, 22(1), 95–113. https://doi.org/10.1108/RAF-07-2021-0178
    14. Kapoor, A., & Singhania, M. (2022). Fraud detection in Indian companies using SHAP, XGBoost, and forensic ratios. Journal of Indian Business Research, 14(1), 33–49. https://doi.org/10.1108/JIBR-09-2021-0287
    15. Lima, V., & Silva, A. (2023). Explainable neural networks in forensic auditing: Empirical evidence from Latin America. Computational Manage-ment Science, 20(2), 357–377. https://doi.org/10.1007/s10287-022-00421-z
    16. Mamun, M. A., Hoque, M., & Yasmeen, F. (2022). Corporate resilience and capital structure dynamics during COVID-19: Evidence from emerging markets. Journal of Risk and Financial Management, 15(4), 190. https://doi.org/10.3390/jrfm15040190
    17. Pandey, R., & Kaur, J. (2024). Revisiting Altman Z-score in post-pandemic Indian MSMEs: Policy-driven changes and risk profiles. Journal of Risk and Financial Management, 17(2), 210. https://doi.org/10.3390/jrfm17020210
    18. Roshan, M., & Hussain, A. (2021). Early warning systems for financial fraud: Combining Altman and Beneish models with Bayesian classification. Review of Quantitative Finance and Accounting, 56(1), 145–165. https://doi.org/10.1007/s11156-020-00926-7
    19. Sharma, R., & Mehrotra, A. (2022). Financial performance modeling of Indian MSMEs: COVID-19 and beyond. Small Enterprise Research, 29(3), 245–264. https://doi.org/10.1080/13215906.2022.2039916
    20. SIDBI. (2022). Annual MSME Sector Report 2021–22. Small Industries Development Bank of India. https://www.sidbi.in/en/msme-sector
    21. Singh, H., Kumar, S., & Roy, P. (2024). Altman Z-score validation for Indian MSMEs: Evidence from longitudinal sectoral data. Global Business Review, 25(1), 87–105. https://doi.org/10.1177/09721509231124319

  • Downloads

  • How to Cite

    Agrawal , D. V. . (2025). Financial Fragility and Sectoral Risk in MSMEs: A Longitudinal Validation of The Altman Z-Score Model in India. International Journal of Accounting and Economics Studies, 12(6), 898-908. https://doi.org/10.14419/zzvvh125