Predictive Modelling for Financial Distress amongst Manufacturing Companies in India


  • Dr. Jyoti Nair N.L.Dalmia Institute of Management Studies and Research
  • Dr. JK Sachdeva



Financial distress, Indian manufacturing sector, distress prediction, logistic regression, distress indicators


This study develops a model predicting financial distress amongst manufacturing companies in India using logistic regression. 18 financial ratios of 574 companies from 34 industries in manufacturing sector were examined for the period 2005 – 2019 to develop and validate the model. The study can be considered as one of the very few which has examined the financial distress indicators of manufacturing sector in India. EBIT margin, Interest coverage, quick ratio, Cash flow from operations to Sales, Debtors Turnover, Working Capital to Total Assets, Fixed Assets to Total Assets are important determinants of financial health of a business. This study provides useful insights to business managers and lenders to review and monitor financial soundness of business. The findings can also help policy makers to design policies and programs to support distressed industries in India. This study also addresses the urgent need for a country specific model for distress prediction. The model developed shows high predictive ability.

Key words: Financial distress, Indian manufacturing sector, distress prediction, logistic regression, distress indicators.

Author Biography

Dr. Jyoti Nair, N.L.Dalmia Institute of Management Studies and Research

Professor- Finance


Abad, Cristina, José.L, Arquero, and Sergio, M. Jiménez (2007). Syndromes Leading To Failure: An Exploratory Research. Investment Management & Financial Innovations, 4(3), 23-32.

Aderemi, A. K., David, I., Adetiloye, K. A., & Eriabie, S. (2017). Financial Reports and Shareholders’ Decision Making In Nigeria: any connectedness? Journal of Internet Banking and Commerce, 22, 1-14.

Agarwal Vineet., and Taffler Richard. (2007). Twenty five years of the Tafflers z –score model: does it really have predictive ability?. Accounting and Business Research. 37(4), 285-300. DOI:

Altman, E. (1968). Financial ratios, discriminant analysis & the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609. DOI:

Altman, E. I., Iwanicz-Drozdowska, M., Laitinen, E. K., and Suvas, A. (2014). Distressed Firm and Bankruptcy Prediction in an International Context: A Review and Empirical Analysis of Altman's Z-Score Model. DOI:

Altman, E., Haldeman, R., and Narayanan, P. (1977). ZETA analysis: a new model to identify bankruptcy risk of corporations. Journal of Banking and Finance.29-54. DOI:

Appiah, K. O., Chizema, A., & Arthur, J. (2015). Predicting corporate failure: A systematic literature review of methodological issues. International Journal of Law and Management, 57(5), 461-485. DOI:

Bardia .S C (2012). Predicting Financial Distress and Evaluating Long-Term Solvency: An Empirical Study. The IUP Journal of Accounting Research & Audit Practices. 11(1), 47-61.

Beaver, W. (1966). Financial ratios as predictors of failure. Journal of Accounting Research 5, 71-111. DOI:

Bhunia, A., & Sarkar, Bagchi (2011). A study of financial distress based on MDA. Journal of Management Research, 3(2), 1-11. DOI:

Brédart , Xavier (2014). Bankruptcy Prediction Model Using Neural Networks. Accounting and Finance Research. 3(2), 124-128. DOI:

Chancharat. N. (2008). An empirical analysis of financially distressed Australian companies: the application of survival analysis. Thesis submitted to School of Accounting and Commerce, University of Wollongong.

Charalambakis, E., & Garrett, I. (2016). On the prediction of financial distress in developed and emerging markets: Does the choice of accounting and market information matter? A comparison of UK and Indian Firms. Review Of Quantitative Finance & Accounting, 47(1), 1-28. DOI:

Coyne, Joseph S., Singh, Sher.G & Smith, Gary J. (2008). The Early Indicators of Financial Failure: A Study of Bankrupt and Solvent Health Systems. Journal of Healthcare Management, 53(5), 333-346. DOI:

Fallahpour, S., Lakvan, E. N., & Zadeh, M. H. (2017). Using an ensemble classifier based on sequential floating forward selection for financial distress prediction problem. Journal Of Retailing & Consumer Services, 34159-167. DOI:

Frydman, H., Altman, E. L., & Duen-Li, K. (1985). Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress. Journal of Finance, 40(1), 269-291. DOI:

Ganesalingam, S., & Kumar, Kuldeep. (2001). Detection of financial distress via multivariate statistical analysis. Managerial Finance, 27(4), 45-55. DOI:

Gepp, Adrian, and Kumar,Kuldeep. (2008). The Role of Survival Analysis in Financial Distress Prediction. International Research Journal of Finance and Economics, 16, 14-34.

Grice, John Stephen, & Dugan, Michael T. (2001). The Limitations of Bankruptcy Prediction Models: Some Cautions for the Researcher. Review of Quantitative Finance and Accounting. 17(2), 151-166. DOI:

Grünberg, Martin & Lukason, Oliver. (2014). Predicting Bankruptcy of Manufacturing Firms. International Journal of Trade, Economics and Finance, 5(1), 93-97. DOI:

H. Jayesh. (2015). Impact of repeal of SICA on debt restructuring. Insolvency and Restructuring International. 9(2).28

Hossari, Ghassan & Rahman, Sheikh. (2005). A Comprehensive Formal Ranking of the Popularity of Financial Ratios in Multivariate Modeling of Corporate Collapse. Journal of American Academy of Business, 6(1), 321-327.

Hui Hu (2011). A study of Financial Distress Prediction of Chinese Growth Enterprises. Doctoral thesis submitted to Faculty of Business and Government, University of Canberra

Hui, Huang., & Jing-Jing (2008). Relationship between Corporate Governance and Financial Distress: An empirical study of distressed companies in China, International Journal of Management; 25(4), 654-664.

Jones, Stewart & Hensher, David A. (2004). Predicting firm financial distress: A mixed logit model. The Accounting review, 79(4), 1011-1038. DOI:

Kumar. Radha Ganesh & Kumar .Kishore (2012). A comparison of bankruptcy models. International Journal of Marketing, Financial Services & Management Research .1(4), 76-86.

Li, X., Wang, F., & Chen, X. (2015). Support Vector Machine Ensemble Based on Choquet Integral for Financial Distress Prediction. International Journal of Pattern Recognition & Artificial Intelligence, 29(4), 1. DOI:

Mensah, Y. M. (1984). An examination of the stationary multivariate bankruptcy prediction models: a methodological study. Journal of Accounting Research, 22(Spring), 380-395. DOI:

Mondal, A., & Roy, D. (2013). Financial indicators of corporate sickness: A study of Indian steel industry. South Asian Journal of Management, 20(2), 85-101.

Murty, A. V .N, & Misra, D. P (2004). Cash Flow Ratios as Indicators of Corporate Failure. Finance India, 18(3), 1315-1325.

Nair (2013). Performance analysis and solvency prediction of Indian Pharma Companies. International Journal of Marketing, Financial Services & Management Research. 2(5). 34-43.

Nair (2015). Examination of Financial distress in Indian Sugar Sector Application of Ohlson’s ‘o’ score model. International Journal of Management and Social Sciences Review. 1(14). 221-226.

Nair and Sachdeva (2016). Indicators of Financial Distress – An empirical study of Indian Textile Sector. Journal of Global Economy 12(2). 101-113. DOI:

Oberholzer, Merwe (2010). The Interrelationship between different performance estimates. Studia Universitatis Babes-Bolyai, 55(3), 3-17.

Ohlson, James. (1980). Financial ratios and probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131. DOI:

Panda,K., & Behera, P. (2015). Financial distress prediction of pharmaceutical industry through z-score model. CLEAR International Journal Of Research In Commerce & Management, 6(2), 17-22.

Reddy .N. Ramana & Reddy .K.Hari prasad (2010). Financial status of select sugar manufacturing units - z score model. International Journal of Marketing, Financial Services & Management Research. .1(4), 64-69.

Sarbapriya Ray (2011). Assessing Corporate Financial Distress in Automobile Industry of India: An Application of Altman’s Model. Research Journal of Finance and Accounting .2(3), 55-168.

Senapati, Manjusha., and Ghosal. Saptarshi (2016). Modelling Corporate Sector Distress in India .RBI Working Paper Series No. 10.

Situm, Mario (2015).The Relevance of Trend Variables for the Prediction of Corporate Crises and Insolvencies.. Zagreb International Review of Economics & Business; 18(1), 17-49. DOI:

Suntraruk (2010). A review of statistical methods in Financial Distress literature. A.U. Journal of Management. 8(2).31-45.

Svabova, Lucia, Marek Durica, and Ivana Podhorska. (2018). Prediction of Default of Small Companies in the Slovak Republic. Economics and Culture 15, 88–95 DOI:

Wang, Zheng (2004). Financial Ratio Selection for Default-Rating Modeling: A Model-Free Approach and Its Empirical Performance. Journal of Applied Finance, 14(1), 20-35.

Xie, C., Luo, C., & Yu, X. (2011). Financial distress prediction based on SVM and MDA methods: The case of Chinese listed companies. Quality and Quantity, 45(3), 671-686. DOI:

Zavgren, C. V. (1985). Assessing the vulnerability to failure of American industrial firms: a logistic analysis. Journal of Business Finance & Accounting, 12: 19–45. DOI:

Zmijewski, M. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22, 59-82. DOI:




How to Cite

Dr. Jyoti Nair and Dr. JK Sachdeva (2022) “Predictive Modelling for Financial Distress amongst Manufacturing Companies in India”, Journal of Global Economy, 18(4), pp. 261–276. doi: 10.1956/jge.v18i4.665.