Corporate Financial Distress: Analysis of Indian Automobile Industry

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  • B. N. Bahadur Institute of Management Sciences, University of Mysore, Manasagangothri, Mysore ,IN
  • B. N. Bahadur Institute of Management Sciences, University of Mysore, Manasagangothri, Mysore ,IN



Financial Distress, Bankruptcy, Stakeholders, Altman Z Score Model, Intermediate Area.
Macroeconomics and Microeconomics


Financial distress leads to bankruptcy of firm which features systemic impact on both macro and micro economy of the country. Industry characteristics too play an important role in endurance of firm and successively with its financial strategies. Compulsion to evaluate the financial strength of firm is a significant aspect for both Internal and External stakeholders, especially creditors. Information that firm is approaching distress can set out managerial actions to anticipate problems before they occur. Drastic changes in automobile policies in India have mixed effects on Automobile Industry. This paper is an attempt to evaluate the financial health of automobile industry in India. Automobile industry has been classified into four categories based on products namely Passenger car, commercial vehicles, motorcycle/ mopeds and scooters & 3-wheelers manufacturers. The Altman Z score model developed for manufacturing firms has been applied for ten years (2007 to 2016) during which period features Automotive Mission Plan framed by Government of India. The study reveals that commercial vehicle manufactures are in intermediate area of financial distress and calls for agile action.


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How to Cite

Shilpa, N. C., & Amulya, M. (2017). Corporate Financial Distress: Analysis of Indian Automobile Industry. SDMIMD Journal of Management, 8(1), 85–93.






Agarwal, V., & Taffler, R. (2008). Comparing the performance of market-based and accounting-based bankruptcy prediction models. Journal of Banking & Finance, 32(8), 1541–1551.

Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 23(4), 589–609.

Altman, E. I. (I983) Corporate Financial Distress. A Complete Guide to Predicting, Avoiding, and Dealing with Bankruptcy. John Wiley & Sons, New York.

Altman, E. I., Iwanicz-Drozdowska, M., Laitinen, E. K., & Suvas, A. (2016). Financial distress prediction in an international context: a review and empirical analysis of altman's Z-score model. Journal of International Financial Management & Accounting. Retrieved from

Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 71–111.

Bulow, J. I., & Shoven, J. B. (1978). The bankruptcy decision. The Bell Journal of Economics, 437–456.

Celli, M. (2015). Can Z-Score Model Predict Listed Companies' Failures in Italy? An Empirical Test. International Journal of Business and Management, 10(3), 57.

Chava, S., & Jarrow, R. A. (2004). Bankruptcy prediction with industry effects. Review of Finance, 8(4), 537–569.

Chen, H. J., Kacperczyk, M., & Ortiz-Molina, H. (2012). Do nonfinancial stakeholders affect the pricing of risky debt? Evidence from unionized workers. Review of Finance, 16(2), 347–383.

Dawkins, M. C., Bhattacharya, N., & Bamber, L. S. (2007). Systematic share price fluctuations after bankruptcy filings and the investors who drive them. Journal of Financial and Quantitative Analysis, 42(2), 399–419.

Fich, E. M., & Slezak, S. L. (2008). Can corporate governance save distressed firms from bankruptcy? An empirical analysis. Review of Quantitative Finance and Accounting, 30(2), 225–251.

Gilson, S. C. (1989). Management turnover and financial distress. Journal of Financial Economics, 25(2), 241–262.

Grice, J. S., & Ingram, R. W. (2001). Tests of the generalizability of Altman's bankruptcy prediction model. Journal of Business Research, 54(1), 53–61.

John, T. A. (1993). Accounting measures of corporate liquidity, leverage, and costs of financial distress. Financial Management, 91–100.

Kahya, E., & Theodossiou, P. (1999). Predicting corporate financial distress: A time-series CUSUM methodology. Review of Quantitative Finance and Accounting, 13(4), 323–345.

Kwak, W., Shi, Y., Cheh, J. J., & Lee, H. (2005). Multiple criteria linear programming data mining approach: An application for bankruptcy prediction. In Data Mining and Knowledge Management (pp. 164–173). Springer. Retrieved from Li,

W. G. (2014). Corporate Financial Distress and Bankruptcy Prediction in the North American Construction Industry. Retrieved from Moyer, R. C. (1977). Forecasting financial failure: a re-examination. Financial Management, 6(1), 11.

Opler, T. C., & Titman, S. (1994). Financial distress and corporate performance. The Journal of Finance, 49(3), 1015–1040.

Platt H. D. & Platt M. B. (2002). Predicting corporate financial distress: reflections on choice-based sample bias. Journal of Economics and Finance, 26(2), 184–199.

Ray, S. (2011). Assessing corporate financial distress in automobile industry of India: An application of Altman's model. Research Journal of Finance and Accounting, 2(3), 155–168.

Shen, C.-H., Chen, Y. K., & Huang, B. Y. (2010). The prediction of default with outliers: Robust logistic regression. In Handbook of Quantitative Finance and Risk Management (pp. 965–977). Springer. Retrieved from

Singhal, R., & Zhu, Y. E. (2013). Bankruptcy risk, costs and corporate diversification. Journal of Banking & Finance, 37(5), 1475–1489. Retrieved From:

Trieschmann, J. S., & Pinches, G. E. (1973). A multivariate model for predicting financially distressed PL insurers. Journal of Risk and Insurance, 327–338. Retrieved From: