Comparative Analysis of Bankruptcy Prediction Models :An Indian Perspective

Dr. Sandeepa Kaur

doi:10.65301/dias.2018.14.2.251

Articles 28th Edition of DTR Oct 2017 – Mar 2018

DOI 10.65301/dias.2018.14.2.251

Comparative Analysis of Bankruptcy Prediction Models :An Indian Perspective

Authors

DR

Dr. Sandeepa Kaur

Assistant Professor, Department of Management, Delhi Institute of Advanced Studies, Rohini, Delh

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Published 2025-08-14

Pages 19-28

Abstract

Bankruptcy is a state of insolvency wherein the company or the person is not able to repay the creditors the debt amount. The purpose of this research is to develop and compare the performance of bankruptcy prediction models using multiple discriminant analysis, logistic regression and neural network for listed companies in India. These bankruptcy prediction models were tested, over the three years prior to bankruptcy using financial ratios. The sample consists of 72 bankrupt and 72 non-bankrupt companies over the period 1991-2016. The results indicate that as compared to multiple discriminant analysis and logistic regression, neural network has the highest classification accuracy for all the three years prior to bankruptcy.

Keywords

Bankruptcy prediction Multiple discriminant analysis Logistic regression Neural network

References

i. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589–609.
ii. Altman, E. I. (1984). The Success of Business Failure Prediction Models. Journal of Banking and Finance, 8, 171–198.
iii. Altman, E. I., Marco, G., & Varetto, F. (1994). Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking & Finance, 18(3), 505–529.
iv. Bankruptcy Code of India (2016).
v. Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 71–111.
vi. Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81(3), 637–654.
vii. Boritz, J., & Kennedy, D. (1995). Effectiveness of neural network types for prediction of business failure. Expert Systems with Applications, 9(4), 504–512.
viii. Bredart, X. (2014). Bankruptcy prediction models using neural networks. Accounting and Finance Research, 3(2), 124–128.
ix. Brockman, P., & Turtle, H. (2003). A barrier option framework for corporate security valuation. Journal of Financial Economics, 67(3), 511–529.
x. Caudill, M. (1989). Neural network primer. The Magazine of Artificial Intelligence in Practice.
xi. Charitou, A., Neophytou, E., & Charalambous, C. (2004). Predicting corporate failure: empirical evidence for the UK. European Accounting Review, 13(3), 465–497.
xii. Coats, P. K., & Fant, L. F. (1993). Recognizing financial distress patterns using a neural network tool. Financial Management, 22(3), 142–155.
xiii. Crosbie, P., & Bohn, J. (1999). Modeling default risk. KMV Corporation.
xiv. Cybinski, P. (2001). Description, explanation, prediction – the evolution of bankruptcy studies? Managerial Finance, 27(4), 29–44.
xv. Dichev, I. (1998). Is the risk of bankruptcy a systematic risk? Journal of Finance, 53(3), 1131–1147.
xvi. Dielman, T.E. (1996). Applied Regression for Business and Economics. Boston: Duxbury Press.
xvii. Etemadi, H., Anvary Rostamy, A., & Dehkordi, H. (2009). A genetic programming model for bankruptcy prediction. Expert Systems with Applications, 36(2), 3199–3207.
xviii. Fitzpatrick, P. (1932). Comparison of ratios of successful and failed firms.
xix. Grunert, J., Norden, L., & Weber, M. (2005). Role of non-financial factors in internal credit ratings. Journal of Banking and Finance, 29(2), 509–531.
xx. Hillegeist, S., Keating, E., Cram, D., & Lundstedt, K. (2004). Assessing probability of bankruptcy. Review of Accounting Studies, 9(1), 5–34.
xxi. Jo, H., & Han, I. (1996). Integration of case-based forecasting, neural network, and discriminant analysis. Expert Systems with Applications, 11(4), 415–422.
xxii. Jones, F. L. (1987). Current techniques in bankruptcy prediction. Journal of Accounting Literature, 6, 131–164.
xxiii. Kerling, M., & Poddig, T. (1994). Classification of enterprises using KNN.
xxiv. Merton, R. (1974). Pricing of corporate debt: risk structure of interest rates. Journal of Finance, 29(2), 449–470.
xxv. Merwin, C. L. (1942). Financing small corporations. National Bureau of Economic Research.
xxvi. Min, J., & Jeong, C. (2009). Binary classification method for bankruptcy prediction. Expert Systems with Applications, 36(3), 5256–5263.
xxvii. Morris, R. (1997). Early Warning Indicators of Corporate Failure.
xxviii. Muthukumar, G., & Sekar, M. (2014). Fiscal fitness of select automobile companies in India.
xxix. Odom, M. D., & Sharda, R. (1990). Neural network model for bankruptcy prediction.
xxx. Ohlson, J. A. (1980). Financial ratios and probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131.
xxxi. Ong, S.W., Yap, V.C., & Roy, W.L. (2011). Corporate failure prediction. Managerial Finance, 37(6), 553–564.
xxxii. Polemis, D., & Gounopoulos, D. (2012). Prediction of distress and identification of M&A targets in UK.
xxxiii. Pongsatat, S., Ramage, J., & Lawrence, H. (2004). Bankruptcy prediction for firms in Asia.
xxxiv. Ramser, J., & Foster, L. (1931). Demonstration of ratio analysis.
xxxv. Reisz, A., & Perlich, C. (2007). Market-based framework for bankruptcy prediction.
xxxvi. Salchenberger, L. M., Cinar, E., & Lash, N. A. (1992). Neural networks for predicting thrift failures. Decision Sciences, 23(4), 899–916.
xxxvii. Scott, J. (1981). Probability of bankruptcy: comparison of empirical predictions and theoretical models.
xxxviii. Shumway, T. (2001). Forecasting bankruptcy more accurately. Journal of Business, 74(1), 101–124.
xxxix. Tavlin, E., Moncarz, E., & Dumont, D. (1989). Financial failure in hospitality industry.
xl. Ugurlu, M., & Aksoy, H. (2006). Prediction of corporate financial distress in Turkey.
xli. Vasantha, S., Dhanraj, V., & Thiayalnayaki (2013). Prediction of business bankruptcy for Indian airline companies.
xlii. Vassalou, M., & Xing, Y. (2004). Default risk in equity returns. Journal of Finance, 59(2), 831–868.
xliii. Virág, M., & Kristóf, T. (2005). Neural networks in bankruptcy prediction. Acta Oeconomica, 55(4), 403–426.
xliv. Winakor, A., & Smith, R. (1935). Financial structure of unsuccessful corporations.
xlv. Xu, M., & Zhang, C. (2009). Bankruptcy prediction: Japanese listed companies.
xlvi. Zanakis, S., & Zopounidis, C. (1997). Prediction of Greek company takeovers.
xlvii. Zavgren, C.V. (1983). Prediction of corporate failure.
xlviii. Zhang, G., Hu, M., Patuwo, B.E., & Indro, D.C. (1999). Artificial neural networks in bankruptcy prediction. European Journal of Operational Research, 116(1), 16–32.

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Dr. Sandeepa Kaur . (2025). Comparative Analysis of Bankruptcy Prediction Models :An Indian Perspective. DIAS Technology Review, 14(2), 19-28.

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[1] i. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589–609.

[2] ii. Altman, E. I. (1984). The Success of Business Failure Prediction Models. Journal of Banking and Finance, 8, 171–198.

[3] iii. Altman, E. I., Marco, G., & Varetto, F. (1994). Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking & Finance, 18(3), 505–529.

[4] iv. Bankruptcy Code of India (2016).

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

[6] vi. Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81(3), 637–654.

[7] vii. Boritz, J., & Kennedy, D. (1995). Effectiveness of neural network types for prediction of business failure. Expert Systems with Applications, 9(4), 504–512.

[8] viii. Bredart, X. (2014). Bankruptcy prediction models using neural networks. Accounting and Finance Research, 3(2), 124–128.

[9] ix. Brockman, P., & Turtle, H. (2003). A barrier option framework for corporate security valuation. Journal of Financial Economics, 67(3), 511–529.

[10] x. Caudill, M. (1989). Neural network primer. The Magazine of Artificial Intelligence in Practice.

[11] xi. Charitou, A., Neophytou, E., & Charalambous, C. (2004). Predicting corporate failure: empirical evidence for the UK. European Accounting Review, 13(3), 465–497.

[12] xii. Coats, P. K., & Fant, L. F. (1993). Recognizing financial distress patterns using a neural network tool. Financial Management, 22(3), 142–155.

[13] xiii. Crosbie, P., & Bohn, J. (1999). Modeling default risk. KMV Corporation.

[14] xiv. Cybinski, P. (2001). Description, explanation, prediction – the evolution of bankruptcy studies? Managerial Finance, 27(4), 29–44.

[15] xv. Dichev, I. (1998). Is the risk of bankruptcy a systematic risk? Journal of Finance, 53(3), 1131–1147.

[16] xvi. Dielman, T.E. (1996). Applied Regression for Business and Economics. Boston: Duxbury Press.

[17] xvii. Etemadi, H., Anvary Rostamy, A., & Dehkordi, H. (2009). A genetic programming model for bankruptcy prediction. Expert Systems with Applications, 36(2), 3199–3207.

[18] xviii. Fitzpatrick, P. (1932). Comparison of ratios of successful and failed firms.

[19] xix. Grunert, J., Norden, L., & Weber, M. (2005). Role of non-financial factors in internal credit ratings. Journal of Banking and Finance, 29(2), 509–531.

[20] xx. Hillegeist, S., Keating, E., Cram, D., & Lundstedt, K. (2004). Assessing probability of bankruptcy. Review of Accounting Studies, 9(1), 5–34.

[21] xxi. Jo, H., & Han, I. (1996). Integration of case-based forecasting, neural network, and discriminant analysis. Expert Systems with Applications, 11(4), 415–422.

[22] xxii. Jones, F. L. (1987). Current techniques in bankruptcy prediction. Journal of Accounting Literature, 6, 131–164.

[23] xxiii. Kerling, M., & Poddig, T. (1994). Classification of enterprises using KNN.

[24] xxiv. Merton, R. (1974). Pricing of corporate debt: risk structure of interest rates. Journal of Finance, 29(2), 449–470.

[25] xxv. Merwin, C. L. (1942). Financing small corporations. National Bureau of Economic Research.

[26] xxvi. Min, J., & Jeong, C. (2009). Binary classification method for bankruptcy prediction. Expert Systems with Applications, 36(3), 5256–5263.

[27] xxvii. Morris, R. (1997). Early Warning Indicators of Corporate Failure.

[28] xxviii. Muthukumar, G., & Sekar, M. (2014). Fiscal fitness of select automobile companies in India.

[29] xxix. Odom, M. D., & Sharda, R. (1990). Neural network model for bankruptcy prediction.

[30] xxx. Ohlson, J. A. (1980). Financial ratios and probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131.

[31] xxxi. Ong, S.W., Yap, V.C., & Roy, W.L. (2011). Corporate failure prediction. Managerial Finance, 37(6), 553–564.

[32] xxxii. Polemis, D., & Gounopoulos, D. (2012). Prediction of distress and identification of M&A targets in UK.

[33] xxxiii. Pongsatat, S., Ramage, J., & Lawrence, H. (2004). Bankruptcy prediction for firms in Asia.

[34] xxxiv. Ramser, J., & Foster, L. (1931). Demonstration of ratio analysis.

[35] xxxv. Reisz, A., & Perlich, C. (2007). Market-based framework for bankruptcy prediction.

[36] xxxvi. Salchenberger, L. M., Cinar, E., & Lash, N. A. (1992). Neural networks for predicting thrift failures. Decision Sciences, 23(4), 899–916.

[37] xxxvii. Scott, J. (1981). Probability of bankruptcy: comparison of empirical predictions and theoretical models.

[38] xxxviii. Shumway, T. (2001). Forecasting bankruptcy more accurately. Journal of Business, 74(1), 101–124.

[39] xxxix. Tavlin, E., Moncarz, E., & Dumont, D. (1989). Financial failure in hospitality industry.

[40] xl. Ugurlu, M., & Aksoy, H. (2006). Prediction of corporate financial distress in Turkey.

[41] xli. Vasantha, S., Dhanraj, V., & Thiayalnayaki (2013). Prediction of business bankruptcy for Indian airline companies.

[42] xlii. Vassalou, M., & Xing, Y. (2004). Default risk in equity returns. Journal of Finance, 59(2), 831–868.

[43] xliii. Virág, M., & Kristóf, T. (2005). Neural networks in bankruptcy prediction. Acta Oeconomica, 55(4), 403–426.

[44] xliv. Winakor, A., & Smith, R. (1935). Financial structure of unsuccessful corporations.

[45] xlv. Xu, M., & Zhang, C. (2009). Bankruptcy prediction: Japanese listed companies.

[46] xlvi. Zanakis, S., & Zopounidis, C. (1997). Prediction of Greek company takeovers.

[47] xlvii. Zavgren, C.V. (1983). Prediction of corporate failure.

[48] xlviii. Zhang, G., Hu, M., Patuwo, B.E., & Indro, D.C. (1999). Artificial neural networks in bankruptcy prediction. European Journal of Operational Research, 116(1), 16–32.