Comparative Credit Risk Assessment Structures in Indian Banking Industry DOCTO

Dr. Sandeepa Kaur

doi:10.65301/dias.2018.15.1.765

Articles 29th Edition of DTR Apr 2018 – Sep 2018

DOI 10.65301/dias.2018.15.1.765

Comparative Credit Risk Assessment Structures in Indian Banking Industry DOCTO

Authors

DR

Dr. Sandeepa Kaur

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84 Downloads

Published 2018-09-30

Pages 58-68

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

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 — 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. Duxbury Press.
xvii. Etemadi, H., Anvary Rostamy, A., & Dehkordi, H. (2009). 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. Certified Public Accountant, 2, 598–605.
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 and neural network for bankruptcy prediction. 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). Klassifikation von Unternehmen mittels KNN.
xxiv. Merton, R. (1974). On the pricing of corporate debt. Journal of Finance, 29(2), 449–470.
xxv. Merwin, C. L. (1942). Financing small corporations in five manufacturing industries.
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 using Z-score and Springate models. XIMB Journal of Management, 11(2).
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 study. Managerial Finance, 37(6), 553–564.
xxxii. Polemis, D., & Gounopoulos, D. (2012). Prediction of distress and potential M&A targets in UK. Managerial Finance, 38(11), 1085–1104.
xxxiii. Pongsatat, S., Ramage, J., & Lawrence, H. (2004). Bankruptcy prediction in Asia. Journal of Accounting and Corporate Governance, 1(2), 1–13.
xxxiv. Ramser, J., & Foster, L. (1931). Demonstration of ratio analysis.
xxxv. Reisz, A., & Perlich, C. (2007). Market-based bankruptcy prediction framework. Journal of Financial Stability, 3(2), 85–131.
xxxvi. Salchenberger, L. M., Cinar, E., & Lash, N. A. (1992). Neural networks for predicting failures. Decision Sciences, 23(4), 899–916.
xxxvii. Scott, J. (1981). Probability of bankruptcy. Journal of Banking and Finance, 5, 317–344.
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. FIU Review, 7(1), 55–75.
xl. Ugurlu, M., & Aksoy, H. (2006). Prediction of corporate financial distress. Cross Cultural Management, 13(4), 277–295.
xli. Vasantha, S., Dhanraj, V., & Thiayalnayaki. (2013). Prediction of business bankruptcy using Altman model.
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). Changes in financial structure of unsuccessful corporations.
xlv. Xu, M., & Zhang, C. (2009). Bankruptcy prediction: Japanese listed companies. Review of Accounting Studies, 14(4), 534–558.
xlvi. Zanakis, S., & Zopounidis, C. (1997). Prediction of Greek company takeovers. Journal of Operational Research Society, 48(7), 678–687.
xlvii. Zavgren, C. V. (1983). Prediction of corporate failure. Journal of Accounting Literature.
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 . (2018). Comparative Credit Risk Assessment Structures in Indian Banking Industry DOCTO. DIAS Technology Review, 15(1), 58-68.

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[1] ii. Altman, E. I. (1984). The success of business failure prediction models. Journal of Banking and Finance, 8, 171–198.

[2] 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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[17] xviii. Fitzpatrick, P. (1932). Comparison of ratios of successful and failed firms. Certified Public Accountant, 2, 598–605.

[18] 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.

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

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

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

[22] xxiii. Kerling, M., & Poddig, T. (1994). Klassifikation von Unternehmen mittels KNN.

[23] xxiv. Merton, R. (1974). On the pricing of corporate debt. Journal of Finance, 29(2), 449–470.

[24] xxv. Merwin, C. L. (1942). Financing small corporations in five manufacturing industries.

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

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

[27] xxviii. Muthukumar, G., & Sekar, M. (2014). Fiscal fitness using Z-score and Springate models. XIMB Journal of Management, 11(2).

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

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

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

[31] xxxii. Polemis, D., & Gounopoulos, D. (2012). Prediction of distress and potential M&A targets in UK. Managerial Finance, 38(11), 1085–1104.

[32] xxxiii. Pongsatat, S., Ramage, J., & Lawrence, H. (2004). Bankruptcy prediction in Asia. Journal of Accounting and Corporate Governance, 1(2), 1–13.

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

[34] xxxv. Reisz, A., & Perlich, C. (2007). Market-based bankruptcy prediction framework. Journal of Financial Stability, 3(2), 85–131.

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

[36] xxxvii. Scott, J. (1981). Probability of bankruptcy. Journal of Banking and Finance, 5, 317–344.

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

[38] xxxix. Tavlin, E., Moncarz, E., & Dumont, D. (1989). Financial failure in hospitality industry. FIU Review, 7(1), 55–75.

[39] xl. Ugurlu, M., & Aksoy, H. (2006). Prediction of corporate financial distress. Cross Cultural Management, 13(4), 277–295.

[40] xli. Vasantha, S., Dhanraj, V., & Thiayalnayaki. (2013). Prediction of business bankruptcy using Altman model.

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

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

[43] xliv. Winakor, A., & Smith, R. (1935). Changes in financial structure of unsuccessful corporations.

[44] xlv. Xu, M., & Zhang, C. (2009). Bankruptcy prediction: Japanese listed companies. Review of Accounting Studies, 14(4), 534–558.

[45] xlvi. Zanakis, S., & Zopounidis, C. (1997). Prediction of Greek company takeovers. Journal of Operational Research Society, 48(7), 678–687.

[46] xlvii. Zavgren, C. V. (1983). Prediction of corporate failure. Journal of Accounting Literature.

[47] 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.