Abhigyan

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Multivariate Discriminant Predictive Modelling of Transactional Credit Risk in SME and Mid-Corporate Lending


Affiliations
1 Mata Sundri College for Women, University of Delhi, New Delhi, India
2 Delhi School of Management, Delhi Technological University, Delhi, India
     

   Subscribe/Renew Journal


This paper aims to develop a credit risk assessment model based on multivariate discriminant analysis (MDA) for predicting default risk in grant of business loans by Indian public sector commercial banks. The study uses a sample of 47 bank loans to small and medium enterprises (SMEs) and mid-corporates, by an Indian public sector bank, to design a three-group discriminant model, based on financial and non-financial factors. The results show that this model performs better than the Altman et al. (1995) - Emerging Markets Z-score model with re-estimated discriminant scores. The combination of quantitative and qualitative risk factors improved credit risk assessment and the model could accurately classify 97.5 per cent and 71.4 per cent in analysis and hold-out samples respectively. The findings confirm that by using both financial and non-financial characteristics of loan counterparties in multi-discriminant analysis, banks can predict credit risk in each loan transaction, and can map rating transitions to develop early warning signals of default which will ultimately help them to capture bad loans.

Keywords

Credit Ratings, Default Risk, Mid-Corporates, Inadequate Safety, Rating History.
User
Subscription Login to verify subscription
Notifications
Font Size

  • Altman, E.I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy.

  • Journal of Finance, 23 (4), 589-609.

  • Altman, E.I., Haldemann, R.G., & Narayan, P. (1977). ZETATM analysis: A new model to identify bankruptcy risk of corporations. Journal of Banking and Finance, 1(1), 29-54.

  • Altman, E.I., Hartzell, J., & Peck, M. (1995). Emerging markets corporate bonds: A scoring system, New York: Saloman Brothers.

  • Altman, E.I. (2000). Predicting financial distress of companies revisiting the Z-score and ZETA models. Working Paper, Stern School of Business, New York University, New York, NY.

  • Altman, Edward, I. (2006). Default recovery rates and LGD in credit risk modeling and practice, pp. 1-40, Available at: < http// www.defaultrisk.com/pp_recov_53.htm>, accessed on 16 July, 2013.

  • Araten, M., & Jacobs, M. Jr. (2001, May). Loan equivalents for revolving credits and advised lines. The RMA Journal, 34-39.

  • Atiya, Amir, F. (2001, July). Bankruptcy prediction for credit risk using neural networks : A survey and new results. IEEE Transactions on Neural Network, 12, 929-935.

  • Bajaj, Richa, V. (2013). Dynamics of PD, National Institute of Bank Management (NIBM), Pune, India, Available at:< http://www.nibm.org.in>, accessed February 31, 2013.

  • Balcaen, S., & Hubert. (2004, June). Alternative methodologies in studies on business failure: Do they produce better results than the classical statistical methods?”, Working Paper Series,249/2004, Universiteit Gent.

  • Bandyopadhyay, Arindam. (2006). Predicting probability of default of Indian corporate bonds: Logistic and Z-score model approaches. The Journal of Risk Finance, 7 (3), 255-272.

  • Beaver,W.H. (1968). Market prices, financial ratios and the prediction of failure. Journal of Accounting Research, 4, 179-92.

  • Chijoriga, M.M. (2011). Application of multiple discriminant analysis (MDA) as a credit scoring and risk assessment model. International Journal of Emerging Markets, 2, 132-147.

  • Dietsch, M., & Petey, J. (2002). The credit risk in SME loans portfolios: Modeling issues, pricing and capital requirements. Journal of Banking and Finance, 26, 303-322.

  • Gama, Ana., & Geralds, H. (2012). Credit risk assessment and the impact of the New Basel Capital Accord on small and medium-sized enterprises- An empirical analysis. Management Research Review, 35 (8), 727-749.

  • Glennon, D., & Peter, N. (2005, October). Measuring the default risk of small business loans: A survival analysis approach. Journal of Money, Credit and Banking, 37 (5), 923-947.

  • Grunert, J., Norden., & Weber, M. (2005). The role of financial factors in Internal credit ratings. Journal of Banking and Finance, 29, 509-31.

  • Hirtle, Beverly, J., Levonian., Saidenbery., Water., & Wright. (2009, March). Using credit risk models for regulatory capital: Issues and options. FRBNY Economic Policy Review, Federal Reserve Bank of New York, 19-36.

  • Jain, K.K., Gupta., & Mittal (2011). Logistic predictive model for SMEs financing in India.Vision: The Journal of Business Perspectives, 15 (4), 331-346.

  • Jayadev, M. (2006, July-September). Predictive power of financial risk factors: An empirical analysis of default companies, Vikalpa, 31 (3), 45-56.

  • Lehmann, Bina. (2003, April 17). Is it worth the while- The relevance of qualitative information in credit rating. Working Paper Series, Centre of Finance and Econometrics, KANSTANZ, Germany.

  • Malhotra, Naresh, K., & Dash,S. (2012). Discriminant and logit analysis. In marketing research-An applied orientation (pp. 552-585.), Ch.18, Sixth Edition, India: Pearson Prentice Hall.

  • Oesterreichische National Bank, Vienna, Austria. (2004). Guidelines on “Credit Rating Models and Validation” November, Available at: < http://www.oenb.at>, pp.1-171, accessed July 16, 2013).

  • Priscila, L., & Ribeiro,J. (2011). A systematic approach to construct credit risk forecast models. Pesquisa Operacional, 31(1), ISSN 0101-7438, Jan-Apr, Rio de Janeiro, Available at: < http://dx.doi.org/10.590.

  • RBI (2011). Implementation of the Internal Rating Based (IRB) approaches for calculation of capital charge for credit risk”, RBI/2011-12/311, dated December 22, 2011, pp. 1-19.

  • Richard, E., Chijoriga., & Kaijage. (2008). Credit risk management systems of a commercial bank in Tanzania. International Journal of Emerging Markets, .3 (3), 323-332.

  • Sinkey, Joseph F., Terja V., & Dince, R. (1987, Autumn). A Zeta analysis of failed commercial banks. Quarterly Journal of Business and Economics, 26 (4), 35-49.

  • Tabachnick,B.G., & Fidell,L.S. (1996). Using multivariate statistics. New York.: Harper Collins College Publishers.

  • The Times of India (14 February, 2016). BOB faces Rs. 3342cr loss in Q3, p.15, Delhi Ed.

  • Treacy, W. F., & Carey, M. (2000). Credit risk rating systems at large US Banks. Journal of Banking and Finance, 24(1/2), 167-201.


Abstract Views: 441

PDF Views: 2




  • Multivariate Discriminant Predictive Modelling of Transactional Credit Risk in SME and Mid-Corporate Lending

Abstract Views: 441  |  PDF Views: 2

Authors

Renu Arora
Mata Sundri College for Women, University of Delhi, New Delhi, India
Archana Singh
Delhi School of Management, Delhi Technological University, Delhi, India

Abstract


This paper aims to develop a credit risk assessment model based on multivariate discriminant analysis (MDA) for predicting default risk in grant of business loans by Indian public sector commercial banks. The study uses a sample of 47 bank loans to small and medium enterprises (SMEs) and mid-corporates, by an Indian public sector bank, to design a three-group discriminant model, based on financial and non-financial factors. The results show that this model performs better than the Altman et al. (1995) - Emerging Markets Z-score model with re-estimated discriminant scores. The combination of quantitative and qualitative risk factors improved credit risk assessment and the model could accurately classify 97.5 per cent and 71.4 per cent in analysis and hold-out samples respectively. The findings confirm that by using both financial and non-financial characteristics of loan counterparties in multi-discriminant analysis, banks can predict credit risk in each loan transaction, and can map rating transitions to develop early warning signals of default which will ultimately help them to capture bad loans.

Keywords


Credit Ratings, Default Risk, Mid-Corporates, Inadequate Safety, Rating History.

References