Credit assessment risk for small and medium enterprises (SMEs) offers special challenges to practitioners, regulators and academics. The lenders’ design of the package of credit to SMEs may affect the propensity to default or lead to delinquency. Considering the fundamental role being played by small-sized industries in Indian economy and emphasis on inclusive economy, we aim to develop a default prediction model, specifically for SMEs. Apart from the conventional ‘ability to pay’ basis of analyzing borrower-centric default risk, there may be a set of other explanatory variables that can be modelled to assess the credit default. The study here examines the behaviour of relevant measures of default risk and explores the most significant variables of the financial package to construct a model for SMEs by applying multinomial logistic regression technique. The study is based on database covering two years of the time period from 2007 to 2009 on 2,864 SMEs pertaining to an emerging cluster obtained from a prominent financial institution in Delhi.
AltmanE. (1968). Financial ratios discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589–609.
2.
AltmanE.BaidyaT.Ribeiro-DiasL.M. (1979). Assessing potential financial problems of firms in Brazil. Journal of International Business Studies, 10, 9–42.
3.
AltmanE.LavalleeM. (1981). Business failure classification in Canada. Journal of Business Administration, Summer, 147–164.
4.
AltmanE.MarcoG.VarettoF. (1994). Corporate distress diagnosis: Comparison using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking and Finance, 18, 505–529.
5.
AltmanE.NarayananP. (1997). An international survey of business failure classification models. Financial Markets, Institutions and Instruments, 6(2), 1–57.
6.
AltmanE.SabatoG. (2007). Modelling credit risk for SMEs: Evidence from the U.S. market. Abacus, 43(3), 332–357.
7.
AzizA.EmanuelD.C.LawsonG.H. (1988). Bankruptcy prediction—An investigation of cash flow based models. Journal of Management Studies, 25(5), 419–437.
8.
BaetgeJ.MussM.NiehausH. (1988). The use of statistical analysis to identify the financial strength of corporations in Germany. Studies in Banking and Finance, 7, 183–196.
9.
BandyopadhyayA. (2007). Credit risk models for managing bank’s agricultural loan portfolio. Munich Personal RePEe Archive (MPRA), 5358, November.
10.
BecchettiL.SierraJ. (2002). Bankruptcy risk and productive efficiency in manufacturing firms. Journal of Banking and Finance, 27, 2099–2120.
11.
BergerAllen N.FrameScott W. (2005). Small business credit scoring and credit availability. Working Paper Series No. 10, Federal Reserve Bank of Atlanta.
12.
BergerAllen N.UdellGregory F. (2004, October14–15). A more complete conceptual framework about SME finance. Paper presented at conference on SME: Overcoming Growth Constraints, World Bank, Washington, DC.
13.
BhatiaU. (1988). Predicting corporate sickness in India. Studies in Banking and Finance, 7, 57–71.
14.
CharitouA.TrigeorgisL. (2002, July4–6). Option-based bankruptcy prediction. Paper presented at 6th Annual Real Options Conference at Paphos, Cyprus.
15.
DietschM.PeteyJ. (2004). Should SME exposures be treated as retail or as corporate exposures—A comparative analysis of default probabilities and asset correlation in French and German SMEs?Journal of Banking and Finance, 28, 303–324.
EarlM.J.MaraisD. (1982). Predicting corporate failure in the UK using discriminate analysis. Accounting and Bussiness Research.
18.
GentryJ.A.NewboldP.WhitfordD.T. (2003). Measuring the discriminative power of rating systems. Retrieved from http://www.defaultrisk
19.
GlorfeldLouis W.HardgraveB.C. (2006). An improved method for developing neural networks: The case of evaluating commercial loan creditworthiness. Computers & OR 2006, 23(10), 933–944.
20.
GloubosG.GrammatikosT. (1998). The success of bankruptcy prediction models in Greece. Studies in Banking and Finance, 7, 37–46.
21.
HandD.J.HenleyW.E. (1997). Statistical classification methods in consumer credit. Journal of Royal Statistical Society, Series A, 160(3), 523–541.
22.
HandzicM.TjandrawibawaF.YeoJ. (2001). How neural networks can help loan officers to make better informed application decisions. Informing Science, June, 97–109.
23.
IzanH.Y. (1984). Corporate distress in Australia. Journal of Banking and Finance, 8(2), 303–320.
24.
Jacobson. (2004). Credit risk versus capital requirement. Working Paper Series, Central Bank of Sweden, Journal of Financial Services Researcher, Springer, 28(1), 43–75.
25.
JayadevM. (2006). Internal credit rating practices of Indian banks. Economic and Political Weekly, 41(11), 1069–1078.
26.
KeaseyK.WatsonR. (1987). Non-financial symptoms and the prediction of small company failure: A test of Argenti’s hypotheses. Journal of Business Finance and Accounting, 14(3), 335–354.
27.
KoC.J. (1982). A delineation of corporate appraisal models and classification of bankruptcy firms in Japan. Thesis, New York University, New York.
28.
KolariJames W.ShinHwan G. (2004). Assessing the profitability and riskiness of small business lenders in the U.S. banking industry. Retrieved from www.sba.gov
29.
LewisE.M. (1992). An introduction to credit scoring. San Rafael, CA: Athena Press.
30.
LizalD.E.LizalL. (2002) Effects of ownership and financial status on corporate environmental performance. CERGE-EIWorking Papers wp203.
31.
MaraisD.A.J. (1979). A method of quantifying companies’ relative financial strength. Working Paper No. 4, Bank of England, London.
MossmanC.E.BellG.G.SwartzL.M.TurtleH. (1998). An empirical comparison of bankruptcy models. The Financial Review, 33(2), 35–54.
34.
OhlsonJ. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131.
35.
OogheH.JoosP.De BourdeaudhuijC. (1995). Financial distress models in Belgium: The results of a decade of empirical research. International Journal of Accounting, 30(3), 245–274.
36.
PlattH.D.PlattM.B. (1990). Development of a class of stable predictive variables: The case of bankruptcy prediction. Journal of Business Finance and Accounting, 17(1), 137–160.
37.
RosenbergGleit. (1994). Quantitative methods in credit management: A survey. Operations Research, 42, 589–613.
38.
SaltelliA.CampolongoF.CariboniJ.LeuvenK.U. (2004). An effective screening design for sensitivity analysis of large models. Amsterdam, The Netherlands: Elsevier Science Publishers B.V.
39.
SaurinaJ.TrucharteC. (2004). The impact of Basel II on lending to small and medium sized firms: A regulatory policy assessment Basel on Spanish credit register data. Journal of Finance Services Research, 26(2), 121–144.
40.
SchwaigerW.S. (2002). Auswirkungen von Basel II auf den österreichischen Mittelstandnach Branchen und Bundesländern. Bankarchiv, 50(6), 21–50.
41.
SmithE.R. (1999). Affective and cognitive implications of group membership becoming part of the self: New models of prejudice and of the self-concept. In AbramsD.HoggM. (Eds), Social identity and social cognition (pp. 183–196). Oxford: Blackwell Publishers.
42.
ThomasL.C. (1992). Financial risk management models. In AnsellJ.WhartonF. (Eds), Risk analysis assessment and management (pp. 149–172). Chichester: John Wiley & Sons.
43.
ThomasL.C. (1998). Methodologies for classifying applicants for credit. In HandD.J.JackaS.D. (Eds), Statistics in finance (pp. 83–103). London: Arnold.
44.
ThomasL.C. (2000). A survey of credit and behavioural scoring: Forecasting financial risk of lending to consumers. International Journal of Forecasting, 16(2), 149–172.
45.
ThomasL.C.CrookJ.N.EdelmanD.B. (1992). Credit scoring and credit control. Oxford: Oxford University Press.
46.
UdellGregory F. (2004). SME lending: Defining the issues in a global prospective. Working Paper, Indiana University.
47.
Van FrederikslustR. (1978). Predictability of corporate failure. Leiden: Martinus Nijhoff Social Science Division.
48.
WeibelP.F. (1973). The value of criteria to judge credit worthiness in the lending of banks. Bern and Stuttgart. Wharton Financial Institutions Centre. U.S.
