The application of brute force logistic regression to corporate credit scoring models: Evidence from Serbian financial statements

Expert Systems with Applications

Volumn 40, Issue 15, 2013, Pages 5932-5944

  Nikolic, Nebojsa ^a   Zarkic Joksimovic, Nevenka ^a   Stojanovski, Djordje ^a   Joksimovic, Iva ^a  

^a UNIVERSITY OF BELGRADE   (Serbia)

Author keywords

Corporate entities; Credit scoring; Logistic regression; Probability of default; Weight of evidence approach

Indexed keywords

CORPORATE ENTITIES; CREDIT SCORING; LOGISTIC REGRESSIONS; PROBABILITY OF DEFAULTS; WEIGHT OF EVIDENCE APPROACH;

CLUSTERING ALGORITHMS; CURVE FITTING; ELECTRIC FAULT CURRENTS; FINANCE; LOGISTICS; REGRESSION ANALYSIS;

COMPUTER SIMULATION;

EID: 84878899844     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2013.05.022     Document Type: Article

References (62)

1. Akaike, H. (1974). Information theory and an extension of the maximum likelihood principle. In Second interantional symposium on information theory (pp. 267-281).
2. E. Altman Financial ratios, discriminant analysis and the prediction of corporate bankruptcy Journal of Finance 23 4 1968
3. E. Altman, and G. Sabato Modeling credit risk for SMEs: Evidence from the US market Abacus 43 3 2007 323 357
4. E. Altman, R. Haldeman, and P. Narayanan ZETA analysis: A new model to identify bankruptcy risk of corporations Journal of Banking and Finance 1 1 1977 29 54
5. R. Anderson The credit scoring toolkit 2007 Oxford University Press New York
6. ASB. (2013). Association of Serbian Banks. Retrieved from < http://www.ubs-asb.com/ >.
7. A. Aziz, D.C. Emanuel, and G.H. Lawson An investigation of cash flow based models Journal of Management Studies 25 5 2007 419 437
8. J. Banasik, J. Crook, and L. Thomas Sample selection bias in credit scoring models Journal of the Operational Research Society 54 8 2003 822 832
9. BCBS. (2005, May). Studies on the validation of internal rating systems. Basel Committee on Banking Supervision.
10. BCBS. (2006). International convergence of capital measurement and capital standards - A revised framework. Basel Committee on Banking Supervision.
11. W. Beaver Financial ratios predictors of failure Journal of Accounting Research 4 1967 3
12. L. Becchetti, and J. Sierra Bankruptcy risk and productive efficiency in manufacturing firms Journal of Banking and Finance 27 11 2003 2099 2120 (Pubitemid 37155252)
13. K. Bijak, and L.C. Thomas Does segmentation always improve model performance in credit scoring? Expert Systems with Applications 39 2012 2433 2442
14. A. Blanco, R. Pino-Mejias, J. Lara, and S. Rayo Credit scoring models for the microfinance industry using neural networks: Evidence from Peru Expert Systems with Applications 40 2013 356 364
15. Blochlinger, A., Leippold, M. (n.d.). Economic benefit of powerful credit scoring. Journal of Banking and Finance 30(3), 851-873.
16. CFA Institute. (2012). Financial Reporting and Analysis (CFA® Program Curriculum) (Vol. 2). Pearson.
17. M.-Y. Chen Predicting corporate financial distress based on integration of decision tree classification and logistic regression Expert Systems with Applications 38 2011 11261 11272
18. J.S. Cramer Scoring bank loans that may go wrong: A case study Statistica Neerlandica 58 3 2004 365 380 (Pubitemid 39137743)
19. S.F. Crone, and S. Finlay Instance sampling in credit scoring: An empirical study of sample size and balancing International Journal of Forecasting 28 2012 224 238
20. J.N. Crook, D.B. Edelman, and L.C. Thomas Recent developments in consumer credit risk assessment European Journal of Operational Research 183 3 2007 1447 1465 (Pubitemid 47030750)
21. G. Derelioglu, and F. Gurgen Knowledge discovery using neural approach for SME's credit risk analysis problem in Turkey Expert Systems with Applications 38 2011 9313 9318
22. A.I. Dimitras, S.H. Zanakis, and C. Zopounidis A survey of business failure with an emphasis on prediction methods and industrial applications European Journal of Operational Research 90 3 1996 487 513 (Pubitemid 126369043)
23. Dwyer, D. (2005). Examples of overfitting encountered when building private firm default prediction models. Moody's KMV, New York, www.moody's. com/whitepapers.
24. Fantazzini, D., & Figini, S. (2009). Random survival forests model for SME credit risk measurement. In Methodology and computing in applied probability, Springer.
25. J. Fidrmuc, and C. Hainz Default rates in the loan market for SMEs: Evidence from Slovakia Economic Systems 34 2 2010 133 147
26. J. Gentry, P. Newbold, and D. Whitford Classifying bankrupt firms with funds flow components Journal of Accounting Research 23 1 1985 146 160
27. Gupta, J., Wilson, N., Gregoriou, A., Healy, J. (2012). The Value of Operating Cash Flow in Modelling Credit Risk for SMEs. Available at SSRN 2180313.
28. M.C. Gupta, and R.J. Huefner A cluster analysis study of financial ratios and industry characteristics Journal of Accounting Research 1972 77 95
29. D. Hand, and A. Niall Defining attributes for scorecard construction in credit scoring Journal of Applied Statistics 27 5 2000 527 540
30. Hermanto, B., Gunawidjaja, S. (2010). Default prediction model for SME's: Evidence from indonesian market using financial ratios. SSRN 1666703.
31. D.W. Hosmer, and S. Lemeshow Applied logistic regression 2000 Wiley New York
32. K. Jain, P. Gupta, and M. Sanjiv Logistic predictive model for SMEs financing in India The Journal of Business Perspective 15 4 2011 331 346
33. T. Johnsen, and R. Melicher Predicting corporate bankruptcy and financial distress: Information value added by multinominal logit models Journal of Economics and Business 46 4 1994 269 286
34. G.V. Kass An exploratory technique for investigating large quantities of categorical data Applied Statistics 29 2 1980 119 127
35. K. Kennedy, B.M. Namee, S. Delany, M. O'Sullivan, and N. Watson A window of opportunity: Assessing behavioural scoring Expert Systems with Applications 40 2013 1372 1380
36. K.-j. Kim, and H. Ahn A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach Computers and Operations Research 39 2012 1800 1811
37. P.R. Kumar, and V. Ravi Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review European Journal of Operational Research 180 2007 1 28 (Pubitemid 46123864)
38. E.K. Laitinen, and T. Laitinen Bankruptcy prediction application of the Taylor's expansion in logistic regression International Review of Financial Analysis 9 2000 327 349
39. K.C. Lee, I. Han, and Y. Kwon Hybrid neural network models for bankruptcy predictions Decision Support Systems 18 1 1996 63 72 (Pubitemid 126363840)
40. M. Leshno, and Y. Spector Neural network prediction analysis: The bankruptcy case Neurocomputing 10 1996 125 147 (Pubitemid 26082145)
41. F. Lin, and S. McClean A datamining approach to the prediction of corporate failure Knowledge Based Systems 14 2001 189 195 (Pubitemid 32491734)
42. F. Louzada, P.H. Ferreira-Silva, and C.A. Diniz On the impact of disproportional samples in credit scoring models: An application to a Brazilian bank data Expert Systems with Applications 39 2012 8071 8078
43. J.H. Min, and C. Jeong A binary classification method for bankruptcy prediction Expert Systems with Applications 36 2009 5256 5263
44. T. Moon, and S. Sohn Technology credit scoring model considering both SME characteristics and economic conditions: The Korean case Journal of the Operational Research Society 2010 666 675
45. J. Ohlson Financial ratios and the probabilistic of bankruptcy Journal of Accounting Research 18 1 1980 109 131
46. P. Peduzzi, J. Concato, E. Kemper, T. Holford, and A. Feinstein A simulation of the number of events per variable in logistic regression analysis Journal of Clinical Epidemiology 49 12 1996 1373 1379 (Pubitemid 27028925)
47. D. Plattner Why firms go bankrupt. The in factors on the insolvency probability of small and medium sized enterprises KfW Research 28 2002 37 51
48. K. Pluto, and D. Tasche Internal-ratings-based approach Encyclopedia of Quantitative Finance 2010
49. D. Ruthenberg, and Y. Landskroner Loan pricing under Basel II in an imperfectly competitive banking market Journal of Banking and Finance 32 12 2008 2725 2733
50. SAS Institute Inc. (2011). SAS/STAT® 9.3 User's Guide. Cary, NC: SAS Institute Inc.
51. G.E. Schwarz Estimating the dimension of a model Annals of Statistics 6 2 1978 461 464
52. N. Siddiqi Credit risk scorecards: Developing and implementing intelligent credit scoring 2005 John Wiley & Sons, Inc New York
53. Sohn, S.Y., Kim, Y.S., 2012. Behavioral credit scoring model for technology-based firms that considers uncertain financial ratios obtained from relationship banking. Small Business Economics (pp. 1-13).
54. M. Sustersic, D. Mramor, and J. Zupan Consumer credit scoring models with limited data Expert Systems with Applications 36 2009 4736 4744
55. D. Tasche Validation of internal rating systems and PD estimates The Analytics of Risk Model Validation 2006 169 196
56. L.C. Thomas Consumer credit models: Pricing, profit, and portfolios 2009 Oxford University Press Inc New York
57. TrimTab Solutions. (2012). ScoreStar® - Credit Scoring Software. < http://www.trimtab.rs >.
58. S. Vukovic, B. Delibasic, A. Uzelac, and M. Suknovic A case-based reasoning model that uses preference theory functions for credit scoring Expert Systems with Applications 39 2012 8389 8395
59. Y.-J. Wang, and H.-S. Lee A clustering method to identify representative financial ratios Information Sciences 178 4 2008 1087 1097 (Pubitemid 350180893)
60. S. Westgaard, and N.v. Wijst Default probabilities in a corporate bank portfolio: A logistic model approach European Journal of Operational Research 135 2001 338 349 (Pubitemid 32842072)
61. D. Yazdanfar Predicting bankruptcy among SMEs: Evidence from Swedish firm-level data International Journal of Entrepreneurship and Small Business 14 4 2011 551 565
62. Zekic-Susac, M., Sarlija, N., & Bensic, M. (2004). Small business credit scoring: A comparison of logistic regression, neural networks, and decision tree models. In 26th International conference on information technology interfaces. Croatia.