CREDIT SCORING BY USING GENERALIZED MODELS: AN IMPLEMENTATION ON TURKEY’S SMEs

Yıl 2017, Cilt: 4 Sayı: 2, 98 - 105, 30.06.2017

Aysegul Iscanoglu Cekic , Kasirga Yildirak

https://doi.org/10.17261/Pressacademia.2017.438

Öz

Purpose - In this study, we make an
empirical research and a comparison study on econometric models used with
logistic link functions. We compare the predictive powers of models in credit
granting process.

Methodology - We collected data belonging
to 87 medium sized companies. 21 of these companies are defaulted. The data set
includes 15 continuous financial ratios for estimation of the models. We
implement three models which are Logistic Regression, Generalized Partially
Linear Models(GPLM) and Generalized Additive Models(GAM). For each model the
best fitted model is selected according to AIC criteria.

Findings-   GPLM have
pointed out that the equity turnover ratio has a significant nonparametric effect.
On the other hand GAM pointed out that (total liability)/(total assets) and
Increase in Sales have significant nonparametric effects. Comparison of the
models have implemented according to their accuracy ratios, Type I and Type II
errors. Results show that generalized additive model with logistic link
outperforms both Logistic Regression and generalized partially linear model in
terms of three performance measures.

Conclusion- After
1980s as a result of the financial crises the default events become a main
issue of the credit agencies. For this reason, a credit agency’ objective is to
determine whether a credit application should be granted or refused. Here, the problem is to learn default
some time before the default event occurs. The empirical studies in this area
have indicated that commonly used classification methods are good to detect
signals of defaults. Especially the models which allow logistic link function
are good choices for modeling default risk. In this study we mainly focused on the
generalized linear models and its semi- and non-parametric extensions with
logistic link function. We compare their performances in a credit granting
procedure. We use a real data
belonging to Turkish SMEs. Our results show that the GAM outperforms the other
two models and it will be a good choice for credit granting procedure.

Anahtar Kelimeler

Credit scoring, logistic regression, GPLM, GAM

Kaynakça

• Altman, E. (1968). Financial Ratios, Discriminant analysis and the prediction of corporate bankruptcy, The Journal of Finance, 589-609.
• Altman, E.I., and Loris, B. (1976). A financial early warning system for over- the counter broker-dealers, The Journal of Finance, 31,4 1201-1217.
• Altman, E., Haldeman, R. and Narayanan, P. (1977). ZETA Analysis: A New Model to Identify Bankruptcy Risk of Corporations, Journal of Banking and Finance, 29-54.
• Awh, R. Y., & Waters, D. A. (1974). Discriminant analysis of economic, demographic, and attitudinal characteristics of bank charge card holders: A case study, The Journal of Finance, 29(3). 973-980.
• Back B., Laitinen T., Sere K. and van Wezel M. (1996). Choosing bankruptcy predictors using discriminant analysis, logit analysis, and genetic algorithms, Turku Centre for Computer Science Technical Report 40
• Beaver, W. (1966). Financial ratios as predictors of failure, Journal of Accounting Research, Empirical Research in Accounting: Selected Studies, 1966, supplement to vol.5, 71-111.
• Coats, K.P. and Fant, L.F. (1991). A neural network approach to forecasting financial distress, The Journal of Business Forecasting, Vol. 10(4). 9-12.
• Coats P. and Fant L. (1993). Recognizing financial distress patterns using a neural network toll, Financial Management, 22, 142-155.
• Dambolena, I.G., and Khoury, S.J. (1980). Ratio stability and corporate failure, The Journal of Finance, 1017-1026.
• Deakin,E.B. (1972). Discriminant analysis of predictors of business failure, Journal of Accounting Research, 10-1, 167-179.
• Fitzpatrick, P. J. (1931). Symptoms of Industrial Failures as Revealed by an Analysis of the Financial Statements of the Failed Companies, Washington, D. C; Catholic University of America.
• Fitzpatrick, D. B. (1976). An analysis of bank credit card profit, Journal of Bank Research, 7, 199- 205.
• Hastie, T. J. (1991). Generalized additive models. Chapter 7 of Statistical Models in S eds. J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.
• Hastie, T. J. and Tibshirani, R. (1987). Generalized additive models: some applications, Journal of the American Statistical Association, Vol. 82, No. 398 pp. 371-386.
• Hosmer D.W., and Lemeshow Jr.S. (2000). Applied Logistic Regression, New York, Wiley.
• Huo, Y., Chen, H., and Chen J. (2017). Research on Personal Credit Assessment Based on Neural Network-Logistic Regression Combination Model, Open Journal of Business and Management, 5, 244-252.
• Galindo, J. and Tamayo, P. (2006) Evaluation of Neural Networks and Data Mining Methods on a Credit Assessment Task for Class Imbalance Problem, Nonlinear Analysis: Real World Applications, 7, 720-747.
• Laitinen, T. and Kankaanpaa, M. (1999). Comparative analysis of failure prediction methods: the finnish case, The European Accounting Review 8:1, 67-92.
• Louviere, J. J., Hensher, D. A. and Swait, J. (2000). Stated choice methods: analysis and application. Cambridge University Press.
• McKee , T.E. and Greensten M. (2000). Predicting bankruptcy using recursive partitioning and a realistically proportioned data set. Journal of Forecasting, 19: pp. 219-230.
• Mears, P. K., Discussion of financial ratios as predictors of failures, Jour- nal of Accounting Research, Emprical Research in Accounting: Selected Studies 4 (1966) 119-122.
• Merwin, C. L. (1942). Financing Small Corporations: In Five Manufacturing Industries, 1926-36, National Bureau of Economic Research.
• Miyamoto, M. (2014). Credit Risk Assessment for a Small Bank by Using a Multinomial Logistic Regression Model, International Journal of Finance & Accounting, 3, 327-334.
• Muller, M. (2000). Semiparametric extensions to generalized linear models, Humboldt Universitat zu Berlin. Working Paper.
• Muller M. and Ronz B. (1999). Credit scoring using semiparametric methods, Humboldt Universitaet Berlin in its series Sonderforschungsbereich 373 with number 1999-93.
• Odom, M.D., and Sharda, R. (1990). A Neural network model for bankruptcy prediction, Neural Networks, IJCNN International Joint Conference on 17-21, 2, 163-168.
• Orgler, Y.E. (1970). Credit scoring model for commercial loans, Journal of Money, Credit and Bank, 2: 435-445. Pantalone, C. and Platt, M. (1987). Predicting commercial bank failure since deregulation, Federal Reserve Bank of Boston New England Economic Review, (July/ August):37-47.
• Ramser, J. & Foster, L., A (1931). Demonstration of Ratio Analysis, Bulletin 40, Bureau of Business Research, University of Illinois, Urbana.IL.
• Shi, Q. and Jin, Y. (2004). A Comparative Study on The Application of Various Personal Credit Scoring Models in China Statistical Research, 21, 43-47.
• Smith, R. and Winakor, A. (1935). Changes in Financial Structure of Unsuccessful Industrial Corporations. Bureau of Business Research, Bulletin No. 51. Urbana:University of Illinois Press,
• Sinkey, J.F. (1975). A Multivariate analysis of the characteristics of problem banks, The Journal of Finance, 30: 21-36.
• Xiao, W. and Fei, Q. (2006). A Study of Personal Credit Scoring Models on Support Vector Machine with Optimal Choice of Kernel Function Parameters, Systems Engineering Theory & Practice, 26, 73-79.
• Yang, S., Zhu, Q. and Cheng, C. (2013). The Building of the Combined Model for Personal Credit Rating: A Study Based on the Decision Tree-Neural Network, Financial Forum, 27, 57-61.