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

Year 2017, Volume: 4 Issue: 2, 98 - 105, 30.06.2017

Aysegul Iscanoglu Cekic , Kasirga Yildirak

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

Abstract

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.

Keywords

Credit scoring, logistic regression, GPLM, GAM

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