Predicting the default risk of companies. Comparison of credit scoring models: LOGIT vs Support Vector Machines

• Authors: Natalia Nehrebecka

Title variants

PL

Przewidywanie ryzyka kredytowego przedsiębiorstw niefinansowych. Porównanie modeli scoringowych: regresja logistyczna vs Support Vector Machine

• Languages of publication: EN

Abstracts

EN

The aim of the article is to compare models on a train and validation sample, which will be created using logistic regression and Support Vector Machine (SVM) and will be used to assess the credit risk of non-financial enterprises. When creating models, the variables will be subjected to the transformation of the Weight of Evidence (WoE), the number of potential predictions will be reduced based on the Information Value (IV) statistics. The quality of the models will be assessed according to the most popular criteria such as GINI statistics, Kolmogorov-Smirnov (K-S) and Area Under Receiver Operating Characteristic (AUROC). Based on the results, it was found that there are significant differences between the logistic regression model of discriminatory character and the SVM for the model sample. In the case of a validation sample, logistic regression has the best prognostic capability. These analyses can be used to reduce the risk of negative effects on the financial sector.

Keywords

EN

Basel III; Internal Rating Based System; credit scoring; Support Vector Machines; logistic regression

• Publisher: Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
• Journal: Econometrics. Ekonometria. Advances in Applied Data Analytics
• Year: 2018
• Issue: vol. 22 no. 2
• Pages: 54-73

Contributors

author

Natalia Nehrebecka