<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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 (<italic>WoE</italic>), the number of potential predictions will be reduced based on the Information Value (<italic>IV</italic>) 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.</p></abstract>

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.

Predicting the Default Risk of Companies. Comparison of Credit Scoring Models: Logit Vs Support Vector Machines

Econometrics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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)...

Predicting the Default Risk of Companies. Comparison of Credit Scoring Models: Logit Vs Support Vector Machines

Data publikacji: 26 maj 2018

Zakres stron: 54 - 73

DOI: https://doi.org/10.15611/eada.2018.2.05

Słowa kluczowe
Basel III, Internal Rating Based System, credit scoring, Support Vector Machines, logistic regression

© 2018 Natalia Nehrebecka, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Predicting the Default Risk of Companies. Comparison of Credit Scoring Models: Logit Vs Support Vector Machines

Data publikacji: 26 maj 2018

Zakres stron: 54 - 73

DOI: https://doi.org/10.15611/eada.2018.2.05

Słowa kluczoweBasel III, Internal Rating Based System, credit scoring, Support Vector Machines, logistic regression

© 2018 Natalia Nehrebecka, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
Basel III, Internal Rating Based System, credit scoring, Support Vector Machines, logistic regression