Credit Scoring and Default Risk Prediction: A Comparative Study between Discriminant Analysis & Logistic Regression

Zaghdoudi Khemais, Djebali Nesrine and Mezni Mohamed

International Journal of Economics and Finance, 2016, vol. 8, issue 4, 39-53

Abstract: This paper aims to develop models for foreseeing default risk of small and medium enterprises (SMEs) for one Tunisian commercial bank using two different methodologies (logistic regression and discriminant analysis). We used a database that consists of 195 credit files granted to Tunisian SMEs which are divided into five sectors ¡°industry, agriculture, tourism, trade and services¡± for a period from 2012 to 2014. The empirical results that we found support the idea that these two scoring techniques have a statistically significant power in predicting default risk of enterprises. Logistic discrimination classifies enterprises correctly in their original groups with a rate of 76.7% against 76.4% in case of linear discrimination giving so a slight superiority to the first method.

Keywords: credit scoring; probability of default; discriminant analysis; logistic regression; SMEs (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://ccsenet.org/journal/index.php/ijef/article/view/56494/31202 (application/pdf)
http://ccsenet.org/journal/index.php/ijef/article/view/56494 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ibn:ijefaa:v:8:y:2016:i:4:p:39-53

Access Statistics for this article

More articles in International Journal of Economics and Finance from Canadian Center of Science and Education Contact information at EDIRC.
Bibliographic data for series maintained by Canadian Center of Science and Education ().