ASSESSING CREDIT DEFAULT USING LOGISTIC
REGRESSION AND MULTIPLE DISCRIMINANT
ANALYSIS: EMPIRICAL EVIDENCE FROM
BOSNIA AND HERZEGOVINA
Deni MemićDepartment of Economics - Sarajevo School of Science and Technology
Sarajevo, Bosnia and Herzegovina
|INDECS 13(1), 128-153, 2015
Full text available here.
Received: 29 September 2014.
This article has an aim to assess credit default prediction on the banking market in Bosnia and Herzegovina nationwide as well as on its constitutional entities (Federation of Bosnia and Herzegovina and Republika Srpska). Ability to classify companies info different predefined groups or finding an appropriate tool which would replace human assessment in classifying companies into good and bad buckets has been one of the main interests on risk management researchers for a long time. We investigated the possibility and accuracy of default prediction using traditional statistical methods logistic regression (logit) and multiple discriminant analysis (MDA) and compared their predictive abilities. The results show that the created models have high predictive ability. For logit models, some variables are more influential on the default prediction than the others. Return on assets (ROA) is statistically significant in all four periods prior to default, having very high regression coefficients, or high impact on the model's ability to predict default. Similar results are obtained for MDA models. It is also found that predictive ability differs between logistic regression and multiple discriminant analysis.
Bosnia and Herzegovina, default prediction, logistic regression, multiple discriminant analysis, banking
JEL: G17, G33, G53