Using Discriminant Analysis To Classify Banks According To Credit Risk
Abstract
This research aims to find the set of macro and microeconomic variables that can be used to classify Commercial Banks Operating in Syria according to their credit risks and to predict these risks. A panel of quarterly data over the period 2008-2018 from 11 banks is used to implement this study. To achieve this, Multiple Discriminant analysis (MDA), Eigenvalue and Wilks' Lambda tests for evaluation quality of model and Receiver operating characteristic curve (ROC) for evaluation model in classification are used.
According to the discriminant model, the classification accuracy rating is 83.7%. In this model, the most important variables are: credit risk in the period t-1 (lCrisk), Capital adequacy ratio (CAR), Non performing credit facilities/ credit facilities (NPL), Market share (MS), Loans/Assets (LAR), Operational risk (OR) and Inflation rate in the period t-1 (lINF) respectively.
The results also show that the proposed classification model has an ability of predication with an accuracy rating equals to 95.1%, 82.9% and 81% in the first year, the second and the third respectively.
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