A Model For Prediction Of Financial Distress In Sudanese Banking System Using A Newly Built Data Set.

Abstract

Bank failures threaten the economic system as a whole. Therefore, predicting bank financial failures is crucial to prevent and/or lessen its negative effects on the economic system. Financial crises, affecting both emerging markets and advanced countries over the centuries, have severe economic consequences, but they can be hard to prevent and predict. This is originally a classification problem to categorize banks as healthy or non-healthy ones in order to design the required measures and policies to mitigate the risks for non-healthy banks. This study aims to apply Discriminant analysis and Support Vector Machines methods to the bank failure prediction problem in Sudan, and to present a comprehensive computational comparison of the classification performances of the techniques tested. Eleven financial and non-financial ratios with six feature groups including capital adequacy, asset quality, Earning, and liquidity (CAMELS) are selected as predictor variables in the study. Credit risk have also been evaluated using logistic analysis to study the effect of Islamic finance modes, sectors and payment types used by Sudanese banks with regard to their possibilities of failure. Feature selection has shown that new groups can be identified from CAMELS ratios and narrowing the data set space to 11 factors instead of eighteen. Discriminant analysis has identified 3 ratios with highest predictive power, which are: EAS (Ratio of equity capital to total asset), LADF (Ratio of liquid assets to deposits and short term funds) and RFR (Rain Fall Ratio). The later ratio is a novel one used for the first time by this research. Financial analysts are focusing on finance sectors in order to determine which sector is subject to special study. Transportation Sector and Short Term Local finance Sector is considered the most significant sector in bank default probability. Payment type was not found the best predictors for Islamic credit risk analysis. The research outputs can be utilized by monetary policy regulator to monitor commercial banks by focusing on the discovered important predictors as well as review all polices with regard to deferred credit finance mode as well as transportation sector finance.