Financing Structures, Bank Specific Variables and Credit Risk ...

4.0 Analysis of Finding4.1 CorrelationMulticollinearity problem have been tested by preparing the correlation matrix usingPearson correlation test. The correlation result indicates there is no seriousmulticollinearity problem. Thus all the variables can be used to test the model. Table4 provide the value of correlation for the variables used in the study. From the result,there are mixed relationship between banks specific variables and credit risk level.Out of five variables, three variables have negative relationship with credit risk. TL,TE and CAPR have a negative correlation with CR. LLP and TA has a positivecorrelation with CR.Table 4: Correlation MatrixCR TL LLP TE CAPR TACR 1.0000TL -0.0743 1.0000(-0.6362)LLP 0.5553 0.1897 1.0000(-5.7044)*** -1.6619TE -0.2207 -0.2645 -0.1735 1.0000(-1.9337)* (-2.3595)** (-1.5158)CAPR -0.1781 -0.5183 -0.1729 0.5121 1.0000(-1.5361) (-5.1783)*** (-1.4996) (5.1291)***TA 0.3345 0.3033 0.1033 -0.2874 -0.2965 1.0000(3.0324)*** (2.7382)*** (-0.8935) (-2.6160)** (-2.6711)******, ** and * denotes significance at 1%, 5% and 10% confidence level, respectively.4.2 Regression ResultFive independence variables and three dummy variables have been regress againstcredit risk (CR). The estimation result shows that four BSV has a significantrelationship with credit risk. TL and TE have a negative significant relationship withCR. Negative relationship between TE and CR is expected as lower equity capitalindicates that higher debt capital. Higher debt capital will increased net interestmargin ratio. TL has contradicted result; TL is expected to have positive relationshipwith CR, however this study finds that TL has negative relationship with CR. Thisresult is same with Rahman and Shahimi (2010).LLP has positive significant relationship with CR as expected. Higher amount ofprovisioning indicates that a bank have a problem with loan quality. Lower quality ofloan will potentially increase loan default.9