The Relationship Between Loan Portfolio Sectoral Concentration and Credit Risk of Commercial Banks in Kenya
The business activity in the banking industry is very sensitive as they handle depositors’ money which on average constitutes 85% of their liability portfolio in their balance sheets. The questions that comes to the mind of commercial banks management while advancing credit facilities is whether they need to minimise their risk through diversification of the loan portfolios by advancing loans to various market sectors or they need to concentrate their loans to a few sectors that they have adequate knowledge. This study pursued to determine the impact of loan portfolio sectoral concentration on credit risk of commercial banks in Kenya. The study’s population comprised of all 42 commercial banks operating in Kenya. Data was obtained from 40 out of the 42 banks giving a response rate of 95.24%. Loan portfolio sectoral concentration was the independent variable and was measured by the HH1 index on an annual basis. The control variables were liquidity as measured by the current ratio, bank size as measured by natural logarithm of total assets and management efficiency as measured by cost to income ratio per year. Credit risk was the dependent variable which the study sought to explain and it was measured by total non performing loans to total advanced loans on an annual basis. Secondary data was collected for a total period of 5 years (from January 2013 to December 2017) on an annual basis. The study employed a descriptive cross-sectional research design and a multiple linear regression model was used to analyze the association between the variables. Data analysis was undertaken using the Statistical package for social sciences version 21. The results of the study produced R-square value of 0.396 which means that about 39.6 percent of the variation in the Kenyan commercial banks’ credit risk can be explained by the four selected independent variables while 60.4 percent in the variation of credit risk of commercial banks was associated with other factors not covered in this research. The study also found that the independent variables had a strong correlation with credit risk (R=0.629). ANOVA results show that the F statistic was significant at 5% level with a p=0.000. Therefore the model was fit to explain the relationship between the selected variables. The results further revealed that management efficiency produced negative and statistically significant values for this study while loan portfolio sectoral concentration, banks size and liquidity were established to be statistically insignificant determinants of credit risk among commercial banks. This study thus recommends that good measures should be put in place to enhance management efficiency among commercial banks as this will help reduce credit risk.
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