Effect Of Bank Specific Factors On Income Diversification Of Listed Commercial Banks In Nairobi Securities Exchange.

Abstract

Banking industry is a very critical industry in economic affairs of any given country not to mention the global economic affairs. Bank run in one country if not well controlled can quickly degenerate into a banking crisis and in the long run into a contagion either locally, regionally or even globally. Supervision and examination of banks using the recommended bank specific factors by CAMELS Model comes in handy to ensure all regulations and requirements are followed. This ensures the banks continues to offer their intermediation services as required. The rise in regulation of traditional income sources of commercial banks, industry deregulation and technology changes has allowed non-bank institutions to offer stiff competition to the banks especially in the traditional income space. This has made commercial banks to shift to non-interest incomes in order to supplement the incomes lost. This study therefore sought to understand how bank specific characteristics influence commercial banks in diversifying incomes in both interest and non-interest based sources as measured by Herfindahl Hirschmann Index (HHI). The study considered Capital Adequacy, Asset Quality, Management Efficiency, Earning Ability and Liquidity to test their effect on income diversification. The study targeted the commercial banks in Kenya but specifically focused on the listed commercial banks at the NSE. Census sampling method was employed since the listed commercial banks are few however commercial banks that did not meet the inclusion criteria were not considered in the analysis. The study process utilized the secondary published data from the CMA, CBK, NSE and Individual banks websites among other Economic reports. The data was collected using a data collection form and then transferred to Microsoft Excel for clean up before exporting it to STATA for the analysis. Panel Data analysis techniques was employed and GLS was used to fit the model since the technique is versatile in dealing with Auto correlation and Heteroscedasticty challenges. The output indicated that Capital adequacy has a strong positive correlation to income diversification while Asset Quality and Management Efficiency have a negative significance degree of association to income diversification. Earning Ability has a negative weak significance to the income diversification. Lastly, liquidity effect on income diversification was inconclusive. The output results implied that bank management, Central Bank and Treasury should develop policies and environment that allows banks to invest the excess capital for income diversification. Banks should develop policies and invest in models that favor Asset quality improvement and credit administration policies to manage asset quality. Additionally management of costs is vital in order to assure income diversification benefits hence cost management’s measures and monitoring tools should be implemented. Lastly liquidity effects on income diversification was insignificant and inconclusive therefore further studies on this is recommended.