Evaluating the Impact of Interest Rate Caping on the Profitability of Commercial Banks in Kenya Using the Camels Model

Abstract

Utilizing the CAMELS model, the research was conducted with the intention of determining the extent to which interest rate ceilings influence profitability. Ceilings on lending rates continue to be a popular policy instrument that is used with the goal of reducing the total cost of credit or protecting customers from outrageous charges. Both of these goals are meant to decrease the overall cost of credit. Interest rate caps may be implemented in a wide range of forms, each with the potential to affect either a narrow segment of the market or the whole market as a whole depending on the logic behind its implementation. In recent years, most countries have passed new rules or increased the harshness of those already in place, while just a small number have repealed or relaxed the restrictions. The study's goal was to look at commercial banks in Kenya from 2014 to 2022, before and after the implementation of interest rate caps. The CAMEL model was used to provide an in-depth analysis of the commercial banks' financial standing. We took into account capital sufficiency, asset quality, managerial effectiveness, earnings potential, and liquidity when assessing performance as assessed by ROA. The purpose of this research is to examine how interest rate limits have influenced the CAMEL framework's and Kenya's commercial banks' ability to generate a profit. The methodology used in this investigation was strictly descriptive. This research strategy was chosen because it presumes that the events being studied have already occurred and that the only remaining task is to establish a causal relationship between them. The full capability of this method was used in order to ascertain whether or not a correlation exists between the response variable (profitability) and the explanatory components (capital adequacy, asset quality, managerial efficiency, earning quality, and liquidity) of Kenyan commercial banks. The purpose of this was to prove the existence of this connection. Primary and secondary data were gathered mostly from audited financial statements and reports annually. We performed some preliminary statistical analyses, including the multicollinearity test and the Durbin-Watson test. Analysis of correlation and linear regression were carried out for both pre- and post-capping of interest rates. In the case of commercial banks, the precapping coefficient of determination, also known as the R-squared of ROA, explains 78.6% of CAMEL ratio, but the post-capping coefficient of determination, R-squared, was just 48.4%.