Factors effecting on the net interest margins of financial institutions of Sri Lanka

Abstract

The financial intermediaries, which include licensed commercial banks (LCB), licensed specialized banks (LSB) and licensed finance companies (LFCs) play a major role in Sri Lankan economy. Net Interest Margin (NIM) can be considered as a main aspect of measuring the performance of these financial intermediaries. When comparing with the other countries in South Asia, the NIM in Sri Lanka is much higher. Unlike in other countries, financial system of Sri Lanka has not been able to lower its NIM value. Hence, the objective of this study is to investigate the industry specific and macroeconomic factors for higher NIM in Sri Lanka and in view of LCB, LSB and LFC. In fact, LCB & LSB and LFCs account for more than 70% of assets of the country’s financial sector, which is the main reason why we have chosen the above three financial intermediaries in this study. Panel data will be extracted from the individual annual reports of each financial institution between 2009 to 2019. 7 LCB, 3 LSB and 5 LFCs were selected as samples, based on their total assets. The primary regression model includes the two major institutes: bank and finance & leasing sectors. Hence Two multiple regression models were deployed to investigate the determinants of LCB & LSB and LFCs separately. Credit risk, firm size, liquidity assets, earning ability and operational expenses are categorized under industry specific variables, while economic growth, inflation rate and interest rate are considered under macroeconomic variables. The results of the primary regression model obtained from SPSS statistics 25 software shows that interest rate, firm size, economic growth, and credit risk have significant impact with NIM. When considering the two resultant models, only the inflation rate and the economic growth have a positive significant impact on NIM. Moreover, in the LCB & LSB sector, liquidity assets and operational expenses have a positive significant relationship with NIM. However, Credit risk shows a negative significant relationship with NIM. This study indicates that firm size and earning ability are not significant variables for LCB & LSB. But in LFCs, firm size is the only industry specific variable that has a negative significant impact with NIM. Furthermore, the R square value of the primary regression model is 0.764. This shows that only 76.4 percent of total variability of the first model is explained by independent variables. The R square value for the second regression model is 0.730. Hence, 73 percent of the total variation for NIM values can be explained by the fitted model for LFCs. These findings fitted well with previous studies. The findings may help the policy makers as well as the investors to make better decisions while dealing with these financial institutions.