Abstract
With profitability objectives conflicting with liquidity objectives of banks, there is need to reconcile these conflicting positions through effective liquidity management so as to ensure the survival and growth of banks and to prepare them against probable financial challenges. This paper examines the link or nexus between liquidity management and bank profitability in Nigeria. An ex-post facto research design was employed as relevant data were collected from the annual report of affected banks and the CBN statistical bulletin for the period 2006 to 2019. A total of 6 variables, split into 3 dependent and 3 independent variables were used in the study. The profitability ratios constitute the dependent variables. They are Return on Equity (ROE), Return on Assets (ROA) and Profit after Tax (PAT) while the Liquidity management ratios that make up the independent variables include Cash Ratio (CAR), Loan to deposit ratio (LTDR) and Loan to Assets ratio (LTAR). A panel data analysis involving the use of Generalized Least Square (GLS) method on a time series data with 14 observations and 10 cross sections were used to ascertain relationships. Outcome of the study indicates that, the coefficient of liquidity management ratios had a mixed bag relationship with profitability ratios of selected commercial banks - While some had a positive impact, others were negative. However, in return to equity (ROE) equation, it maintained a strictly negative relationship with loan to asset ratios (LTAR) of all the selected commercial banks except for Sterling bank. It was also a mixed bag scenario with other profitability ratios and the panel cross section fixed effects. Conclusively, it could be said that the actual sway of each policy is a function of other endogenous variables inherent in each bank. For example, how come it was only Stirling Bank that sustained a positive interface between return to equity and loan to asset ratio as a liquidity management tool? The answer to this question is not farfetched as every level of liquidity has a different effect on the level of profitability. It is thus recommended that Banks should evaluate and redesign their liquidity management strategies so that it will not only optimize returns to shareholders equity but also optimize the use of the assets. In this regard, the current liquidity management policies as put forward by the central bank of Nigeria should be sustained as they are helping to mop up excess liquidity. In a situation where a bank is experiencing excess liquidity crises, the following lines of action should be considered - such excesses should be invested in profitable financial outlets and in the real sectors at home or abroad. Again, such excesses could be used for expansion, where there is a positive synergy for such an expansion but where these are not feasible then, the bank should lodge in such excesses with the Central Bank of Nigeria. Keywords: Profitability ratio, Liquidity ratio, Return on Equity, Return on Assets, Profit after Tax, Cash Ratio, Loan to deposit ratio, Loan to Assets ratio. DOI: 10.7176/RJFA/12-20-01 Publication date: October 31 st 2021
Highlights
Background of the studyCommercial banks are important institutions in the financial system as they function as retail banking units facilitating the transfer of financial assets from fund lenders to fund seekers
This implies that holding all the independent variables constant, the value of the banks return on asset growth will increase by 26.64 .From the result table above, the cash ratio variable Cash Ratio (CAR) indicate a positive signs for seven www.iiste.org banks, ACCESS, FIDELITY,FCMB, STERLING, UBN,UBA and UNITY respectively
Summary and conclusion It could be said that the various liquidity management policies as put up by the Central Bank of Nigeria is helping to mop up excess liquidity in the banking system
Summary
From table 1 above, the panel descriptive statistics for the variables under consideration indicated that from 2006 to 2019, all the variables under study showed an averaged positive mean values with 140 observations in ten cross sections. The relationships amongst the variables under consideration are tested using correlation matrix and the result presented in table 2 below:. Other variables maintained a positive correlation with one another except for LTAR This implies that issues of multi collinearity are not likely to be present in the data. The null hypothesis of the Hausman test is that random effect is the preferred model and the alternative hypothesis is that the fixed effect model is preferred. When the null hypothesis is rejected, it indicates that cross sectional unit random effects are correlated with the regressors; the fixed effect model is superior to the random effect model. If we fail to reject the null hypothesis the random effect is preferable implying there is no correlation between the unique errors and the explanatory variables
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