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EFFECT OF LIQUIDITY MANAGEMENT ON FINANCIAL PERFORMANCE OF
DEPOSIT MONEY BANKS IN NIGERIA
By
D. O. Gbegi Ph.D
Department of Accounting and Finance,
College of Management Sciences,
University of Agriculture, Makurdi
S.R. Abdullahi Ph.D
Department of Accounting and Finance,
Faculty of Management Sciences,
Kogi State University, Anyigba
Wuave Terseer
Department of Accounting and Finance,
College of Management Sciences,
University of Agriculture, Makurdi
Email:[email protected]
Abstract
The Banking Institution have contributed significantly to the effectiveness of the entire financial
system in Nigeria as it offers efficient institutional mechanism through which resources can be
mobilized and channeled from the surplus unit to the deficit unit. Therefore, the study of the effect
of liquidity management on financial performance of the Deposit Money Banks in Nigeria is
paramount. The main objective of this study is to investigate the effect of liquidity management on
financial performance of deposit banks in Nigeria for the periods 2010 to 2015. For the purpose
of this study, secondary source of information was utilized. Five banks were chosen across Nigeria
and researched upon. The liquidity indicators are: Liquidity ratio (LQR), Loan to deposit ratio
(LDR), Cash reserve ratio (CRR) and deposit rate (DR), while return on assets (ROA), return on
equity (ROE) and return on net interest margin (NIM) are proxies for financial performance
(Profitability). The study made use of panel regression analysis in estimating results and Hausman
test for the choice between fixed effect and random effect model. The study found out that liquidity
ratio (LQR) and deposit rate (DR) have positive and significant effect on financial performance of
DMB as measured by return on assets (ROA), return on equity (ROE) and net interest
margin(NIM).The researcher recommends that the central bank of Nigeria and other regulatory
agents should evolve polices that aim at enhancing the liquidity ratio of DMB, place rules that peg
loan to deposit ratio to ascertain margin beyond which it will be a crime and reduce loan to deposit
ratio and cash reserve to ensure increase to net interest margin of Deposit Money Bank (DBM) in
Nigeria.
Keywords: Liquidity Management, Deposit Money Banks, Return on Asset, Return on Equity,
Net Interest Margin and Financial Performance.
Introduction
Liquidity is a financial term that means the amount of capital that is available for investment. In
other words liquidity simply means the ability to convert an asset to cash with minimum delay and
minimum loss/cost. The adequacy of liquidity plays very crucial roles in the successful functioning
of all business firms. However, the issue of liquidity though important to other businesses, is most
paramount to banking institutions and that explains why banks showcase cash and other liquid
securities in their balance sheet statement annually (Ngwu, 2006). With respect to finance and
financial institutions, liquidity may be defined as the bank’s ability to meet maturing obligations
without incurring unacceptable losses. Liquidity shortage, no matter how small, can cause great
damage to a financial institution’s operations and customer relationship in particular. Managing
liquidity is therefore a core daily process requiring managers to monitor and project cash flows to
ensure that adequate liquidity is maintained at all times (Anyanwu, 1993).
There is consensus in theoretical literature that profitability and liquidity constitute the most
prominent issues in corporate finance literatures. While it may be true that the ultimate goal for
any firm is to maximize profit, too much attention on profitability may lead the firm into a pitfall
by diluting the liquidity position of the organization (Niresh, 2012). Therefore the need to strike a
balance between the firm’s desire to make profit and the desire to remain liquid cannot be over-
emphasized and there arises the issue of liquidity management. Against this backdrop, this research
study seeks to examine the effect of liquidity management on banks performance in Nigeria.
The deposit money banks play their mediation role by absorbing financial surpluses from their
holders (depositors) and put them at the disposal of investors (borrowers) to be directed towards
various investment channels. This investment activity carried out by the bank is hardly devoid of
risks and problems, because the bank is seeking to maximize its expected profits on these
investments, and this requires optimum utilization of the available resources, since the bank is
exposed at any moment to meet the obligations of its clients and depositors who want to withdraw
their savings, and so the bank should be ready to meet these demands at any time.
The problem arises when the Bank is not able to meet these demands, especially those unexpected
ones, which may embarrass the bank with its clients and may lose their trust over the time, in light
of the intensive competition in the banking sector resulting from the increasing number of local
banks, as well as intensive competition from the banks that work in the banking sector.
Furthermore, in a study conducted by Ibe (2013) on the impact of liquidity management on the
profitability of banks in Nigeria focuses on profit after tax as the proxy for profitability or financial
performance and not taking into consideration other variables such as Return on Assets (ROA),
Return on Equity (ROE) and Net Interest Margin (NIM), illiquidity problem still persist with
deposit money banks. However, the impact of banks’ liquidity management on bank performance
remains ambiguous and further research is required. To bridge this gap of counter intuitiveness,
inconclusive results, unattended inverse relationships between liquidity and profitability, this
research combines all three variables (ROA, ROE and NIM) as a means to measure the effective
performance of Banks in Nigeria. This research also tries to improve on the published studies about
the effects of liquidity management on financial performance of deposit money banks. It
contributes to the existing literature by providing anew addition to the previous literature about the
effects of liquidity management on financial performance of deposit money banks in Nigeria.
Conclusively, based on the empirical review, Kosmidou (2008) in his analysis used ROA and find
out that there is a relationship between liquidity management and profitability in Greek. This study
therefore wants to adopt the same variables in Nigerian Deposit Money Banks (DMB) and add to
ROE and NIM for ultimate result. Olagunju et al., (2012) major aim was to find empirical evidence
of the degree to which effective liquidity management affects profitability in Deposit Money
Banks, the researchers used both primary and secondary sources of data and concluded that
profitability in commercial banks is significantly influenced by liquidity and vice versa. The
present study focuses on secondary sources of data and considers ROA, ROE and NIM to measure
financial performance of money deposit banks.
This research seeks to answer the following questions:
i. Is there an effect of bank liquidity management on Return on Asset (ROA)?
ii. To what extent has liquidity management affect Return on Equity?
iii. What is the effect of liquidity management on Net Interest Margin?
The main objective of this research will be investigating the effects of the liquidity management
on financial performance of deposit money banks in Nigeria.
The specific objectives of the research are as follows:
i. To examine the effects of bank liquidity management and Return on Asset (ROA).
ii. To find out if liquidity management affects Return on Equity (ROE).
iii. To examine the effect of bank liquidity management and Net Interest Margin (NIM)
The following hypotheses have been formulated;
HO1: There is no significant relationship between liquidity management and Return on Assets
(ROA).
HO2: Liquidity management has no effect on Return on Equity (ROE).
HO3: Liquidity management has no significant relationship with Net Interest Margin.
Literature Review and Theoretical Framework
Bourke (2009) in his study on performance of banks in twelve countries in Europe, North America
and Australia found evidence that there is a positive relationship between liquid assets and bank
profitability. These results seem counterintuitive, as it is expected that illiquid assets have a higher
liquidity premium and hence higher return. Kosmidou, Tanna and Pasiouras (2005) realized that
the ratio of liquid assets to customer and short term funding is positively related to ROA and
statistically significant. Also, they found a significant positive relationship between liquidity and
bank profits.
Kosmidou (2008) examined the determinants of performance of Greek banks during the period of
EU financial integration (1990-2002) using an unbalanced pooled time series data set of 23 banks
and found that less liquid banks have lower ROA. This is consistent with the previous findings of
Bourke (2009) who found out that there is a positive relationship between liquidity risk and bank
profitability.
Olagunju, David and Samuel (2012) investigated Liquidity Management and deposit money
bank’s Profitability in Nigeria. The major objective of the study was to find empirical evidence of
the degree to which effective liquidity management affects profitability in deposit money banks
and how deposit money banks can enhance their liquidity and profitability positions. Considering
the nature of the survey, quantitative methods of research were applied. In attempt to achieve the
objectives of the study, several findings were made through the analysis of both the structured and
unstructured questionnaire on the management of banks and the financial reports of the sampled
banks. The data obtained from the Primary and Secondary sources were analyzed through
collection, sorting and grouping of the data in tables of percentages and frequency distribution.
The study formulated hypotheses, which were statistically tested through Pearson correlation data
analysis. Findings from the testing of this hypothesis indicate that there is significant relationship
between liquidity and profitability. That means profitability in deposit money banks is significantly
influenced by liquidity and vice versa. The study concluded that for the success of operations and
survival, deposit money banks should not compromise efficient and effective liquidity
management and that both illiquidity and excess liquidity are" financial diseases" that can easily
erode the profit base of a bank as they affect banks attempt to attain high profitability-level. Finally
the study recommends that the Central Bank should be encourage maintaining a flexible Minimum
Monetary Policy [MPR] or discount rate so as to enable the deposit money bank take advantage
of the alternative measures of meeting the unexpected withdrawal demands, and reduce the
tendency of maintaining excess idle cash at expense of profitability, the monetary authority should
as a matter of urgency encourage and legitimate the use of credit cards and enforce cheque usage
for huge amounts in the day to day business transaction, finally , interested researchers should
dwell on the same area of this research extensively using a wider data and area of coverage.
A study which investigated the relationship between liquidity and profitability of some selected
banks and companies quoted in Nigerian Stock Exchange was that of Obiakor and Okwu (2011).
The central objective of the study was to examine the nature and extent of the relationship between
liquidity and profitability and also to determine whether any cause and effect relationship existed
between the two performance measures. Analysis was based on accounts of the banks and the
companies for the relevant period. A model of perceived functional relationship was specified and
estimated using correlation and regression analysis. The results indicated that while a trade-off
existed between liquidity and profitability in the banks with a negative but insignificant impact,
the two variables were positively correlated.
Uremadu (2012) carried out a study on the effect of capital structure and liquidity on the
profitability of selected Nigerians banks. Time series data for the 1980 to 2006 period was used
for the study. The data was analyzed using descriptive statistics and regressive distributed lag
(ARDL) model. The empirical results indicated a positive and significant relationship between
cash reserve ratio, liquidity ratio, corporate income tax and banks’ profitability. On the other hand,
there was negative and significant relationship between savings deposit rate, gross national
savings, balances with the central bank, inflation rate, foreign private investment and bank
profitability.
Ibe (2013) investigated that impact of liquidity management on the profitability of banks in
Nigeria. Three banks were randomly selected to represent the entire banking industry in Nigeria.
The proxies for liquidity management include cash and short-term fund, bank balances and
treasury bills and certificates, while profit after tax was the proxy for profitability. Elliot Rosenberg
Stock (ERS) stationary test model was used to test the association of the variables under study,
while regression analysis was used to test the hypothesis. The result showed that there is a
statistically significant relationship between the variables of liquidity management and
profitability of the selected banks.
The study by Kehinde (2013) critically examined the relationship between credit management,
liquidity position and profitability of selected banks in Nigeria using annual data of ten banks over
the period of 2006 and 2010. The results from ordinary least squares estimate found that liquidity
has significant positive effect on Return on Asset (ROA).
Agbada and Osuji (2013) explored the efficacy of liquidity management and banking profitability
performance in Nigeria. Profitability and Return on Capital Employed (ROCE) were adopted as
proxy variables. Findings from the empirical analysis were quite robust and clearly indicated that
there was a statistically significant relationship between efficient liquidity management and
banking performance, and that efficient liquidity management enhances the soundness of the
banks.
Adeyinka (2013) examined the effect of capital adequacy on profitability of deposit-taking banks
in Nigeria. It sought to assess the effect of capital adequacy of both foreign and domestic banks in
Nigeria and their profitability. The study presented primary data collected by questionnaires
involving a sample of five hundred and eighteen (518) distributed to staff of banks with a response
rate of seventy six percent. Also, published financial statements of banks were used from 2006 to
2010. The finding from the primary data analysis revealed a non-significant relationship but the
secondary data analysis showed a positive and significant relationship between liquidity adequacy
and profitability of bank. This implies that for deposit-taking banks in Nigeria, liquidity adequacy
plays a key role in the determination of profitability. It was discovered that liquidity and
profitability are indicators of bank risk management efficiency and cushion against losses not
covered by current earnings.
Therefore conclusions about the impact of banks’ liquidity management on bank performance
remain ambiguous and further research is required. To bridge this gap of counter intuitiveness,
inconclusive results, unattended inverse relationships between liquidity and profitability, this
research combines all three variables (ROA, ROE and NIM) as a means to measure the effective
performance of Banks in Nigeria. This research also tries to improve on the published studies about
the effects of liquidity management on financial performance of deposit money banks. It
contributes to the existing literature by providing anew addition to the previous literature about the
effects of liquidity management on financial performance of deposit money banks in Nigeria.
Conclusively, based on the empirical review, Kosmidou (2008) in his analysis used ROA and find
out that there is a relationship between liquidity management and profitability in Greek. This study
therefore wants to adopt the same variables in Nigerian Deposit Money Banks (DMB) and add to
ROE and NIM for ultimate result. Olagunju et al. (2012) major aim was to find empirical evidence
of the degree to which effective liquidity management affects profitability in Deposit Money
Banks using both primary and secondary sources of analyzing data and concluded that profitability
in commercial banks is significantly influenced by liquidity and vice versa, so commercial banks
should not compromise efficient and effective liquidity management and that both illiquidity and
excess liquidity are financial diseases that can easily erode the profit base of a bank as they affect
banks attempt to attain high profitability level. The present study focuses on secondary sources of
data and considers ROA, ROE and NIM to measure financial performance of money deposit banks.
For the purpose of this research work, liquidity asset theory, anticipated income theory, shift ability
theory, commercial loan theory and liability management theory are used because of their
relevancy to the study.
Liquid Asset Theory focuses on the asset side of the balance sheet and argues that banks must hold
large amount of liquid assets against possible demand or payment cushion of readily marketable
short term liquid assets against unforeseen circumstances. This approach is however very
expensive in a current world of dynamic money market (Ngwu, 2006). The theory focuses on Bank
assets which happen to be one of the variables measured in this research (Return on Assets).
Anticipated Income Theory holds that a bank’s liquidity can be managed through the proper
phasing and structuring of the loan commitments made by a bank to the customers. Here the
liquidity can be planned if the scheduled loan payments by a customer are based on the future of
the borrower. According to Nzotta (2004) the theory emphasizes the earning potential and the
credit worthiness of a borrower as the ultimate guarantee for ensuring adequate liquidity.
Nwankwo (1991) posits that the theory points to the movement towards self-liquidating
commitments by banks. This theory has encouraged many deposit money banks to adopt a ladder
effects in investment portfolio. This theory also relates to the research for the fact that the
anticipated income of the Bank forms the basis for the Shareholders equity.
Shiftability Theory posits that a bank’s liquidity is maintained if it holds assets that could be shifted
or sold to other lenders or investors for cash. This point of view contends that a bank’s liquidity
could be enhanced if it always has assets to sell and provided the Central Bank and the discount
Market stands ready to purchase the asset offered for discount. Thus this theory recognizes and
contends that shiftability, marketability or transferability of a bank's assets is a basis for ensuring
liquidity. This theory further contends that highly marketable security held by a bank is an
excellent source of liquidity. Dodds (1982) contends that to ensure convertibility without delay
and appreciable loss, such assets must meet three requisites. Liability Management theory
according to Dodds (1982) consists of the activities involved in obtaining funds from depositors
and other creditors (from the market especially) and determining the appropriate mix of funds for
a particular bank.
Commercial Loan Theory has been subjected to various criticisms by Dodds (1982) and Nwankwo
(1992). From the various points of view, the major limitation is that the theory is inconsistent with
the demands of economic development especially for developing countries since it excludes long
term loans which are the engine of growth. The theory also emphasizes the maturity structure of
bank assets (loan and investments) and not necessarily the marketability or the shiftability of the
assets. Also, the theory assumes that repayment from the self-liquidating assets of the bank would
be sufficient to provide for liquidity. This ignores the fact that seasonal deposit withdrawals and
meeting credit request could affect the liquidity position adversely. Moreover, the theory fails to
reflect in the normal stability of demand deposits in the liquidity consideration.
This obvious view may eventually impact on the liquidity position of the bank. Also the theory
assumes that repayment from the self-liquidating assets of a bank would be sufficient to provide
for liquidity. This ignores the fact that seasonal deposit withdrawals and meeting credit request
could affect the liquidity position adversely.
Liability Management Theory: Advocate of liability management theory of liquidity of deposit
money bank maintain that banks can meet liquidity requirement by biding the marked for
additional funds. This approach originally found its strongest advocates in the large money market
centers, the banks, and later develops the negotiable type of certificate of deposit (CD) as a major
money market instrument (Dodds, 1982).
The above five theories will guide the researcher in looking at the balance sheet, or annual reports
presented by Deposit Money Bank carefully to ascertain actual liquidity assets that will be of profit
and benefits to the equity holders bearing in mind the dual purpose of the existence of those banks.
Also, to ensure that Deposit Money Bank carefully planned for their anticipated income by
restructuring the debtors (customers) based as regard to their loanable funds. Take into
consideration, assets that can be shifted or sold to other lenders or investors for cash to enhance
Deposit Money Banks liquidity position. Be guided too, to assume the repayment for self-
liquidating assets of the banks would be sufficient to promote for liquidity and conclusively, using
liability management theory of Deposit Money Banks by bidding to mark for additional funds i.e.
bidding in the money market centers.
Research methodology
This research study is designed to investigate the effects of liquidity management on financial
performance of deposit money Banks in Nigeria. The study adopts ex-post facto research design
because the data used for the study is historical in nature. The research adopts the Cluster sampling
technique. Sample size of Five (5) Banks were selected based on personal judgmental sampling.
These banks are First Bank Plc, UBA, Union Bank, Guaranty Trust Bank and Access Bank. The
time period (2010-2015) was put to use by applying the Data issued by Central Bank reports and
the annual reports of deposit money banks that is relying on the use of secondary data. The study
used regression to analysed data, the method of panel data analysis is used in estimating the result.
Panel data are cross-sectional data observed over time. Panel data are also known as longitudinal
data. The general form of the Panel analysis is stated below;
ititit Xy
Where i = 1..N cross-sectional observations and t = 1..T year
There are basically two types of panel models, the fixed effects and the random effects model.
They differ by their assumptions how the heterogeneity is captured and estimation techniques
(fixed = OLS, random = GLS).
The fixed effect model assumes that individual heterogeneity is captured by the intercept term.
This means every individual gets its own intercept i while the slope coefficients are the same.
This also means that the heterogeneity is associated with the regressors on the right hand side.
The fixed effects model is also known as least square dummy variable estimator (LSDV) because
we assign pretty much a dummy to every individual.
The random effects model assume in some sense that the individual effects are captured by the
intercept and a random component i . This random component is not associated with the
regressors on the right hand side and part of the error term. The intercept becomes . That is
the reason why some textbooks write both capture the heterogeneity by the intercept term.
The assumption of the random effects model that individual effects are not associated with
explanatory variables is a big one! But it allows us to estimate the effect of time-invariant variables
which cancel out in fixed effects estimation.
The use of panel analysis for this study is justified in that; the dataset is both time series and cross
sectional. That is, the data is collected across different banks for a period of five years. Under this
type of data, the appropriate technique to use is the Panel Data Analysis.
The model adopted for this study is a modification (dropping and/or including some variables) of
the Kargi (2011) model which measured profitability with Return on Asset (ROA) as a function
of the ratio of Non-performing loan to loan & Advances (NPL/LA) and ratio of Total loan &
Advances to Total deposit (LA/TD) used as indicators of credit risk. However, this study improved
on the model by incorporating other measures of financial performance of deposit money banks
such as return on equity and net interest margin.
Therefore, the functional form of the model for the study becomes;
ROA =f(LQR, LDR,CRR,DR)-------------------------------------------- (1)
ROE = f(LQR,LDR,CRR,DR)-------------------------------------------- (2)
NIM=f(LQR,LDR,CRR,DR)---------------------------------------------- (3)
Where
ROA = Return on Assets
ROE = Return on Equity
NIM = Net Interest Margin
LQR = Liquidity Ratio
LDR = Loan-to-deposit Ratio
CRR = Cash Reserve Ratio
DR = Deposit Rate
The implicit form of the model is expressed
143210 DRCRRLDRLQRROA --------------------------- (4)
243210 DRCRRLDRLQRROE -------------------------- (5)
343210 DRCRRLDRLQRNIM ---------------------------- (6)
Where
1 - 4 , 1 - 4 and 1 - 4 are the parameter estimates or coefficients of models 4, 5 and 6
respectively. 0 , 0 and 0 are the intercept terms of models 4, 5 and 6 respectively and 1 , 2
and 3 are the error or random terms of the respective models.
It is expected on a priori that, 1 - 4 , 1 - 4 and 1 - 4 will be positively signed.
Decision Rule
Reject the null hypothesis if the probability of the F-statistic is less than the critical value of 0.05
for the fixed effect model for each of the hypothesis.
Data Presentation and Analysis
Result of the Constant Effect Model:
The major assumption under this model is that all coefficients are constant across time period and
individual bank.
The Panel Least Squares results of the three models are given below;
Table 4.1: Pool Effect Model Estimates.
Dependent
Variable
Independent
Variables
Coefficients Std.
Error
t-
statistic
Prob.
EFFECT OF LIQUIDITY MANAGEMENT ON RETURN ON ASSETS
LQR 0.018894 0.067955 0.278041 0.7832
ROA LDR
-0.016143 0.138652
-
0.116430 0.9082
DR 0.763370 1.684781 0.453097 0.6542
CRR
-0.163905 0.280625
-
0.584072 0.5642
R-Squared
0.065
Adj R-Sqr -
0.043
S.E Reg.
3.423
DW Stat
2.29
EFFECT OF LIQUIDITY MANAGEMENT ON RETURN ON EQUITY
LQR 0.011783 0.574190 0.020521 0.9838
ROE LDR
-0.065265 1.171543
-
0.055708 0.9560
DR 4.640293 14.23558 0.325964 0.7471
CRR
-1.707105 2.371147
-
0.719949 0.4780
R-Squared
0.088
Adj R-Sqr -
0.017
S.E Reg.
28.925
DW Stat
2.10
EFFECT OF LIQUIDITY MANAGEMENT ON NET INTEREST MARGIN
LQR 0.052986 0.032795 1.615662 0.1182
NIM LDR
-0.120603 0.066913
-
1.802383 0.0831
DR 1.956396 0.813070 1.906182 0.0535
CRR
-0.233812 0.135429
-
1.726452 0.0961
R-Squared
0.325
Adj R-Sqr
0.248
S.E Reg.
1.652
DW Stat
2.08
An examination of the results of the panel data in Table 4.1 for the three models show that all the
coefficients are individually statistically insignificant at both 1% and 5% level of significance. The
slope coefficients of liquidity ratio (LQR) and Deposit Rate (DR) have the expected positive signs.
Similarly, the coefficients of loan to deposit rate (LDR) and cash reserve rate (CRR) also have the
expected negative sign. The R2 adjusted is relative low for all the three models. That is 0.043,
0.017 and 0.248 for the first, second and third models respectively. The estimated Durbin Watson
statistics is relatively high, suggesting that there is no problem of autocorrelation in the data.
The intercept value is negative (not significant). By assumption the intercept value is the same for
all the 5 banks. Also, the slope coefficients of the three variables are assumed to be identical for
all five banks
The Hausman test is a test that compares the fixed and random effect models. If both fixed and
random effects turn out significant, Hausman test will give you a good idea when choosing one
between the two. The null is that the two estimation methods are both satisfactory and that
therefore they should yield coefficients that are "similar". The alternative hypothesis is that the
fixed effects estimation is justify and the random effects estimation is not; if this is the case, then
we would expect to see differences between the two sets of coefficients. The Hausman Test for
the three models is presented in the Table below;
Table 4.2: Hausman Test
Test Summary Chi-Square d.f Prob.
Model 1 Cross-section random 8.587445 4 0.0112
Model2 Cross-section random 8.254455 4 0.0125
Model 3 Cross-section random 15.087453 4 0.0000
The result of the Hausman test in table 4.2 revealed that the null hypothesis is rejected in favour
of the alternative implying that the fixed effect estimation is most appropriate to use in estimating
the effect of liquidity management on return on assets (ROA), return on equity (ROE) and net
interest management (NIM) for the five banks.
In the basic fixed effects model, the effect of each predictor variable (i.e., the slope) is assumed to
be identical across all the groups (banks), and the regression merely reports the average within-
group effect.
One way to take into account the individuality of each bank is to let the intercept vary for each
bank but still assume that the slope coefficients are constant across the banks. The term “Fixed
Effect “is due to the fact that although the intercept may differ across individuals (that is, the five
banks), each individual bank’s intercept does not vary over time. That is, it is time invariant. This
is the major assumption under this model. That is, while the intercept are cross-sectional variant,
they are time invariant. The results of the Fixed Effect Model under this assumption for the three
models are presented in Table 4.3.
Table 4.3: Fixed Effect Model Estimates.
Dependent
Variable
Independent
Variables
Coefficients Std.
Error
t-statistic Prob.
EFFECT OF LIQUIDITY MANAGEMENT ON RETURN ON ASSETS
C -23.84301 12.02565 -1.982680 0.0606
LQR 0.182669 0.121002 1.509627 0.1460
ROA LDR -0.042679 0.134562 -0.317172 0.7542
DR 3.670570 2.190221 1.675890 0.1086
CRR -0.713120 0.387520 -1.840217 0.0799
R-Squared
0.874
Adj R-Sqr
0.828
S.E Reg.
0.306
DW Stat
2.49
F-Stat.
9.104
P(F.Stat.)
0.0025
EFFECT OF LIQUIDITY MANAGEMENT ON RETURN ON EQUITY
C -113.8849 104.0130 -1.094910 0.2860
LQR 0.974538 1.046582 0.931162 0.3624
ROE LDR -0.061482 1.163860 -0.052826 0.9584
DR 18.52638 18.94379 0.977966 0.3392
CRR -4.330400 3.351758 -1.291979 0.2104
R-Squared
0.715
Adj R-Sqr
0.704
S.E Reg.
0.592
DW Stat
2.18
F-Stat.
7.023
P(F.Stat.)
0.0049
EFFECT OF LIQUIDITY MANAGEMENT ON NET INTEREST MARGIN
C 1.491608 5.730092 0.260311 0.7972
LQR 0.040376 0.057656 0.700288 0.4914
NIM LDR -0.118943 0.064117 -1.855085 0.0777
DR 1.774522 1.043617 1.700358 0.1038
CRR -0.199453 0.184649 -1.080174 0.2923
R-Squared
0.744
Adj R-Sqr
0.716
S.E Reg.
0.575
DW Stat
2.24
F-Stat.
8.6757
P(F.Stat.)
0.0037
Comparing this regression result with the one in Table 4.1. It is evident that, the coefficients of the
independent variables for all the models are highly significant as the probability values of the
estimated “t” statistics are smaller. The intercept values of the five banks are statistically the same
as shown below.
The major assumption under this model is that all coefficients are fixed across time period and
individual bank. That is, in the basic fixed effects model, the effect of each predictor variable (i.e.,
the slope) is assumed to be identical across all the groups, and the regression merely reports the
average within-group effect.
The intercept value is negative (not significant). By assumption the intercept value is the same for
all the 5 banks for each of the models. Also, the slope coefficients of the three variables are
assumed to be identical for all the five banks. Obviously, these are highly restricted assumptions.
This result obviously distorts the true picture of the relationship between bank performance and
all the independent variables across the five banks.
For the first model (effect of liquidity management on return on assets), the slope coefficients of
liquidity ratio (LQR) and Deposit Rate (DR) have the expected positive signs and the coefficients
of loan to deposit rate (LDR) and cash reserve rate (CRR) have the expected negative sign. A unit
increase in LQR and DR will lead to increase in ROA by 0.182669 and 3.67057 respectively. On
the other hand, a unit increase in LDR and CRR will lead to decrease in ROA by 0.04268 by
0.71312 respectively.
The R2 adjusted for the first model is 0.828. The implication therefore is that, 82.8% of the
variation in the dependent variable (ROA) for all the five banks is explained by the independent
variables (LQR, LDR, DR and CRR). This value is relatively high enough to conclude that the
model has goodness of fit.
For the second model (Effect of liquidity management on return on equity), the slope coefficients
of liquidity ratio (LQR) and Deposit Rate (DR) have the expected positive signs and the
coefficients of loan to deposit rate (LDR) and cash reserve rate (CRR) have the expected negative
sign. A unit increase in LQR and DR will lead to increase in ROE by 0.974538 and 18.52638
respectively. Also, a unit increase in LDR and CRR will lead to decrease in ROE by 0.06148 and
4.3304 respectively.
The R2 adjusted for the second model is 0.704. The implication therefore is that, 70.4% of the
variation in the dependent variable (ROE) for all the five banks is explained by the independent
variables (LQR, LDR, DR and CRR). This value is relatively high enough to conclude that the
model has goodness of fit. This value is relatively high enough to conclude that the model has
goodness of fit.
The estimated Durbin Watson statistics for all the three variables are relatively high, suggesting
that there is no problem of autocorrelation in the data sets for the three models. Also, the relative
high values of the F-statistics coupled with their low probability values indicated that, all the
models are statistically significant.
For the third model (Effect of liquidity management on Net Interest Margin), the slope coefficients
of liquidity ratio (LQR) and Deposit Rate (DR) have the expected positive signs and the
coefficients of loan to deposit rate (LDR) and cash reserve rate (CRR) have the expected negative
sign. A unit increase in LDR and DR will lead to increase in NIM by 0.040375 and 1.774522
respectively while a unit increase in LDR and CRR will lead to a decrease in NIM by 0.11894 and
0.19945 respectively.
The R2 adjusted for the third model is 0.716. The implication therefore is that, 71.6% of the
variation in the dependent variable (NIM) for all the five banks is explained by the independent
variables (LQR, LDR, DR and CRR). This value is relatively high enough to conclude that the
model has goodness of fit.
Test of the Hypotheses
Hypothesis One
The first null hypothesis of the study stated as “liquidity management has no effect on the Return
on Assets (ROA) of deposit money banks in Nigeria” was tested using the probability approach.
Since the probability value of the F-statistic (0.0025) is less than the critical value of 0.05, we
reject the null hypothesis and conclude that, liquidity management has significant effect on Return
on Assets (ROA) of deposit money banks in Nigeria.
Hypothesis Two
The second null hypothesis of the study stated as “liquidity management has no effect on the
Return on Equity (ROE) of deposit money banks in Nigeria” was tested using the probability
approach. Since the probability value of the F-statistic (0.0049) is less than the critical value of
0.05, we reject the null hypothesis and conclude that, liquidity management has significant effect
on Return on Equity (ROE) of deposit money banks in Nigeria.
Hypothesis Three
The second null hypothesis of the study stated as “liquidity management has no effect on the Net
Interest Margin (NIM) of deposit money banks in Nigeria” was tested using the probability
approach. Since the probability value of the F-statistic (0.0037) is less than the critical value of
0.05, we reject the null hypothesis and conclude that, liquidity management has significant effect
on Net Interest Margin (NIM) of deposit money banks in Nigeria.
Discussion of Findings
The study found out that, for the pooled effect model all the coefficients of the independent
variables are statistically insignificant at 5% level of significance. This point to the fact that, the
effect of liquidity management (LDR, LDR, DR and CRR) on the financial performance of the
five banks measured in terms of ROA, ROE and NIM may be by chance. Furthermore, the result
of the Hausman Test for the choice between the Random effect and the Fixed effect indicated that,
fixed effect estimation is most appropriate to use in estimating the effect of liquidity management
on return on assets (ROA), return on equity (ROE) and net interest management (NIM) for the five
banks.
Using the fixed effect result in Table 4.3 for all the variables, the study found out that, liquidity
ratio (LQR) and deposit rate (DR) have positive and significant effect financial performance of
deposit money banks as measured by return on assets (ROA), return on equity (ROE) and net
interest margin (NIM). This implies that, the more liquid the banks are, due to high liquidity ratio
and deposit rate, the better their financial power to meet up with their financial obligations. On the
other hand, the higher the cash reserve ratio and loan to deposit ratio, the lesser the ability of the
amount of cash with the banks and the weaker their ability to meet up with their financial
obligations.
Conclusion and Recommendations Arising from the findings, the study concludes that, financial performance of the deposit money
banks in Nigeria can be improved by adjusting the amount of cash which the deposit money banks
keep with the central bank of Nigeria, ensuring increase in the deposit rate, increasing the liquidity
ratio of the deposit money banks and ensuring a reduction in loan-to-deposit ratio of the deposit
money banks.
Based on the findings, the study made the following recommendations:
1. The Central Bank of Nigeria and other banks’ regulatory agents should evolve policies that
aim at enhancing the liquidity ratio of the deposit money banks in Nigeria. This is because;
the financial performance of the deposit money banks will be enhanced if the liquidity ratio
of the banks is improved. This can be done by lowering the capital requirement of the
Deposit Money Banks.
2. The Central Bank of Nigeria and other banks’ regulatory agents should put in place rules
that peg loan-to-deposit ratio to a certain margin beyond which it will be a crime. For
example, a rule could be put up that will place a limit to the percentage of deposits that can
be loaned to customers, let say 30% maximum. This will protect the deposit money banks
from loaning all their funds to the public at the expense of their critical financial
obligations.
3. The Central Bank of Nigeria and other banks’ regulatory agents should evolve policies that
aim at enhancing the liquidity ratio and deposit rate of the money deposit bank on one hand
and reduce loan to deposit ratio and cash reserve ratio on the other hand so as to ensure
increase in net interest margin (NIM) of the deposit money banks in Nigeria.
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