10406

FACTA UNIVERSITATIS
Series: Economics and Organization Vol. 19, No 2, 2022, pp. 139 - 154

https://doi.org/10.22190/FUEO220108011A

Original Scientific Paper

THE IMPACT OF LIQUIDITY MANAGEMENT ON FINANCIAL

PERFORMANCE OF DEPOSIT MONEY BANKS

IN WEST AFRICA1

UDC 658.153:005]:336.713(6)

Mayowa Gabriel Ajao, Emmanuel Nosagie Iyekekpolor

University of Benin, Faculty of Management Sciences,

Department of Banking and Finance, Benin City, Nigeria

ORCID iD: Mayowa Gabriel Ajao https://orcid.org/0000-0002-2063-6888

Emmanuel Nosagie Iyekekpolor N/A

Abstract. This study investigates the effect of liquidity management on financial performance of

deposit money banks (DMBs) by considering the banking sector of selected countries of the West

African Monetary Zone (WAMZ). Aggregate banking sector data for the four selected economies

were used from 1991 to 2020. The panel data regression analysis is also adopted for the empirical

analysis after a statistical evaluation of the datasets are performed. The results from the empirical

analysis reveal that reducing the cash to deposit ratio is the most efficient liquidity management

strategy that may deliver enhanced performance for the DMBs among the sampled countries.

Moreover, it was found that loans and advances to total assets have significant impact on financial

performance of DMBs in West African countries. The ratio of loans and advances to total assets is

limited to return on equity. There is also evidence that the ratio of loans and advances to deposit

does not have a significant effect on financial performance of DMBs in West African countries.

There is need for banks in the selected countries to monitor their deposit mobilization capacity

since it has shown to have major implications on the liquidity management strategies in the

banking sectors for the selected countries.

Key words: liquidity management, financial performance, deposit money bank and panel

regression.

JEL Classification: H62, L25, G21

Received January 08, 2022 / Revised April 21, 2022 / Accepted April 29, 2022

Corresponding author: Mayowa Gabriel Ajao
Department of Banking and Finance, Faculty of Management Sciences, University of Benin, PO Box 16122

Jhon Street, Benin 8007, Nigeria | E-mail: ajao.mayowa@uniben.edu

https://orcid.org/0000-0002-2063-6888
mailto:ajao.mayowa@uniben.edu

140 M. G. AJAO, E.N. IYEKEKPOLOR

1. INTRODUCTION

The banking sector globally is a major player in the financial system of any country,

and has undergone profound innovation and technical changes which have determined

their growth, profitability and competitiveness in recent times. The profitable operations

of banks are essential for the hitch free operation of the financial system of any country,

(Thevaruban, 2017). Increasing profit at the expense of liquidity can cause serious

problems for banks, hence, a trade-off between the two contradictory objectives of

liquidity and profitability needs to be struck. Liquidity management involves the planning

and controlling of the demand and supply of the amount of liquid funds available to meet

the banks’ immediate needs without distorting the banks’ regular operations and financial

performance (Agbada & Osuji, 2013).

The global financial crisis of 2008 made Basel committee for bank supervision, as a

financial body to advocate for the active management of liquidity (Marozva, 2015). In

order to prevent any loss of confidence and trust that may lead to a bank run, banks have

ensured they generate enough profit to meet the financial obligation of their clients and

depositors (Idowu, Essien & Adegboyega, 2017). The relationship between funds

liquidity profitability of DMBs is well documented in literature such as: Agbada and

Osuji (2013), Bassey and Moses (2015), Okaro and Nwakoby (2016), Marozva (2015).

According to World Bank (2006:25) “there is need to undertake deeper analysis of

financial sector performance in sub-Sahara Africa; where performance has not been

impressive, as this would provide more information on commercial banking system in the

sub-region”. Besides, the selected countries (Nigeria, Ghana, Gambia and Sierra Leone)

possess identical financial system frameworks based on the West African Monetary Zone

(WAMZ), they also constitute the Anglophone (English speaking) countries in that sub-

region. Also, the banking sector in these countries in recent past (though at different time)

experienced strategic restructuring (consolidation) of their financial system toward

enhancing their performance and meeting their stakeholders’ needs. The practical

contributions of this study cannot be overemphasized as it harps on the role of liquidity

management on the financial performance of deposit taking institutions with specialized

cross-country data set for the West African sub-region. Most importantly, in measuring

the liquidity management factors, the study includes both loans and advances-related

(Loan and Advances to Deposits [LAD] and Loan and Advances to Total Assets [LNA])

and other liquidity and cash-related factors (Cash to Total deposits [CTD] and Liquid

Assets to total Assets [LTA]) as relevant in the analysis. Thus, the study contributes to

the literature on the liquidity-performance argument by providing an expanded set of

factors in the empirical analysis.

This study seeks to examine how the financial performance of banks in selected West

African countries is influenced by various liquidity management factors using panel data

regression analysis for the empirical analysis, after a statistical evaluation of the datasets

was performed. Hence, the null hypothesis of this study is specified as follows:

Ho: Liquidity management does not have significant impact on the financial

performance of Deposit Money Banks in West Africa.

The other sections of this paper are in the following order: We review extant literatures in

section two while the research methods adopted for the study were discussed in section three.

The presentation and interpretation of data analysis was covered in section four while section

five contains the summary of major findings, recommendations and conclusion.

The Impact of Liquidity Management on Financial Performance of Deposit Money Banks in West Africa141

2. LITERATURE REVIEW

2.1. Conceptual Review

Financial Performance: Financial performance explains the difference between

banks’ operating expenses and income (Bassey & Moses, 2015). It explains how well a

bank manages its assets, liabilities and earned revenues for the financial interest of its

stakeholders: these outcomes are seen in the bank’s financial performance. The overall

evaluation of DMBs in relation to financial performance greatly influences their

continuous growth (Bikker, 2008). Every business needs continuous return on investment

for long term growth and survival as argued by Agbada and Osuji (2013).

Liquidity: Liquidity is the capacity to retire current financial obligations as they come

due. It depends on the capacity of a company’s cash plus near cash assets to offset any

awaiting current liabilities. Bank’s liquidity can be described “as the ability of bank to

offset its current primary cash commitments, as at when due”. For the operational activities

of any deposit money bank to run smoothly, optimum liquidity must be maintained. It

depends on the quantity of cash and near cash (quasi cash) to offset any awaiting current

liabilities. The continuous process to maintain the availability of cash with little or no cost

in line with the cash reserve requirement specified by the federal monetary authority is

known as liquidity management. It is the quantity of liquid cash (or quasi cash) available

to offset short term maturing deposits and contractual obligation. The inflows and

outflows of liquidity in the economy by banks at a desired level without affecting profits

generated in known as liquidity management (Agbada & Osuji 2013). It was argued by

Eljelly (2004) that when current assets and liabilities are planned and controlled in such a

way that it eliminates default risk requirement and reducing investment in these assets is

known as effective liquidity management.

2.2. Liquidity Management and bank Performance

One main goal of DMBs includes maximizing revenue because of their shareholders

as well as the staff and management of the organization because profit maximization is a

primary objective of any bank. The financial sector is the pillar and bedrock of any viable

country, and this is the reason why bank failure should not be an option. To avoid bank

failure, liquidity must be maximizing to meet current liabilities as they come due. A bank

that over maximize liquidity, sacrifices liquidity and reduces the profit of the bank

because the idle fund cannot generate investment returns.

Liquidity is one of the drivers of deposit money banks profitability, hence must be

maintained to ensure the financial health of banks. A bank that maintains a high level of

liquidity provides sufficient funds to lend, improve on the return on interest generated

from operations as well as financial performance. But poor liquidity planning and control

reduce the financial performance of DMBs. The overall financial performance of these

DMBs is important for the smooth operation of the financial system of any country.

Therefore, liquidity management is important for a bank to sustain steady cash inflow so

as to boost its financial performance for fair shareholders returns.

Bank customers are majorly interested in banks’ ability to meet their primary

responsibility of paying deposits whenever withdrawal is made, which is usually done

within short or no notice (Bassey & Moses, 2015). Effective liquidity management helps

the bank to have more operational funds in the short-run to satisfy the needs of its

142 M. G. AJAO, E.N. IYEKEKPOLOR

depositors, other creditors and loan customers, thereby maintaining public confidence and

boosting economic activities. The concept of liquidity management has not been treated

with kid gloves by banks management because it helps to determine the solvency or

insolvency of the organization.

Figures in accounting are irrelevant except when they convey some important financial

information, hence for liquidity management statistics to make sense to the financial

analyst, it must be related to other variables; in this case banks quantitative performance can

be used to make qualitative judgment. Bank liquidity is commonly estimated by the current

ratio, which is the ratio between the balance sheet current assets and liabilities. But excess

liquidity is not good for the financial health of any bank because idle assets do not earn any

return. When liquidity is related to financial performance, they are inversely related,

meaning that as liquidity increases, there will be reduction in financial performance. Hence

the reason to sustain a maximum level of adequate cash that will maximize the profitability

of the bank.

2.3. Empirical Review

In Nigeria, Bassey and Moses (2015) investigated the relationship between liquidity

and performance of DMBs from 2010-2012. The panel data was estimated with OLS

techniques and the findings suggested that a statistically significant relationship exists

amongst liquidity ratio and performance. In Ghana, Nkegbe and Ustarz (2015) examined

determinants of banks’ profitability in Ghana from the period 2000 – 2010 using trend

analysis with cross sectional data. The result revealed an inverse trend in banks

profitability during the period covered by the study.

Song’e (2015) reveals a direct links between financial performance and liquidity

variables, when a study was carried out to examine the linkage between liquidity and

profitability of deposit taking Saccos in Nairobi county between the period 2010 – 2014.

The secondary data collected from 27 deposit Saccos was analysed with regression

analysis. In Nigeria, Duruechi, Ojiegbe and Otiwu (2016) measured the effectiveness of

liquidity management and banks performance from 1999-2014. Time series data was

analysed with some preliminary tests and diagnostic tests. The result revealed the

presence of dual and long-term relationship between liquidity management and banks

performance. Okaro and Nwakoby (2016) assessed how DMBs are affected by liquidity

management from (2000-2015). The secondary data was analysed with the OLS

regression and the result revealed that an increase in liquidity ratio leads to decrease in

banks’ profitability. DMBs should adopt other measures of meeting depositor’s demand

at the expense of holding excess liquid cash. The shiftability theory and anticipated

income theory are recommended here. Salim and Mohamed (2016) investigated the

impact of liquidity management on financial performance in Omani banking sector from

2010-2014. The study concluded that a significant relationship exists between the

measures of liquidity and bank’s ROA and ROE.

Thevaruban (2017) investigated the factors influencing banks profitability in Sri

Lanka from the year 2012 to 2016. The study employed multiple regression analysis and

Pearson correlation test to analyse the secondary data. The findings of the study

established a significant relationship between liquidity and profitability of commercial

banks in Sri Lanka. Hence higher liquidity in DMBs enhances the availability of adequate

funds to generate loans, thereby leading to higher financial performance. Hasanovic and

The Impact of Liquidity Management on Financial Performance of Deposit Money Banks in West Africa143

Latic (2017) identified determinants of excess liquidity in Bosnia & Herzegovina (B&H)

banking sector from 2006 – 2015 using the Generalised Method of Moment (GMM). The

results suggested bank non-performance loans as an important factor of excess liquidity

amongst internal factors. Mucheru, Shukla and Kibachia (2017) revealed a positive

relationship between cash management and financial performance of commercial banks

when they determined the effect of liquidity management on the financial performance of

commercial banks in Rwanda from 2014 to 2016. The secondary data was analysed with

multiple regression and it was concluded that excess liquidity will lead to reduction in

banks’ income and profit.

In Nigeria, Edem (2017) studied liquidity management and profitability of DMBs

between the period 1986 and2011. The 24 DMBs operating in the country were the sample

size, using linear regression. The analyzed results revealed a direct relationship between

liquidity management and ROA, and a significant relationship between liquidity management

and performance of DMBs, hence optimum liquidity should be kept to maximize returns.

Idowu, Essien and Adegboyega (2017) examined liquidity management and banks

performance in Nigeria between the period 2006 and 2015, using a sample size of four

DMBs. The study analyzed the data with Pearson correlation coefficient with ROA and

ROE as measures of performance and liquidity ratios as explanatory variables. The

findings showed that bank liquidity has significant relationship on ROE and ROA.

Onyekwelu, Chukwuani and Onyeka (2016) appraised the effect of liquidity management

on financial performance of deposit money banks in Nigeria from 2007 to 2016. Using

multiple regression, the results show direct and significant relationship between liquidity

and financial performance. Shah, Khan, Shah and Tahir (2018) investigated determinants

of banks liquidity in Pakistan from 2007-2016.The sample size was 23 banks operating in the

country. Panel regression technique was used to estimate the relevant data. The findings

indicate that an insignificant relationship exists between liquidity and profitability. Bayoud,

Sifouh, and Chemlal (2018) examined the factors of financial Moroccan banks performance

between the period 2004 and2016. The fully modified ordinary least squares (FMOLS)

method was used to analyze the co-integrated panel data. The findings show that a set of

internal variables explain the financial performance of banks.

Wuave, Yua and Yua (2020) established that positive and significant relationship exists

between liquidity and profitability of DMBs in Nigeria from 2010 to 2018 when data was

analyzed with panel regression analysis. Adewusi and Adeleke (2020) concluded that

banks’ performance is significantly influenced by liquidity risk management in Nigeria

from 2013 to 2017 when pooled regression was used as analytical technique. Sathyamoorthi,

Mapharing and Dzimiri (2020) studied the effect of liquidity management on the profitability

of banks in Botswana from 2011 to 2019. The correlation and regression analyses show that

liquidity management has significant positive influence on the profitability of Botswana

banks.

Hacini Boulenfad and Dahou (2021) study revealed that liquidity risk management

has negative impact on Saudi Arabian banks’ performance (ROE) from 2002 to 2019

with the use of panel data analysis technique. Dahiyat, Weshah and Aldahiyate (2021)

employed ROA and EPS as performance variables in their study of the impact of liquidity

on the financial performance of Jordanian manufacturing firms from 2010 to 2019. The

study results show a statistically significant impact of liquidity on financial performance.

144 M. G. AJAO, E.N. IYEKEKPOLOR

From these empirical reviews, it is evident that most studies such as Bassey and Moses

(2015), Duruechi et al (2016), Thevaruban (2017), Onyekwelu et al (2016), Sathyamoorthi

et al (2020) and Adewusi et al (2020) have examined liquidity management and financial

performance of DMBs of an individual country. However, to the best of the researchers’

knowledge, no study has been done on a group of West African countries. Hence this study

differs from those above in that it specifically assesses the effect of liquidity management

on financial performance of DMBs in selected West African Countries for the period 1991

to 2020.

3. METHODOLOGY

The study adopts the casual and historical research designs. Its causal nature is hinged

on how to explore the cause and effect linkage between the liquidity management and

profitability as specified in the models over a time scope of 1991-2020. The population

and sample of this study constitute all DMBs in the Gambia, Ghana, Nigeria and Sierra

Leone from 1991-2020. The main source of the data was annual financial reports submitted

to the selected countries Central banks, hence aggregate and countrywide data for each

country were adopted for this study. The sample consists of all DMBs (Gambia [13], Ghana

[24], Nigeria [20] and Sierra Leone [12]) that had been in operation from 1991 to 2020.

3.1. Model Specification

In order to provide an analytical basis to test the empirical validation, this study will

adopt one of the banks liquidity and performance model by Bassey and Moses (2015).

The mathematical form of the mode is given as:

ROEit =  (CRRit, LTAit, LADit, CTDit, LNAit) (1)

Where:

ROE, = Return on equity of banks in period t

CRR, = Current ratio i.e. current asset to current liability

LTA, = Liquid assets to total assets ratio

LAD, = Loans and advances to deposits ratio

CTD, = Cash to total deposit ration

LNA, = Loan and advances to total assets ratio

However, due to the nature of DMBs whose stock in trade is cash deposited by their

customers, CRR and CTD in the model above are essentially one and the same type of

liquidity measure. Thus CRR will be removed from the model. Also, to take cognizance

of the heterogeneous nature of the banks to be included in this study, Bank size (BSIZE)

(measure as shareholders’ fund) will introduced into equation (1) above as control

variable. From the foregoing, the mathematical model for this study is specified as:

ROE = (CTD, LTA, LAD, LNA and BSZ) (2)

However, for the purpose of comparison, this study adopts two distinct measures of

profitability “Return on Equity (ROE) and Return on Asset (ROA)”. Hence, equation 2 is

specified in its econometrics form using the two performance measures as:

The Impact of Liquidity Management on Financial Performance of Deposit Money Banks in West Africa145

ROEit = 0 + 1CTDit + 2LTAit + 3LADit + 4LNAit + 5BSZit + it. (3)

ROAit = 0 + β1CTDit + β2LTAit + β3LADit + β4LNAit + β5BSZit + Uit (4)

0, 0 =constant term,

1 - 5 and β1 – β5 = coefficients to be estimated.

 and U = error term.

i, t = bank i, time t.

The a priori expectation are given as: 1 & β1 < 0 ; 2 < 0 ; 3 & β3 > 0 ; 4 & β4 > 0

and 5 & 5 & β5 > 0.

3.2. Data Analysis Techniques

The analytical technique that was applied to estimate models (3 and 4) is the panel

regression to minimize the effect of aggregation bias and estimate both time series and

cross sectional data. Other tests that were conducted are cross-section dependence test,

unit root and co-integration tests for stationarity of data.

4. DATA ANALYSIS AND INTERPRETATION

4.1. Descriptive Statistics

Table 1 Descriptive Statistics

Variable Mean Max. Min. Std. Dev. Skewness Kurt. J-B Prob

ROA 3.25 8.71 -1.04 2.03 0.43 2.89 3.73 0.15

ROE 30.86 93.82 -8.67 20.35 0.77 3.21 10.14 0.01

LTA 6.11 12.43 0.99 3.14 -0.08 1.87 6.51 0.04

LNA 21.02 65.98 2.14 14.80 0.71 2.38 12.11 0.00

LAD 52.13 88.98 20.94 16.22 0.14 1.99 5.46 0.07

CTD 38.35 87.86 1.74 25.52 0.09 2.06 4.61 0.10

BSIZE 19.81 43.40 2.50 10.55 0.23 2.14 4.75 0.09

Source: Authors’ computation E-view 10.0,2021.

The summary statistics for the datasets are also reported in Table 1. Average ROA is 3.25
percent while average ROE is 30.86 percent. This implies that there is almost a ten-fold size
of ROE over that of ROA among the banks in the WAMZ sub-region. Average liquidity ratio
(LTA) is 6.11 percent, suggesting that only about 6 percent of assets among the banks is
liquid. This is not an impressive level, and the regulators need to encourage the banking
system to improve on liquidity in terms of asset size. The other measure of direct liquidity
(CTD) however has an average ratio of 38.35, indicating that the CTD is high. The LDR is
over 52 percent, which puts the banks at a higher risk when there are defaults. The standard
deviations of the variables are low, indicating the reported mean values are all representative
of the banking systems of the selected countries. Figure 1 shows the correlation chart between
the two dependent variables. It shows a very steep positive slope between the variables,
indicating that profitability among the banks for the countries have similar characteristics in
terms of direction of movement. When banks’ ROA is rising, the ROE is also simultaneously
increasing. The shape of the chart appears to be a positive exponential curve, indicating that

146 M. G. AJAO, E.N. IYEKEKPOLOR

higher levels of increases in ROA are linked to faster increases in ROE and vice versa for the
banking systems of the four countries.

GambiaGambia

Gambia

Gambia
Gambia

Gambia

GambiaGambia

Gambia

Gambia
Gambia
Gambia

Gambia

Gambia
Gambia

Gambia

GambiaGambia

Gambia

Gambia
Gambia

Gambia

Gambia Ghana

Ghana

Ghana
Ghana

Ghana

Ghana
Ghana

Ghana

Ghana
Ghana

GhanaGhana

Ghana

Ghana
Ghana

Ghana

Ghana
Ghana

GhanaGhanaNigeria
Nigeria

Nigeria

Nigeria
Nigeria

Nigeria

Nigeria
Nigeria

NigeriaNigeria

Nigeria

NigeriaNigeria
Nigeria

Nigeria

Nigeria
NigeriaNigeriaNigeria

Nigeria

NigeriaNigeria

Sierra Leone

Sierra LeoneSierra Leone

Sierra Leone

Sierra Leone
Sierra Leone

Sierra Leone

Sierra Leone
Sierra Leone

Sierra LeoneSierra Leone

Sierra Leone

Sierra LeoneSierra LeoneSierra Leone

Sierra Leone
Sierra LeoneSierra Leone

Sierra Leone

0
20

40
60

80
10

0
R

O
E

-2 0 2 4 6 8
ROA

Fig. 1 Relationship between ROA and ROE
Source: Authors’ computations, Eview 10.0, 2021.

4.2. Correlation Analysis

Table 2 Correlation Matrix

Variable LTA LNA LAD CTD

LNA -0.633

(0.000)

LAD -0.519 0.395

(0.000) (0.000)

CTD 0.727 -0.445 -0.594

(0.000) (0.000) (0.000)

BSIZE 0.514 -0.020 -0.397 0.437
(0.000) (0.842) (0.000) (0.000)

Source: Authors’ computation, E-view 10.0, 2021.

From the correlation matrix in Table 2, it is seen that the relationships among the
explanatory variables are strong in either direction. Essentially, none of the correlation
coefficients is too large to elicit multicollinearity problems in the estimation of the models in
the study.

A strong positive correlation is noted between LTA and BSIZE, suggesting that the level
of liquidity in the banking systems for the countries is positively related with cash to deposit
ratio and the bank size. Thus, banks with larger deposits are more liquid and bigger banks also
exhibit higher levels of liquidity. On the other hand, LTA is negatively related to LNA and
LAD, indicating that banks with bigger loans and advances ratios tend to be limited in terms
of liquidity. Thus, the analysis shows that deposits and size matter for better liquidity systems,
while loans tend to depreciate liquidity within the banks. This calls for better management of
deposits and loans in order to ensure that liquidity is maintained among the banks in the
countries. There is also a positive correlation between LNA and LAD and between bank size
and CTD. This is to be expected since loan size and deposits appear to be attractive within the
banking sectors of the selected countries.

The Impact of Liquidity Management on Financial Performance of Deposit Money Banks in West Africa147

4.3. Stationarity Tests

Table 3 Stationarity Tests Results

Variable

Homogenous Unit

Root Process
Heterogeneous Unit Root Process

Remarks
Intercept and Trend

LLC IPS ADF-Fisher

I(0) I(1) I(0) I(1) I(0) I(1)

ROA -0.742 -6.914 -1.330 -8.030 13.471 66.67 Stationary

ROE -0.379 -3.514 -0.504 -5.419 8.576 43.86 Stationary

CTD -0.899 -6.282 -0.654 -6.702 1.566 54.53 Stationary

LAD -0.242 -6.616 -0.657 -6.453 2.525 52.70 Stationary

LNA 0.993 -6.228 1.352 -6.890 7.431 57.25 Stationary

LTA 0.626 -5.516 0.049 -7.239 6.763 59.95 Stationary

BSIZE -0.235 -5.708 0.612 -5.487 5.960 44.53 Stationary

Note: ** and * indicate significant at 1% and 5 % levels respectively;

IPS = Im, Pesaran & Shin; LLC = Levin, Lin & Chu

Source: Estimated by the Authors

The stationarity properties of the data were examined using three distinct tests “Levin,

Lin and Chu (LLC), Im, Pesaran and Shin and the Augmented Dickey-Fuller tests”. This

is to enable us identify and understand the homogenous and heterogeneous characteristics

of the cross sectional data. The tests results are presented in Table 3

4.4. Cointegration Test

Table 4 Panel Cointegration Tests Results

ROA Equation

Within-dimension Between-dimension Kao

Unweighted Weighted

Statistic Prob. Statistic Prob. Statistic Prob -3.37 (0.00)

Panel v-Statistic -6.59 0.16 -9.57 0.12

Panel rho-Statistic 10.34 0.033 10.439 0.01 13.94 0.03

Panel PP-Statistic -20.89 0 -29.44 0 -42.5 0

Panel ADF-Statistic -18.35 0 -23.38 0 -47.77 0

ROE Equation

Within-dimension Between-dimension Kao

Unweighted Weighted -5.04 (0.00)

Statistic Prob. Statistic Prob. Statistic Prob

Panel v-Statistic -6.6 0.06 -9.95 0.16

Panel rho-Statistic 9.86 0.03 10.16 0.04 13.73 0.04

Panel PP-Statistic -23.1 0 -33.07 0 -44.41 0

Panel ADF-Statistic -28.09 0 -35.48 0 -48.89 0

Source: Authors’ computations, Eview 10.0, 2021.

148 M. G. AJAO, E.N. IYEKEKPOLOR

Table 4 displays the results of the Pedroni and Kao panel co-integration tests. The

coefficients of the IPS and Augmented Dickey Fuller test statistics are significant at the 5

percent level which is also supported by Kao panel cointegration test. The Kao residual

cointegration test shown in Table 4 shows that the null hypothesis of no co-integration

can be rejected for each of the equations. Thus, the cointegration tests results show that

there are strong long run relationships among the variables in the study. The panel

estimation framework can therefore be employed in the empirical analysis.

Table 5 Hausman Test for Cross-Section Random Effects

Model Chi-Sq. Statistic Chi-Sq. d.f. Prob.

ROA 5.34 6 0.5006

ROE 7.89 6 0.667

Source: Authors’ computations, Eview 10.0, 2021

For the traditional panel data analysis procedure there is need to select between the

fixed effects or random effects models as the best representation of the relationships.

From the test results in Table 5, the Chi-Square statistic is not significant at any level,

thereby rejecting the fixed-effects estimation technique. This implies we adopt the Random

Effects for the estimation.

Table 6 Liquidity Management and Bank Performance

Variable
Dep variable = ROA Dep. variable = ROE

Coefficient t-Statistic Prob. Coefficient t-Statistic Prob.

C 4.578 4.135 0.000 37.258 3.725 0.000

LTA 0.139 2.357 0.037 1.002 1.025 0.308

CTD 0.016 2.048 0.043 0.301 3.230 0.002

LAD -0.015 -1.265 0.209 0.032 0.263 0.793

LNA -0.014 -0.866 0.389 0.190 3.302 0.001

BSIZE -0.088 -3.993 0.000 -1.012 -4.981 0.000

Adj R-sq 0.230 0.348

F-stat 8.119 11.654

Source: Authors’ computations, Eview 10.0, 2021

The random effect estimates for examining how financial performance are impacted

by liquidity management of the banking systems among WAMZ countries is analyzed in

this section. In the results shown in Table 6, the adjusted R-squared values are 0.23 and

0.348, indicating that a significant proportion of the dependent variables were effectively

explained in the models. More importantly, the significant F-test indicates that the dependent

variable was significantly related to all independent variables and that bank performances

are influenced by liquidity dynamics.

The relevance and effectiveness of each of the explanatory variables in terms of

influencing changes in performance indicators are evaluated by considering the

coefficients of the explanatory variables in terms of signs and significance. In the results,

the coefficient of LTA is significant at the 5 percent level for the ROA equation but fails

the significance test in the ROE equation. This indicates that liquidity ratio only matters

for operational efficiency of the banks but not in explaining the direction of market

The Impact of Liquidity Management on Financial Performance of Deposit Money Banks in West Africa149

outcomes of the banks. The coefficient of LTA in the ROA equation is positive and

shows that increase in the liquidity of the banks in relation to assets significantly boosts

return on asset of the banks. Thus, banks are essentially better off when they allow more

of their asset base to be more liquid. This may effectively promote the capacity of banks to

lend and perform core intermediary functions in the financial system. The coefficient of the

other direct liquidity variable (CTD) is significant in both the ROA and ROE equations,

suggesting that rising cash to deposit ratios effectively influences overall performances of the

banking system among WAMZ countries. For the loan related variables, the results in Table 6

show that the coefficient of LNA (loan to asset ratio) fails the significance test in the ROA

equation but passes the test in the ROE equation. This indicates that when loans increase in

relation to assets in the banks, the return on equity improves. Thus, shareholders’ funds are

better managed by expanding loans in the banks in relation to assets. On the other hand, loans

to deposit ratio (LAD) fails the significance test for both ROA and ROE equations. This

implies that loans do not expand financial performance among WAMZ banking sectors

over time. The coefficient of bank size is negative in both equations and suggests that

bigger banks tend to perform less among WAMZ countries.

Test of Hypothesis: From the results in Table 6, the coefficient of the direct liquidity

variable Cash to Total Deposit (CTD) is significant in both the ROA and ROE equations. We

can sufficiently reject the null hypothesis that liquidity management does have significant

impact on the financial performance of Deposit Money Banks in West Africa.

Diagnostic Tests: The post-estimation tests are carried out to examine the significance of

estimations conducted in this study. The “Variance Inflation Factor (VIF)” test was conducted

to ascertain the existence of a linear relationship among the variables. The results of the VIF

are reported in Table 7. Theoretically, “Variance inflation factors (VIF)” ranges from 1

upwards. The results from Table 7 show that all the variables have variance inflation ratio

of less than 10 for each of the countries. This is a critical condition for observing the

absence of multicollinearity in the estimates. The VIF results complement those reported in

the correlation matrix in Table II and suggest absence of multicollinearity in the models of

the study.

Table 7 Variance Inflation Factor (VIF) Test for MultiCollinearity

Statistics
ROA ROE LNA LTA

Max |z| Prob. Max |z| Prob. Max |z| Prob. Max |z| Prob.

ROA equation

Gambia 2.56 0.04 1.96 0.19 1.35 0.54 1.18 0.66

Ghana 11.3 0.00 2.02 0.16 2.34 0.07 3.30 0.00

Nigeria 1.00 0.79 1.54 0.41 0.62 0.95 6.25 0.00

Sierra Leone 0.25 1.00 2.44 0.06 1.76 0.28 3.50 0.00

ROE equation

Gambia 1.32 0.56 1.2 0.65 1.35 0.54 4.03 0.01

Ghana 1.83 0.24 1.84 0.24 2.01 0.17 1.08 0.32

Nigeria 1.75 0.29 0.96 0.81 0.9 0.84 2.99 0.19

Sierra Leone 2.19 0.11 1.16 0.68 1.51 0.43 0.93 0.33

* Probability approximation using studentized maximum modulus

with parameter value 14 and infinite degrees of freedom

Source: Authors’ computations, Eview 10.0, 2021

150 M. G. AJAO, E.N. IYEKEKPOLOR

-4 -3 -2 -1 0 1 2 3 4 5

Series: Standardized Residuals

Sample 1990 2019

Observations 120

Mean -1.72e-16

Median 0.064513

Maximum 5.012296

Minimum -4.425709

Std. Dev. 1.772098

Skewness 0.252282

Kurtosis 3.628799

Jarque-Bera 3.249869

Probability 0.196925

Fig. 2 Normality test for ROA equation

-40 -30 -20 -10 0 10 20 30 40 50

Series: Standardized Residuals

Sample 1990 2019

Observations 101

Mean 2.63e-15

Median 0.389718

Maximum 51.46479

Minimum -42.86992

Std. Dev. 15.86536

Skewness 0.296985

Kurtosis 3.710327

Jarque-Bera 3.608077

Probability 0.164633

Fig. 3 Normality test for ROE equation

The tests of normality for the probability function of the estimated models are also

conducted. Figures 2 and 3 show the histogram plot of the errors or residuals in the estimates,

which is used to measure the probability density of the residual estimates. Clearly, the charts

show that the distribution of the estimate errors or residuals is non-normal, given the bell-like

shape of the diagram. This is also demonstrated by the J-B statistic values of 3.25 (pr = 0.196)

for ROA and 3.61 (pr = 0.164) for ROE which both fail the significance tests. This therefore

implies that even though the data sets were not normally distributed, the estimated equations

pass the normality conditions for the residuals. This further improves on the robustness of the

estimates in the study.

5.1. Findings

The results found in the empirical analysis of this study provide basic background for
the evaluation of the roles of liquidity management patterns on bank performance. First,
the study has shown that liquidity management of banks, in terms of cash management,
generally plays strong roles in explaining performance of the banking sectors among the
selected West African countries. Apparently, efficient liquidity management patterns are
likely to yield positive effects that both improves overall efficiency of the banks and also

The Impact of Liquidity Management on Financial Performance of Deposit Money Banks in West Africa151

boosts long term performance. This line of findings is also shown in previous studies by
Adewusi et al., (2020) and Wuave et al., (2020).

The role of liquidity in the performance of banks among the selected countries in the
study has also been shown to essentially vary on the basis of the performance term.
Although previous studies have indicated that liquidity matters, in general terms, for the
overall performance of banks among several African and developing country economies
(Ferrouhi, 2014; Song’e, 2015), our study has demonstrated that this may not fully be the
case for the West African countries. The findings from the study indicate that liquidity ratio
does not explain the direction of market outcomes of the banks. Thus, liquidity of banks for
the WAMZ economies is more related to the immediate efficiency of banking activities.

Moreover, the cash to deposit ratio was found to significantly impact on return on equity
among the West African DMBs. Apparently, cash management that favours more liquid
holdings against the pattern of deposits presents more facilities for the banks to maintain
more efficient banking functions. Rising cash holdings to deposits ratio aids in the servicing
of net withdrawals from customer as well as facilitating other activities like customers
drawing from their deposit (checking and savings) accounts (Biswal & Gopalakrishna,
2014; Goel & Kumar, 2016). This outcome confirms the work of Bassey & Moses (2015)
and Agbada & Osuji (2013) who found that cash-dominated strategies significantly
influence banks’ profitability. From the empirical analysis, there is evidence that the ratio of
liquid assets to total assets does not have any meaningful effect on ROE. This indicates that
overload of cash in the system tends to weaken operational efficiency in the short run
among the banks. Indeed, indicating that increasing cash reserves in the short run will limit
market performance of the banks in WAMZ (Biswal & Gopalakrishna, 2014). The results are
also in agreement with previous findings concerning the role of liquid asset management in
the banking system (Kagoyire & Shukla, 2016; Wadike, Abuba & Wokoma, 2017).

The study also found that the ratio of loans and advances to deposit does not have a

significant impact on financial performance of DMBs in West Africa. This is based on

the insignificant effects of loans and advances to deposit ratio on return on equity in the

study. This implies that the lending habits of the banks are essentially not efficient in

terms of promoting their profit earnings. In this direction, loan strategies that focus on

managing customers’ deposits will not yield ultimate performance outcomes. This

outcome may be largely linked to the financial climate in many developing economies,

where credit management involves more unique strategies for banks that seek to excel

(Alobari et al., 2018; Olaoye & Fajuyagbe, 2020). The ratio of loans and advances to

total assets has significant impact on the financial performance of DMBs in West Africa.

The findings in this direction support the findings of Sathyamoorthi et al. (2020) with

significant and positive effect on ROE.
Finally, there is also evidence in the study that shareholders’ funds are better managed by

expanding loans in the banks in relation to assets. This outcome is feasible given that loan
activitiy is a major segment of banking activities, among the banking systems of the WAMZ
economies (Bassey & Moses, 2015; Nkegbe & Ustarz, 2015). The focus of shareholders is
on building assets and critical aspects of the financial management of the firms. Hence, they
would always pursue activities that improve on loan management in terms of minimizing
loan default and weak application of the loan systems for the banks. Hence, return on equity,
which is related to the stimulation of shareholder’s funds is more related to loan
management strategies that are efficiently targeted in line with overall liquidity management
of the banks. This finding is also in line with the outcomes of previous studies (Taiwo et al.,
2017; Tuffour, Owusu & Ofori-Boateng, 2018; Kafidipe et al., 2021).

152 M. G. AJAO, E.N. IYEKEKPOLOR

5.2. Recommendations and Conclusion

The results obtained in the study provide effective background for policy directions.

These policy recommendations from the study therefore include:

The Central Monetary Authority should seek to maintain cash balances that are

optimal for the banking system. The action of the monetary authority to always tend to

push the banks into more lending systems, need to be checked given that our study has

shown that overt loan activities may hamper bank performance in the long run among the

West African economies. In meeting short term cash requirements of customers, banks in

the sub-region need to evolve innovative measures that do not put pressure on the cash

reserve systems. For instance, rather than holding excessive liquidity, commercial banks

may employ the system of borrowing and discounting bills. Finally, management of the

surplus funds (usually in form of cash) needs to improve and focus more on investing in

short-term instruments on a seasonal basis. Furthermore, when funding, sourcing by

banks is concentrated in the wholesale markets, then the risks of liquidity shocks tend to

be more concentrated both for individual banks and within the banking system. In the

same vein, “heavy dependence on inter banking funding tends to expose banks to

unmanageable risks once confidence weakens”.

This study has shown that indeed, liquidity management that relates to loan and cash

management is critical for ensuring improvements in performance for the banks in the

sampled countries. It is shown that loans and other liquidity management is more strategic and

efficient when deposits of the banks are taken into cognizance. This is because the managers

realize that the interactions among lending, cash positions portend much risk to the banking

sector which require constant consideration. It is also clear from the study that risks that

require efficient liquidity management among the banking sectors of the selected countries are

relatively high and appear to be rising. Essentially, managing risk in banks is a crucial issue

which requires expertise knowledge with strategic liquidity management solutions that can

minimize its ravaging effects on the banking operations. There is also the need for banks in

the selected countries to monitor their deposit mobilization capacity and asset base since these

elements have been shown to have major implications on the liquidity management strategies

in the banking sectors of the selected countries. The panel regression analysis used in this
study is not without its limitations and one of such limitations comes from the possible over

parameterization of the regressors which could weaken the significance of the model.

However, this was addressed by using suitable lag length criteria to choose appropriate lag

length for the study. But for further research in this topic, the use of advanced econometric

methods and expanding the scope of the study to cover the Sub-Saharan African countries is

recommended.

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UTICAJ UPRAVLJANJA LIKVIDNOŠĆU NA FINANSIJSKE

PERFORMANSE DEPOZITNIH BANAKA U ZAPADNOJ AFRICI

Ovaj rad istražuje efekte upravljanja likvidnošću na finansijske performanse depozitnih banaka

novca baveći se bankovnim sektorom odabranih zemalja u okviru monetarne zone zapadne Afrike

(WAMZ). Uzeti su zbirni podaci bankovnog sektora za četiri odabrane zamlje za period od 1991 do

2020. Regresiona analiza panel podataka je takođe usvojena za empirijsku analizu nakon što je

izvršena statistička procena skupova podataka. Rezultati empirijske analize oktrivaju da je

smanjenje razmere gotovine i depozita najefikasnija strategija upravljanja likvidnošću koja može

da dovede do poboljšanja performansi banaka u proučavanim zemljama. Štaviše, ustanovljeno je

da krediti i plasmani u ukupnu aktivu imaju značajnog uticaja na finansijske performanse banaka u

Zapadnoafričkim zemljama. Odnos kredita i avansa u ukupnoj aktivi ograničen je na prinos na

kapital. Ima dokaza i da odnos kredita i avansa za deposit nema značajnog uticaja na finansijske

performanse depozitnih banaka u Zapadnoafričkim zemljama. Postoji potreba da banke u

odabranim zemljama prate svoje kapacitete za mobilizaciju depozita jer se pokazalo da to ima

značajnog uticaja na strategije upravljanja likvidnošću u bankovnom sektoru odabranih zemalja.

Ključne reči: upravljanje likvidnošću, finansijske performanse, depozitne banke novca i panel regresija

https://journalofbusiness.org/index.php/%0bGJMBR/article/view/2368
https://journalofbusiness.org/index.php/%0bGJMBR/article/view/2368
https://dx.doi.org/10.3923/ijaef.2018.1.8