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456

LIQUIDITY AND PERFORMANCE OF BANKS LISTED ON THE

GHANA STOCK EXCHANGE

Magnus Kofi Quansah, Oscar Agyemang Opoku

University of Cape Coast, Ghana

Email: magn[email protected], [email protected].edu

Abstract

The aim of the study was to examine the effect of liquidity on the performance of banks listed on the Ghana

Stock Exchange. The study employed annual panel data of 8 out of the 9 banks listed on the Ghana Stock

Exchange. The random effect model was used with the aid of Hausman test. The data used for the study spans

from 2015 to 2019. The study found among other things that 34.8% variation in the return on asset of banks

listed on the Ghana Stock Exchange can be explained by sales growth rate, firm size and liquidity. The study

revealed that liquidity and sales growth do not have any statistically significant effect on the profitability of

banks listed on the Ghana Stock Exchange. However, the size of a bank in terms of its total assets has a

significant effect on the profitability of banks listed on the Ghana Stock Exchange. The study finds that

liquidity and sales growth do not have any statistically significant effect on the profitability of banks listed

on the Ghana Stock Exchange, the size of a bank has statistically significant effect on the return on assets of

banks listed on the Ghana Stock Exchange. It was recommended that banks listed on the GSE must work on

reducing their interest expense to enhance their liquidity positions. Managers of banks listed on the GSE must

work at increasing their asset base as there exists a significant relationship between size and profitability

Keywords

: liquidity; performance; banks listed;

INTRODUCTION

There can be no undermining of the role of the financial sector in economic life. Banks'

role in financial intermediation means that funds are channelled from surplus units to deficit

units. The Bank is a financial intermediary that provides excess funds for parties which need

funding at the specified time (Purbaningsih & Fatimah, 2014). "Acceptable financial

intermediation needs a decisive approach from the management of the banks to profitability

and liquidity, which are two conflicting goals of banks," (Imegi & Ogbeide, 2017).

The Bank has an undeniable role to play in boosting the national economy, as banks are

collectors of surplus funds and credit providers, deficit units, reliable and active community

savings and promote payments across all economic sectors (Malayu & Hasibuan, 2005: 3).

Trust is a very important factor in the achievement of this important role in financial

intermediation.

In 2017, the financial sector crisis led to the collapse of Ghana's banks, as well as the

merger of other financial institutions. Financial sector investors in Ghana are worried that this

all-important element anchors a strong financial industry. Liquidity is one of the most

significant objectives in managing working capital and the key goal of maximizing sales and

financial output of the business. After the 2007-2008 global financial crisis (also known as

GFC), it has gained the forefront of banking and finance. This is due to an abundance of

liquidity from the high risk-taking in United States (US) banks which led to the GFC of 2007-

Injuruty: Interdiciplinary Journal and Humanity

Volume 2, Number 5, Mei 2023

e-ISSN: 2963-4113 and p-ISSN: 2963-3397

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2008. A liquidity danger and/or deteriorating liquidity situation means that companies are

deprived of the liquidity required by the Du Pont equation to handle operations that form a key

part of profitability.

Liquidity has survived over time since the era of the “Great Depression in the 1930s” till

date. This period renders liquidity management the difficulty task from its source, uses, risk

and its effect on firm performance (Nyamador, 2021). Liquidity is the bank's capacity to fund

asset rises and to satisfy debts, without inadmissible losses (BIS, 2008). The IFRS 2005

indicates that liquidity risks are the possibility of an organization experiencing difficulties in

fulfilling financial liabilities commitments. GARP (2015) believed that liquidity was basically

a short-term issue triggered by unforeseen short-term liabilities and long-term liabilities that

had negative impacts on corporate financial performance. The market power theory aids in

examining the performance of banks. It postulated that some banks make higher profit because

they are more effective than others and especially in minimizing risks (Olweny & Shipho,

2011).

(Ejike & Agha, 2018) perceive liquidity management as managing the risk of resources

of a firm to have efficient liquidity equity with the profit of the firm. In their work, Elloumi

and Gueyie (2001) concluded that failure to fulfil contractual debt obligations because of bad

income is the main factor in identifying liquidity companies. This has a negative influence on

the financial condition of businesses. Halling & Hayden, (2006) describe the need to define the

liquidity risk of an organization. The liquidity requirements of a company and the sources of

liquidity at its disposal greatly depend on its on-and off-balance sheet commitments

performance in its market and product mix, balance sheet structure and cash flow profile.

Any negative developments in the banking field would have a negative effect on all other

sectors of the economy, the opposite is also true, because banks are interrelated with other

economic sectors. This makes the industry a very significant contributor to every economy's

progress. This makes it important to research how events in this field are fully understood. A

search in the area of liquidity on performance shows less work done in the banking and

financial sector. There is a significant investigative void which must be filled. The literature on

the impact of liquidity on banks was reviewed systematically in (Mairafi et al., 2018). The

authors observed that bank liquidity has a major effect upon bank outcomes like banks'

profitability, banks' risk-taking behaviour.

Mairafi et al., (2018) considers, however, that all these empirical data were primarily

distorted in the growth of the market. This is a strong argument for emerging market studies in

the field. In this report, the influence of liquidity on banks' success in Ghana is assessed. The

bank's determinants in Ghana have been analyzed by (GEORGE et al., 2014). Amongst other

items, the authors found that greater liquidity risks contributed greatly to banks' success. (Ware,

2015) examined the relationship between liquidity administration and the viability of particular

companies trading on the Ghana Stock Exchange. The study made use of information from 33

companies over the course of five years, from 2005 to 2009. Liquidity was assessed by the cash

change cycle, the average collection duration, and the average payment period, had no

significant impact on the returns of the companies.

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Agyemang et al., (2023) conducted a study using data from Ghana's publicly traded non-

financial companies. Liquidity had a strong relationship with the firms' return on assets (ROA),

but had a negligible relationship with the firms' return on equity (ROE) and return on capital

employed (ROCE). Moreover, several other studies have found that liquidity risk is one of the

factors influencing bank performance. This makes the liquidity risk and efficiency discussion

very relevant. The relationship between liquidity risk and other risk types, which it is subjected

to, should be fully understood and considered. In carrying out their operations, businesses, in

particular financial institutions, face a variety of financial and operational risks. These include

risks in terms of interest, loans, operational, law and reputation. These risks have a big effect

on the liquidity profile of a firm.

Studies including (Hamdi & Hakimi, 2019; Partovi & Matousek, 2019) indicate that

liquidity serves as a basic component of banks’ operations and therefore, poor liquidity result

in the instability of banks performance and in the long run affects the financial sector in a

whole. Hamdi & Hakimi, (2019) posit that low liquidity is realize when banks fail to meet all

the request of depositors either in part or whole in a particular time.

The recent developments in the banking sector have resulted in the merging of six banks

and consolidation of five banks due to mainly their inability to meet the current minimum

capital requirement of the Central Bank of Ghana which is Gh400million. Aside this, some

banks’ license was revoked due to their insolvency. However, the after-mouth situation led to

reduction in deposit by 17 percent and panic withdrawal increased by 13 percent due to

liquidity management challenges that have also affected their return on equity (Ghana Banking

Survey, 2019). The Central Bank of Ghana has recommended effective liquidity management

among the commercial banks. This study aimed at adding to existing knowledge by examining

the effect of liquidity on financial institution performance from the perspective of an emerging

market.

METHOD RESEARCH

The analysis used quantitative testing methods. Kumar, (2014) defines quantitative

research as a structured, objective, systematic method for defining and evaluating relationships,

and for analysing interactions between causes and effects of variables. Quantitative research

focuses on data collection and interpretation calculation (Agbodjah, 2008). In this approach,

mathematical and statistical methods are used to classify evidence and causal relations. It is

used for the compilation of numerical data that can be calculated by statistical analysis.

The study was longitudinal research. Ployhart & Vandenberg, (2010) described

"longitudinal research as a study of change, containing, on at least one substantive interest

building, at least three repetitive observations (although more than three are better). Typically,

a longitudinal study makes many findings over a period of time about the same phenomena or

subjects (lasting many years or seasons). The analysis adopted panel research design.

Typically, panel designs are often longitudinal. These designs calculate each wave of data

collection with all the variables of interest. This design was appropriate since it helped the

researcher to gather data on 8 solvent listed banks on GSE for 5 years.

Data Type and Source

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Basically, the analysis relied on secondary data. They are already data collected on the

same or related interest variables of the researchers by various writers and institutions. The

data came from the released 2015-2019 GSE Financial Reports. There were 23 solvent banks

in Ghana (between the period of 2015 to 2019) as the study population. Nevertheless, the

analysis included 8 of those solvent banks. These were the current banks after the

recapitalization of banks in 2018 whose have up-to-date information on their financial reports.

Study data is accessed on the Ghana Stock Exchange website (GSE). ROA and ROE assess

bank efficiency. Other researchers also use ROA and ROE. These include: Najid and Rahman

2011, (Alkhatib & Harasheh, 2012; Almumani, 2013), Roman and Sargu 2014, Rose and

Hudgins 2013, and Saeed and Rahman. The liquidity ratio was used as a metric of liquidity

calculation. Company size and revenue growth are used as controls to differentiate liquidity's

effects on banks' financial performance from other factors that affect financial performance.

Operationalization of variables

The dependent variables used in this research are Ret urn on Equity (R OE) and Earnings

per share (R OA) indicating financial profitability. The independent variable, which in this case

is liquidity, is denoted by current ratio, sales growth and firm size. The table bel ow summarizes

the variables used and h ow they were computed.

Table 1: The summary of variables used and their expected signs

Variable

Legend

Measurement

Sign

Return on Equity

ROE

net profit before interest and taxes

over Equity

Return on Assets

ROA

Net profit before interest and Tax

over total asset

Independent variables

Current Ratio

Current Assets/Current liabilities

Firm size

FIRMSIZE

Natural logarithm of total assets

Sales growth

Sales growth of firm i in time t

Time series and cross-sectional data from audited companies' financial statements have

been used to produce panel data. It has also been used ideally because it helps to distinguish

results that cannot easily be indicated by simply using cross-sectional or series data and other

relevant aspects. This study employed explanatory variables such as current ratios, business

size and revenue growth debts, thus re-evaluated and re-established assets-based variable.

The data analysis is the process of the systematic application of statistical and logical

methods to describe, explain, condense and recap and assess data, according to Prabhat and

(Meenu et al., 2015). After this information has been obtained, the data was processed and

handled by coding, editing the data if possible, entering the data into the software (STATA

software version 15.0) to produce results and cleaning the data finally, in order to eliminate

types of error that could not be detected. In addition, the results have been analyzed using

descriptive statistics, an analysis of correlation and regression.

RESULT AND DISCUSSION

Table 1: Descriptive Statistics

Variables

Mean

Std Dev

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ROA

2.3130

2.21921

.3513

.14502

22.1315

.64799

17.0228

29.04004

Source: Data from Ghana Stock Exchange (2000-2019)

The Table 2 shows the descriptive statistics of the study. The mean return on asset of

banks listed on the Ghana Stock Exchange (GSE) is 2.31, mean current ratio is 0.3513, mean

firm size is 22.1315 and the mean sales growth rate is 17.0228% The performance of banks

listed on the GSE is very good in terms of ROA. However, banks listed on the GSE are in a

very poor liquidity position.

Table 2: Model Summary

Adjusted R

Durbin-Watson

.590

.348

.294

1.523

The results from the Durbin-Watson of 1.523 indicates that there is not autocorrelation

in the residuals (Table 3). This is because the DW figure is greater than 1.5 but less than 2.5.

The Adjusted R-square figure means that 29.4% variation in the return on asset of banks listed

on the GSE can be explained by sales growth rate, firm size and liquidity. Thus, about 70

percent of the variation is caused by other factors that were not included in the model. Table 4

shows the strength and nature of relationship between the variables of the study.

Table 3: Correlation Matrix

ROA

0.212

0.541*

0.223

0.113

-0.103

0.115

N= 40, *Significant (p<0.05)

Table 4 shows that there is a moderate positive relationship between the profitability

(ROA) of banks listed on the GSE and sales growth (SG) while there was weak positive

relationship between profitability (ROA) and liquidity (CR) and firm size (FS). However, there

were weak relation among the independent variables depicting that there was no problem of

multicollinearity.

Table 5: Collinearity Statistics

Variable

Tolerance

VIF

0.974

1.027

0.971

1.030

0.973

1.028

Multicollinearity is a general term for the existence of linear associations between

explanatory variables. It is used to test the relationship between independent variables. There

is no multicollinearity if Tolerance value is greater than 0.10. Also, if Variance Inflation Factor

is less than 10, then it means there is no multicollinearity and vice versa. Therefore, there was

no problem of multicollinearity among the independent variables because the tolerance values

were all greater than 0.10 and the VIF values were all below 10 (Table 5).

Table 6: Effect of Liquidity and banks’ performance (ROA)

Model

Std. Coefficient (β)

Error

Sig

(Constant)

0.401

-3.579

0.001

.174

0.055

1.278

0.209

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.500

0.010

3.660

0.001

.183

0.040

1.343

0.188

6.406

Prob(F-stat)

0.001

The Table 6 shows the test of significance. From the results presented (F=6.406,

p=0.001), it can be concluded that variation of the profitability (ROA) of banks listed on the

GSE can be explained by sales growth, liquidity and firm size. Table 6 shows no statistically

significant impact on the profitability of GSE-listed banks on liquidity and sales growth. The

analysis also does not refute the null hypothesis since they have Sign values greater than 0.05.

However, the size of a bank has major consequences for the profitability of the GSE-listed

banks in terms of its total assets. Since the test is designed to unravel the relationship between

cause and effect, we use standardized coefficients. With a continuing rise in unit size, the

profitability of such banks rises by 0.182.

This finding confirms that of Marozva (2015) who points to other studies that observed

parallel relationship between these variables. He found that there was a positive relationship

between bank liquidity and performance (profitability). Similarly, Agbada and Osuji (2013)

examined the efficiency of liquidity management and bank profitability using a survey design,

their findings also indicated a positive relationship between effective liquidity management

and bank profitability. In addition, Isik (2017) researched on the profitability determinants of

153 quoted real sector businesses in Turkey. The study’s results discovered liquidity as a

significant determinant of the firms’ profitability as measured by ROA.

On firm size, Mpesum (2010) researched on Cal Bank Ghana Ltd. and the factors that

influenced profitability of the bank. Results suggest that industry Size was a the most important

factor with regards to banks profitability. He observed an “inverse relationship between size

and profitability”, he therefore concluded that for a bank to be profitable they need to be

mindful to their size as well as the bureaucratic issues associated with it.

Table 7: Descriptive Statistics

Variables

Mean

Std Dev

ROE

38.0325

94.48556

.3513

.14502

22.1315

.64799

17.0228

29.04004

The Table 7 is the descriptive statistics. It can be seen that the mean return on equity of

banks listed on the GSE is 38.0325%, mean current ratio is 0.3513, mean firm size is 22.1315

and mean sales growth rate is 17.0228%. The Table 8 shows the correlation between the

variables.

Table 8 Correlation Matrix

ROE

-0.094

0.102

0.155

0.113

-0.103

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0.115

Table 8 shows that there is a weak positive relationship between the profitability (ROE)

of banks listed on the GSE, Firm size (r=0.102, p>0.05) and sales growth (0.155, p>0.05) while

there was weak negative relationship between profitability and liquidity (r= -0.094, p>0.05).

However, these relationships were not statistically significant. Nevertheless, there were weak

relation among the independent variables depicting that there was no problem of

multicollinearity.

Table 4: Model Summary

Adjusted R

Durbin-Watson

0.198

0.039

-0.041

2.127

0.491

Prob(F-stat)

0.091

a. Predictors: (Constant), SG, CR, FS

b. Dependent Variable: ROE

From the model summary (Table 9), it can be seen that there is no autocorrelation in the

residuals. This is because the DW results is greater than 1.5 and less than 2.5. It can also be

that there exists a weak positive (19.8%) relationship between the return on equity of banks

listed on the GSE and the regressors.

F Statistics is used to evaluate if the mean difference between more than two independent

(unrelated) groups is important. It thus contrasts the difference in more than two categories.

The dependent variable is evaluated on a constant scale and more than two independent classes

can be used in the independent variable. When performing the test, F-Test is the value arrived

and will decide if the means are different for three or more classes.

The Table 9 shows the test of significance. From the results presented above (F=0.491,

p=0.091), it can be concluded that variation of the profitability (ROE) of banks listed on the

GSE cannot be explained by sales growth, liquidity and firm size. The above result shows that

variation in the return on equity of banks listed on the GSE cannot be explained by regression

model due to the fact that mean difference between more than two independent variables are

not significant. Table 10 depicts the collinearity statistics of the variables.

Table 5: Collinearity Statistics

Variable

Tolerance

VIF

0.974

1.027

0.971

1.030

0.973

1.028

From Table 10, the test of multicollinearity can be assessed using the Tolerance and

Variance Inflation Factor (VIF). From the results, it can be concluded that there is no

multicollinearity among the independent variables. The tolerance values are all greater than

0.10 and VIFs are also less than 10. Table 11 presents on the relationship between liquidity and

banks’ performance (ROA).

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Figure 1: Hausman Test

The thumb rule about hausman test is that when the p-value is less than or equal to 0.05,

the null hypothesis is rejected and vice versa. According to Figure 1, chi 2 (4.83), p=0.1847

which is greater than the p-value of 0.05. Therefore, the study accepts the hypothesis that

stipulated that the unique errors (ui) are not correlated with the regressors. Therefore, random

effect is reported from the ordinary Least Square (OLS) and the results is presented in Table

13.

Table 6: Effect of liquidity on banks’ performance (ROE)

Model

Std. coefficient (β)

Error

Sig

(Constant)

0.011

.626

-.091

0.851

.585

.097

0.744

.563

.134

0.381

.422

The results in Table 11 indicates that current ratio, firm size and sales growth do not have

any statistically significant effect on the return on equity of banks listed on the GSE. This

finding was contrarily to that of Kosmidou et al., (2005) who found out that there was a positive

relationship between banks’ ROA and the ratio of liquid assets to customer and short funding

obligations. Similarly, Olangunji et al. (2012) in their study found that there is a significant

positive relationship between bank liquidity and its performance. In addition, Mohammed and

Yusheng (2019b) conducted a study on quoted non-financial firms in Ghana. From the study’s

findings, liquidity had a material connection with the firms’ ROA, but insignificant link with

the firms’ ROE. Moreover, Mutwiri (2015) analyzed the economic viability of five quoted

energy and petroleum sector firms in Kenya. From the study’s multiple regression analysis,

liquidity was an insignificant determinant of the firms’ ROE

CONCLUSION

Panel design of the Longitudinal study was conducted appropriately and the following

conclusions can be drawn: the study concludes that, liquidity and sales growth do not have any

effect on the profitability of banks listed on the GSE. However, the size of a bank positively

affect on the return on assets of banks listed on the Ghana Stock Exchange. In all, liquidity

affect the ROA but not ROE of banks listed on the GSE

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Journal of Research in Business Studies and Management, 2(11).

Magnus Kofi Quansah, Oscar Agyemang Opoku (2023)

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