DETERMINANTS OF BANKS’ STABILITY: A
CASE STUDY OF BANKS LISTED ON THE GHANA STOCK EXCHANGE
|
Abstract The study was to analysed
the determinants of stability of banks listed on the Ghana Stock Exchange
(GSE). The study used 8 of the 9 banks listed on the Ghana Stock Exchange for
the study. The study used annual data of the sampled banks on the GSE from
2015 to 2019. Panel regression analysis was used to unravel the determinants
of bank stability in Ghana. The study found that Income diversity, the size
of a bank, inflation, regulation and gross domestic product do not determine
the stability of banks listed on the Ghana Stock Exchange (GSE). A weak
positive relationship was found between income diversity, the size of a bank,
inflation, regulation and gross domestic product and the stability of banks
listed on the Ghana Stock Exchange. The study concluded that income
diversity, size of a bank, inflation rate in the country, the gross domestic
product do not determine the stability of banks
listed on the Ghana Stock Exchange. The study makes the following
recommendations. Future studies to be conducted into the determinants of bank
stability using variables. The Bank of Ghana (BoG)
and other bodies to pay more attention to other factors other than size,
income diversity, inflation, regulation, gross domestic product in their bid
to enhancing banking stability as these factors do not affect the stability
of banks in Ghana. Keywords: Ghana, Stock Exchange, Bank’s
stability, |
INTRODUCTION
Stability of the financial system is a key to
economic development (Batuo, Mlambo, &
Asongu, 2018). The economic prospects of any country are dramatically
enhanced by sound finances Rajan & Zingales,
2003; Saif-Alyousfi & Saha, (2021) The role played by the banking sector is a very critical
one. It appears all the economic prospects on the economy are hinged on a
vibrant banking sector. Tiwari & Sontakke,(2013) observe that various sectors of the economy (Industry,
mining, agriculture, manufacturing, personal and government) benefit from this
role played by Banks.
The banking sector of every economy thrives on
confidence; thus, banking sector stability remains a major concern for
governments all over the world. The
critical financial intermediation role played by banks in the economy is
hamstrung if banks are unstable. Thimann, (2014) believes that if the financial system fails to function
correctly, the consequences will be severe for the economy as a whole. As a
result, policymakers, regulators, researchers, and practitioners in all
countries are concerned about the sector's health and stability (Head, 2016). The United States government, then headed by President
Bush, signed the Emergency Economic Stabilization Bill into an Act to restore
the financial system to health after the financial crisis (Shah, 2009). This created a Treasury Fund of $700 billion to buy
bank assets which have collapsed. The government of Ghana in order to correct
the financial sector crisis had to institute a number of measures such as
increasing bank capitalization from 120 million cedis to 400 million cedis. The
government is estimated to spend 20 billion cedis equivalent to 3.5 billion
dollars to bring confidence in the financial sector back. The shocks to the
financial system can be triggered by bank-specific or macroeconomic factors.
The essence of banks' work is that they are
subjected to risk from a multitude of outlets. Alkalha, Al-Zu’bi,
Al-Dmour, Alshurideh, & Masa’deh, (2012) states that the origins of financial institutions at
risk can be divided into two main categories: systemic and non-systematic. In
addition, the author considered that systemic risk factors have an important
influence on all financial institutions on the market and that systematic risk
sources refer to variables outside of the control of the bank. The risk sources
that are non-systematic differ and are partly related to the bank's variables.
The Financial Stability Index, according to Stock & Watson, (2003), is a delicate predictor of financial stability as all
financial, company, and business operations and economies are flexible to
easily withstand financial crises and low losses, as many structural, financial
and behavior-based factors interact in developing a financial system. Menurut Nasreen, Anwar, &
Ozturk, (2017) Financial
stability variables reduce the power of financial crises in countries through
the provision of a financial crisis early warning system, and vice-versa, by
having a system of early warning that financial instability will negatively
impact economies and financial markets, demolishing the financial system of the
country and in the long-term affecting the size of itself. This study
contributes aims at contributing to the ongoing debate from an emerging market
perspective examining the factors that determine bank stability in Ghana.
In 2007, several developed and emerging
countries introduced models to warn early on the financial crisis, as well as
initiatives by the countries to find the frameworks and studies and experiments
to absorb possible losses. Notwithstanding the relevance of financial sector
stability in the life of an economy, the literature on bank stability
determinants in Africa is rather scanty, this gap in knowledge must be filled.
This research contributes to the current debate through the empirical
investigation from an emerging market perspective of predictors of the
financial sector crisis. Thus, the study analysed the
determinants of Ghana's bank stability and its influence on the country's
economy on sustainable growth.
Banking sector instability or crisis means a
lot of economic loss to a country. This loss comes in the form of government
huge budget with the view to correcting the situation, loss of confidence in
the banking sector, and an overall reduction in the national output of the
economy. Therefore, there is the need for studies to be conducted in
identifying the factors that cause instability in the banking sector, the
relative weight of these factors and to prevent instability in the banking
sector.
METHOD RESEARCH
The
study used an explanatory design which was quantitative approach in nature. In
quantitative resea rch,
data a re captu red
in nume rical fo rm and analysed
quantitatively (Teddlie
& Tashakkori, 2011).
The
study used secondary data. These includes financial reports and statements of
selected banks on the Ghana Stock Exchange. The research used data from the
Ghana Stock Exchange's audited financial statements (GSE). Each bank’s
financial reports from 2015 to 2019 were reviewed. The study consisted of 8 out of 9 banks for
the study listed on the Ghana Stock Exchange.
Many
reports, such as Gan, (2004) and Fell and
Schinasi, have addressed and concentrated on financial banking stability (Fu, Lin, & Molyneux, 2014). The model used by the
authors has been adapted slightly. The model is specified below:
Financial Banking
Stability BSit = β0 +
β1IDit + β2SBit+ β3INFit
+ β4GDPit+ €it…. 1
The model is a
combination of banks and macroeconomic factors.
Where:
BSit = Banking
Stability defined as insolvency risk measured by Z-score companyi
at time t.
Β0 = is
the constant f or each bank.
Β1,
β2, β3 β4 β5 =
is the regression coefficients values
IDit = is income diversity banki at time
t
SBit = is the size of banki at time t
INFit = is
the inflation rate of Ghanai at time t
GDPit = is
the Gross Domestic Producti at time t
εit = is the err or term
Dependent Variable
Banking
stability (BS) defined as the calculated risk of insolvency is a dependent
variable by Z-score: [ROA +(E/TA)] / SD of ROA.
Independent Variables
Bank
Specific Factor (BS): This measure refers to the bank's internal factors
and its sensitivity to the bank's financial stability, in which internal
adjustments represent the Bank's rules of procedure, then this effect applies
to the whole of the national financial banking system and defines the degree of
stability of the financial banking system and measures it by: Income Diversity
(ID) = 1 - │
(Net interest income - Other operating income) / Total operating income│
Size of Bank (SB)=
Logarithm of the total assets of a bank
Banking Sector
Factor (BSE): This measure demonstrates that the banking sector as a whole is
vulnerable to banking stability. P/E ratio is used to index banking sector
factor.
External
Governance (Economic Freedom) (EG): This measure refers to the scale of economic freedom
variables, by which the share of foreign trade and the magnitude of the
contribution it makes to the gross national product and its reflection on
financial banking stability calculated by government size (SG) and Regulation
are measured (RE).
Data
was analyzed for measurement, comparison, examination of relations, forecasts,
test hypotheses, concepts and theories to be built, exploration, monitoring and
clarification. In this investigation the determinants of banks at the Ghana
Stock Exchange are investigated using quantitative research technologies. In
this analysis, regression panels are used for analyzing results. The analysis
was carried out with the aid of STATA software (version 14.0).
RESULT
AND DISCUSSION
|
Variable |
N |
Mean |
Std. Deviation |
|
BS |
40 |
9.8120 |
32.15822 |
|
ID |
40 |
-2.1278 |
8.42309 |
|
INFL |
40 |
12.3920 |
4.44642 |
|
GDP |
40 |
58.0200 |
6.55498 |
|
SIZE |
40 |
22.1500 |
.55787 |
|
REG |
40 |
1.0000 |
.00000 |
Source: Author’s Construct (2020)
The descriptive
statistics of the analysis are given in Table 1 above. As other statistical
statistics, the key characteristics of the data set used for the analysis are
listed. From the tables it can also be seen that the average of BS is 9,8120.
Usually that is a deviation of the value of 32,15822, the mean is -2.1278 and
the standard deviation is 8,42309. INFL has a mean of 12,3920 and standard
deviations are 4,44642 and the mean of GDP is 58,0200.
|
Variable |
BS |
ID |
INFL |
GDP |
SIZE |
REG |
|
BS |
- |
.039 |
-.203 |
.198 |
.097 |
|
|
ID |
|
- |
.187 |
-.178 |
-.115 |
|
|
INFL |
|
|
- |
-.934* |
-.098 |
|
|
GDP |
|
|
|
- |
.138 |
|
|
SIZE |
|
|
|
|
- |
|
|
REG |
|
|
|
|
|
- |
The Table 2 above shows the correlation between the
variables (both dependent and independent) used in the study. Correlation
explains the nature and strength of relationship between variables. The sign
describes the direction of the relationship whilst the values describe the
magnitude of the relationship between the variables. From the table above, it
can be seen that the correlation coefficient for bank stability and income
diversity is 0.039, this means that there is a weak positive relationship
between income diversity and bank stability. The Pearson Correlation
coefficient for bank stability and gross domestic product (GDP) is 0.198 (Ausloos, Eskandary, Kaur, & Dhesi,
2019). This means that
there is a weak positive relationship between bank stability and the GDP of
Ghana.
|
R |
R square |
Adjusted R square |
Durbin Watson |
|
.235a |
.055 |
.053 |
1.996 |
a.
Predictors:
(constant), SIZE, INFL, ID, GDP
b.
Dependent
Variable: BS
The Table 3 shows
the extent to which variations in banking stability is explained by the
dependent variables put together. The R value of 23.5% illustrates the
connection between the BS and SIZE, INFL, ID and GDP. The R value implies that
the relationship between the dependent variable and the independent variables
is small. The R Square describes the variance in bank stability caused by size,
inflation, income diversity and GDP. This means that the independent variables
account for only 5.3% of changes in bank stability.
The 1.996 Durbin Watson value shows that
the majority of the residues in the regression model are not autocorrelated.
This is because the Durbin Watson has a maximum 1.5 and a minimum of 2.5. A DW
figure below 1.5 indicates that the residuals are autocorrelated.
Autocorrelation violates classic linear regression criteria. Autocorrelation.
|
Model |
Sum of Squares |
df |
Mean Square |
F |
Sig. |
|
Regression |
2219.252 |
4 |
554.813 |
.510 |
0.029 |
|
Residual |
38112.633 |
35 |
1088.932 |
|
|
|
Total |
40331.885 |
39 |
|
|
|
The Table 4
measures or tests the appropriateness of the model used for the study. From the
test of significance above, it can be seen that, the regression model used for
the study is very significant in explaining the relationship between the
variables used for the study. This is because the sig. value is less than 0.05
which means we reject the null hypothesis which says that the model is not
relevant in explaining the relationship between the variables.
|
Variables |
Tolerance |
VIF |
|
ID |
.955 |
1.047 |
|
INFL |
.127 |
7.899 |
|
GDP |
.126 |
7.937 |
|
SIZE |
.964 |
1.038 |
Table 5
shows the Multicollinearity status of the 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 less than 1.0 and
the VIF values were all above 1.0.
|
Model |
Unst Coeff. |
Stand Coeff. |
t |
Sign |
|
(Constant) |
-91.469 |
|
-.356 |
.724 |
|
ID |
.340 |
.089 |
.529 |
.600 |
|
INFL |
-1.290 |
-.178 |
-.386 |
.702 |
|
GDP |
.176 |
.036 |
.077 |
.939 |
|
SIZE |
4.866 |
.084 |
.504 |
.617 |
From the results above (Table 6), it can be seen that
Income diversity of banks, inflation, the size of a bank and the GDP of Ghana
at any particular time do not impact on the stability of banks in Ghana. This
is due to the fact that they all return sig values greater than 0.05 which
means we fail to reject the null hypothesis.
The study found
Income diversity, the size of a bank, inflation, regulation and gross domestic
product do not determine the stability of banks listed on the Ghana Stock Exchange
(GSE). The study found no autocorrelations among the residuals (Adjasi,
Harvey, & Agyapong, 2008). The multi-collinearity test also implies that the independent
variables are not multi-linear. The study also found that the relationship
between bank stability and all the independent variables used in the study had
been marginally positive. This is attributed to a R value of 23.5% in the study
review. Therefore, the relationship between income diversity, a bank's size,
inflation, regulation, and the Ghana Stock Exchange's gross national product is
slippery.
CONCLUSION
The study concludes that income diversity,
size of a bank, inflation rate in the country, the gross domestic product does
not determine the stability of banks listed on the Ghana Stock Exchange. This
means that the effect of these variables on the stability of banks listed on
the GSE is not statistically significant at 5%. These variables therefore do
not determine the stability or otherwise of banks listed on the GSE.
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Copyright
holders:
Daniel
Dwamena Kofi, Oscar Agyemang Opoku, Henry Okudzeto (2023)
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