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DETERMINANTS OF BANKS’ STABILITY: A CASE STUDY OF
BANKS LISTED ON THE GHANA STOCK EXCHANGE
Daniel Dwamena Kofi, Oscar Agyemang Opoku, Henry Okudzeto
University of Cape Coast, Ghana
Email: danield[email protected]m, oscar.o[email protected], henry.okudzeto001@stu.ucc.edu.gh
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
Injuruty: Interdiciplinary Journal and Humanity
Volume 2, Number 6, June 2023
e-ISSN: 2963-4113 and p-ISSN: 2963-3397
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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
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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 BS
it
= β
0
+ β
1
ID
it
+ β
2
SB
it
+ β
3
INF
it
+ β
4
GDP
it
+ €
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.
Β
0
= is the constant f or each bank.
Β
1
, β
2
, β
3
β
4
β
5 =
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
Definition of Variables
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 Processing and Analysis
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).
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RESULT AND DISCUSSION
Table 1: Descriptive Statistics
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.
Table 2: Correlations Matrix
BS
ID
INFL
GDP
SIZE
REG
-
.039
-.203
.198
.097
-
.187
-.178
-.115
-
-.934*
-.098
-
.138
-
-
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.
Table 3: Model Summary
R
R square
Adjusted R square
Durbin Watson
.235
a
.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
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of 2.5. A DW figure below 1.5 indicates that the residuals are autocorrelated. Autocorrelation
violates classic linear regression criteria. Autocorrelation.
Table 4: ANOVA
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.
Table 5: Multicollinearity Test
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.
Table 6: Coefficient
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.
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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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Commercial Banks in India. International Journal of Application or Innovation in
Engineering & Management.
Copyright holders:
Daniel Dwamena Kofi, Oscar Agyemang Opoku, Henry Okudzeto (2023)
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