Syntax Idea: p�ISSN: 2684-6853 e-ISSN: 2684-883X�
Vol. 3, No.11, November 2021
BANKING
FINANCIAL SYSTEM STABILITY ANALYSIS
Anindita Neng Pda
Insitut Agama Islam Negeri (IAIN) Salatiga Jawa Tengah, Indonesia
Email: [email protected]
Abstract
The purpose of
this study was to determine the effect of the competition�s level, the level of
credit growth on financial system stability. The method in this study is panel
data regression with three approaches, namely common effect, fixed effect and
random effect. The data used is monthly time series data starting from June
2015 to June 2018 and cross section data from all types of commercial banks
covering four types of banks based on the distribution of BUKU (Business
Commercial Banks). The data obtained from the official website of Bank
Indonesia, the Financial Services Authority, the internet and other references
sourced from literature, literature studies, scientific journals, supporting
books and various other sources related to research. The results of this study
indicate that the Fixed Effect Model is the best model for further estimation based
on the results of the Chow test and Hausman test. These results also explain
that the Bank's Competition Level has a positive effetc
but not significant on bank stability. Meanwhile, credit growth has a negative impact
and significant on bank stability, and profit sharing
financing has a positive impact and significant on bank stability.
Keywords: Bank
Competition Level; Credit Growth; Inflation; Bank Stability; Fix Effect Panel
Model
Received: 2021-10-22; Accepted: 2021-11-05; Published: 2021-11-20
Introduction
Banking has undergone a metamorphosis since the era of
deregulation. Deregulation is a policy set by the government to facilitate the
establishment of new banks which has an impact on increasing the number of
domestic banks. Banks are currently one of the financial institutions that have
a major influence on the process of economic growth and stability. This can be
seen from the dominance of banks in the share of financial sector assets, where
banks reached 79% (Kajian Stabilitas Keuangan, 2018).
According to the Banking Law Law
No. 21 of 2008 concerning Islamic Banking, one of the functions of a bank is to
carry out an intermediation role. The main function of intermediation carried
out by banks has a role in the process of stabilizing the financial system
because banks are the largest fund-raising institutions. This is in accordance
with the main authority of the banking system itself to regulate the distribution
and payment system properly to all levels of society.
The proliferation of banking in Indonesia is marked by
a very large number. Currently, there are 95 conventional banks in Indonesia.
Meanwhile, there are 14 Islamic banks. The increase in the number is not based
on strong regulations. This increase was accompanied by moral hazard by private
banks as well as the implementation of a financial liberalization system which
resulted in a banking crisis in Indonesia. This was marked by the monetary crisis
in 1998. As a result, 23 banks were liquidated and public confidence in banking
was lost (Bank Indonesia, 2018).
To deal with the crisis, the government implemented
the Indonesian Banking Architecture (API) policy in 2004. With this API, which
is the basic framework for the banking system in Indonesia (Kajian Stabilitas Keuangan, 2018). With the
implementation of the API system, many banks are conducting mergers and acquisitions.
This is the main objective of the API policy with the formation of 60 banks in
Indonesia, of which 2 to 3 international banks, 3 to 5 national banks and 30 to
50 specialist banks within a period of 10 to 15 years. It is hoped that this
policy can provide a strong role for bank institutions in maintaining financial
stability in the economy. Financial stability in banking itself is influenced
by various factors which the authors simplify into three influencing variables,
namely the level of banking competition, credit growth and also profit sharing financing.
Stability is a measure of economic success and an
indicator of economic resilience from various crises, because it is this crisis
that provides many lessons for the economy in many countries. Various efforts,
overcoming, preventing and mitigating crisis risks have become homework for
many countries, especially Indonesia. Two crises that occurred in 1998 and 2008
have created instability and eroded public confidence. The sustainability of
the economic mechanism, namely in setting prices, allocation of funds, risk
management, resilience to crises, the ability to carry out the intermediation
function and the driving force of economic growth can be achieved with good
stability. On the other hand, the crisis triggered by the increasingly
integrated financial system due to the globalization of the financial sector,
increasingly dynamic financial product innovation, and various transactions
will lead to the opposite condition (Budi Santoso, 2014).
Banking stability and financial stability are
interrelated. This is because healthy banking conditions are usually reflected
by the condition of banks that carry out their functions properly, namely in
distributing third party funds in the form of credit and financing. To increase
economic growth, it can be seen in the availability of credit which can later
be converted into investment. Giving credit is also the main activity of banks
to generate profits (Shijaku, 2017).
However, credit growth in banks always fluctuates which can be shown in the
following table:
Table 1
Banking Credit Growth in 2020
|
Bulan |
Pertumbuhan
Kredit (%) |
|
Januari |
5.70 |
|
Februari |
5.50 |
|
Maret |
7.20 |
|
April |
5.73 |
|
Mei |
2.40 |
|
Juni |
1.34 |
|
Juli |
1.53 |
|
Agustus |
1.04 |
|
September |
0. 12 |
|
Oktober |
-0.47 |
|
November |
-1.39 |
|
Desember |
-2.7 |
Source: Bank Indonesia, processed in
2021
Based on the table above,
credit growth in 2020 is slowing down. Although in March there was an increase,
from April to December there was a decrease. Even in October began to see a significant
decline up to -0.47%. Which then in the following month also decreased. This is
due to the weak demand for credit, where credit risk in the banking sector is
still very high.
To avoid this risk, in running
the financial system and its financing, it uses a profit-sharing scheme. In the
last thirty years the main successful schemes in Islamic countries. In
Indonesia, this scheme was only used in 2004. However, the current results have
not been able to describe the actual potential of the Islamic financial system.
So there is a need for further studies on this profit-sharing system which will
later be able to stabilize the financial system in banking (Bank Indonesia, 2018).
From this study, the author wants to find out how the influence of competition�s level, credit growth and profit sharing financing on the
financial stability of banks in Indonesia. In research that has been conducted (Apriadi et al., 2017) show that increased bank competition will reduce banking
stability. However, research conducted by (Shijaku, 2017) show that bank stability has a positive and significant
effect on financial stability in banking. Subsequent research from (Bilan et al., 2016) showed a significant influence.
Another study on credit
growth conducted by (SE, 2020) showed that credit growth had no effect on banking
stability. In contrast, research conducted by (Zevananda & Pangestuti, 2017) showed a negative and significant influence on banking
stability. The positive results between the influence of credit growth and
banking stability were investigated by (Faruqinata, 2019). Subsequent research
conducted by (Pujianti & Sitorus, 2016)
showed a negative and significant influence between profit sharing financing
and banking stability. Negative results were also obtained in (Elvani et al., 2017) but were not significant. In contrast to (Tawami, 2017)
which results that profit sharing financing has a positive influence on banking
stability.
With
the differences in results from previous researchers, a research gap was
obtained in previous studies. Differences in the results of this study caused
by different in the amount of data and methods
used. Furthermore, the researchers expanded
the object of research in this study, namely banking in Indonesia, Islamic and
conventional banking.
Research Methods
The data used is monthly time series data starting from June
2015 to June 2018 and cross section data from all types of commercial banks
covering four types of banks based on the distribution of BUKU (Business
Commercial Banks). If the data is paneled, 148 observations will be formed from
a total of 37 month periods and 4 types of banks. The data obtained from the
official website of Bank Indonesia, the Financial Services Authority, the
internet and other references sourced from literature, literature studies,
scientific journals, supporting books and various other sources related to
research.
1.
Stationarity
Test
According to (Arifin, 2016), the stationarity
test is used to analyze time series data to see if there is a unit root between
variables so that the relationship between variables becomes valid. The
stationarity test is used to test time series data so that it is not flat, does
not contain trend components with constant diversity and does not occur
periodic fluctuations (Sujarweni, 2015).
The test used is the Unit Root Test developed by
Dickey-fuller. There is a provision if the probability value is less than
0.05, it shows stationary data, but if the probability value is more than 0.05, it means the
data is not stationary (Wahyu Winarno, 2015).
2.
Panel
Data Regression Estimation
1)
Common
Effect Method
This method will assume that the combined data shows the
actual condition where the intercept value of each variable is the same as the
slope coefficient of the variable used and the same in all cross section units.
2)
Fixed
Effect Model
This model is characterized by the presence of an object
that has a constant value that remains large for various time periods.
3)
Random
Effect Model
This approach used to overcome the weakness of the fixed
effect method which uses quasi-variables. Therefore, this model results in
uncertainty.
Panel Data Model Selection
a.
Chow-Test
This test determine the PLS or FEM model will be chosen
to process the data. If the cross section probability value is > 0.05, it
means the model chosen is PLS. Then, if the value is < 0.05, it means the model chosen is
fixed effect.
b.
Hausman
Test
This test determine the FEM or REM model will be used in
the analysis. If the cross section probability value is > 0.05, the model
chosen is random effect. Then, if the value is < 0.05, the model chosen is fixed
effect.
3.
Classic
Assumption Test
1)
Normality
Test
In general, before doing the test to get a conclusion, it
is necessary to do a normality test. This test used to find out the data in the
study are normally distributed or not. Normality test carried out using the Jarque Berra (JB) test. If
the probability of Jarque Berra (JB) > 0.05, the residuals are normally
distributed.
2)
Heteroscedasticity
Test
Heteroscedasticity test is a condition which the variance
and confounding error not constant for all independent variables. A good
regression model if there is no heteroscedasticity. If the test result is
above the significant level (r > 0.05), it means that there is no heteroscedasticity and vice
versa. Then, if the label is below significant (r < 0.05), it means that there is no heteroscedasticity (Sujarweni, 2015).
3)
Multicollinearity
Test
This test used to test the regression model found a correlation between
the independent variables. In a good regression model, there is no correlation between the independent variables (Ghozali, 2005). The method for testing the existence of this
multicollinearity can be seen from the tolerance value or variance inflation
factor (VIF). The limit of the tolerance value > 0, 1 or the VIF value is
less than 10, so there is no multicollinearity.
4)
Autocorrelation
Test
According to (Sujarweni, 2015)
the autocorrelation test used to determine the
correlation between the confounding variable in a certain
period and the previous variable.
Results and Discussions
Hypothesis testing begins with testing classical
assumptions. The tests are multicollinearity test, heteroscedasticity test, and
normality test. Multicollinearity test is a test to determine a correlation
between the regression model and the independent variables. The result of
multicollinearity test can be seen from the VIF value < 10. In this study,
all variables had a VIF value < 10 so there was no multicollinearity of
symptoms.
While the heteroscedasticity test is a test to find
out the regression model has the same residual variance from one observation to
another. The regression model is homoscedastic or no heteroscedasticity. If the
significance probability > 0.05, it means that there is no
heteroscedasticity. Subjective knowledge variable with sig. 1,260, country
image sig. 1,064, and halal label sig. 1,093. It can be concluded that all variables
have a sig value > 0.05 means there are no symptoms of heteroscedasticity.
Last, a normality test used to determine the residual
variables were normally distributed or not. This test done by checking the
significance value of Asymp.Sig (2-tailed). If 0.05,
it means that the distribution is normal. From this study, the value of Asymp.Sig (2-tailed) is 0.259 > 0.05, it means that the
residual value is normally distributed.
Furthermore, this study uses panel data, so the
researcher uses the Hausman test in the regression selection model which is
shown in the following table:
Table
1
Model
Selection Test
|
Correlated
Random Effects - Hausman Test Equation:
Untitled Test
cross-section random effects |
||
|
Test Summary |
Chi-Sq. Statistic |
Chi-Sq. d.f.���� Prob. |
|
Cross-section random |
0.058746 |
3��������� 0.0063 |
Because the value is < 0.05, the model chosen is
fixed effect. With fashion; The results of research to test the following hypotheses
are obtained:
Table 2
Hypothesis Test
|
Determinant Coefficient and Adjusted R
Square |
||
|
R |
0.940662 |
|
|
Adjusted
R Square |
0.913960 |
|
|
Result of t-Test |
||
|
Standardized
Coeffisient Beta (X1) |
8.83E-10 |
|
|
t |
1.006875 |
|
|
Sig. |
0.3200 |
|
|
Annotation |
Tidak
signifikan |
|
|
Standardized
Coeffisient Beta (X2) |
-2.204888 |
|
|
t |
-5.612165 |
|
|
Sig. |
0.000 |
|
|
Annotation |
Signifikan
|
|
|
Standardized
Coeffisient Beta (X3) |
0.183092 |
|
|
t |
0.054073 |
|
|
Sig. |
0.001 |
|
|
Annotation |
Signifikan |
|
|
Result of the F-Test |
||
|
F |
35.22791 |
|
|
Sig. |
0.000 |
|
|
Annotation |
Signifikan |
|
|
*Significant
(Sig.<0.05) |
|
|
From the table above, it can be concluded that the
adjusted R2 value is (0.913960) with the dependent variation is
91.39%. While 8.61% influenced by variations of the other dependent variables
model. Then, in the Bank Competition Level Test (X1), the probability value is
0.3200 > 0.05 with a coefficient of 8.83E-10, then the competition�s level
has a positive effect but not significant on bank stability. Therefore, the
first hypothesis rejected. This is because the results in this study do not
show significant results but have a positive coefficient. It means that the
level of banking competition in Indonesia does not have a major influence on
financial stability in Indonesian banks. In accordance with the theory of
Structure Conduct Performance (SCP) which explain how companies will behave
when facing certain market structures in an industry. This behavior will later
create a certain performance. From this result, the banking parties perform
well in the face of an increasingly tight market. The results of this study have
similarities with research conducted by (Bilan et al., 2016).
In the credit growth variable (X2), the probability
value obtained is 0.0000 <0.05 with a coefficient of -2.204888, then credit
growth has a negative and significant effect on bank stability. It means that
the second hypothesis in this study also rejected. Thus, it can be conclude that the decline in credit to banks will also
reduce the level of banking stability in Indonesia. It happens because credit
growth is the key to economic progress in a country. With the decline in credit
growth, it will affect several aspects. This research supports the data for the
last one year in 2020 where credit growth in Indonesian banks has decreased
every month. This research is the same as the research conducted (Zevananda & Pangestuti, 2017)
Finally, the profit-sharing financing variable (X3) obtained a probability value of 0.0001 <0.05 with a coefficient of 0.183092, then profit-sharing financing has a positive influence and significant on bank stability. Due to the expectation of the profit-sharing system to improve the financial system in banking. Although at this time, profit sharing financing is still rarely used, it has proven to be able to stabilize the banking financial system. This is reinforced by the research conducted by (Ichsan, 2016) which also gave the same results in this study. This research is the same as the research conducted by (Tawami, 2017)
Conclusion
An empirical study of the level of competition, credit
growth and profit sharing financing on banking
stability in Indonesia illustrates that the impact of the API (Indonesian
Banking Architecture) policy has an influence on the structure and performance
of the banking system. By using the method of panel data analysis and the FEM
approach, the level of bank competition has a positive effect but not
significant on banking stability. It can be concluded that the more competitive
the bank, the stability will increase but not significantly.
The results of the credit growth variable have a
negative effect and significant on bank stability. Due to the factors of
receiving funds such as interest rate income and third party
funds which in some phenomena have a more influential influence than the credit
growth variable. It can also interpreted that credit
growth also influenced by other factors such as interest rates and
profit-sharing financing as well as GDP has far measured to affect stability.
Finally, the profit-sharing financing variable has positive and significant
results on bank stability. The results can be concluded that profit sharing
financing has a lot of involvement in bank stability.
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