ASSESSMENT OF BANK HEALTH LEVEL USING RGEC
METHOD AND ITS IMPACT ON ISLAMIC FINANCIAL DISTRESS
Ditha
Khiswaradewi1, Agus Eko Sujianto2, Mashudi3, Qomarul
Huda4, Dede Nurohman5
Universitas Islam Negeri Sayyid Ali
Rahmatullah Tulungagung, Jawa Timur, Indonesia
KEYWORDS |
ABSTRACT |
bank soundness level, financial distress, risk
profile, good corporate governance, earnings, capital. |
Bank Indonesia has established rules regarding bank
health so that banks are always expected to be healthy it will not harm the
people interested in banking. This study aimed to analyze the effect of bank
soundness level on financial distress using the RGEC method at Islamic
Commercial Banks in Indonesia either partially or simultaneously. This
research uses a descriptive quantitative approach using the RGEC method; the
object of this research is Islamic Commercial Banks in Indonesia from
2013-2020. The sample collection technique used purposive sampling, with a
sample of 11 Islamic Commercial Banks. Source of data obtained through
secondary data. Data analysis used panel data regression with the help of
Eviews 10 software. Financial Distress (Z-Score) is the dependent variable.
While the Risk Profile (NPF and FDR), Good Corporate Governance (GCG),
Earnings (ROA), and Capital (CAR) as independent variables. The study results
show that the soundness level of Islamic commercial banks in 2013-2020 in
terms of NPF is very healthy, and FDR is quite healthy. Meanwhile, GCG is in
the good (healthy) category. Regarding ROA, it has decreased, so Islamic
banks generating profits have decreased. Meanwhile, the CAR level has
increased to a very healthy category. Partially NPF, GCG, and ROA
significantly negatively affect Financial Distress. Meanwhile, FDR and CAR do
not affect Financial Distress. Simultaneously NPF, FDR, GCG, ROA, and CAR
significantly affect Financial Distress. |
DOI: 10.58860/ijsh.v2i6.56 |
|
Corresponding Author: Ditha Khiswaradewi
E-mail: dithakhiswara@gmail.com
INTRODUCTION
Banking in the life of a country has a vital role in
advancing the country's economy and becoming one of the agents of development (Putera
& SH, 2020). In Indonesia, banking is
the primary need for the community to meet financial needs. Therefore, in
running a banking business, one must consider the risks resulting from its
operational activities. Bank health is the ability of a bank to carry out its operational
activities usually and fulfill its obligations properly (Liyas,
2022). The soundness level of a
bank is fundamental and influences customer trust (Quang
Trinh et al., 2023). This is because the better
a bank's health, the more interested customers are in saving their funds in
that bank.
Islamic banking in Indonesia has become one of the
fastest-growing banks. According to Banjaran Surya Indrastomo as the Chief
Economist of PT Bank Syariah Indonesia (BSI), the growth of Islamic banking
assets reached 12.8 percent or higher than conventional banking and the
national banking industry. (Nora,
2016). In addition, the Islamic
banking sector survived amid the Covid-19 pandemic crisis, as seen from the
increase in business and performance and movements that tended to be stable in
the capital market.
According to Agus, financial distress is when a company
experiences financial difficulties and is threatened with bankruptcy (Utami,
2021). Financial distress is a
signal and an early warning of the coming bankruptcy of a company. The earlier
the signs of bankruptcy are known, the better it is for management to improve (Ir
Agus Zainul Arifin, 2018). From an Islamic
perspective, bankruptcy is categorized in files (bankrupt), individuals whose
debts are more significant than their wealth. Fiqh experts think bankruptcy is
viewed through an Islamic perspective, namely when the amount of debt is
greater than the assets owned. For companies that are currently operating,
bankruptcy is a fatal thing. So, bankruptcy must be addressed immediately, one
of which is by predicting bankruptcy so that companies can minimize the risk of
bankruptcy.
Based on Law Number 21 of 2011 concerning the Financial
Services Authority, as of 31 December 2013, the banking regulatory and
supervisory duties were transferred from Bank Indonesia to the Financial
Services Authority (Number
21 CE). This resulted in several
regulations previously regulated in Bank Indonesia Regulations being converted
into Financial Services Authority Regulations. Regulations regarding the
Soundness Rating of Islamic Commercial Banks and Sharia Business Units are
contained in POJK No. 8/POJK.03/2014, which explains that the scope of the
assessment consists of four factors, namely the risk profile (Risk Profile),
Good Corporate Governance (GCG), Earnings (profitability) and capital (capital)
or commonly called the RGEC method.
The risk profile in this study uses Non-Performing Financing
(NPF) and Financing to Deposit Ratio (FDR) proxies. NPF (Non-Performing
Financing) is a ratio for assessing problem financing. In contrast, FDR
(Financing to Deposit Ratio) measures a bank's ability to fulfill financing by
utilizing Third Party Funds (Wasiaturrahma
et al., 2020). According to previous
research, weak governance is one of the causes of the economic crisis in Indonesia.
Regarding the Corporate Governance mechanism, researchers will test using a
Self Assessment proxy (Sarmigi
& Putra, 2013).
Earnings in this study use ROA (Return on Assets) proxies. A
critical indicator in achieving optimal company performance is profit. This
ratio provides an overview of the level of effectiveness of company management.
According to previous research, ROA measures a bank's ability to obtain profit
(profit) (Saputra,
2020). Capital, in this study,
uses the CAR (Capital et al.) proxy. CAR compares the ratio of capital to
risk-weighted assets (Rampai,
2013).
The following research is related and can be used as a
reinforcement for this condition or writing, namely, Non-Performing Financing
(NPF) and FDR (Financing to Deposit Ratio) affect financial distress (Haq
& Harto, 2019). Meanwhile, other research
states that Non-Performing Financing (NPF) and FDR (Financing to Deposit Ratio)
does not affect financial distress (Prabawati
et al., 2021). Similar results were also
found by Prianti and Musdholifah (2018).
Previous research suggests that Good Corporate Governance
(GCG) affects financial distress (Wijayanti
et al., 2018). While other research states
that Good Corporate Governance does not affect financial distress (Prabawati
et al., 2021). Previous research stated
that Return On Assets (ROA) affected financial distress (Suhartanto
et al., 2022). Meanwhile,
the ROA variable in Mugiarti and Mranani's research (2019) does not affect
financial distress (Mugiarti, 2019). Previous research suggested
that the Capital Adequacy Ratio (CAR) affected financial distress (Mahmud & Waskito, 2021). Meanwhile, the results found
by other studies state that the Capital Adequacy Ratio does not affect financial
distress (Nisak, 2021).
The emergence of various bankruptcy prediction
models is an anticipation and early warning system for financial distress.
These models can identify and even improve conditions before and before a
crisis or bankruptcy. For companies that are considered in the bankrupt
category but immediately make internal improvements to the company, the
company's finances may improve and become a non-bankrupt category. For this
reason, this prediction also depends on the company's feedback on the results
of the bankruptcy prediction. One of the bankruptcy prediction models is the Altman Z-Score
model (Putra
et al., 2021). In this study, the Altman
Z-Score model used is a modified Altman Z-Score model. As a result, this model
can predict bankruptcy with a relatively high degree of accuracy before the
company goes bankrupt.
Based on the phenomenon of Islamic banking in Indonesia,
action is needed to predict the bankruptcy aspect of Islamic commercial banks
in Indonesia registered with the Financial Services Authority (OJK) for the
period 2013-2020 in order to determine the company's financial condition,
considering its ability to maintain business continuity as an opportunity for
Sharia economic growth in Indonesia. This research was conducted with the aim
of understanding and analyzing the Assessment of Bank Soundness Level with the
RGEC Method and its Influence on Islamic Financial Distress in Islamic
Commercial Banks in Indonesia.
METHODS
Judging from the approach used, the research method used in
this study uses a quantitative approach. Quantitative research is a type of
research that produces data findings in the form of numbers obtained using
statistical procedures or other methods of quantification (measurement). In the
quantitative approach, the nature of the relationship between variables is
analyzed using an objective theory.
The type of research used in this research is associative research.
Associative research aims to determine the relationship between two or more
variables (Jaya,
2020). The form of the
relationship in this study is a clausal relationship, namely a causal
relationship arising from the independent variables, namely Risk Profile, Good
Corporate Governance, Earnings, and Capital, to the dependent variable, Islamic
Financial Distress.
The population of this study is Islamic Commercial Banks
registered with OJK, which consists of 14 Islamic Commercial Banks. At the same
time, research sampling is a sampling technique that provides equal
opportunities for each element (member) of the population to be selected as a
sample member (Yusuf,
2016). The sampling technique used
is a purposive sampling technique, namely a sampling technique based on
specific considerations.
Data collection techniques are methods used by researchers to
capture or capture quantitative information from respondents according to the
scope of the research. In this study, researchers used documentation, library,
and observation techniques.
RESULTS
AND DISCUSSION
Descriptive
Analysis
Descriptive statistics provide an overview or
description of data from the maximum, minimum, and standard deviation values.
Descriptive statistics provide an overview of the distribution and behavior of
the sample data. The variables used in this study are Non-Performing Financing (NPF),
Financing to Deposit Ratio (FDR), Good Corporate Governance (GCG), Return On
Assets (ROA), and Capital Adequacy Ratio (CAR). as the independent variable, and Financial Distress as
the dependent variable. The results of the descriptive analysis are as follows:
Table 1. Descriptive
Statistical Test
|
X1 |
X2 |
X3 |
X4 |
X5 |
Y |
Means |
2.254659 |
89.03682 |
1.965909 |
0.930341 |
20.27375 |
5.796136 |
Median |
2.220000 |
88.34000 |
2,000000 |
0.910000 |
19.13500 |
6.080000 |
Maximum |
4.990000 |
196.7300 |
3,000000 |
5.100000 |
45.30000 |
9.680000 |
Minimum |
0.010000 |
63.94000 |
1.000000 |
-10.77000 |
11.10000 |
0.760000 |
std. Dev. |
1.608773 |
17.33679 |
0.614927 |
1.796887 |
6.846361 |
1.861090 |
Source: Data Processed by Researchers, 2023
The variable (X1) has
a mean value of 2.25 and a standard deviation of 1.60. This means that the mean
value is greater than the standard deviation, indicating that the results are
promising. Because the standard deviation illustrates high deviation, data that
is not spread shows average and unbiased results. The minimum value (X1)
of 0.01 was found for BCA Syariah companies in 2020, and the maximum value was
4.99 for BRI Syariah companies in 2018.
The variable (X2) has a mean value of
89.03 and a standard deviation 63.94. This means that the mean value is greater
than the standard deviation, indicating that the results are promising. Because
the standard deviation illustrates high deviation, data that is not spread
shows average and unbiased results. The minimum value (X2) of 63.94
was found in the Bank Mega Syariah company in 2020, and the maximum value was
196.73 in the Bank KB Bukopin Syariah company in 2020.
The variable (X3) has a mean value of
1.96 and a standard deviation of 0.61. This means the mean value exceeds the
standard deviation, indicating good results. Because the standard deviation
illustrates high deviation, data that is not spread shows average and unbiased
results. The minimum value (X3) is 1 in several companies; the maximum
is 3 in several companies.
The variable (X4) has a mean value of
0.93 and a standard deviation of -10.77. This means the mean value exceeds the
standard deviation, indicating good results. Because the standard deviation
illustrates high deviation, data that is not spread shows average and unbiased
results. The minimum value (X4) of -10.77 was found in the Bank
Panin Dubai Syariah company in 2017, and the maximum value was 5.1 in the BPD
NTB Syariah company in 2013.
The variable (X5) has a mean value of
20.27 and a standard deviation 6.84. This means the mean value exceeds the
standard deviation, indicating good results. Because the standard deviation
illustrates high deviation, data that is not spread shows average and unbiased
results. The minimum value (X5) of 11.10 was found for the KB Bukopin Syariah
Bank company in 2013, and the maximum score was 45.3 for the BCA Syariah
company in 2020.
Variable (Y) has a mean value of 5.79 and a
standard deviation of 1.86. This means the mean value exceeds the standard
deviation, indicating good results. Because the standard deviation illustrates
high deviation, data that is not spread shows average and unbiased results. The
minimum value (Y) of 0.76 was found in the NTB Syariah BPD company in 2017, and
the maximum value was 9.68 in the NTB Syariah BPD company in 2019.
Classic Assumption Test
1. Normality test
The
normality test was carried out to test whether, in the regression model, the
confounding or residual variables have a normal distribution or not. This study uses Eviews 10 to
detect whether the residuals are normally distributed. The results
of the normality test using the Jarque-Bera Test can be seen in the image
below:
Figure 1. Normality test
Source: Eviews Output Results (Processed
Data), 2023
Based on the results of the normality test above,
it is known that the probability value is
0.0002 <0.05. So there is a problem with the normality test. To improve
normality, it is necessary to delete
data (outliers) that are
considered to have extreme data so that the results can pass normality.
Figure 2. Improved
Normality Test
Source: Eviews Output
Results (Processed Data), 2023
Based on the image above, after the
outlier is done, it is known that the probability value is 0.0747> 0.05. So
there are no symptoms of normality in this study. The picture above shows
that the number of sample data used is 75 from 88 samples of previous data. The
data reduction was due to the outlier data released in the study. So that for
further research, it will use 75 sample data.
2. Multicollinearity Test
The multicollinearity test tests whether the
regression model found a correlation between independent (independent)
variables. A good regression model should not correlate with the independent
variables. Based
on the data processing performed, the following results are obtained:
Table 2. Multicollinearity Test
Variance Inflation
Factors |
|
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Date: 02/15/23 Time:
20:12 |
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Samples: 1 88 |
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|
|
Included
observations: 75 |
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||
|
coefficient |
Uncentered |
Centered |
Variables |
Variances |
VIF |
VIF |
C |
4.014085 |
128.8524 |
NA |
X1 |
0.036405 |
8.310961 |
2.702797 |
X2 |
0.000369 |
88.44922 |
1.087436 |
X3 |
0.130894 |
17.42305 |
1.281799 |
X4 |
0.047201 |
3.679862 |
2.155811 |
X5 |
0.001490 |
20.17287 |
1.520543 |
Source: Eviews Output
Results (Processed Data), 2023
From the results of the table above, it can be concluded that
all the independent variables used in the equation are free from multicollinearity problems because all
the variables used in this study have a VIF value of <10, which means that
the data used for the study do not experience multicollinearity.
3. Heteroscedasticity Test
The
heteroscedasticity test aims to test whether, in the regression model, there is
an inequality of variance from the residuals of one observation to
another. Suppose
the variance from the residual observation to other observations
remains. In that case, it is called homoscedasticity, and if the variance is
not constant or changes, it is called heteroscedasticity. A good
regression model is homoscedasticity, or there is no heteroscedasticity.
Table 3. Heteroscedasticity Test
Test Equation: |
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|
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Dependent
Variable: LRESID2 |
|
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Method: Least
Squares |
|
|
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Date: 02/15/23
Time: 20:13 |
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Samples: 3 88 |
|
|
|
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Included
observations: 75 |
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Variables |
coefficient |
std. Error |
t-Statistics |
Prob. |
C |
-6.856322 |
2.849771 |
-2.405921 |
0.0188 |
X1 |
-0.368449 |
0.271392 |
-1.357624 |
0.1790 |
X2 |
0.048716 |
0.027327 |
1.782668 |
0.0790 |
X3 |
0.337196 |
0.514608 |
0.655249 |
0.5145 |
X4 |
0.229733 |
0.309023 |
0.743415 |
0.4598 |
X5 |
0.085638 |
0.054903 |
1.559795 |
0.1234 |
Source: Eviews Output
Results (Processed Data), 2023
Based on the table above using the
heteroscedasticity test, the probability values for all research variables are
above 0.05. So, in this study, there was no heteroscedasticity problem because
the Sig value > 0.05.
4. Autocorrelation Test
This test determines whether there
is a deviation from the classic autocorrelation assumption, namely the
correlation between the residuals in the i-th and k-th observations. A good
regression model is a regression that is free from autocorrelation.
Table 4 . Autocorrelation Test
Breusch-Godfrey Serial Correlation LM Test: |
|||
F-statistics |
0.985172 |
Prob. F(2,67) |
0.3787 |
Obs*R-squared |
2.142598 |
Prob. Chi-Square(2) |
0.3426 |
Source: Eviews Output
Results (Processed Data), 2023
Based on the table above, it is known that the
value of Prob. F is 0.3787 > 0.05, so the autocorrelation test has no
symptoms or problems.
Panel Data Regression Model Selection Test
1.
Chow test
The Chow test or Chow test is a test to determine the most
appropriate Common Effect or Fixed Effect model used in estimating panel data.
The hypothesis in the Chow test is:
H0:
Common Effect Model or pooled OLS
Ha: Fixed Effects Model
Table 5. Chow
test
Redundant Fixed Effects Tests |
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Equation: Untitled |
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Test cross-section fixed effects |
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Effect Test |
Statistics |
df |
Prob. |
Cross-section F |
2.074954 |
(10.59) |
0.0411 |
Chi-square cross-sections |
22.601516 |
10 |
0.0123 |
Source: Eviews Output Results (Processed
Data), 2023
Based on the results of the Chow test using Eviews 10, a
probability value of 0.0411 is obtained. This shows that the probability value
is smaller than the significance level (0.05), then H0 for this model is
rejected. Ha is accepted, so a better estimate is used using the Fixed Effect Model (FEM) method, then
proceed to the Hausman test.
2.
Hausman test
The Hausman test is a statistical test to choose whether the fixed
effect or random effect model is the most appropriate. Suppose the Hausman statistical
value is less than the critical value (0.05). In that case, Ha is accepted (the
correct model is the fixed effect model) and vice versa. The hypothesis put
forward is as follows:
H0 : Random Effects Model
Ha : Fixed Effects Model
From the results of the
model analysis using the Hausman test, the following results are obtained:
Table 6.
Hausman test
Correlated Random Effects - Hausman Test |
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Equation: Untitled |
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Test cross-section random effects |
|
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Test Summary |
Chi-Sq. Statistics |
Chi-Sq. df |
Prob. |
Random cross-sections |
7.021571 |
5 |
0.2190 |
Source: Eviews Output Results (Processed Data),
2023
Based on the results of the Hausman test using
Eviews 10, a probability of 0.2190 is obtained, indicating that the probability
value is greater than the significance level (0.05) so that it can
be concluded that H0 for this model is
accepted and Ha is rejected. The appropriate estimation model used is the Random Effect Model (REM). Because
there are differences in the model used from the results of the Chow and
Hausman tests, it is necessary to carry out the Lagrange Multiplier test.
3.
Lagrange Multiplier Test (LM)
The Lagrange Multiplier test
determines the model for panel data regression analysis. The hypothesis put forward is as follows:
H0 : Common Effect Model
Ha : Random Effects Model
Table 7.
Lagrange Multiplier Test
Lagrange
multiplier (LM) test for panel data |
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Date: 02/15/23
Time: 20:02 |
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Sample: 2013 2020 |
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Total panel
observations: 75 |
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probability in() |
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Null (no rand.
effect) |
Cross-section |
period |
Both |
Alternatives |
One-sided |
One-sided |
|
Breusch-Pagan |
0.198614 |
1.078639 |
1.277253 |
|
(0.6558) |
(0.2990) |
(0.2584) |
Honda |
0.445661 |
-1.038575 |
-0.419254 |
|
(0.3279) |
(0.8505) |
(0.6625) |
King-Wu |
0.445661 |
-1.038575 |
-0.510244 |
|
(0.3279) |
(0.8505) |
(0.6951) |
GM |
-- |
-- |
0.198614 |
|
-- |
-- |
(0.5543) |
Source:
Eviews Output Results (Processed Data), 2023
Based on the output results
above, it is known that the Breusch-Pagan probability value is 0.6558 >
0.05. Then H0 is
rejected, and Ha is accepted, so the suitable model for the following analysis
is the Common Effect Model (CEM).
Panel Data Regression Analysis
This analysis is used to see the effect of the independent
variable on the dependent variable in the form of panel data which consists of
a combination of time series and cross-section data. Using panel regression
estimation with the Common Effect Model
(CEM) approach. The estimation results using Eviews 10 are as follows:
Table 8. Panel Data Regression Model Test Results
Dependent Variable: Y |
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Method: Panel Least Squares |
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Date: 02/15/23 Time: 20:19 |
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Sample: 2013 2020 |
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The period included: 8 |
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Cross-sections included: 11 |
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Total panel (unbalanced) observations: 75 |
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Variables |
coefficient |
std. Error |
t-Statistics |
Prob. |
C |
10.45591 |
2.003518 |
5.218774 |
0.0000 |
X1 |
-0.390736 |
0.190801 |
-2.047871 |
0.0444 |
X2 |
-0.002909 |
0.019212 |
-0.151420 |
0.8801 |
X3 |
-0.869143 |
0.361793 |
-2.402324 |
0.0190 |
X4 |
-0.934342 |
0.217258 |
-4.300621 |
0.0001 |
X5 |
-0.045664 |
0.038599 |
-1.183031 |
0.2409 |
R-squared |
0.309080 |
Mean dependent var |
5.807733 |
|
Adjusted R-squared |
0.259013 |
SD dependent var |
1.775711 |
|
SE of regression |
1.528543 |
Akaike info criterion |
3.763125 |
|
Sum squared residue |
161.2146 |
Schwarz criterion |
3.948524 |
|
Likelihood logs |
-135.1172 |
Hannan-Quinn criteria. |
3.837153 |
|
F-statistics |
6.173357 |
Durbin-Watson stat |
1.528445 |
|
Prob(F-statistic) |
0.000087 |
|
|
|
Source: Eviews Output
Results (Processed Data), 2023
The results of the
equation from the table above are:
Y = α + b1X1 +
b2X2 + b3X3 + b4X4 + b5X5 + e
Y = 10.45 –
0.39 X1 - 0.002 X2 – 0.86 X3 – 0.93 X4 – 0.04 X5 + e
Information:
Y = Financial
Distress
α
= Constant
β1
= Variable Coefficient
X1
= Non-Performing
Financing
X2
= Financing
to Deposit Ratio
X3 = Good
Corporate Governance
X4 = Return
On Assets
X5 =
Capital Adequacy Ratio
ε
= Error Term
From the equation above, it can be explained that:
a.
A constant value of 10.45 indicates that if the dependent
variable is Financial Distress is zero, then Financial Distress is a constant of 10.45%.
b.
The coefficient value of Non-Performing
Financing of -0.39
indicates that a decrease in Non-Performing Financing
in one unit number will decrease Financial
Distress by -0.39%
per unit, assuming other variables are constant.
c.
The coefficient value of the
Financing to Deposit Ratio
of -0.002 indicates that a decrease in the
Financing to Deposit Ratio in one unit number will decrease Financial Distress by -0.002% per unit, assuming other variables are
constant.
d.
A good Corporate Governance coefficient value of -0.86 indicates that a
decrease in Good Corporate Governance in one
unit number will decrease Financial Distress by -0.86% per unit, assuming
other variables are constant.
e.
The coefficient value Return On
Assets of -0.93
indicates that a decrease in Return On Assets in
one unit number will decrease Financial Distress by -0.93% per unit, assuming
other variables are constant.
f.
The capital Adequacy Ratio coefficient value of -0.04 indicates that the
decline in Capital Adequacy Ratio in one unit
of numbers will result in a decrease in Financial
Distress of -0.04%
unit, assuming other variables are constant.
Hypothesis test
1.
T-test
The decision to reject or accept the hypothesis with the amount of data
is 75 and with a significance level of 5% with the formula t table = t(α/2;nk-1) =
t(0.05/2;75 -5 -1) = (0.025; 69) so
that the t-table value in data 69 is
1.9949 based on the following
criteria.
Based on the comparison of the values of 𝑡count
and 𝑡𝑡able the basis for the decision is:
1)
If 𝑡count<, then H0 is accepted, and Ha is rejected (no effect).
2)
If 𝑡count>, then H0 is rejected, and Ha is
accepted (there is influence).
So, the results of the hypothesis from Table
4.9 include the following:
1)
There is a significant negative effect of the variable Non-Performing Financing (X1) on Financial
Distress (Y) because the Prob value is 0.0444 <0.05. So
that there is an influence between the variable X1 on Y,
or in other words, H0 is rejected, and Ha is accepted.
2)
There is no effect of the
Financing to Deposit Ratio (X2) variable on Financial Distress (Y)
because the Prob value is 0.8801 > 0.05. So that there
is no influence between variable X2 on Y,
or in other words, H0 is accepted, and Ha is rejected.
3)
There is a significant negative effect of the variable
Good Corporate Governance (X3) on
Financial Distress (Y) because the Prob value is 0.0190 <0.05. So
that there is an influence between the variable X3 on Y,
or in other words, H0 is rejected, and Ha is accepted.
4)
There is a significant adverse effect on the variable Return On Assets (X4) on Financial Distress (Y)
because the Prob value is 0.0001 <0.05. So that there
is an influence between the variable X4 on Y,
or in other words, H0 is rejected, and Ha is accepted.
5)
There is no effect of the Capital
Adequacy Ratio variable (X5) on Financial Distress (Y)
because the Prob value is 0.2409 > 0.05. So that there
is no influence between the variable X5 on Y,
or in other words, H0 is accepted, and Ha is rejected.
2.
F test
Eviews data processing on the F test
is to see whether or not there is an influence of the independent variables on
the dependent variable and to test whether the model used is fixed or not. The data
processing results in Table 8 above show a significant value at 0.0000 (Sig 0.0000 <0.05).
This means indicating that the regression equation obtained is reliable or the
model used is fixed, so this means that
the variables X1, X2, X3, X4, and X5
can explain the dependent variable (Y) together or there is a simultaneous
influence of the independent variable on the variable dependent.
3.
Determination Coefficient Test (R2)
The coefficient of determination aims
to see or measure how far the model's ability to explain the dependent variable
is. From the output display Eviews 10 in Table 8 above, the magnitude of R
Square is 0.2590. This indicates that the
contribution of the independent variable to the dependent variable is 25.90%. In comparison, the remaining 74.1%
(100-25.90) is determined by other factors outside the model which were not
detected in this study.
This study measures the soundness of Islamic Commercial Banks
using the RGEC ( Risk et al., Earnings, and Capital ) method. Where in
measuring the Risk Profile using Non-Performing
Financing (NPF) and Financing to Deposit Ratio
(FDR) proxies. Non-Performing Financing (NPF) is an indicator of the
health of the quality of a bank's assets; the higher the NPF value (above 5%),
the bank is unhealthy (Z.,
2012). Table 2
shows that the average NPF of Islamic Commercial Banks in the 2013-2020 period
is categorized as very healthy. This shows at least bad credit and problematic
financing by customers at Islamic banks. The category of all banks is still in
the very healthy category, which is interpreted as being in a safe position
because the NPF value of each Islamic Commercial Bank shows results of less
than 5%.
Apart from Non-Performing Financing
(NPF), the Risk Profile is also proxied using the
Financing to Deposit Ratio (FDR). This ratio indicates the health of bank
liquidity (Z., 2012). Based on Table 2, it is concluded that the average FDR of
Islamic Commercial Banks in the 2013-2020 period is categorized as relatively
healthy, meaning that the bank, in fulfilling its short-term obligations and
collecting third-party funds, is in a reasonably good category.
Good Corporate Governance (GCG) is a system that manages and
controls companies to create added value for stakeholders (Faridah
et al., 2023). Table 2 shows that the
average GCG of Islamic Commercial Banks in 2013-2020 is categorized as good
(healthy). This reflects that Islamic Commercial Banks fulfill and are adequate
to Good Corporate Governance (GCG) principles. If there are weaknesses in
applying the principles of Good Corporate Governance (GCG), then in general,
these weaknesses are less significant and can be resolved by everyday actions
by Islamic bank management.
Earnings are proxied using Return On Assets (ROA), where ROA
is the ratio used to measure the ability of bank management to gain overall
profit (profit). The greater the ROA of a bank, the greater the level of profit
the bank achieves and the better the bank's position in terms of asset use.
Based on Table 2, the average ROA of Islamic Commercial Banks in the 2013-2020
period is categorized as relatively healthy because changes in ROA for each
bank vary. The higher the ROA means that the bank can use its assets well to
earn profits and vice versa.
Capital is proxied using the Capital Adequacy Ratio (CAR).
The higher the Capital Adequacy Ratio (CAR), the better the bank's performance
because with sufficient CAR, the bank can operate can generate profits. Table 2
shows that the average CAR of Islamic Commercial Banks in the 2013-2020 period
is categorized as very healthy. This shows that Islamic Commercial Banks have a
higher level of capital, are adequate, and can anticipate all the risks faced and support the expansion of business banks forward.
The purpose of maqasid Sharia is to maintain maslahah or
suitable for the public interest and the company, which means the company can
generate profits. For the company to fulfill its obligations in realizing
profits for the public interest, it must maintain its financial condition
healthy, without the potential for bankruptcy. This can be realized by
anticipating bankruptcy prediction using one of the methods, the Altman
Z-Score. By predicting bankruptcy, the goal of maqashid Sharia in maintaining
the company's survival ( going concerned) can be achieved.
Effect of Risk Profile on Financial Distress in Islamic Commercial
Banks in Indonesia
This study shows that the Risk Profile proxied by Non-Performing Financing (NPF) significantly
negatively affects Financial Distress. The higher the NPF value, the poorer the
quality of the bank's financing and the potential for bankruptcy. The low NPF
value can be caused by Islamic Commercial Banks being able to provide financing
using the precautionary principle before providing financing and restructuring
financing if there are obstacles in repayment.
The results of this study are supported by the existing
theory that a high NPF can reduce the level of profitability so that it affects
the ability of banks to expand their financing business, and financing
performance will decrease (Z.,
2012). In addition, this study
aligns with previous studies, which stated that the Risk Profile variable
proxied by Non-Performing Financing (NPF) had
a significant adverse effect on Financial Distress. In contrast, the results of
previous research conducted by Wahyuni stated that Non-Performing
Financing ( NPF) significantly negatively affects Financial Distress.
That way, the two studies produce the same decision: the Risk Profile variable
proxied by Non-Performing Financing (NPF)
significantly negatively affects Financial Distress in Islamic Commercial Banks
in Indonesia.
The Risk Profile is also proxied from NPF using the Financing
to Deposit Ratio (FDR). This ratio measures a bank's ability to fulfill
financing by utilizing Third Party Funds (DPK). If the bank cannot distribute
financing and many funds are collected, the bank will suffer losses (Hasibuan
et al., 2020). However, this study does
not strengthen the existing theory. Based on the tests that have been carried
out, it is found that FDR has no effect on Financial Distress.
This shows that the amount or amount of funds channeled by
banks to the public does not affect the possibility of banks experiencing
financial distress. The amount of FDR disbursed indicates that the bank's
management can market good funds, but this cannot reflect the low probability
of Islamic Commercial Banks experiencing financial distress. Islamic Commercial
Banks have sufficient liquidity capacity to fulfill bank obligations so that FDR
does not affect financial distress.
This study's results align with previous studies, which
stated that the Risk Profile variable proxied by the Financing to Deposit Ratio
(FDR) did not affect Financial Distress. In contrast, the results of previous
studies stated that the Financing to Deposit Ratio (FDR) did not affect the
probability of occurrence of banking distress (financial distress) (Prianti
& Musdholifah, 2018). That way, the two studies
produce the same decision; namely, the Risk Profile variable proxied by FDR
does not affect Financial Distress at Islamic Commercial Banks in Indonesia.
Good corporate governance ( GCG) is the structure and
mechanism that regulates the company's management to produce sustainable
long-term economic value for shareholders and stakeholders. This study shows
that Good Corporate Governance significantly negatively affects Financial
Distress. These results illustrate that the better the implementation of
corporate governance, the less financial distress can be. Conversely, if the
implementation of corporate governance worsens, it will increase the
possibility that the company or bank will experience financial distress. This
is supported by the statement that one of the causes of financial distress can
be seen in the form of corporate governance that is not managed correctly (Hutabarat,
2020).
This study's results align with previous research, which
stated that Good Corporate Governance (GCG) had a significant adverse effect on
Financial Distress. In contrast, the results of previous research conducted by
Mahmud, Handajani, and Waskito stated that Good Corporate Governance (GCG) had
a negative effect significant impact on Financial Distress (Prianti
& Musdholifah, 2018). In this way, the two
studies make the same decision: Good Corporate Governance (GCG) has a
significant negative effect on Financial Distress in Islamic Commercial Banks
in Indonesia.
The Effect of Earnings on Financial Distress in Islamic Commercial
Banks in Indonesia
Earnings are proxied using Return on Assets (ROA). This ratio
is a comparison of net income to total assets. The higher the ROA value, the
higher the profit generated (Fauziah,
2017). This study shows that ROA
has a significant negative effect on Financial Distress. This shows that the
company has implemented asset effectiveness in obtaining profits that can be
used to fund its operational activities so that the company has a slight
(negative) possibility of experiencing financial distress.
This study's results align with previous research, which
stated that the Earnings variable proxied by Return on Assets (ROA)
significantly adversely affected Financial Distress (Yuliani
& Haryati, 2023). Meanwhile, the results of
previous studies state that Return on Assets (ROA) has a significant negative
effect on Financial Distress (Mahmud
& Waskito, 2021). That way, the two studies
produce the same decision: the Earnings variable proxied by Return on Assets
(ROA) has a significant negative effect on Financial Distress. in Islamic
Commercial Banks in Indonesia.
The Effect of Capital on Financial Distress in Islamic
Commercial Banks in Indonesia
Capital is proxied using the Capital Adequacy Ratio (CAR).
This ratio indicates a bank's ability to cover declining assets due to losses
on bank assets using its capital (Siswanti
et al., 2020). This study found that CAR
does not affect Financial Distress. These results indicate that an increase in
CAR does not affect the possibility of a company experiencing financial
distress, which means that the bank's ability to cover its risky assets is
good. Additional capital makes the bank's capital sufficient. It supports
assets that contain or generate risk so that it does not affect financial
distress.
The results of this study strengthen the existing theory, in
which one of the factors of financial distress is a lack of working capital (Hery
& Si, 2017). Based on the research
results above, CAR in Islamic Commercial Banks is in a very healthy category,
so CAR does not affect financial distress. This research is also in line with
previous research, which stated that the variable capital proxied by the
Capital Adequacy Ratio (CAR) did not significantly affect Financial Distress.
In contrast, the results of previous studies stated that the Capital Adequacy
Ratio (CAR) did not significantly affect Financial Distress. Distress (Nisak,
2021). That way, the two studies
produce the same decision, namely, the variable capital, which is proxied by
the Capital Adequacy Ratio (CAR), does not significantly affect Financial
Distress. in Islamic Commercial Banks in Indonesia.
Based on the study's results, the effect of Risk Profile,
Good Corporate Governance, Earnings, and Capital proxied by NPF, FDR, GCG, ROA,
and CAR simultaneously significantly influences Financial Distress in Islamic
Commercial Banks in Indonesia. The results of this study show that the
influence of the five factors measured, namely NPF, FDR, GCG, ROA, and CAR, can
change the condition of Financial Distress in Islamic Commercial Banks in
Indonesia. Financial and non-financial distress conditions depend on the high or
low value of NPF, FDR, GCG, ROA, and CAR. In addition, if Islamic banks can
maintain their health, then the
bank's performance will automatically be good so that the bank can avoid
financial distress.
The results of this study are in line with previous research,
which stated in this study that the assessment of the soundness level of a bank
using the RGEC method, which was proxied by NPF, FDR, GCG, ROA, and CAR
simultaneously, had a significant effect on Financial Distress. In contrast,
the results of previous research conducted by Ermar and Suhono stated that the
soundness level of a bank using the RGEC method, which is proxied by NPL, LDR,
GCG, ROA, and CAR, simultaneously has a significant effect on Financial
Distress (Prabawati
et al., 2021). In this way, the two
studies produce the same decision, namely the assessment of the soundness of a
bank using the RGEC method, which is proxied by NPF, FDR, GCG, ROA, and CAR,
which simultaneously have a significant effect on Financial Distress at Islamic
Commercial Banks in Indonesia.
CONCLUSION
In conclusion, the
assessment of the soundness of Islamic commercial banks in Indonesia using the
RGEC method reveals positive results. The banks demonstrate healthy levels of
risk profile, as indicated by low NPF and relatively healthy FDR values. The
implementation of Good Corporate Governance (GCG) principles is satisfactory,
contributing to lower financial distress. Earnings, represented by ROA, are
relatively healthy, indicating sufficient profitability. Capital, proxied by
CAR, is at a very healthy level, supporting the bank's ability to manage risks
and expand its business.
The perception of
maqashid Sharia aligns with the goal of bankruptcy prediction, emphasizing the
importance of maintaining the benefit and preventing harm. The analysis
highlights the significance of risk profile, GCG, earnings, and capital in
influencing financial distress. Effective risk management, strong corporate
governance practices, and profitability contribute to reducing the likelihood
of financial distress in Islamic commercial banks.
Overall, the findings
emphasize the importance of maintaining a healthy financial position and
implementing robust risk management practices to ensure the stability and
resilience of Islamic commercial banks in Indonesia.
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