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https://ajesh.ph/index.php/gp
Liquidity
Risk, Credit Risk and Capital as Determining of Predicting Financial Distress in
Rural Banks in Indonesia
I Nengah Arsana1*, I Made Suardana2,
Indah Ariffianti3
1,2,3STIE AMM Mataram, West Nusa Tenggara,
Indonesia
Email: inengaharsana2@gmail.com1*,
imade_suar@yahoo.com2,
indahariffianti99@gmail.com3
ABSTRACT:
This study aims to
analyze the level of accuracy of financial distress prediction models and to
test the ability of liquidity risk ratio, credit risk and capital ratio in
predicting the possibility of financial distress in rural banks (BPR) in
Indonesia. The data used is sourced from secondary data and collected from
BPR's financial statements published on the Financial Services Authority (OJK)
website during the 2014-2023 period. The population in this study is all rural
banks as many as 1,402 rural banks and the number of samples is 312 rural banks
spread throughout Indonesia. Determination of samples by the Slovin method by proportionate stratified random sampling
technique. The results of the study that the liquidity risk ratio, credit risk
and capital ratio in predicting financial distress can be used with an accuracy
rate of 95.90%. Liquidity risk ratio and credit risk ratio have a positive and
significant effect, capital ratio and primary ratio have a negative and
significant effect, while capital adequacy ratio has a positive and significant
effect on the possibility of financial distress in rural banks in Indonesia.
Keywords: Financial Distress,
Liquidity Risk, Credit Risk, Capital, Rural bank.
INTRODUCTION
People's
credit banks (BPR) are one of the bank's financial institutions that have a
role as an intermediary institution, whose role is to collect public funds and
then distribute them back to people who need capital for business purposes.
Therefore, the existence of rural banks is still needed to serve the interests
of the community, especially in areas that have not been reached by commercial
banks. But in fact at this time commercial banks also
expand their service areas to villages, so competition becomes quite tight. Not
to mention the existence of competitors from microfinance institutions,
especially savings and loan cooperatives. The intense competition makes BPR have to improve its financial performance and improve
service to customers.
BPR's
financial performance over the last 10 years is mainly seen from the number of
assets, core capital, third party funds raised, and the number of loans
successfully disbursed continue to increase, as can be explained in figure 1
below.
Figure 1. Development of Total Assets,
Total Deposits, Total Loans and Total Equity of BPR in Indonesia for the Period
2014 – 2023
Source: Indonesian Banking Statistics
(data processed)
Based on data obtained from the
Indonesian Banking Statistics for the period 2014-2023 as explained in Figure 1
above, the total assets of rural banks in Indonesia continue to increase from
Rp.89.88 trillion in 2014 to Rp.194.98 trillion in 2023. The increase in the
number of assets was caused by an increase in the amount of third party funds
(DPK) that were successfully raised, where in 2014 DPK amounted to Rp.72.91
trillion increased to Rp.158.75 trillion in 2023 this means that public trust
in BPR continues to increase and is also followed by an increase in BPR equity
which in 2014 Rp.12.80 trillion increased to Rp.25, 14 trillion by 2023.
Increasing public confidence also affects the ability of rural banks to
disburse credit to their debtors, where total loans in 2014 amounted to
Rp.59.18 trillion increased to Rp.140.79 trillion in 2023.
Increasing public confidence in rural
banks and increasing total loans disbursed were not followed by improvements in
other financial performance. Where the return on assets of rural banks as one
of the benchmarks that rural banks succeed in financial performance is
precisely the ROA of rural banks continues to decrease, one of the causes is an
increase in non-performing loans, as can be explained in figure 2 below.
Figure 2. Development of NPL, ROA and
Banks in Liquidation of BPR in Indonesia for the 2014-2023 Period
Source: Indonesian Banking Statistics
(data processed)
Indicators of the success of rural
banks in disbursing loans can be seen from how much the loans disbursed are
problematic, the greater the loans disbursed are problematic in addition to
disrupting liquidity capabilities will also hamper and even reduce BPR
revenues, eventually BPR profits also fall. This can be explained as figure 2
above, that BPR's NPL over the last 10 years has continued to increase, in 2014
it was 4.75% and continued to increase to 9.87% in 2023 and has implications
for ROA which continues to decrease. This condition will cause the possibility
of BPR experiencing financial difficulties and eventually going bankrupt. This
condition causes banks to be liquidated by the Financial Services Authority
(OJK), as it is known that every year there are banks liquidated by the OJK,
during the 2014-2023 period OJK has liquidated as many as 61 rural banks, which
is an average of 6 rural banks every year. The highest number of liquidated
rural banks occurred in 2017 and 2019 as many as 9 rural banks each, while the
lowest in 2022 was 1 rural bank.
Banks as intermediary institutions,
which attract and distribute public funds. These two activities are
interrelated and contain elements of risk in the form of credit risk and
capital adequacy
Research Results of
While research by
Based on the above, this study will
examine the ability of liquidity risk ratios, credit risk ratios, and capital
ratios to predict the occurrence of financial distress in rural banks in
Indonesia.
RESEARCH
METHODS
The data used is sourced from secondary data and
collected from conventional rural bank (BPR) financial statements published on
the Financial Services Authority (OJK) website during the 2014-2023 period. The
population in this study is all conventional rural banks in Indonesia as many
as 1,402 rural banks. The number of samples in this study was 312 rural banks
spread throughout Indonesia, whose sampling was determined using the Slovin method
Research
Variables and Their Measurement
The independent variables in this study are
liquidity risk ratio, credit risk and capital ratio (Xi), while to
predict bank financial distress used Zmijewski model
(1984), which is a dependent variable (Y) and is a dummy variable with a
nominal scale, namely rural banks that experience financial distress are given
a value of 1 and those that do not experience financial distress is rated 0.
The 1984 Zmijewski model with the formula
Zm = -4.336 – 4.513 X1 + 5.679 X2 – 0.004 X3
Where:
Zm = Overall Zmijewski index
X1 =
Net profit/total assets
X2 =
Total debt/total assets
X3 =
Current assets/current debt
Zmijewski classifies companies as follows:
-
Companies with a probability smaller than 0.5
are classified into companies that do not experience financial difficulties
-
Companies with a greater probability of 0.5
are classified as companies experiencing financial difficulties
Capital Adequacy Ratio = equity capital to risk
weighted assets
|
X1 = LRR |
: |
Liquidity risk ratio = (liquid assets
– short term borrowing) to total deposit |
|
X2 = CRR |
: |
Credit risk ratio = bad debt to total
loans |
|
X3 = CR |
: |
Capital ratio = (equity capital +
reserve for loan losses) to total loans |
|
X4 = PR |
: |
Primary ratio = equity capital to
total assets |
|
X5 = CAR |
: |
Capital Adequacy Ratio = equity
capital to risk weighted assets |
Data
Analysis Methods
The data analysis method in this study uses logit
regression and data processing using the Statistical Package for Social 24.0
for Windows software tool. Tests conducted using descriptive statistics aim to
describe the minimum and maximum values. Mean and standard deviation of the
variables present in this study. Hypothesis testing in this study uses binary
logit analysis to examine the classification power and significance of the
ratio of liquidity risk, credit risk and capital to the possibility of financial
distress in rural banks.
1 - FD2CRR + b3CR+ b4PR+ b5CAR
|
Y = Ln |
FD
|
= |
a + b1LRR + b2CRR
+ b3CR+ b4PR+ b5CAR |
|
1 - FD |
where:
FD = Financial
Distress CR = Capital Ratio
LRR =
Liquidity Risk Ratio PR = Primary Ratio
CRR =
Credit Risk Ratio CAR = Capital Adequcy
Ratio
To test the feasibility of the binary logit
regression model above, the feasibility test of the model and the overall
feasibility test of the model are carried out, as follows:
Model
Feasibility Test
Model Feasibility Test is used to test the
feasibility of the model
Overall Fit Model Test
The overall feasibility test of the
model can be obtained by doing the Chi Square test (X2), the use of the value
of X2 is done by comparing the value of -2log likelihood of the
beginning (block number 0) with the value of -2log likelihood of the result
(block number 1), if there is a decrease in the value of -2log likelihood then
the model shows a good regression model or vice versa.
Furthermore, to answer the first
hypothesis in this study, a Classification Table test was carried out to
predict the possibility of rural banks experiencing financial distress or not
experiencing financial distress, so that the level of classification or
accuracy of the financial distress prediction model can be known using the
liquidity risk ratio, credit risk ratio and capital ratio.
W Test (wald)
To test the
second hypothesis of this study, the W (Wald) test is conducted to examine the
significance of the partial effects of independent variables on the dependent
variable, using the formula:
|
Wi = |
|
SE Bj |
2 |
If Wi > X2 at alpha 0.05 (the
independent variable has no significant effect on the dependent variable)
If Wi < X2 at alpha 0.05 (the
independent variable has a significant effect on the dependent variable)
RESULTS
AND DISCUSSION
This study uses financial statement data of 312 conventional rural
banks during the period 2014-2023 so the number of samples in this study is
3,120 years of observation. Descriptive statistics can be presented in Table 1
below.
Table 1. Descriptive Statistics
|
|
N |
Minimum |
Maximum |
Mean |
Std. Deviation |
|
Liquidity Risk Ratio |
3120 |
5.6400 |
995.5800 |
43.281349 |
34.9551020 |
|
Credit Risk Ratio |
3120 |
.0003 |
101.7800 |
6.613997 |
6.7564505 |
|
Capital Ratio |
3120 |
8.4600 |
542.7300 |
36.492821 |
28.1362738 |
|
Primary Ratio |
3120 |
3.5100 |
91.9200 |
22.529766 |
13.7162507 |
|
Capital Adequacy Ratio |
3120 |
1.0300 |
304.0800 |
26.288147 |
18.9880999 |
|
Valid N (listwise) |
3120 |
|
|
|
|
Source: Bank Publication Report (processed)
Based on Table 1. above, it is known
that the average value of Liquidity risk ratio is 43.28% with a minimum value
of 5.64% and a maximum value of 995.58% and a standard deviation of 34.96%
shows that the average BPR in Indonesia has a fairly high
LRR ratio. The average credit risk ratio of 6.61% with a minimum value of
0.0003% and a maximum value of 101.78% with a standard deviation of 6.76% shows
that the average ratio of loans containing risk is still high.
The average value of Capital ratio of
36.49% with a minimum value of 8.46% and a maximum value of 542.73% with a
standard deviation of 28.14% shows that the average ratio of capital owned to
loans is quite good. The average Primary ratio value is 22.53%, the minimum
value is 3.51% and the maximum is 91.92% with a standard deviation of 13.72%,
and the average Capital adequacy ratio is 26.29% with a minimum value of 1.03%
and a maximum value of 304.08% with a standard deviation of 18.99%.
By looking at the output of the Hosmer
and Lemeshow Model Feasibility Test, the results of
the model feasibility test can be seen in Table 2 below:
Table
2. Model Feasibility Test Results
|
Hosmer
and Lemeshow Test |
|||
|
Step |
Chi-square |
df |
Sig. |
|
1 |
5.740E+15 |
8 |
.000 |
Based on Table 2 above, the value of Hosmer and Lemeshow (Chi Square) > α (0.05) then the model is
feasible to use in this study.
The overall feasibility test results of
the model can be obtained by comparing the value of -2 log likelihood (block
number 0) with the value of -2 log-likelihood result (block number 1), as shown
in Table 3 below.
Table
3. Overall Model Feasibility Test Results
|
Overall Fit Model Test |
||
|
Block |
-2 log likelihood |
|
|
Block Number 0 |
3532.935 |
|
|
Block Number 1 |
849.986 |
|
Source: processed data
Based on Table 3 above, the value of -2
log likelihood initial (block number 0) of 3,532,935 and the value of -2 log
likelihood of results (block number 1) of 849,986 can be said to indicate that
the value of -2 log likelihood in this study decreased; therefore, the
regression model in this study is feasible and good to use.
Furthermore, to answer the first
hypothesis in this study, the classification table test was carried out. The
results of the classification of rural banks that are predicted to experience
financial distress and rural banks that do not experience financial distress
can be seen in Table 4 below.
|
Classification Tablea |
|||||
|
|
Observed |
Predicted |
|||
|
|
Rural Banks |
Percentage Correct |
|||
|
|
Not in financial distress |
Financial distress |
|||
|
Step 0 |
Rural Banks |
Not in financial distress |
2264 |
65 |
97.2 |
|
Financial distress |
64 |
727 |
91.9 |
||
|
Overall Percentage |
|
|
95.9 |
||
|
a. The cut value is .500 |
|||||
Based on Table 4 above, it can be seen,
from the number of research samples as many as 3,120 BPR with the results of
the overall logistic regression classification, the accuracy of the prediction
results is very good, which is 95.9%. The accuracy of predictions in rural
banks that did not experience financial distress was very high at 97.2%, where
as many as 2,264 observations were predicted correctly and only 65 observations
were predicted otherwise. The accuracy of predictions in rural banks
experiencing financial distress was 91.9%, of which there were 727 observations
that could accurately be predicted to experience financial distress, and there
were 64 observations that could not be predicted to experience financial
distress.
Based on the results mentioned above,
the percentage of truth or accuracy in predicting rural banks experiencing
financial distress is very high, at 91.9%; it can be stated that the financial
distress prediction model is correct and accurate; thus, the first hypothesis
(H1) can be accepted. This is also reinforced by the results of the Omnibus
Test of Model Coefficients and Model Summary, as can be explained in Table 5
below.
Table 5.
Output of Omnibus Tests of Model Coefficients and Model Summary
|
Omnibus Tests of Model Coefficients |
|||||||
|
|
Chi-square |
df |
Sig. |
||||
|
Step 1 |
Step |
2682.949 |
5 |
.000 |
|||
|
Block |
2682.949 |
5 |
.000 |
||||
|
Model |
2682.949 |
5 |
.000 |
||||
|
Model Summary |
|||||||
|
Step |
-2 Log likelihood |
Cox & Snell R Square |
Nagelkerke R Square |
||||
|
1 |
849.986a |
.577 |
.851 |
||||
|
Estimation terminated at iteration number
10 because parameter estimates changed by less than .001. |
|||||||
In Table 5 above, the significance
value of the regression model is obtained at 0.000 which < 0.05, so it can
be concluded that the independent variables (liquidity risk ratio, credit risk
ratio and capital ratio) can have a real influence simultaneously on the model.
While the Nagelkerke R Square value of 85.10% shows
that the contribution of independent variables in the form of liquidity risk
ratio, credit risk ratio and capital ratio in predicting financial distress is
85.10% and the remaining 14.90% can be predicted and explained by other
independent variables that are not included in this research model.
Results of Binary Logit Regression Test and Model Formation
Logit regression testing is carried out
with the Wald test to partially test the effect of the independent variable on
the dependent variable with a significance level of 5%. As can be seen in Table
6 below, a logit regression model can be formed.
Table 6. Wald
Test Results
|
Variables
in the Equation |
|
|||||||
|
|
B |
S.E. |
Wald |
df |
Sig. |
Exp(B) |
||
|
Step 1a |
Liquidity Risk Ratio |
.031 |
.008 |
16.499 |
1 |
.000 |
1.032 |
|
|
Credit Risk Ratio |
.150 |
.020 |
54.174 |
1 |
.000 |
1.161 |
|
|
|
Capital Ratio |
-.061 |
.023 |
6.917 |
1 |
.009 |
.941 |
|
|
|
Primary Ratio |
-1.457 |
.082 |
314.521 |
1 |
.000 |
.233 |
|
|
|
Capital Adequcy Ratio |
.154 |
.032 |
22.856 |
1 |
.000 |
1.166 |
|
|
|
Constant |
16.011 |
.847 |
357.716 |
1 |
.000 |
8984179.420 |
|
|
|
a. Variable(s) entered on step
1: Liquidity Risk Ratio, Credit Risk Ratio, Capital Ratio, Primary Ratio,
Capital Adequacy Ratio. |
|
|||||||
Based on Table 6 above, the variable
liquidity risk ratio proxied with liquidity risk ratio and credit risk variable
proxied with credit risk ratio can be used as determinants of financial
distress with a significance value of both < α (0.05), this means that it
has proven to be statistically significant as a determinant of influencing the
occurrence of financial distress at people's precredit banks in Indonesia.
While the value of the variable coefficient liquidity risk ratio (0.031) and
variable credit risk ratio (0.150) have a positive influence, which means that
the higher these two ratios, the greater the probability of BPR experiencing financial
distress. Vice versa, if this ratio is small and decreases, the probability of
BPR experiencing financial distress is getting smaller.
The variable capital ratio proxied with
capital ratio, primary ratio and capital adequacy ratio all have a significant
effect, with the significance value of each capital ratio of 0.009, primary
ratio and capital adequacy ratio of 0.000 < α (0.05) this shows that these
three variables are also determinants that BPR has the potential to experience
financial distress or vice versa. The value of the coefficient of the capital
ratio variable (-0.061) and primary ratio (-1.457) means that these two
variables have a negative influence, meaning that the higher these two ratios,
the potential for rural banks to experience financial distress is smaller or
the bank is healthier and vice versa, the smaller these two ratios, the higher
the potential for rural banks to experience financial distress. While the variable
capital adequacy ratio has a positive influence with a coefficient value of
0.154 which means that the higher this ratio, the greater the probability of
BPR experiencing financial distress and vice versa, the lower or smaller this
ratio, the smaller the probability of BPR experiencing financial distress.
Based on the results of the above
research, the second hypothesis (H2) can be accepted, that liquidity risk
proxied with liquidity risk ratio and credit risk is proxied with credit risk
ratio, and capital ratio proxied with capital ratio, primary ratio and capital
adequacy ratio have a significant effect on the probability of financial
distress or non-financial distress in rural banks in Indonesia. With these
results, a financial distress prediction model is formed, as follows:
1 - FD
|
727 |
91.9
|
= |
Overall PercentageLRR1+ 0,150CRR –
0,061 CR– 1,457PR + 0,154CAR. |
|
1 - FD |
Based on the results of the logistic
regression model mentioned above, the effect of liquidity risk ratio, credit
risk and capital ratio on financial distress predictions can be analyzed. To
analyze it can be explained using the ln odd ratio. Ln odd ratio is obtained
from the results of the expondential logistic
regression coefficient (expβ), and the probability in the logistic
regression model can be found by the formula: Probability = expβ/(1+ expβ)
Table
7. Variable Proportion and Probability Figures
|
Variabel |
Koefisien ( β ) |
Proporsi (expβ
) |
Probabilita = expβ/(1 + expβ) |
|
Liquidity Risk Ratio |
.031 |
1.032 |
0.4907 |
|
Credit Risk Ratio |
.150 |
1.161 |
0.5373 |
|
Capital Ratio |
-.061 |
.941 |
0.4848 |
|
Primary Ratio |
-1.457 |
.233 |
0.1890 |
|
Capital Adequcy Ratio |
.154 |
1.166 |
0.5383 |
|
Constant |
16.011 |
8984179.420 |
1.0000 |
Source: Results of
data processing with excel
From table 4 above, it can be seen the
value of each variable liquidity risk ratio, credit risk and capital ratio, the
value of the liquidity risk ratio coefficient of 0.031 has a positive effect
with the probability of financial distress of 49.07%. The value of the credit
risk ratio coefficient of 0.150 has a positive effect with the probability of
financial distress of 53.73%. The value of the capital ratio coefficient of
0.061 has a negative effect with the probability of financial distress of
48.48%. The value of the primary ratio coefficient of 1.457 has a negative
effect with the probability of financial distress of 18.90% and the capital adequacy
ratio coefficient value of 0.154 has a positive effect with the probability of
financial distress of 53.83%.
From the results of the analysis of liquidity risk ratio, credit
risk and capital ratio consisting of liquidity risk ratio, credit risk ratio,
capital ratio, primary ratio and capital adequacy ratio can be used to classify into groups of rural banks (BPR) that experience financial
distress and those that do not experience financial distress. The financial
distress model used using Zmijewski's
The liquidity risk ratio reflects the risk faced by banks failing
to prepare liquid tools to fulfill their short-term obligations to depositors.
The higher the liquidity risk ratio, the greater the bank's liquidity, a liquid
bank is a bank that is able to fulfill its
obligations, especially short-term obligations so that depositors' trust in the
bank increases. The results of the study that the liquidity risk ratio has a
positive and significant influence, meaning that the higher this ratio will
reflect that the bank has increased liquidity, but the high liquidity of the
bank illustrates that the bank is not optimal in using its source of funds to
be distributed in the form of credit, the small credit channeled affects the
bank's income or the low income of the bank will result in losses and
eventually the bank will potentially experience financial distress. This result
is in line with the research of
Credit risk ratio has a positive and significant effect on the
probability of financial distress, the greater this ratio, the higher the
probability of financial distress. Credit risk ratio is a risk faced by banks as a result of failure to return loans provided by banks to
their debtors, failure to return these loans will have an impact on the bank's
liquidity ability. In addition, the bank's source of income will decrease,
because the bank's main income is from credit interest income
and it is known that this source of income can also be used to fulfill
obligations related to the bank's operations. So the
failure to return the credit given to the debtors will potentially increase the
probability of financial distress. The results of this study are in line with
research conducted by
Capital ratios that have a negative and significant effect on
financial distress are the capital ratio and primary ratio. The results of this
study are in line with several previous researchers, namely
Captal ratio has a
negative and significant effect on the probability of financial distress, the
greater this ratio, the prediction of financial distress is smaller and vice
versa. Capital ratio is a ratio that describes the amount of capital and
write-off reserves owned by banks to bear the risk of default on loans provided
to their debtors. The greater the formation of reserves carried out by the
bank, the more able the bank is to protect failures and the more guaranteed the
sustainability of the bank's business, so that the probability of financial
distress will decrease.
Primary ratio has a negative and significant effect on the
probability of financial distress, the greater this ratio, the prediction of
financial distress is smaller and vice versa. Equity to total assets is a ratio
that describes the proportion of net worth of assets owned by the bank, the
higher the proportion of net worth of bank assets, the smaller the proportion
of bank debt. If the proportion of net worth is greater than the proportion of
debt on bank assets, this indicates that the bank has more flexibility in its
operations and a smaller proportion of debt will have implications for
operating costs. The smaller proportion of debt on a bank's assets will reduce
interest expense, so the bank's chances of earning profits will increase.
Increasing profits will reduce the probability of financial distress.
Capital adequacy ratio is the ratio between capital owned by the
bank compared to risk-weighted assets, meaning how much capital the bank is able to bear the risks that will occur on the assets
owned by the bank. The smaller the risk of assets owned by the bank, the
greater the bank's capital adequacy ratio, so that it is more efficient in the
use of its capital and more flexible in the implementation of bank operational
activities. The higher the bank's capital adequacy ratio, the smaller the
probability of rural banks experiencing financial distress. However, in this
study the capital adequacy ratio has a positive and significant effect on
financial distress, the greater this ratio, the greater the probability of BPR
experiencing financial distress. However, when the larger capital adequacy
ratio does not immediately get good results, the high capital adequacy ratio
illustrates that BPR is too cautious in expanding investment in risky
productive assets, especially expansion in lending. Small expansion in productive
assets that are risky will reduce income, small sources of BPR income will
increase the chances of BPR experiencing financial distress.
CONCLUSION
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I Nengah Arsana, I Made Suardana,
Indah Ariffianti (2024) |
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