INTRODUCTION
The quality of banking assets is
one of the leading indicators capable of showing the soundness of a bank. The
quality of banking assets is reflected in the Non-Performing Loan (NPL) ratio,
which is one of the measurements of the bank's business risk ratio, which shows
the amount of non-performing credit risk that exists in a bank (Harun,
2016). Non-Performing Loans (NPL) are loans classified into several
groups, namely current loans, doubtful loans, and bad loans (Supeno, 2017). A non-Performing Loan (NPL), according to Ismail (2010)
is a condition where the debtor
cannot pay his obligations to the bank, namely the obligation to pay the
instalments that have been promised
at the beginning
of the agreement
(Putriningtyas, 2021).
The 2019 coronavirus disease, or
covid-19 pandemic, which has become an international pandemic, is no exception
in Indonesia. The impact of the COVID-19 pandemic is the weakening of economic
sectors in almost all of the world, including Indonesia. Economic growth from
January-September 2020 decreased to 2.03%. In the second quarter of 2020, the
Indonesian economy was recorded at -5.32% (y-o-y) (BPS, 2020). This decline
occurred for two consecutive quarters, meaning that Indonesia was experiencing
an economic recession due to the COVID-19 pandemic (Sumarni, 2020).
The economic downturn and
recession have a direct impact on entrepreneurs where the level of business
income decreases and has an impact on the ability of the company. The declining
financial capacity of the company certainly has an impact on the ability to pay
obligations to banks, so there is a risk of an increase in the Non-Performing Loan
(NPL) ratio in Indonesian banking in general (Dewantara & Nufitasari, 2021).
As an intermediary institution,
the main business activity of a bank is to provide loans, wherein the bank will
benefit in the form of interest income which is helpful for activities for bank
operations. Banks earn income on interest on loans given
to customers (Harun, 2016). Borrowing funds given to the public by banks can
not be separated from the failure to pay. Failure to pay or so-called
non-performing loans is a bank's inability to collect back the loans issued. As
a result, the bank's operational activities cannot be carried out correctly.
Therefore, non-performing loans are a reference to determine bank performance.
The higher the value of non-performing loans, the bank will be considered less good (Chosyali & Sartono, 2019).
The Covid-19 condition caused a
spike in non-performing loans (NPL) at banks in Indonesia. Data published by
the Financial Services Authority (OJK) notes that NPLs at commercial banks that
provide credit to the public continued to increase from 2016 to 2020 (Amalia, 2022). Based on OJK Statistics
December 2020, the trend of Non-Performing Loans for Commercial Banks in
Indonesia between 2016 and 2020 tends to fluctuate. However, at the end of
2020, it was recorded that the NPL of Commercial Banks in Indonesia had
increased and recorded the highest number compared to the previous periods both
in percentage terms (3, 06%) or nominally (Rp 167,707 billion).
Failure to pay can be caused by
several factors, either internal or external. External factors are in the
activities of the bank's operational efficiency, loan amount, bank liquidity
and margin on interest. Meanwhile, external factors include world oil prices,
inflation and Bank Indonesia interest rates. All of these factors can have an
influence on the performance of the bank in running its business (Aviliani et al., 2014).
Operational costs and operating
income (BOPO), or the so-called efficiency ratio, is the ratio used to measure
whether the bank has used all of its production factors effectively and
efficiently (Harun, 2016). Bank managerial
activities can be measured so that it can be seen the efficiency of costs
incurred by the bank to support its
business (Pandia, 2012). The smaller the
value of the efficiency ratio means the bank has good efficiency. Banks that
are efficient in controlling costs in management will lead to better bank
performance. So that the efficiency ratio is thought to have an effect on non-performing
loans. Efficiency ratio can have an influence on debtors' non-performing loans (Aryani et al., 2016).
Loan
to Deposit Ratio (LDR) is the amount of funds that commercial banks can release
on credit (Harun, 2016). The loan to deposit ratio will measure the amount of credit that
will be given by the bank based on the funds raised by the bank (Wild et al., 2005). The higher the
loan to deposit ratio, the higher the profit generated by the bank. In
addition, the high loan to deposit ratio will lead to higher risks to be faced
by banks. This is due to the debtor being unable to complete the payment,
resulting in non-performing loans.
Capital
adequacy ratio is a capital that shows the bank's ability to provide funds for
the bank's operational needs and accommodate the risk of loss of funds caused
by operating activities (Harun, 2016). The capital adequacy ratio owned by the bank
is at least 8% based on Bank Indonesia Regulation No. 3/21/PBI/2001. The higher
the capital adequacy ratio owned by the bank will indicate high bank liquidity
so that the risk to the bank will be smaller. However,
Aryani et al. (2016) actually got different results that the
capital adequacy ratio had no effect on non-performing loans.
Net
interest margin is the managerial ability to manage productive assets to
generate net interest income (Pandia, 2012). Bank managerial
looks to be productive if the value of the net interest margin gets bigger. The
more productive the management of the bank, the lower the risk of problems
faced by the bank. Thus, banks can manage loans given to debtors, thus it is
hoped that non-performing loans will not occur (Setiyaningsih et al., 2015).
World
crude oil prices, inflation and interest rates are external factors that cause
bad loans to occur in banks. With the increase in world crude oil prices,
inflation and changes in interest rates, there will be an increase in prices
due to an increase in the cost of distributing goods in the world. As a result,
cost overruns arise so that costs become higher, so that the risk of the
debtor's ability to repay credit increases. Bad credit. Research
conducted by Al-Khazali and Mirzaei (2017)
states that world crude oil
prices can have an impact
on banks due to the
inability of corporations to pay their credits
(Attar & Islahuddin, 2014).
Non-Performing
Loans is the inability of banks to collect back the credit that has been issued
by the bank (Harun, 2016). The change
in credit classification from current credit to NPL is gradual through a process
of decreasing credit quality. NPL shows that the ability of bank management in
managing non-performing loans provided by banks. So that the higher this ratio,
the worse the quality of bank credit, causing the number of non-performing
loans to be greater, so the possibility of a bank in a problematic condition is
greater. According to Bank Indonesia Regulation No. 15/2/PBI/2013 concerning
Status Determination and Follow-Up Supervision of Conventional Commercial Banks
article 4 paragraph (2), Banks are considered to have potential difficulties
that endanger their business continuity if non-performing loans (NPLs) on a net
basis are more than 5% of the total credit (Fattawi, 2021).
Based
on the formulation of the problem in this study, the purpose of this study is
to analyze the effect of BOPO on non-performing loans of banks listed on the
Indonesia Stock Exchange during 2016-2020. Then analyze the effect of the loan
to deposit ratio on non-performing loans in banks listed on the Indonesia Stock
Exchange during 2016-2020. As well as analyzing the effect of the capital
adequacy ratio on non-performing loans for banks listed on the Indonesia Stock
Exchange during 2016-2020.
Margaretha and Kalista (2016)
found that BOPO, Financial ratio, Loans to asset ratio, Credit growth Deposit
rate and rupiah reserve requirement had no impact on NPL, Bank size, ROA, ROE
had a significant impact on NPL. In addition, Vanni and Rokman
(2017) found that FDR had a negative and significant effect on the
NPF, the exchange rate had a positive and significant effect on the NPF,
Inflation had no effect on the NPF, Simultaneously the three variables had a
significant effect on the NPF. Bolarinwa et al. (2021)
found that PROF, Leverage, Liquidity had a significant negative effect on NPL,
Bank Size, Capital, Inflation, Economic growth had a positive effect on NPL.
Based
on the research objectives, the benefits of this research are to contribute
ideas and input in the development of knowledge and analysis, especially those
directly related to the analysis of non-performing loans. The results of the
research are expected to be a reference in the process of analysis or study of
factors that affect non-performing loans in banking. And this research can be
used as study material for further research in academic circles.
Effect
of Capital Adequacy Ratio on Non-Performing Loans
Capital
adequacy ratio is a capital that shows the bank's ability to provide funds for
the bank's operational needs and accommodate the risk of loss of funds caused
by operating activities (Harun, 2016). The capital adequacy ratio owned by the bank is at least 8% based on Bank
Indonesia Regulation No. 3/21/PBI/2001. The higher the capital adequacy ratio
owned by the bank will indicate high bank liquidity so that the risk to the
bank will be smaller.
The size of the banking
capital adequacy ratio (CAR) indicates the level of cash adequacy ratio owned
by the bank to meet operational needs and accommodate the risk of loss of funds
caused by operating failures. The greater the CAR of the banking system, it is
projected that the bank will be more able to withstand losses from failure, so
that the relationship between CAR has a negative effect on Non-Performing Loans
(Hermina & Suprianto, 2016).
The results of previous
research by Ad�hadini and Kusumawardhani (2016)
have investigated the factors that affect banking NPLs (a case study on
conventional commercial banks) where the results obtained are that CAR has a
negative effect on NPL while BOPO, LDR and credit growth have a positive effect
(Siringoringo, 2017). Research from Mikri (2014) on the determinants of NPL with a case
study in the European zone also shows the results that CAR and ROE have a
negative effect on NPL. In addition, the results of research from Usman (2015)
regarding the NPL of the banking industry stated that the CAR ratio had a
negative effect on NPL. From the description of the theory and previous
research, the author proposes a hypothesis:
H3: Capital Adequacy Ratio
has a negative effect on Non Performing
Loans.
Effect of Net Interest
Margin on Non-Performing Loans.
Net interest margin is the
managerial ability to manage productive assets to generate net interest income (Pandia, 2012). Bank managerial
looks to be productive if the value of the net interest margin gets bigger. The
more productive the management of the bank, the lower the risk of problems
faced by the bank. Thus, banks can manage loans extended to debtors, thereby
preventing non-performing loans from occurring.
Net
Interest Margin (NIM) is a financial ratio resulting from the comparison
between interest income to assets, which is also the difference between deposit
interest and loan interest. The higher the Net Interest Margin (NIM) will
indicate the more effective a bank is in placing earning assets in the form of
credit, so that the Net Interest Margin
ratio has a negative effect on Non Performing Loans.
The results of previous
research from Barus (2016) have investigated the factors that affect
NPL where the results obtained are that NIM, BOPO, LDR, firm size and inflation
together affect NPL. Meanwhile, Ozili et al. (2020) mention other results where
the NIM ratio has no effect
on NPL. From the
description of the theory
and previous research, the author proposes a hypothesis:
H4:
Net Interest Margin has a negative effect on Non Performing Loans
Effect
of Oil Price on Non-Performing Loans.
World
oil is included in the main trading commodity where the fluctuations in the
price of world oil have an impact and become a determinant of the rise and fall
of the prices of goods as a whole. Oil price is one component of the fixed cost
for some companies, so that if a company has credit to the bank, the
fluctuations in the oil price will also have an impact on the company's ability
to pay credit obligations to the bank. Therefore, under these conditions, Oil
Price has a positive effect on Non-Performing Loans.
The results of previous research from Nadalizadeh et al. (2019)
with the title "The Impact of Oil Price Movements on Bank Nonperforming
Loans (NPLs): The Case of Iran" suggest that Oil Price has a significant
positive effect on NPL. While other research from the description of the theory
and previous research, the author proposes a hypothesis:
H5: Oil Price has a positive effect on Non-Performing
Loans
Effect of
Inflation on Non-Performing Loans.
Inflation can be interpreted as a
process of increasing the prices of goods continuously. However, this does not
mean that the prices of various goods have increased by the same percentage,
but that they do not occur simultaneously, the most important thing is that
there is an increase in the general price of goods continuously during a
certain period (Nopirin, 2000). The occurrence of
inflation will affect the development and economic growth of a country and have
an impact on the ability of the debtor to pay credit obligations to the bank.
For this condition, oil price has a positive effect on Non-Performing Loans
because the higher the inflation rate will have an impact on the increase in
the bank's NPL ratio.
The results of previous research from Bolarinwa et al. (2021)
with the research title �determinants of non-performing loans after
recapitalization in the Nigerian banking industry: Does efficiency matter?�
which discusses the relationship between failure to pay, efficiency in banks
and re-capitalization. The results show that inflation and economic growth have
a positive effect on NPL, while BOPO, LDR and credit growth have a positive
effect. Research from Mikri (2014) on the
determinants of NPL with a case study in the European zone also shows the
results that inflation, LDR, ROA have a positive and significant impact on NPL.
Other research from Barus (2016)
also mentions that inflation has a positive impact on NPLs. From the
description of the theory and previous research, the author proposes a
hypothesis:
H6: Inflation has a positive effect on Non Performing Loans
Effect of Interest Rate (BI Rate) on Non-Performing
Loans.
Interest rate (BI Rate) is a common reference for
banks in Indonesia in determining the price (Pricing) given to customers. An
increase in the interest rate will directly have an impact on increasing bank
lending rates, thereby increasing the risk of default. For these conditions,
the Interest rate has a positive effect on Non-Performing Loans.
The results of a previous study by Barus (2016)
with the title "Analysis of factors that affect Non-Performing Loans at
Commercial Banks in Indonesia" suggest that the benchmark interest rate
has a significant impact on NPL. From the description of the theory and
previous research, the author proposes a hypothesis:
H7: Interest Rate has a positive effect on Non Performing Loans.
METHOD
This study is
useful for knowing and analyzing the determination that can affect
non-performing loans in banks listed on the Indonesia Stock Exchange. This
study suspects that there are 7 variables that can affect non-performing loans
(NPL), namely efficiency ratio (BOPO), Loan to Deposit Ratio (LDR), Capital
Adequacy Ratio (CAR), Net Interest Margin (NIM), World Oil (OIL PRICE) ,
Inflation (INFLATION), and Interest Rates (BI RATE).
According to Sugiyono (2019), the object of research is an
attribute or nature or value of people, objects or activities that have certain
variations that are determined by researchers to be studied and then drawn
conclusions. This study uses the object of research, namely general banking
listed on the Indonesia Stock Exchange from 2016-2020 as many
as 32 companies/ banks (Ridha, 2017).
The dependent
variable in this study is Non Performing Loan (NPL). Non-Performing Loans are
defaults made by the debtor. Non-Performing Loans are calculated by the number
of defaults divided by the amount owed. The NPL variable is a measure of the
quality of bank assets (credit disbursed) that affects the soundness and
profitability of a bank (Adicondro & Pangestuti, 2015).
The population is a generalization
area consisting of: objects/subjects that have certain qualities or
characteristics determined by researchers to be studied and then
drawn conclusions (Sugiyono, 2019). The population of
this study is all banks listed on the Indonesia Stock Exchange in the 2016-2020
period with a total of 46 banks. According to Sugiyono (2019)
the sample
is part of the number and
characteristics possessed by the population. Samples taken from the population
must be truly representative. The sample in this study is part of 46 banks
listed on the Indonesia Stock Exchange for the period 2016-2020 with certain criteria using purposive sampling.
Table 1
Research Sample
|
Criteria
|
Quantity
|
Information
|
|
Number of
Banking Companies on IDX 2016-2020
|
46
|
Whole
|
|
Number of
Banking Companies Publishing Incomplete Financial Statements 2016-2020
|
(14)
|
Sample
Reduction Factor
|
|
Number of Banking Companies Publishing Complete
Financial Statements 2016-2020
|
32
|
Samples for analysis
|
Source: BEI accessed in June 2021
The type of data in this study uses secondary data. Secondary data is data
obtained indirectly from data sources. The data sources for this research are
from the official website www.idx.co.id, www.bi.go.id, www.bps.go.id, as well
as other library materials, namely journals, books and previous research.
This research is a quantitative research so that the data analysis method
used is a statistical method. The data obtained will be analyzed using the
method of linear regression analysis of multiple panel data or more commonly
known as panel data regression analysis. The data analysis with the help of
software Econometric Views (Eviews) version 12.0.
According to Karini and Filianti
(2018), panel data
regression is used to determine whether there is a significant effect of more
than one independent variable on the dependent variable and is a regression
technique that combines time series data and cross section data.
RESULTS AND
DISCUSSION
Banking is a company engaged in finance that has the main products, namely
savings and financing. The banking sector is currently growing very rapidly
every year both in terms of financial statements and shares that have gone
public. Business prospects in the banking sector also prove to be very
profitable every year which will later attract investors to invest their
capital in the company. The shares of banking companies also increase every
year because many investors are interested in investing in this company sector
for investment purposes to meet future needs. The research population used in
this study were all banking companies listed on the Indonesia Stock Exchange
for the 2016-2020 period as many as 46 issuers consisting of BUKU 1, BUKU II,
BUKU III and BUKU IV banks.
The object of research is an object or place that is targeted in research.
The objects in this research are some of the banks listed on the Indonesia
Stock Exchange (IDX) as many as 32 banks. The research object was 32 banks,
which were chosen with the consideration that the research object had complete
financial reports from 2016-2020.
A. Descriptive
statistics
Table 2
Descriptive Statistics
|
N
|
Minimum
|
Maximum
|
Mean
|
Std.
Deviation
|
|
BOPO
|
60
|
40.03
|
235.2
|
90.13391
|
24.64428
|
|
LDR
|
60
|
34.645
|
163
|
86.39255
|
18.98872
|
|
CAR
|
60
|
9.01
|
66.43
|
22.42499
|
8.051019
|
|
NIM
|
60
|
0.47
|
9.30
|
6.05563
|
12.53803
|
|
OIL PRICE
|
60
|
47.78
|
67.18
|
56.9816
|
6.686526
|
|
INFLASI
|
60
|
1.68
|
3.61
|
2.832
|
0.645305
|
|
Interest
Rate /BI rate
|
60
|
3.75
|
6
|
4.75
|
0.760668
|
|
NPL
|
60
|
0
|
9.92
|
2.72075
|
1.692631
|
Source: Eviews version 12
(processed)
Analysis of the results of descriptive
statistics, namely N is the amount of data processed in the core research,
which is 160 data consisting of BOPO, LDR, CAR, NIM, Oil Price, Inflation,
Interest Rate, and NPL.
In general, based on the results of the analysis
per each of the above variables, it can be explained as follows:
1. The average BOPO value is 90.13%,
this shows that the overall BOPO ratio is still not efficient because every 100
banks have to incur operational costs of 90. The average BOPO is still above
the regulatory requirements in which based on Bank Indonesia Circular No.
15/7/DPNP dated March 8, 2013, it is explained that the BOPO ratio that must be
maintained is not more than 85%.
2. The average value of
LDR is 86.39%, this shows that of the total third party funds and own capital
of 100 banks channeling in the form of credit is 86.39. In accordance with the
Circular Letter of Bank Indonesia No.13/24/DNP of 2011 concerning the soundness
of banks based on the LDR ratio, it can be determined that the LDR value of
86.39% generally falls into the criteria of a bank with a fairly healthy
category (85% < LDR < 100% ).
3. The average value of
CAR is 22.42%, this shows that the minimum amount of capital compared to all risk-weighted
assets (RWA) is 22.42%. In accordance with Bank Indonesia Regulation
No.15/PBI/2013 of 2013 concerning the soundness of banks based on the CAR
ratio, it can be determined that the CAR value of 22.42% generally falls into
the criteria of a very healthy bank (12% < CAR).
4. The average value of
NIM is 6.05%, this shows that the margin of net interest income compared to
earning assets (credit disbursed) is 6.05%. In accordance with the Circular
Letter of Bank Indonesia No.13/24/DNP of 2011 concerning the soundness of banks
based on the NIM ratio, it can be determined that the NIM value of 6.05% is
generally included in the criteria for banks in the very healthy category (3%
< NIM).
5. The average value of
NPL is 2.72%, this shows the number of non-performing loans compared to total
loans is 2.72%. In accordance with the Circular Letter of Bank Indonesia
No.13/24/DNP of 2011 concerning the soundness of a bank based on the NPL ratio,
it can be determined that the NPL value of 2.72% is generally included in the
criteria for a bank in the healthy category (2% LDR 5%).
Based on the results of the
above calculations, it can be seen that the lowest (minimum) BOPO of 40.03 is
found at PT. Bank Capital Indonesia, and the highest (maximum) value of 235.2
is found at PT. Bank of India Indonesia, while the average is 90,13391. This
means that for every 100 income, 90 operating costs are incurred. In the
Circular Letter of Bank Indonesia No. 15/7/DPNP dated March 8, 2013, it is
explained that the BOPO ratio that must be maintained is not more than 85%.
Based on the results of the
above calculations, it can be seen that the lowest LDR (minimum) of 34.6 is
found at PT. Bank Capital Indonesia, and the highest (maximum) value of 163 is
found in PT. Bank BTPN, while the average score is 86.3925. The value of the
standard deviation of the LDR variable is smaller than the average value, so it
can be interpreted that the LDR has a low level of data variation. Bank
Capital's low LDR condition in 2020 was due to internal policies to maintain
liquidity stability by increasing the portion of deposits with higher interest
rates and a decrease in loans extended to maintain credit quality, while the
high LDR condition at Bank BTPN in 2019 was due to very high credit growth
reaching 108.05% compared to 2018 from the original total loan disbursement of
Rp. 68.1 trillion to Rp. 141.7 trillion, but it was not matched by the growth
of Third Party Funds (DPK) which only grew 22.7% in
2019.
The CAR ratio based on the
above calculation shows the lowest (minimum) CAR value of 9.01 which is found
in PT. Bank BPD Banten, and the highest (maximum) value of 66.43 is found at
PT. Bank INA Perdana, while the average value is 22,42499. The value of the
standard deviation of the CAR variable is smaller than the average value, so it
can be interpreted that the CAR has a low level of data variation. The capital
condition (CAR) of PT Bank INA Perdana was strong in 2017 due to a very high
capital increase and was getting stronger after the 2nd right issue with total
equity from Rp482.71 billion in 2016 to Rp1,204.18 billion in 2017. While the
CAR ratio of Bank BPD Banten in 2019 was low due to the condition of the bank's
capital being increasingly eroded due to current year losses and plans for
additional capital from the regional government and the issuance of new debt
securities to be carried out in 2020, the capital recorded in 2019 was Rp.
173.994 billion with an RWA value. reached IDR 1.93 trillion.
From the results of the calculation of the NIM ratio
recorded for the lowest number (minimum) of 0.47 contained in PT. Bank Mayapada Internasional, and the
highest (maximum) value of 9.30 is found at PT. Danamon
Indonesia, while the average score is 6.05563. The value of the standard
deviation of the NIM variable is greater than the average value, so it can be
interpreted that the NIM has a high level of data variation. The low NIM
condition at Bank Mayapada International in 2020 was
due to a decrease in outstanding credit so that interest income also decreased,
in 2020 the realization of interest income was Rp. 5.1 trillion, down from 2019
which was Rp. 8.9 trillion. As for the NIM ratio of PT. Danamon
Indonesia's high in 2017 was influenced by the condition of interest income
growth of 3% from the original in 2016 of Rp13.1 trillion to Rp14.1 trillion in
2017 and on the other hand, the cost of TPF interest was successfully reduced
by 14.2% in 2017. 2017 compared to the previous year.
Based on the above calculation results for the Oil
Price variable, the lowest (minimum) value is 47.78 in 2020 and the highest
(maximum) value is 67.18 in 2019, while the average value is 56.9816. The
standard deviation value of the Oil Price variable is smaller than the average
value, so it can be interpreted that Oil Price has a low level of data
variation. The condition of this value is in accordance with the condition of
oil price fluctuations in the research year.
For the Inflation variable based on the calculation
results, the lowest (minimum) value is 1.68 in 2020 and the highest (maximum)
value is 3.61 in 2017, while the average value is 2.832. The value of the
standard deviation of the inflation variable is smaller than the average value,
so it can be interpreted that the inflation variable has a low level of data
variation. The condition of this value is in accordance with the conditions of
fluctuations in Inflation in Indonesia in 2016 to 2020.
Based on the research results for the Interest Rate
(BI rate) variable, the lowest (minimum) value of 3.75 is in 2020 and the
highest (maximum) value of 6 in 2018, for the average value is 4.75. The value
of the standard deviation is smaller than the average value, so it can be
categorized as Interest Rate variable having a low level of data variation. The
value of the Interest Rate is taken from data sources throughout the research
year period.
Based on the results of the above calculations, it can be seen that the
lowest (minimum) NPL of 0 is found at PT. National Nobu, and the highest
(maximum) value of 9.92 is found in PT. Bank Yudha Bhakti,
while the average value is 2,72075. The value of the standard deviation of the
NPL variable is smaller than the average value, so it can be interpreted that
the NPL has a low level of data variation. The good NPL condition of Bank
National Nobu in 2016 occurred due to maintained credit quality, while the high
NPL condition at Bank Yudha Bhakti in 2018 was due to
a decrease in the collectability of one of the dominant Debtors a.n Altamoda Group so that the
NPL ratio increased from the previous year of 2.07% to 9.92% in 2018.
B. Inferential Statistics
1. Research Model Selection
In this study, the estimation of the panel data regression model used
is based on three models, namely: Common Effect Model, Fixed Effect Model, and
Random Effect Model. To find out which model is the best that will be used in
this study, it must be analyzed further.
Table 3
Conclusion of Panel Data Regression Model Testing
|
No
|
Model
|
Test
|
Results
|
|
1
|
Test Chow
|
Common Effect vs Fixed Effect
|
Fixed Effect
|
|
2
|
Test Hausman
|
Fixed Effect vs Random Effect
|
Random Effect
|
|
3
|
Test Lagrange Multiplier
|
Common Effect vs Random Effect
|
Random Effect
|
Source: Processed by researchers (2022)
2. Panel Data Analysis with
Selected Model
Based on the Chow, Hausman and LM tests, it can be
seen that the best estimation model in this study is the Random Effect Model
(REM). So this study uses the model as the basis for determining the hypothesis. The panel data regression estimation using the random effect model in
Table 4.6 proves that, BOPO, CAR, NIM, OIL PRICE and INTEREST (reference
interest rate) have an effect on the Non-Performing Loans (NPL) of banks.
Meanwhile, LDR and INFLATION have no effect on the Bank's
Non-Performing Loans (NPL).
3. Simultaneous Test (F Test)
The calculated F is 6.621 and the significance level
is 0.0000. F count (6.621) > F table (1.94) or probability value (0.000)
< 0.05 so that the panel data regression model can be used to predict the
dependent variable simultaneously or simultaneously has a positive and
significant effect on Non-Performing Loans.
4.
Model Feasibility (Coefficient
of Determination) � R2
Based on the selection of the best model with REM,
the results of the analysis can be seen as follows:
Table 4
Total panel (balanced) observations:
160
|
|
|
|
|
|
|
|
Variable
|
Coefficient
|
Std. Error
|
t-Statistic
|
Prob.
|
Information
|
|
|
|
|
|
|
|
|
C
|
-0.610914
|
0.416937
|
-1.465244
|
0.1449
|
|
|
BOPO
|
0.026767
|
0.004745
|
5.641513
|
0.0000
|
H1 Accepted
|
|
LDR
|
0.002063
|
0.006217
|
0.331737
|
0.7405
|
H2 Rejected
|
|
CAR
|
-0.025668
|
0.012949
|
-1.982303
|
0.0492
|
H3 Accepted
|
|
NIM
|
-0.018358
|
0.006487
|
-2.829984
|
0.0053
|
H4 Accepted
|
|
OIL
|
0.012703
|
0.004856
|
2.615762
|
0.0098
|
H5 Accepted
|
|
INFLASI
|
-0.150926
|
0.083606
|
-1.805212
|
0.0730
|
H6 Rejected
|
|
INTEREST
|
0.238150
|
0.058200
|
4.091941
|
0.0001
|
H7 Accepted
|
|
|
|
|
|
|
|
|
|
Weighted Statistics
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
R-squared
|
0.233677
|
Mean dependent var
|
1.240577
|
|
|
Adjusted R-squared
|
0.198385
|
S.D. dependent var
|
1.281967
|
|
|
S.E. of regression
|
1.147783
|
Sum squared resid
|
200.2457
|
|
|
F-statistic
|
6.621379
|
Durbin-Watson stat
|
1.843128
|
|
|
Prob(F-statistic)
|
0.000001
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Unweighted Statistics
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
R-squared
|
0.237659
|
Mean dependent var
|
2.720750
|
|
|
Sum squared resid
|
347.2728
|
Durbin-Watson stat
|
1.051259
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Based on the results of the analysis above, it can be
seen that the R-squared value is 0.234. As is known, the coefficient of
determination is used to measure how far the model's ability to explain the
dependent variable is. If the value of R Squared is close to 1, it can be said
that the strength of the model is strong. In this study, the value of R square
is 0.234, which means that the independent variables in explaining the
dependent variation have limited (weak) abilities.
The results of a low R-squared indicate the weak
influence of the independent variable, where the influence of the independent
variable is only 23.4% or in other words, other factors outside the independent
variables studied reach 76.6%. This can happen partly because the research
sample used is the NPL balance at each position at the end of the year (31
December) for the research period in 2016-2020 but does not include or take into account the previous NPL balanced position so that
it does not accumulate in nominal terms.
In addition, other factors that can affect the
condition of the bank's NPL are external factors, especially the decline in the
financial condition of the borrower (debtor) so that payment of obligations to
the bank also decreases, resulting in a new NPL.
5. Partial Test
(t-Test)
Hypothesis
test is used with t-test. To determine the significant decision, it can be seen
in 2 ways, namely t count > t table (df = n-k, 1.655) or probability value
< 0.05.
Table 5
Total panel (balanced) observations:
160
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Variable
|
Coefficient
|
Std. Error
|
t-Statistic
|
Prob.
|
Information
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
BOPO
|
0.026767
|
0.004745
|
5.641513
|
0.0000
|
H1 Accepted
|
|
LDR
|
0.002063
|
0.006217
|
0.331737
|
0.7405
|
H2 Rejected
|
|
CAR
|
-0.025668
|
0.012949
|
-1.982303
|
0.0492
|
H3 Accepted
|
|
NIM
|
-0.018358
|
0.006487
|
-2.829984
|
0.0053
|
H4 Accepted
|
|
OIL
|
0.012703
|
0.004856
|
2.615762
|
0.0098
|
H5 Accepted
|
|
INFLASI
|
-0.150926
|
0.083606
|
-1.805212
|
0.0730
|
H6 Rejected
|
|
INTEREST
|
0.238150
|
0.058200
|
4.091941
|
0.0001
|
H7 Accepted
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Explanation of the t-test based on the table above
with reference to the REM model obtained information that:
1. The probability value of BOPO is 0.0000 (p < 0.05)
so that H1 is accepted.
2. The probability value of LDR is 0.7405 (p > 0.05)
so that H2 is rejected.
3. The probability value of CAR is 0.0492 (p < 0.05)
so that H3 is accepted.
4. The NIM probability value is 0.0053 (p < 0.05) so
that H4 is accepted.
5. OIL probability value is 0.0098 (p < 0.05) so that
H5 is accepted.
6. The probability value of INFLATION is 0.0073 (p >
0.05) so H6 is rejected.
7. The probability value of INTEREST is 0.0001 (p <
0.05) so that H7 is accepted.
Based on the results of the panel data regression
selection to determine the determinants of the NPL of banks listed on the IDX
in the 2016-2020 period, it can be seen the coefficient value of each variable
with the panel data regression equation as follows:
The above equation can be interpreted as follows:
1. The BOPO coefficient value is 0.0267 with a
significance of 0.0000. The significance value is <0.05, the effect of the
BOPO variable is positive and significant on the NPL. If there is an increase
in BOPO by one unit, the NPL variable will increase by 0.0267 with the
assumption that the BOPO, LDR, CAR, NIM, OIL PRICE, Inflation and Interest Rate
(BI Rate) variables are constant.
2. The LDR coefficient value is 0.0021 with a
significance of 0.7405. The significance value >0.05 means that the effect of
the LDR variable is positive but not significant on the NPL. If there is an
increase in LDR by one unit, then the NPL variable will increase by 0.0021 with
the assumption that the BOPO, LDR, CAR, NIM, OIL PRICE, Inflation and Interest
Rate (BI Rate) variables are constant.
3. The CAR coefficient value is -0.0257 with a
significance of 0.0492. The significance value <0.05 means that the effect
of the CAR variable is negative and significant to the NPL. If there is an
increase in CAR by one unit, the NPL variable will decrease by 0.0257 with the
assumption that the BOPO, LDR, CAR, NIM, OIL PRICE, Inflation and Interest Rate
(BI Rate) variables are constant.
4. The NIM coefficient value is -0.183 with a
significance of 0.0053. The significance value is <0.05, the effect of the
NIM variable is negative and significant on the NPL. If there is an increase in
NIM by one unit, then the NPL variable will decrease by 0.0267 with the
assumption that the BOPO, LDR, CAR, NIM, OIL PRICE, Inflation and Interest Rate
(BI Rate) variables are constant.
5. The OIL coefficient value is 0.0127 with a
significance of 0.0098. The significance value is <0.05, the effect of the
OIL variable is positive and significant on the NPL. If there is an increase in
OIL by one unit, the NPL variable will increase by 0.0127 with the assumption
that the BOPO, LDR, CAR, NIM, OIL PRICE, Inflation and Interest Rate (BI Rate)
variables are constant.
6. The value of the inflation coefficient is
-0.1509 with a significance of 0.0730. The significance value >0.05 means
that the effect of the inflation variable is negative and not significant to
the NPL. If there is an increase in inflation by one unit, the NPL variable
will decrease by 0.1509 with the assumption that the BOPO, LDR, CAR, NIM, OIL
PRICE, Inflation and Interest Rate (BI Rate) variables are constant.
7. Interest coefficient value is 0.2381 with a
significance of 0.0001. The significance value <0.05 means that the effect
of the Interest variable is positive and significant on the NPL. If there is an
increase in Interest by one unit, the NPL variable will increase by 0.2381 with
the assumption that the BOPO, LDR, CAR, NIM, OIL PRICE, Inflation and Interest
Rate (BI Rate) variables are constant.
C. BOPO against Non-Performing Loans
In
this study, the results showed that BOPO had a positive and significant effect
on NPL. The level of effectiveness of banking companies in carrying out their
efficiency is reflected in the BOPO ratio, as it is known that the BOPO ratio
is used to measure the ability of bank management to manage operational costs
so as to produce optimal operating income.
According
to Bank Indonesia regulations, BOPO is a comparison between total operating
expenses and operating income. Operational efficiency is carried out by the
bank in order to find out whether in its operations related to the bank's main
business, it is carried out correctly (in accordance with the expectations of
management and shareholders) and is used to show whether the bank has used all
its production factors effectively and effectively. The operating efficiency of
a bank projected by BOPO affects the bank's performance. The lower the BOPO
Ratio, the more efficient the bank is in its operations and vice versa.
A
bank with a high BOPO ratio indicates that the bank is not operating
efficiently because the high value of this ratio shows the large amount of
operational costs that must be incurred by the bank to obtain operating income.
In addition, a large amount of operating costs will reduce the amount of profit
that will be obtained because operating costs or expenses act as a deduction
factor in the income statement.
�The results of this study are in line with the
research proposed by Agustiningtyas (2018) where the results of his research suggest
that BOPO has an effect on NPL. In addition, there are also research results
from Rizal and Taswan (2020) which state that BOPO has a positive and
significant effect on NPL.
D. LDR to Non-Performing
Loans
This
study shows that LDR has a positive but not significant effect on NPL. The loan to deposit ratio will measure the amount of credit that
will be given by the bank based on the funds raised by the bank (Kasmir, 2016). The
higher the loan to deposit ratio, the higher the profit generated by the bank.
In addition, the high loan to deposit ratio will lead to higher risks to be
faced by banks. This is due to the debtor being unable to complete the payment,
resulting in non-performing loans.
The LDR variable shows
the amount of savings in the form of savings, current accounts and time
deposits in banks and is used for lending. This is due to the higher savings
deposits, current accounts and time deposits in banks, the higher the bank's
ability to channel credit so that the potential for non-performing loans also
increases.
The higher the loan to
deposit ratio, the higher the profit generated by the bank. In addition, the
high loan to deposit ratio will lead to higher risks to be faced by banks. This
is due to the debtor being unable to complete the payment, resulting in
non-performing loans.
The results
of this study are in line with the
research proposed by Halim (2016) where the results of
his research, namely LDR have a positive effect on NPL. In addition, there are also research results
from Suryanto (2015) which state that LDR has a significant effect on NPL. As well as the results of
research from Ad�hadini and Kusumawardhani (2016) which states that LDR has a positive effect on NPL.
D. CAR against
Non-Performing Loans
The results showed that CAR had a negative
and significant effect on NPL. Capital
adequacy ratio is capital that shows the bank's ability to provide funds
for the bank's operational needs and accommodate the risk of loss of funds
caused by operating activities (Harun, 2016).
Capital is one of the important factors for banks in carrying out their
operations, developing business businesses, and anticipating the risk of loss.
Banks are required to anticipate the emergence of risks, this is because
various forms of large risks can occur in banks. The regulator requires every
banking company to be able to meet the adequacy of the CAR to ensure that each
bank has sufficient cushion to absorb a reasonable amount of losses so that the
resilience of the banking industry in general in Indonesia can be maintained
properly. Resilience in capital adequacy (CAR) is one of the greater risk
mitigations, namely the collapse of one bank which can have a systemic impact
on the banking industry as a whole in Indonesia.
The higher the capital
adequacy ratio owned by the bank will indicate high bank liquidity so that the
risk to the bank will be smaller. The results of the study are in line with
research from Usman (2015) which states that CAR has a negative effect on NPL, besides that it is
also in accordance with research from
Mikri (2014) which suggests that CAR has a negative effect on NPL. The results of
testing the effect of CAR on NPL have a negative and significant effect on NPL.
The test results are in accordance with the developed theory and framework.
E. NIM to Non-Performing Loans
The results showed that
NIM had a negative and significant effect on NPL. According to Moussa and Majouj (2016), Net Interest Margin (NIM) is a ratio used to measure the
ability of bank management to manage their productive assets to generate net
interest income. In addition, NIM is also an important policy factor because it
shows how efficient the bank's performance is in managing Third Party Funds
(DPK) which will be allocated as loans and will generate interest for the bank.
The greater this ratio, the higher interest income on productive assets managed
by the bank, so that the probability of a bank being in trouble is getting
smaller. Net Interest Margin (NIM) is one of the most important factors that
measure the efficiency of a bank as an intermediary in managing savings and
providing loans. According to several studies, high NIM is a barrier to
investment and is likely to affect economic growth in various ways, especially
in developing countries.
The higher the Net Interest Margin (NIM) will
indicate the more effective a bank is in placing earning assets in the form of
credit, which shows the bank's ability to maintain the quality of loans
disbursed so as to provide optimal interest income for banks. This is the same
as when the Net Interest Margin (NIM) shows a low percentage, this shows that
the bank is not able to manage productive assets properly so that the interest
income earned is not optimal. The low NIM is an indication of a tendency for
the emergence of bad loans (NPL) which erodes bank interest income. The results
of testing the effect of NIM on NPL have a negative and significant effect, so
that the test results are in accordance with the theory and framework that was
developed.
The results of this study contradict the research
of Ayopo and Benet (2018), where
the results of the research
state that the NIM variable has a significant positive impact on NPL, in
addition to the results of Melnik's research (2018) which states that NIM has
no effect on NPL. However, it
is in line with research from
Barus (2016) which suggests
that NIM has a significant impact on NPL.
F.
OIL to Non-Performing Loans
The results showed that Oil Price had a positive and
significant effect on NPL. Oil prices
are an important determinant in showing the
performance of the global economy. Overall, rising oil prices encourage
transfers from oil-importing countries to importing countries. The magnitude of
the direct effect of an increase in oil prices depends on the share of the cost
of oil from national income, the degree of dependence on oil imports and the
ability of end users to reduce their consumption and shift from petroleum to
non-petroleum. In addition, another factor is how the increase in gas prices
can affect the increase in oil prices, the intensity of gas for the economy as
well as the impact of higher price increases from the form of substitute energy
reserves. Naturally, the increase in oil prices and higher price increases in
the long term are sustained, giving a larger macroeconomic impact. For
oil-exporting countries, price increases will directly increase real national
income through an increase in the value of exports. This condition will be
reversed if there is an economic crisis from its trading partner country which
will reduce export demand.
The conditions mentioned above illustrate that world
oil is included in the main trading commodity where the fluctuations in the
price of world oil have an impact and become a determinant of the rise and fall
of the prices of goods as a whole. Oil price is one component of the fixed cost
for some companies, so that if a company has credit to the bank, the
fluctuations in the oil price will also have an impact on the company's ability
to pay credit obligations to the bank. The results of testing the effect of Oil
Price on NPL have a positive and significant effect on NPL. The results of this
study contradict the research of Idris and Nayan (2016) which
states that oil price has a negative and significant effect on NPL, as well as
research from Ayopo and Benet
(2018) which also suggests
that oil price has a negative impact on NPL.
G. Inflation on Non-Performing Loans
The results showed that inflation had a negative and
insignificant effect on NPL. In addition to having an impact on the economy and
investment, the effect of inflation also has an impact on bank interest rates.
Inflation plays an important role in the process of economic tug-of-war so that
the country's economy remains and continues to grow. A growing economy will
show market activities where the economic activity of every level of society
continues to move. When inflation can still be said to be at a normal level,
not too high or not too low, then economic stability is maintained.
In controlling inflation, the central bank will use
interest rates. Inflation is the continuous increase in general prices in the
economy. While the interest rate is the fee that must be paid on the loan funds
provided. As for when a customer deposits money or applies for a loan to a
bank, the interest rate applied is the nominal interest rate. The interest rate
includes the real interest rate as well as a premium for inflation.
Rising inflation will have an effect on rising
nominal interest rates. Even though real interest rates are fixed, the premium
for inflation may rise. In order for economic growth to continue steadily, the
interest rate must be higher than the inflation rate. The explanation of the
two is that to lend money to encourage economic growth, interest rates must be
higher than inflation. This is because higher interest rates than inflation can
increase the value of the currency. It would be different if interest rates
were lower than the inflation rate.
The effect of inflation on bank interest rates can
be divided into two, namely when inflation is high and when inflation is low.
When inflation is high, and the general price of goods and services increases,
the central bank will make policies to reduce the inflation rate. However, to
control when inflation is high, the central bank will raise interest rates to
reduce inflation.
According to the explanation above, inflation does
have an impact on bank lending rates, but this does not show a direct
relationship that inflation has an impact on increasing NPLs because there are
still other factors that have an impact on increasing bank NPLs.
The results of testing the effect of inflation on
NPL have a negative and insignificant effect. This means that inflation does
not have a direct role in the process of bad credit in banks. This study is in
line with the research of Hada
et al. (2020) which states
that inflation has a
negative effect on NPL, but contradicts the research of Bolarinwa
et al. (2021) which suggests
that inflation has a positive impact on NPL and also
contradicts the research of Indrawan (2013) which states that inflation has no impact on
NPL.
H. Interest (BI Rate) on Non-Performing Loans
The results of the study show that interest (BI
rate) has a positive and significant effect on NPL. The increase
in the BI Rate will have an impact on the economy and the real sector. Economic
growth will slow down. On the other hand, an increase in the BI Rate will
result in an increase in bank interest rates. Banks can increase interest rates
on deposits or loans. The increase in central bank interest rates will also
trigger the cost of funds or the cost of borrowing funds for business actors to
be much more expensive. Consumers will also bear the rising interest costs such
as financing for Home Ownership Credit (KPR) and motor vehicle loans.
The increase in the BI Rate will have an impact on the
economy and the real sector. Economic growth will slow down. On the other hand,
an increase in the BI Rate will result in an increase in bank interest rates.
Banks can increase interest rates on deposits or loans. An increase in deposit
interest rates will encourage people to postpone consumption activities because
they choose to save their funds in banks. An increase in deposit interest rates
will increase the bank's cost of funds. If you don't want margins to be
depressed, banks must raise lending rates. The bank's move to increase loan
interest rates will face the risk of non-performing loans.
In accordance with the explanation above, any increase
in Interest (BI Rate) will directly have an impact on increasing bank lending
rates, thereby increasing the risk of default. The results of testing the
effect of Interest Rate on NPL have a positive and significant effect on NPL.
This means that the test results are in accordance with the developed theory
and framework. The results of this study contradict the research of Ahmad and Bashir
(2013) which
states that the interest rate has a negative effect on NPL. However, it is in
line with research by Fabio (2021) where it was stated that the benchmark interest rate
had a positive effect on NPL and research from Barus and Erick (2016) which stated that the BI rate had a significant
effect on NPL.
CONCLUSION
Based on the results of the analysis and discussion, it is further
concluded that BOPO has a positive and significant effect on the NPL of banks
listed on the IDX in the 2016-2020 period. Then the LDR has a positive but not
significant effect on the NPL of the Bank listed on the IDX in the 2016-2020
period. CAR has a negative and significant effect on the NPL of banks listed on
the IDX in the 2016-2020 period. NIM has a negative and significant influence
on the NPL of the Bank listed on the IDX in the 2016-2020 period. Oil Price has
a positive and significant effect on the NPL of Banks listed on the IDX in the
2016-2020 period. And inflation has a negative but not significant effect on
Bank NPLs listed on the IDX in the 2016-2020 period. And the Interest Rate (BI
Rate) has a positive and significant effect on the NPL of Banks listed on the
IDX in the 2016-2020 period.
Banks
must pay attention to the variables that in the research results have a
negative and positive impact on the Non-Performing Loans variable. References
and rules from the regulator, namely the Financial Services Authority, serve as
guidelines where the maximum limit for the NPL ratio is 5% so that the quality
of bank credit can be maintained.
Banks
can also pay attention to other factors outside of research variables that can
affect the level of NPL. Several other factors that can influence and need
attention include the increase or decrease in the financial condition of the
debtor (borrower), the quality factor of collateral (collateral) where there is
a delay in the execution process due to the lengthy auction process.
Banks
also need to pay attention to the quality factor of credit analysis carried out
by HR in the credit unit to ensure that the credit distribution process is late
on target and in accordance with applicable SOPs.
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DETERMINANTS OF
NON-PERFORMING BANK LOANS LISTED ON THE INDONESIA STOCK EXCHANGE FOR THE
2016-2020 PERIOD