DETERMINATION OF CREDIT RISK DIVERSIFICATION
Amabel Nabila
Faculty of Ekonomi dan Bisnis, Universitas Brawijaya amabelnabila@student.ub.ac.id
Supervisor:
Dias Satria, SE., M.App.Ec., Ph.D.
ABSTRACT
This study aims to evaluate the factors affecting credit risk or Non Performing Loans by using 10 banks that are the main entities of the Financial Conglomerate from 2015 to 2020 as samples. Sample selection was done by using the purposive sampling method. The variables tested were Non Performing Loan (NPL) as the dependent variable, credit risk diversification based on the type of use (HHI1) and the economic sector (HHI2), and Operational Efficiency Ratio (OER) as the independent variables, and Good Corporate Governance as the moderating variable.
The analytical methods employed were descriptive statistics, classical assumption test, panel data regression analysis and model feasibility test. The findings revealed HHI1 and HHI2 are insignificant on credit risk (NPL). However, the OER is positive and significant to the NPL. Then, GCG is unable to moderate the effect of credit risk diversification by HHI1 and OER on NPL. However, GCG is positively and significantly moderate the effect of HHI2 on NPL.
Keywords : Bank, Diversification, NPL
INTRODUCTION
According to the Financial Services Authority or Otoritas Jasa Keuangan (OJK) Regulation Number 18/POJK.03/2016 about the
Implementation of Risk Management for Commercial Banks, bank is responsible for managing eight types of risks, namely credit risk, liquidity risk, market risk, operational risk, legal risk, strategic risk, reputation risk, and compliance risk. One of the most significant sources of risk for banks is credit risk. One such quantitative indicator that can be used to evaluate credit risk is the Non-Performing Loan (NPL), which is the number of non-earning assets divided by the sum credit provided by the bank. The lower the NPL value, the lower the credit risk.
The effect of the COVID-19 pandemic in Indonesia is projected to increase NPL. OJK Regulation Number 15/POJK.03/2017 regarding Status Determination and Follow-Up Supervision of Commercial Banks, non-performing loans are substandard, doubtful, or of bad quality. The lower the NPL value, the lower the credit risk.
NPL is classified into two types based on the type of use and the economic sector. The largest number of NPL gross based on type of use is noted in Working Capital Credit which in 2018, accounting for 2.82% then increased to 3.92% in 2020. Meanwhile the lowest was credit consumption with a total of 1.79% in 2020, 1.60% in 2019, 1.54% in 2018. Subsequently, in NPL gross based on the economic sector, the largest sector is noted in the mining sector, reaching 7.26% in 2020, while in 2018 was 4.66%. However, the largest NPL gross in 2019 was processing industry at 3.88%. However, the lowest was electricity, gas, and water from 2018 to 2020 (1.33%, 0.89%, 1.24% respectively).
Diversification of the credit portfolio is a method of managing credit risk. Company diversification tries to reduce risk while providing a potential degree of profit. If one business sector experiences a loss because of diversification, the profits of the other business segments can compensate for the loss. Banks must manage their credit portfolios to ensure that they are properly diversified to reduce the level of credit risk (Widyatini et al., 2015).
According to OJK Regulation Number 55/POJK.03/2016 on Commercial Bank Governance Implementation, Good Corporate
Governance (GCG) is required to improve bank performance, safeguard stakeholders' interests, and promote compliance with laws, regulations, and generally accepted ethical principles in the banking industry. Due to greater risks and challenges facing the banking sector, governance in the banking sector is essential now and in the future. One of the attempts to boost the bank's internal condition is to improve the quality of governance implementation. This indicates that GCG will help reduce risk s, particularly credit risk.
Operational Efficiency Ratio (OER) or in Indonesian it is Biaya Operasional Pendapatan Operasional (BOPO). OER is the comparison between operational costs and operating income. The lower the OER ratio, the better the bank's management performance as existing resources are used more efficiently. In addition, managing GCG in terms of the lending process and controlling OER can also prevent an increase in NPL.
The purpose of this paper was to evaluate the impact of credit diversification by type of use and economic sector, as well as OER, on credit risk, utilizing GCG as a moderating variable.
LITERATURE REVIEW
According to a review of past research, the influence of credit diversity on credit risk has been explored, with varying result, such as Šeho et al. (2021) indicate that expanding a bank's loan and financing portfolios into new sectors have no effect on either returns or risk and diversification increases the risk. Meanwhile, Al-kayed et al. (2020) discovered that sectoral diversification boosts return but has no influence on risk.
According to Saajidah et al. (2020), the Herfindahl-Hirschman Index is an important type of concentration measure. This method measures the degree of market concentration among all companies in a given industry.
According to Abidin et al. (2020), the findings demonstrated that GCG can enhance the impact of credit risk diversification by type of usage and sectoral economy on credit risk. Credit risk diversification depending by type of usage and sectoral economy, on the other hand, are insignificant on
NPL. Moreover, Artantino (2020), GCG can moderates the impact of portfolio diversification on credit risk. In line with the study of Widyatini et al. (2015), good corporate governance moderates loan portfolio diversification, which can reduce credit risk. The beneficial impact of good corporate governance in moderating the effect of loan portfolio diversification on credit risk level was consistent across loan types and economic sectors
According to Moudud-Ul-Huq et al. (2018), the bank benefits from diversification, and diverse banks perform better and have reduced risk.
Income diversification improves bank performance and stability; asset diversification has a different influence depending on the country. In comparison, Adzobu et al. (2017) studies show that diversifying a bank's credit portfolio has no influence on profitability or credit risk.
Abuzayed et al. (2018) examined diversification of income or assets in general does not improve bank stability. There is evidence of a non-linear link between non-interest revenue and stability, indicating that banks can lower risk at a higher level of diversification. Improved institutional quality, macroeconomic conditions, and other bank-specific variables all contributed to increased stability.
Cinar et al. (2018) discovered that income and product (loan) diversification boosts asset return while lowering credit risk, but sectoral diversification lowers profits while increasing risk.
Based on Ryzkita et al. (2017), BOPO has a significant effect on Non-Performing Loan. The results are the same as the research from Barus, et al. (2016), The findings revealed that BOPO influences NPL, and that it has a partially significant positive effect on NPL.
In contrast to Hakimi et al. (2015), who concluded that diversification had a positive influence on bank performance, Ajide et al. (2015) found that diversification had a negative impact on bank performance.
Satria & Subegti (2010) stated that profit obtained by commercial banks can provide its own encouragement for commercial banks to increase profits or profits by specializing in certain financing sectors or focusing on
lending that can generate maximum profits with the lowest level of risk, as commercial banks can see from track financing records that have been achieved.
METHODOLOGY
The population in this study is the banking sector (Indonesia Banking Statistics - Vol. 19 No. 1 December 2020), the total number of commercial banks are 109 banks, while the number of conglomerate banks are 48 banks.
The purposive sampling method was used in this study to obtain a representative sample based on the specified criteria, such as the bank has issued financial statements for six consecutive years from 2015 to 2020, the bank is still operating from 2015 to 2020 or not liquidated by the government, credit data by the type of use and the economy sectoral, as well as bank reports, are available. GCG implementation is documented in the form of a GCG report. Based on these criteria, there are ten banks as samples in this study as follows:
Table 1 Sample of Research
Source: Purposive Sampling Results Sample (processed)
Due to limited and incomplete data availability regarding credit diversification by type of use and economic sector, only 10 of the 48 banks have comprehensive data from 2015 to 2020. However, the total assets of the 10 sample banks in this research are 5,363,470,901 billion.
Meanwhile, the total assets of all banks are 8,780,681,000 billion. So the
No Bank Name
1 PT Bank Mandiri (Persero), Tbk
2 PT Bank Rakyat Indonesia (Persero), Tbk 3 PT Bank Central Asia, Tbk
4 PT Bank Negara Indonesia (Persero), Tbk 5 PT Bank Permata, Tbk
6 PT Bank CIMB Niaga, Tbk.
7 PT Bank Danamon Indonesia, Tbk.
8 PT Bank Bukopin, Tbk 9 PT Bank Mega, Tbk
10 PT Bank MNC Internasional, Tbk
total assets of the 10 sample banks in this research have reached 61% and it can be considered that these 10 samples are representative for this research.
Based on the previous literature review, the variables in this analysis can be formulated as follows:
a. Dependent Variable
The variable that is described or influenced by the independent variable is referred to as the dependent variable. The dependent variable in this study is corporate credit risk. The likelihood of a debtor failing to repay a bank's credit is defined as credit risk. In this study, credit risk is assessed using the Non-Performing Loans (NPL) ratio, which is the amount of non assets divided by total credit granted by the bank. The higher this ratio is, the more likely the bank is to have non-performing loans. NPL unit in this research is in percentage.
NPL =Non Performing Loans Loans given b. Independent Variable
The variable that helps to explain or impact the other variables.
There are three independent variables, including credit risk diversification based on type of use, credit risk diversification based on economic sector, and operational efficiency ratio.
1. Credit Risk Diversification
In this study there are independent variables which include: the credit risk diversification by the type of use and economic sector. The Hirschman Herfindahl Index (HHI) evaluates the diversification. HHI is a market concentration indicator with a value ranging from 0 to 1.
If the HHI value is near zero, the credit portfolio is becoming increasingly diverse. The closer the credit portfolio is near one, the more concentrated it is. HHI unit in this research is in percentage The credit diversification approach is determined by:
• The type of use
Diversified loans based on the type of use consist of investment, working capital, and consumption credit.
• Economic sector
Diversified loans by economic sector consist of agriculture, hunting and forestry, mining and quarrying, manufacturing, electricity, gas and water, construction, wholesale and retail trading, transportation, warehousing and communications, and others.
HHI₁ and HHI₂ = ∑ 𝑟2
𝑛
𝑖=1
Information:
2. Operational Efficiency Ratio (OER)
The operational efficiency ratio, abbreviated as BOPO in Indonesian, is a profit ratio. The ratio that is used to assess a bank's ability to control operational expenditures in relation to operating income. The lower this ratio is, the more efficient the bank's operational costs are, and the likelihood of a bank being insolvent decreases. Bank accomplishment is based on a quantitative evaluation of bank profitability, which can be calculated using the operating cost-to-income ratio. A company's OER can be said to be good if it is below 90%. OER unit in this research is in percentage.
HHI₁ : Hirschman Herfindahl Index of credit portfolios by type of use HHI₂ : Hirschman Herfindahl Index of credit portfolios by economic sector n : Number of companies observed
i : Number of categories of use types or economic sectors
r : The number of credits per category divided by the total credits
OER = Operating costs
Operating income × 100%
3. Moderating Variable
Moderating factors are variables that enhance or weaken the direct relation between the independent and dependent variables. The moderating variable in this study is Good Corporate Governance (GCG), which is proxied by the composite value of the company's self-assessment results given in the GCG implementation report or the annual report. The findings of the GCG self-assessment are reported as a composite value that represents the bank's GCG implementation results rating. The lower the value, the better the outcome. The unit of GCG is the composite value of self-assessment.
Table 2 Composite Value
Source: OJK (2017)
The data was analyzed using Multiple regression analysis through the use of Eviews10 to study the impact of the independent variables on the dependent, with GCG as a moderator factor.
Regression testing is performed by constructing the regression equation, which is stated as follows:
Simultaneously examine the impact of the independent variable and the moderating variable on the independent variable's relation to the dependent variable.
NPL= α + β₁HHI₁ + β₂HHI₂ + β3OER + β4GCG + β5 (HHI₁.GCG) + β6 (HHI₂.GCG) + β7 (OER.GCG) + e
Composite Value (CV) Composite Predicate Rank
CV< 1,50 Excellent 1
1,5 ≤ CV < 2,5 Good 2
2,5 ≤ CV < 3,5 Good Enough 3
3,5 ≤ CV < 4,5 Less good 4
4,5 ≤ CV < 5,0 Not good 5
Information:
RESULT AND DISCUSSION
Descriptive statistics is used to provide a summary or description of the data that is statistically employed in this research. The data of HHI1, HHI2, OER, GCG, and NPL, data from 2015 to 2020 timeframe are used in this study.
Table 3 Descriptive Statistics Test Results
HHI1 HHI2 OER GCG NPL
Mean 0.374667 0.286833 84.3545 1.917 3.409
Median 0.37 0.26 81.385 2 2.805
Maximum 0.46 0.63 180.62 3 10.16
Minimum 0.33 0.19 58.24 1 0.7
Std. Dev. 0.0269 0.105726 22.5796 0.513266 1.894134 The total number of observations in this study is 60, with cross section data from 10 banks and time series data over 6 years. Based on table 3, it can be concluded that all mean values for all variables are greater than the standard deviation, which means the mean value can be used as a representation of the entire data. The following are the results of descriptive statistical analysis of research variables:
a. Credit Risk (NPL)
The average NPL value of banks that are the Financial Conglomerate's Main Entities from 2015 to 2020 is 3.409%. The banks that contributed the highest NPL were PT Bank Permata Tbk and PT Bank Bukopin. Tbk both of 10.16% and 8.8% in NPL : Non-Performing Loan
α : Constant
β₁ - β7 : Regression Coefficient
HHI₁ : Hirschman Herfindahl Index of credit portfolios by type of use HHI₂ : Hirschman Herfindahl Index of credit portfolios by economic
sector
GCG : Good Corporate Governance
e : Error Term, which is the level of estimating error in research
2020 and 2016 respectively. The bank with the lowest NPL contributors was PT Bank Central Asia Tbk, with a total value of 0.7% in 2015.
b. Credit Risk Diversification Based on Type of Use (HHI1) The average HHI for credit by type of use is 0.37%, indicating that conglomerate bank loans are well diversified from 2015 to 2020. Moreover, PT Bank Danamon Indonesia. Tbk recorded the highest HHI1 value in 2020 of 0.46%, indicating that credit is becoming more diversified. Meanwhile, PT Bank Mega. Tbk recorded the lowest HHI1 value of 0.33% in 2017 and 2018, indicating that Conglomerate bank loans from 2015 to 2020 are well diversified.
c. Credit Risk Diversification Based on Economic Sector (HHI2) The average HHI for credit by economic sector is 0.28%, indicating that conglomerate bank loans are well diversified from 2015 to 2020. Furthermore, PT Bank MNC Internasional Tbk obtained the highest HHI2 value of 0.63% in 2017, implying that conglomerate bank credit becomes increasingly concentrated from 2015 to 2020. While PT Bank Negara Indonesia (Persero). Tbk has the lowest HHI2 value in 2016, 2018, and 2020 are 0.19% for each year, this indicates that Conglomerate bank credit is becoming more diverse from 2015 to 2020.
d. Operational Efficiency Ratio (OER)
The average value of OER is 84.3545%. Thus, it can be said that the average OER value of most companies are very healthy. The highest OER value is PT Bank MNC Internasinoal Tbk in 2017 with a value of 180.62% and it can be indicated as not healthy, while the lowest was at PT Bank Central Asia. Tbk in 2018 with a value of 58.24% and it can be indicated as very healthy.
e. Good Corporate Governance (GCG)
The average composite value of the Conglomerate bank's GCG is 1.9, indicating "Good" (1,5 ≤ CV < 2,5). The highest CV GCG value is 3 which means "Good Enough" (2.5 CV < 3.5) which is noted on PT Bank Permata Tbk in 2016 and PT Bank MNC Internasional Tbk from 2015 to 2019. While the CV GCG value the lowest is 1 which means "Excellent" (CV < 1.50) which is located at PT Bank Mandiri (Persero). Tbk in 2015, 2016, and 2018; PT Bank Rakyat Indonesia (Persero). Tbk in 2016, 2017, and 2020; and PT Bank Central Asia. Tbk from 2015 to 2017.
Panel data regression is used to test how well a model can describe the dependent variable's variation and how much the independent variable influences the dependent variable. The optimal panel data regression model was chosen prior to examining the effect of independent factors on the dependent variable. The Common Effect (CE) model was selected as the panel data regression model that best fits the data, based on the results of the Chow, Hausman, and Lagrange Multiplier Tests. The panel data regression analysis utilizing the CE model yielded the results shown in Table 4.
Table 4 Common Effect Model Panel Data Regression Results Variable Coefficient Std. Error t-Statistic Prob.
C 2.436492 0.231778 10.51217 0
HHI1 0.104784 0.103345 1.013924 0.3153
HHI2 -0.052765 0.129843 -0.40638 0.6861
OER 0.540024 0.12012 4.495705 0
GCG 0.150858 0.136173 1.107837 0.273
HHI1GCG -0.011264 0.147606 -0.07631 0.9395 HHI2GCG 0.464976 0.212246 2.190737 0.033 OERGCG 0.069555 0.165313 0.420747 0.6757 Adjusted R-
squared 0.331613
F-statistic 5.181747
Prob(F-
statistic) 0.00016
Source: Processing results with Eviews 10
The CE model of panel data regression fulfilled all the regression model's requirements, including multicollinearity, heteroscedasticity, and
autocorrelation, as well as the normality test over the residual regression model, as shown in Table 4. The constructed regression model passed the model's feasibility test, according to the F test. The F-statistic is 5.181747, with a probability of 0.00016, which is less than the alpha of 0.05, indicating that the regression model can explain the effect of the independent variable on the dependent variable. The independent variable has a 33.16 percent effect on the dependent variable, whereas other variables influence the remaining 66.84 percent, according to the coefficient of determination (Adj.
R-Squared) of 0.331613.
Several factors that affect the NPL are economic conditions, the level of concentration of credit granted, the condition of GCG in banking, as well as the cost of funds charged to debtors. Based on the results of panel data regression that tested the independent variables and their interactions with moderating variables simultaneously on the dependent, it is known that the diversification of credit portfolios by the HHI₁ and the HHI₂ are insignificant on NPL as both probability’s value are above 5% (0.3153 and 0.6861 sequentially) . The results of this study are in line with the results of research from Al-kayed et al. (2020), Abidin et al. (2020), and Adzobu et al. (2017) whose research results state that Credit diversification has no influence on credit risk. Credit risk cannot be handled by diversifying credit portfolios based on type of usage, and credit risk cannot be controlled by diversifying credit portfolios based on economic sectors. This means that credit risk control in Indonesian banks cannot be achieved only by diversifying the credit portfolio; other measures to stimulate low credit risk must be made concurrently. (Abidin et al., 2020).
However, this study contrasts with the results of research from Šeho et al. (2021) and Hakimi et al. (2015) which claim that diversification has a positive significant effect and the results of research from Moudud-Ul-Huq et al. (2018), Abuzayed et al. (2018), and Ajide et al. (2015) which state that diversification has a negative significant effect and can reduce risk.
Furthermore, the results of this study are not in line with the results of research from Cinar et al. (2018) which states that diversification can significantly
inclining and declining the risk where income and product (loan) diversification boosts asset return while lowering credit risk, but sectoral diversification lowers profits while increasing risk.
Moreover, the findings of this study are consistent with the findings of Satria & Subegti (2010), who state that commercial banks' profits can encourage them to increase profits by concentrating in certain financing sectors that can generate huge returns with the least amount of risk, as attested by track financing records.
On the other hand, OER has a significant positive effect on NPL as the probability’s value is 0, which is below 5%. This shows that OER can increase NPL. The results of this study are in line with the results of research from Ryzkita et al. (2017) and Barus et al. (2016) which state that OER has a positive and significant effect on NPL. This implies that the higher the OER, the greater the NPL. This is possible because if operational costs exceed operating income, it indicates that the operational costs incurred are inefficient, putting the bank in a precarious position (Barus et al., 2016).
Based on the results of a panel data regression that tested the independent variables and their interactions with the moderating variables on the dependent, it was discovered that GCG was unable to moderate the effect of the credit portfolio diversification variable by the HHI1 and OER on NPL as the output states that the probability variables are greater than 5%. (0.9395 and 0.6757 respectively), indicating the variables are not significant on the NPL. This is not in line with the results of research from Artantino (2020) and Widyatini et al. (2015) which both state that GCG can moderate the impact of portfolio diversification on credit risk and also the research results from Abidin et al. (2020) which shows the results that GCG could strengthen the effect of credit risk diversification based on type of use on credit risk or NPL. Considering that the results of GCG values are only based on bank self-assessments, the assessment can be less objective. As a result, GCG results may have no effect and cannot moderate credit diversification by type of use and OER on NPL.
However, GCG can moderate in a positive and significant way the effect of credit portfolio diversification variables based on HHI2 on NPL. It demonstrates that GCG enhances the impact of credit portfolio diversification by economic sector on credit risk and nonperforming loans.
The findings of this study corroborate those of Abidin et al. (2020), who found that GCG could enhance the effect of economic sector credit risk diversification on NPL. It is feasible because GCG implementation is expected to assist banks in establishing credit diversification and undertaking the proper lending process, ultimately reducing bank credit risk.
Thus, due to the current Indonesian economic factor (covid19 epidemic) which is more prominent in affecting NPL, the rationale for diversifying credit based on kind of use and economic sectors is unlikely to have a substantial impact on NPL.
IMPLICATIONS OF THE RESEARCH
The objective of this research is to investigate the effect of credit risk diversification by the type of use (HHI1) and economic sector (HHI2), as well as the Operational Efficiency Ratio (OER) on NPL, with GCG as the moderating variable at conglomerate banks from 2015 to 2020.
Based on the results, only the OER has a positive and significant effect and GCG strengthen the effect of credit risk diversification based on HHI₂ on the NPL. This can happen because the higher the OER, the higher the NPL, if operational expenditures exceed operating income, it means the operational costs incurred are inefficient, placing the bank in hazard. Moreover, due GCG deployment is projected to help banks diversify their credit portfolios and carry out proper lending processes, lowering bank credit risk.
However, the other variables, namely Credit risk diversification by HHI1 and HHI2, GCG moderates the effect of HHI₁, and GCG moderates the effect of OER are insignificant on NPL as the current Indonesian economic factor during this covid19 epidemic is more prominent in influencing NPL, it is most likely that these variables have no effect on NPL.
Thus, based on the research results the author recommend the banking industry is expected to have a better understanding of the impact of diversification on credit risk depending on economic sector for banking management by evaluating the adoption of GCG and cost efficiency.
Additionally, it is intended to offer regulators with information on the interaction between significant variables such as the OER and GCG, which moderates the positive and significant effect of credit risk diversification by on HHI2 on NPL. Nevertheless, this does not take out the importance of other variables like credit risk diversification by HHI1 and HHI2, GCG moderates the effect of HHI1, and GCG moderates the effect of OER. As a result, the government must pay attention to the economic condition in order to reduce NPL. Finally, the next researcher can investigate other types of diversification, such as cost diversification, by employing other credit risk measurement indicators, such as Return on Assets (ROA).
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Baca selengkapnya di artikel "Jenis Pasar Berdasarkan Strukturnya:
Monopoli & Persaingan Sempurna", Retrieved January 18, 2022, from https://tirto.id/jenis-pasar-berdasarkan-strukturnya-monopoli-persaingan- sempurna-gl2z
Yetkin Çınar Arş Gör Gökçe Gürsel Sevgi Eda Tuzcu Ankara Üniversitesi Ankara Üniversitesi Ankara Üniversitesi Siyasal Bilgiler Fakültesi Siyasal Bilgiler Fakültesi Siyasal Bilgiler Fakültesi, D. (n.d.). THE IMPACTS OF DIVERSIFICATION STRATEGIES OF TURKISH BANKS ON THEIR PROFITABILITY AND RISK: A PANEL DATA ANALYSIS *.
