International Journal of Business and Economy (IJBEC) eISSN: 2682-8359 [Vol. 4 No. 2 June 2022]
Journal website: http://myjms.mohe.gov.my/index.php/ijbec
DETERMINANTS OF FINANCING RISK BASED ON BANK SPECIFIC AND MACROECONOMIC VARIABLES ON
ISLAMIC BANKS IN INDONESIA AND MALAYSIA
Veno Renardi Putra1* and Permata Wulandari2
1 2 Economy and Business Faculty, University of Indonesia, Jakarta, INDONESIA
*Corresponding author: [email protected]
Article Information:
Article history:
Received date : 15 June 2022 Revised date : 18 June 2022 Accepted date : 28 June 2022 Published date : 30 June 2022
To cite this document:
Putra, V. R., & Wulandari, P. (2022).
DETERMINANTS OF FINANCING RISK BASED ON BANK SPECIFIC AND MACROECONOMIC
VARIABLES ON ISLAMIC BANKS IN INDONESIA AND MALAYSIA.
International Journal of Business and Economy, 4(2), 133-146.
Abstract: The Global Banking sector is overcoming challenges in the past year. With the growth of conventional banks, Islamic banks also making great growth in the past year. Besides, the growth and development banks will also face risk and one of them are financing risk. Financing risk is one of the crucial risks in banking activities. This research aims to determine the bank specific and macroeconomic variables that affected the financing risk in Indonesia and Malaysia. This research uses data from Islamic banks in Indonesia and Malaysia from 2010 until 2020 using One Step Difference Generalized Method of Moments (GMM) method. In Indonesia Islamic bank the result was Inflation rate have positive influence on financing risk. Nevertheless, Profitability (ROA) have a negative influence on Indonesia Islamic banks financing risk. For Malaysia Islamic bank we find that Capitalization (CAR) have positive influence in financing risk. However, Profitability (ROA) and Efficiency (OEOI) have a negative influence on Malaysia Islamic banks financing risk. The overall results indicated that Islamic banks financing risk is not only influenced by internal factors but also affected by external factors.
Keywords: Financing Risk, Bank Specific Variables, Macroeconomic Variables, Islamic Banks, Generalize Method of Moments.
1. Introduction
In its daily operational activities’ banks will also face risks. Banks operational activities were collecting and receiving funds from public with various savings and deposit products that have different periods, this activity creates a mismatch of time periods that poses risks to banking operations. The banking sector must pay attention in managing risk to avoid the recurrence of crisis. Safiullah and Shamsuddin (2018) explained that there are several risks that banks could encounter, there are insolvency risk, credit/financing risk, liquidity risk and operational risk.
The most important is financing risk. Financing risk is important because it is the core activity of banks and loans are one of the main assets. Financing risk happens when a counterparty fails in fulfilling its responsibilities. To measure the level of financing risk the ratio of Non- Performing Finance (NPF) can be used. High NPF ratio indicates that the bank has a weakness in managing financing risk (Othman et al., 2020).
Previous studies advise that there are two aspects that drive financing risk. Researchers proposed that financing risk is determined by the banks specific variables (BSV). Bank specific variables were the internal factors that controlled the factors of banks. Bank specific variables were the variables that affected banks performance namely capitalization, profitability, management efficiency, liquidity and bank size (Misman et al., 2015). The influence of the macroeconomic condition on financing risk is crucial and financing quality mainly driven by economic growth (Castro, 2013; Misman et al., 2015). Koju et al. (2019) added that the macroeconomic variables that mainly affected financing risk were GDP growth and inflation rate.
Islamic finance industry has a strong performance in the last two years. With the challenges of Covid-19 Pandemic and the global oil price fall, Islamic finance industry assets grew by 10.6%
on 2020. The largest contributor was Islamic banking with 69% contribution. There are two advantages that Islamic banks has over conventional banks. First, there is an insight that there was a high moral hazard in Islamic banks. Islamic bank cannot take careless quantities of risk or pay outsize bonuses. Second, Islamic banks’ earnings come from identifiable assets (Wood, 2021).
Indonesia and Malaysia were the two countries that the banking industry dominates financial industry. Also, Indonesia and Malaysia have a similar political economy where dual banking system were implemented on both countries. Besides that, Indonesia and Malaysia are the two countries that developed Islamic finance and banking industry in Southeast Asia region (Prasetyo et al., 2020). Malaysia and Indonesia were the first ASEAN countries that developed in Islamic banking. Different approach was used by Malaysia and Indonesia in developing Islamic banking, Malaysia used state driven approach, while Indonesia used market driven approach with led them to a slower development of Islamic Banking (Ghozali et al., 2019).
2. Literature Review Islamic Banks
Islamic finance is constructed with Islamic Sharia philosophies and the main purpose of Islamic Bank is to provide a Conventional Bank’s facilities in a suitable model based on the principle of Islam. The main principles of Islamic Banks are prohibiting interest (Riba) and promoting profit-loss sharing (PLS) (Görmüş & Alkhawaja, 2019). Islamic banking has become an alternative to conventional banking. With the development and expansion of the conventional bank the Islamic banking industry also grown significantly which is expected by 10% to 15%
on annual basis since it was first introduced in 1970 (Sobarsyah et al., 2020).
Financing Risk
Islamic banks also face risk and one of them was financing risk. Financing risk explained is the risk that happened because the failure of the client accomplishing its responsibilities to the bank in accordance with the agreed promise. Financing risk comes from various bank business activities especially in fund financing activities (Rustam, 2018). To measure financing risk one of the pointers is Non-Performing Finance (NPF) for Islamic banks. In banking studies, the loan or financing is classified as NPF when the payment of interest and principal are unsettled by 90 days or more. High level of NPF ratio could interpreted as potential banking uncertainty caused the banks to experience lower profit margins and if the problem becomes more thoughtful, it can lead to a crisis (Misman et al., 2015).
Bank Specific Variables
The importance bank specific variables on financing risk are described with numerous hypotheses. The agency theory framework debates a condition where one party involves with another party in performing tasks on her behalf. Since there is difference between the motivations of the agents and the principal, the agents may tempt to act in their individual interest rather that the principal interest (Susamto et al., 2020).
Macroeconomic Variables
Systematic risk was the second aspects that affected financing risk. Systematic risk was influenced by: macroeconomic factors like growth in Gross Domestic Product (GDP, inflation rate and changes in economic policies as well as import and export limitations and changes on political condition (Castro, 2013). Macroeconomic factors give an unfavourable path, the components in macroeconomic triggers credit/financing loss in systematic financing risk. With that there was a significant correlation between systematic financing risk and macroeconomic factors (Yurdakul, 2014)
2.1 Problem Statement
The purpose of this research is to analyze the determinants of financing risk using bank specific and macroeconomic variables in Indonesia and Malaysia Islamic banks. Author used dynamic panel data model with One Step Generalized Method of Moments (GMM) model. Author hopes that the results of this research will provide important information that can provide recommendations in risk management for Islamic banks and banking authorities.
3. Method 3.1.1 Samples
The sample of the research is Islamic banks in Indonesia and Malaysia. Using purposive sampling technique with four conditions that were (1) Islamic Commercial Banks in Indonesia and Malaysia; (2) Islamic bank with unconsolidated financial statement; (3) Banks published their financial statement during the research period that was 2010 until 2020. We can conclude the total research sample were 12 Islamic banks in Indonesia and 12 Islamic banks in Malaysia.
Indonesia and Malaysia were chosen because both countries using dual banking system.
For bank specific variables data were obtained from the financial report of selected banks that were collected from banks official website and Thomson Reuters-Eikon. For the macroeconomic variables the data were obtained from world bank website, Thomson Reuters- Eikon and Indonesia Central Bureau of Statistic.
3.1.2 Procedures
A number of studies have already explained the performance of Islamic banks. Though, there were inconclusive and mixed results. There were more data, measurement, rules, estimation, methods and work needed to determine a more suitable result. Misman et al. (2015) stated that in determining financing risk there are two main factors. The first is bank specific variables (BSV) and the other one is the macroeconomic factors. From past research and literatures informed that normally they used one of the factors or both. Below is the hypothesis of this research.
Bank’s Capital ratio showed the ability of banks to cover assets decline that caused by risky assets on banks operations. Supriani and Sudarsono (2018) finds that Capital Adequacy Ratio (CAR) have a positive and significant influence on financing risk (NPF) in Indonesia Islamic banks. Also, İncekara and Çetinkaya (2019) in Turkey finds that Capital Adequacy Ratio (CAR) have a positive significant influence on Islamic banks financing risk. This result means that when there was an increase in capital the financing risk will also increase.
H1: Capitalization has a significant positive influence on financing risk in Islamic Bank on Indonesia and Malaysia
Bank’s profitability (ROA) is an amount of profitability before loan loss provisions are listed on banks’ balance sheet. Ningrum et al. (2019) find that ROA partially has a negative effect on Non-Performing Financing (NPF). Also, Isnaini et al. (2019) finds that Return on Asset (ROA) have a negative influence on Islamic banks financing risk. This implies that the greater the return, the better the company's performance, which means the ability to overcome the risks faced is easier and the NPF ratio decreases.
H2: Profitability has a significant negative influence on financing risk in Islamic Bank on Indonesia and Malaysia
The ability of bank in turning resources into revenue is called bank efficiency. Widarjono and Rudatin (2021) finds that Operating Efficiency Ratio (OER) using OEOI have a significant negative influence of Islamic banks financing risk in Indonesia. Kadir et al. (2021) finds that efficiency have a negative and significant effect on Islamic banks financing risk.
H3: Efficiency has a significant negative influence on financing risk in Islamic Bank on Indonesia and Malaysia
Liquid assets are also required in Islamic banks because the limited resource of Islamic banks in lending source for emergency situations. Kadir et al. (2021) finds that Financing Deposit Ratio (FDR) have a positive and significant influence on financing risk (NPF) in Islamic rural banks. Supriani and Sudarsono (2018) finds that Financing Deposit Ratio (FDR) have a positive and significant influence in Islamic banks financing risk. Because the greater banks give financing banks can be neglect in financing analysis.
H4: Liquidity has a significant positive influence on financing risk in Islamic Bank on Indonesia and Malaysia
Risk is impacted by bank size. Economies of scale can benefit banks with large assets. With that, large bank can benefit from big market power and gain advantage in cumulated profits. J.
Effendi et al. (2017) research in Indonesia finds that bank size has a significant negative influence on Islamic banks financing risk. Also, Alzoubi et al. (2020) finds that bank size has a significantly negative relationship to financing risk in Islamic banks because large banks are more diversify and reduce risk.
H5: Bank Size has a significant negative influence on financing risk in Islamic Bank on Indonesia and Malaysia
Increase in consumption following by a decrease in investment and the level of real GDP indicates a decline in the production of goods and services. This will affect the level of operating results obtained by the company which is a source of funds in credit payments from banking institutions. Farika et al. (2018) research they find that GDP growth have a negative and significant impact on financing risk in Indonesia Islamic banks. Also, İncekara and Çetinkaya (2019) finds that gross domestic product has a statistically negative relationship with financing risk.
H6: GDP Growth has a significant negative influence on financing risk in Islamic Bank on Indonesia and Malaysia
Inflation is a representation of monetary policy and measures the general increase in the price level. Banking sectors is affected by inflation by price stability and money supply. Sukmana (2018) finds that inflation have a significant and positive relationship to financing risk in Indonesia Islamic banks. Mahdi (2022) finds that inflation rate has a positive significant relationship with Islamic banks financing risk in Indonesia.
H7: Inflation rate has a significant positive influence on financing risk in Islamic Bank on Indonesia and Malaysia
3.1.2 Measurement
The approach used in this research was quantitative approach. Where the data of this research is in the form of numbers or which can be calculated using nominal. Based on the purpose of the research this is descriptive research. Descriptive research was research that have a purpose to explain and summarize different condition and location or variable that arise and which become the objective of the research (Bungin, 2017).
This research used dynamic panel data method using Generalized Method of Moments (GMM) in determining the bank specific variables that affected Islamic banks in Indonesia and Malaysia. Using dynamic panel data model that involves the lagged dependent variable as a regression variable in the model. One of the main advantages in using dynamic panel data method is the dynamic panel data can examine the dynamic adjustment analysis (Baltagi, 2005). The research model of this research as follows:
𝑁𝑃𝐹𝑖,𝑡= 𝛼 + 𝑁𝑃𝐹𝑖,𝑡−1+ 𝛽1𝐶𝐴𝑅𝑖,𝑡+ 𝛽2𝑅𝑂𝐴𝑖,𝑡+ 𝛽3𝑂𝐸𝑂𝐼𝑖,𝑡+ 𝛽4𝐹𝐷𝑅𝑖,𝑡+ 𝛽5𝐿𝑛𝑆𝐼𝑍𝐸𝑖,𝑡+ 𝛽6𝐺𝐷𝑃𝐺𝑖,𝑡+ 𝛽7𝐼𝑁𝐹𝑖,𝑡+ 𝜀𝑖,𝑡
Where;
𝑁𝑃𝐹𝑖,𝑡 : Represents the logit transformation of NPF ratio for bank 𝑖 at the end of period 𝑡. 𝑁𝑃𝐹𝑖,𝑡−1: Represents the NPF variable lagged by one period. 𝐶𝐴𝑅𝑖,𝑡: Capital Adequacy Ratio (CAR) of bank 𝑖 in year 𝑡. 𝑅𝑂𝐴𝑖,𝑡: Return in Assets (ROA) of bank 𝑖 in year 𝑡. 𝑂𝐸𝑂𝐼𝑖,𝑡: Operation Expense to Operation Income of bank 𝑖 in year 𝑡. 𝐹𝐷𝑅𝑖,𝑡: Financing Deposit Ratio (FDR) of bank 𝑖 in year 𝑡. 𝐿𝑛𝑆𝐼𝑍𝐸𝑖,𝑡: Bank Size of bank 𝑖 in year 𝑡. 𝐺𝐷𝑃𝑖,𝑡: GDP Growth of country 𝑖 in year 𝑡. 𝐼𝑁𝐹𝑖,𝑡: Inflation rate of country 𝑖 in year 𝑡. 𝜀𝑖,𝑡: Represents the error term. Explanation for the variables that author used in the research model is given below.
Table 1: Research Variables
Variable Description Formula
Dependent Variable
Financing Risk NPF Non-Performing Financing to Total
loans 𝑵𝒐𝒏 − 𝑷𝒆𝒓𝒇𝒐𝒓𝒎𝒊𝒏𝒈 𝑭𝒊𝒏𝒂𝒏𝒄𝒊𝒏𝒈
𝑻𝒐𝒕𝒂𝒍 𝑭𝒊𝒏𝒂𝒏𝒄𝒊𝒏𝒈 Independent Variable
Bank Specific Variable
Capitalization CAR Ratio of a bank's capital to its risk. 𝑪𝒂𝒑𝒊𝒕𝒂𝒍 𝑹𝒊𝒔𝒌 𝑾𝒊𝒆𝒊𝒉𝒋𝒕𝒆𝒅 𝑨𝒔𝒔𝒆𝒕 Profitability ROA Return on total assets 𝑵𝒆𝒕 𝑰𝒏𝒄𝒐𝒎𝒆
𝑻𝒐𝒕𝒂𝒍 𝑨𝒔𝒔𝒆𝒕𝒔 Efficiency OEOI Operating expenses to operating
income
𝑶𝒑𝒆𝒓𝒂𝒕𝒊𝒏𝒈 𝑬𝒙𝒑𝒆𝒏𝒔𝒆 𝑶𝒑𝒆𝒓𝒂𝒕𝒊𝒏𝒈 𝑰𝒏𝒄𝒐𝒎𝒆 Liquidity FDR Totoal Financing to Totoal deposits 𝑻𝒐𝒕𝒂𝒍 𝑭𝒊𝒏𝒂𝒏𝒄𝒊𝒏𝒈
𝑻𝒐𝒕𝒂𝒍 𝑫𝒆𝒑𝒐𝒔𝒊𝒕𝒔
Bank Size LnSize Bank’s total assets Natural logarithm of total assets Macroeconomic Factors
GDP Growth GDPG Percentation change in real Gross Domestic Product (GDP)
𝑵𝒐𝒎𝒊𝒏𝒂𝒍 𝑮𝑫𝑷 𝑫𝒆𝒇𝒍𝒂𝒕𝒐𝒓 Inflation Rate INF Percentation Consumer Price Index
(CPI)
(𝑪𝑷𝑰𝒙+𝟏− 𝑪𝑷𝑰𝑿) 𝑪𝑷𝑰𝑿
3.2 Data Analysis
There were several types of data in econometrics. First is cross section data, second one is time series data the last is panel data. Panel data is a combination of cross-sectional data and time series data. In other words, panel data consist the same parameters in cross section data but in certain period. There are several advantages in using panel data regression. First, as explained before panel data was a combination of cross section and time series data that using data panel can add extra data so that it can produce a larger degree of freedom. The second advantage was the information from combination cross section and time series can solve problems that arise when there is an omitted variable (Widarjono, 2018).
This study uses the dynamic panel data model. The relationship between economic variable is mainly dynamic. In using dynamic panel data model can be used on dynamic models that involved the lagged dependent variable as a regression variable in the model. One of the main advantages in using dynamic panel data method is the dynamic panel data can examine the dynamic adjustment analysis (Baltagi, 2005).
There was an econometric trick that designed a large cross-sectional panel for a short dimension where the independent variable isn’t strictly exogenous is called the Generalized Method of Moments (GMM) (Koju et al., 2019). To avoid of a bias in the dynamic panel this research used Generalized Method of Moments (GMM) that have been proposed by Arellano and Bover (1995) and Blundell and Bond (1998). Using dynamic model can erase some econometric bias rather than using traditional panel data estimators (Pooled OLS, Fixed Effect and Random Effect). This research used the One Step Difference Generalized Method of Moments (GMM) approach in determining the financing risk in Indonesia and Malaysia Islamic banks.
3.2.1 Validity dan Reliability
Before the analysis using GMM Method Castro (2013) and Waqas et al. (2017) undergo unit root test in determining the stationarity of the data. Unit root test is used in detecting the trendrandom walk on time series data. In this test author used the Levin, Lin & Chu t approach with null hypothesis (H0=0). For decision making, author use error rate/significant rate of 0.05 (5%). If the test finds a probability value above 0.05 (5%), then H0 is accepted. This means that there is a unit root in the data and the data is not stationer. Meanwhile, if the test finds the probability value below 0.05 (5%), then H1 is accepted. This means that the tested data does not contain a unit root and the data is stationer.
Autocorrelation is a condition where there is a relationship between the error of one period and the error of another period, and usually occurs in time series data. This of course will result in a biased estimate of the coefficient and the resulting variance is not the true value (Ekananda, 2019). Widarjono (2018) explained that using time series data, the disturbance variables between time will be interconnected. Therefore, it is suspected that time series data often contains an element of autocorrelation. The Autocorrelation test that author uses in this research is the Arellano-Bond Test for Serial Correlation I the First-Difference Error on the m order which was displayed as AR (1) and AR (2). The moments condition was valid if there was no correlation on the idiosyncratic error (𝜀𝑖,𝑡). If the null hypothesis is rejected on order 0 and 1 which means the model is fit for the research. However, if on order >1 the null hypothesis is still rejected there was a problem in the model.
In testing the validity of the instrument in the analysis we can use the Sargan and Hansen Test results. Hansen test is used to check the overidentification on the research model. A theoretically superior overidentification test for the one-step estimator is that based on the Hansen statistic from a two-step estimate. The Hansen test should not be relied upon too faithfully, because it is prone to weakness (Roodman, 2009). The interpretation of the Hansen test is if the probability ≥ 0.05 (5%) we can’t reject the null hypothesis of “All the restrictions of overidentification are valid”.
4. Results and Discussion
In this research the factors that determining financing risk Islamic banks are tried to be determined empirically by using dynamic panel data method. Below were the number of observations, mean, standard deviation, minimum and maximum values of the variables in the model.
Table 2: Descriptive Statistic Results Descriptive Statistics
Variables Indonesia Malaysia
Obs Mean Std Dev
Min Max Obs Mean Std Dev
Min Max Financing
Risk (NPF)
132 0.0348 0.0296 0.0000 0.2204 132 0.0235 0.0298 0.0031 0.2323
Bank Specific Variables Capitalization
(CAR)
132 0.2238 0.1812 0.1060 1.9514 132 0.1685 0.0415 0.1173 0.3641
Profitability (ROA)
132 0.0108 0.0205 -0.1077 0.0693 132 0.0106 0.0112 -0.0734 0.0610
Efficiency (OEOI)
132 0.9098 0.1873 0.5076 2.1740 132 0.6120 0.4819 -0.3373 5.5836
Liquidity (FDR)
132 0.8915 0.1884 0.1693 1.9673 132 0.9326 0.3383 0.4364 2.7618
Bank Size 132 20.5101 1.1913 17.6238 22.9191 132 22.3698 0.8153 21.0052 24.088 Macroeconomic Variables
GDP Growth 132 0.0473 0.0221 -0.0206 0.0622 132 0.0435 0.0327 -0.0564 0.0742 Inflation Rate 132 0.0449 0.0147 0.0192 0.0641 132 0.0183 0.0132 -0.0113 0.0387
The mean of Financing risk (NPF) in Indonesia Islamic banks was 3.48% and this result is higher than the Financing risk (NPF) in Malaysia Islamic banks with 2.35%. In Capitalization (CAR) Indonesia Islamic banks have was higher with 22.38% than Malaysia Islamic banks with 16.85%. Profitability (ROA) for Indonesia and Malaysia Islamic banks were almost identical with 1.08% for Indonesia and 1.06% for Malaysia. For Efficiency (OEOI) Malaysia Islamic banks was better than Indonesia Islamic banks with 61.20% for Malaysia and 90.98%
for Indonesia. Indonesia Islamic banks have better Liquidity (FDR) management than Malaysia Islamic banks with 89.15% and for Malaysia Islamic banks with 93.26%. Malaysia Islamic banks have bigger bank asset with 22.3698 than Indonesia with 20.5101. Indonesia and Malaysia GDP Growth was almost identical with 4.73% for Indonesia and 4.35% for Malaysia.
For Inflation rate Indonesia have bigger inflation rate with 4.49% than Malaysia with 1.83%.
Table 3: Unit Root Test Results Islamic Banks
Variables Indonesia Malaysia
Levin, Lin & Chu Levin, Lin & Chu Statistics P-Value Statistics P-Value Financing Risk (NPF) -5.0678 0.0000 -7.2244 0.0000 Capitalization (CAR) -4.2934 0.0000 -4.5354 0.0000 Profitability (ROA) -4.7596 0.0000 -6.6453 0.0000 Efficiency (OEOI) -7.8696 0.0000 -3.8455 0.0001 Liquidity (FDR) -3.2874 0.0005 -2.2150 0.0134 Bank Size -5.2079 0.0000 -7.5664 0.0000 GDP Growth -8.0778 0.0000 -3.8873 0.0001 Inflation Rate -3.0073 0.0013 -3.7031 0.0001
The results from Table 3 showed that there was no unit root or the data of bank specific and macroeconomic variables of Indonesia and Malaysia Islamic bank with all of the probability (P-Value) was below the significant rate of 0.05 (5%). Author can continue the analysis to the One Step Difference Generalized Method of Moments (GMM). The results of the One Step Difference Generalized Method of Moments (GMM) are given in Table 3 below.
Table 4: One Step Difference GMM Results Islamic Bank
Variables Model 1 Model 2 Model 3
Indonesia Malaysia Indonesia Malaysia Indonesia Malaysia NPF (-1) 0.4768***
(4.98)
0.6599***
(11.51)
0.4931**
(2.57)
0.7226***
(9.13)
0.4449***
(4.66)
0.6654***
(11.01) Bank Specific Variables
Capitalization (CAR)
0.03474 (0.90)
0.1236***
(3.45)
0.0605 (1.23)
0.1277**
(2.82) Profitability
(ROA)
-1.0471***
(-3.07)
-1.5058***
(-13.41)
-1.0728***
(-2.98)
-1.5147***
(2.82) Efficiency
(OEOI)
0.0205 (0.88)
-0.0196***
(-7.92)
0.0179 (0.72)
-0.0189***
(-4.91) Liquidity
(FDR)
-0.0031 (-0.25)
0.0000 (0.03)
-0.0035 (-0.27)
0.0001 (0.04) Bank Size 0.0226
(1.39)
-0.0077 (-1.74)
0.0297 (1.74)
-0.0070 (1-.68) Macroeconomic Factors
GDP Growth -0.0464
(-1.05)
-0.0979 (-1.28)
0.0393 (0.73)
0.0026 (0.06)
Inflation Rate 0.1451
(0.70)
0.1722 (1.02)
0.2091*
(1.82)
0.0133 (0.19)
AR (1) -1.46 (0.144)
-1.96 (0.50)
-1.32 (0.186)
-1.25 (0.213)
-1.55 (0.120)
-1.96 (0.050) AR (2) -1.34
(0.179)
0.31 (0.753)
-0.72 (-.473)
-0.41 (0.685)
-1.48 (0.140)
0.35 (0.727)
Sargan Test 0.083 0.352 0.364 0.271 0.128 0.277
Hansen Test 0.695 0.784 0.573 0.671 0.876 0.772
Note: t-statistics are reported in parenthesis, AR (1) and AR (2) and Sargan and Hansen Test are also reported respectively. Imply significant *** p-value < 1% (0.01), ** p-value < 5% (0.05), * p-value < 10% (0.10)
From table the test result and the AR (1) and AR (2) or namely the serial correlation, Sargan Test and Hansen Test results confirmed the soundness and validity of the individual lag one step difference General Method of Moments (GMM) coefficient estimations. Therefore, results are retained to the One Step Difference Generalized Method of Moments (GMM). There were
three (3) models that have been regressed to confirm the soundness of the variable. Model 1 contains only bank specific variables; Model 2 contains macroeconomic variable and Model 3 contains bank specific and macroeconomic variables.
First, in this research we used the lag of dependent variable on the right side of the research model. This research finds that in Indonesia the lagged Non-Performing Finance (NPF−1) is statistically significant and positive influencing financing risk with 0.4449 coefficient in Indonesia Islamic banks and 0.6654 coefficient for Malaysia Islamic banks. From this result we can indicate that in 1% increase in last period Non-Performing Finance (NPF) will increase the Non-Performing Finance (NPF) as 0.4449% in Indonesia Islamic banks and in 1% increase in last period Non-Performing Finance (NPF) will increase the Non-Performing Finance (NPF) as 0.6654% in Malaysia Islamic banks. This result is aligned with the findings from Susamto et al. (2020) that explained that the lagged Non-Performing Loan (NPF−1) indicates that the Non-Performing Loan (NPF) were likely to improve whatever financial firms or banks have risen provision against it during the year before write offs.
The first independent variable in this research is Capitalization (CAR). From table the findings were in Indonesia Islamic banks Capitalization (CAR) is have a positive influence but not significant on financing risk. For Malaysia Islamic banks we find that Capitalization (CAR) is statistically significant and positively influence financing risk with 1.277 coefficient. From this result we can indicate that in 1% increase in Capital Adequacy Ratio (CAR) will increase Financing risk (NPF) as 1.277% in Malaysia Islamic banks. This finding for Malaysia Islamic bank was aligned with the findings of İncekara and Çetinkaya (2019) that explained the theory when there was an increase in financing risk bank’s capital will also increase because banks will tend to keep capital in facing risky condition.
For the Profitability (ROA) the findings were in Return on Assets (ROA) have a significant negative relationship on financing risk with -1.0728 coefficient in Indonesia Islamic banks and For Malaysia Islamic banks we also find that Return on Asset (ROA) have a significant negative relationship on financing risk with -1.5147 coefficient. From this result we can indicate that in 1% increase in Return on Asset (ROA) will decrease Non-Performing Finance (NPF) as 1.0728% in Indonesia Islamic banks and in 1% increase in Return on Asset (ROA) will decrease the Non-Performing Finance (NPF) as 1.5147% in Malaysia Islamic banks. These findings were aligned with the findings of Ningrum et al. (2019) that proves the theory of the higher the profitability ratio (ROA) the more effective bank is in placing productive asset in the form of financing and banks will have better performance and the ability to overcome risk is easier.
In Efficiency (OEOI) the findings were in Indonesia Efficiency (OEOI) have a positive but not significant influence on financing risk. Destiana (2018) also have the same result in Indonesia stated that the bank's efficiency factor does not necessarily reduce the ratio of Non-Performing Finance (NPF). This is because Non-Performing Finance (NPF) is an external problem of Islamic banks related to the debtor's obligation to pay its debts and does not depend on the operational efficiency of Islamic banks. In Malaysia Islamic banks the finding was Efficiency (OEOI) have a negative and significant influence on financing risk with -0.0189 coefficient.
From this result we can indicate that in 1% increase in Operating Expense to Operating Income (OEOI) will decrease Non-Performing Finance (NPF) as 0.0189% in Malaysia Islamic banks.
This finding was aligned with findings of Widarjono and Rudatin (2021). That prove the theory
of in reducing bad loans (financing risk) banks need to improve their management and banks with high efficiency will increase their financing risk.
For Liquidity (FDR) the findings were in Indonesia there is a negative but not significant relationship from Financing to Deposit Ratio (FDR) on financing risk. For Malaysia Islamic banks we find that Financing to Deposit Ratio (FDR) have a negative but not significant influence on financing risk. This result can happen if banks maintaining FDR and they do not have to pay the cost of maintaining idle cash flows. This continues to happen because the banking industry can reduce the number of NPF. The amount of financing does not increase the NPF ratio because the financing channelled by the bank is more selective (Ahmad &
Widodo, 2018).
The last bank specific variable was bank size the findings was bank size have a positive but not significant relationship on financing risk in Indonesia Islamic banks. For Malaysia we find that bank size has a negative but not significant influence on financing. The size of the bank has no effect on the financing risk, which means that the size of Islamic bank does not have an impact on the amount of Non-Performing Financing (NPF). This means that the Non-Performing Financing (NPF) factor is more determined by how the bank's operations is managed (Firmansyah, 2014).
The GDP Growth in Indonesia Islamic banks have a positive but not significant relationship on financing risk and for Malaysia we also find that inflation rate has a positive but not significant on financing risk. Gross Domestic Product (GDP) is an increase in people's income levels in a certain area or area. This indirectly causes the Non-Performing Financing (NPF) value of the Bank to decrease, due to many factors, one of which is the consumptive nature of the community and overrides obligations. So that borrowers of funds are late in paying or unable to pay instalments because the income earned is used for things that are consumptive (Yuniarti et al., 2022).
The second macroeconomic factor is inflation rate. The result indicate that the finding of Indonesia inflation rate has a positive significant relationship on financing risk with 0.2091 coefficient in Indonesia Islamic banks. This finding is aligned with the findings of Sukmana (2018) and Mahdi (2022). The findings proven the theory of when there is an increase in inflation will affected the financial condition and make an increase on the inability to repay the financing. For Malaysia we find that inflation rate has a positive not significant relationship on financing risk. This result is possible because if high inflation rate usually happens in high interest rate of the bank will cause customers to save funds and avoid loans. So, despite high inflation and high interest rates, this does not affect the performance or financing risk Malaysia Islamic Bank (K. A. Effendi & Yuniarti, 2018).
5. Conclusion
The results of the research using One Step Difference Genialized Method of Moments (GMM) reveal that in Indonesia Islamic bank the bank specific variable of Profitability (ROA) positively affected financing risk and for the macroeconomic variable of Inflation rate have a positive and significant influence in financing risk. In Malaysia Islamic banks the result for bank specific variable was Capitalization (CAR) positively influence financing risk. However, Profitability (ROA) and Efficiency (OEOI) also influence financing risk but in a negative relationship.
From the research results both bank-specific and macroeconomic variables have an impact on the financing risk. The study differs from other studies and is unique with comparing banks financing risk in Indonesia and Malaysia. In this respect, it is considered that banks should take these factors into account for good financing risk management and for banking authorities could help banks in strengthen banking regulations.
6. Acknowledgement
The authors would like to thank Master of Management Department, Economic and Business Faculty, University of Indonesia in supporting this research and authors would also thank the peer reviewers for a great feedback and assistance in doing this research.
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