International Journal of Business and Economy (IJBEC) eISSN: 2682-8359 [Vol. 3 No. 1 March 2021]
Journal website: http://myjms.mohe.gov.my/index.php/ijbec
ANALYSIS OF FACTORS AFFECTING LIQUIDITY RISK IN INDONESIAN ISLAMIC BANKING
Takuanara Lalu Gogo1* and Tika Arundina2
1 2 Magister of Management, University of Indonesia, Jakarta, INDONESIA
*Corresponding author: [email protected]
Article Information:
Article history:
Received date : 20 February 2021 Revised date : 14 March 2021 Accepted date : 14 March 2021 Published date : 15 March 2021 To cite this document:
Gogo, T., & Arundina, T. (2021).
ANALYSIS OF FACTORS
AFFECTING LIQUIDITY RISK IN INDONESIAN ISLAMIC
BANKING. International Journal Of Business And Economy, 3(1), 98-114.
Abstract: This study aims to find out the causes of the high liquidity risk in Islamic banking in Indonesia, to find the factors that influence the occurrence of liquidity risk, and to find out what can be done to mitigate liquidity risk in Islamic banking in Indonesia. Because at the end of the research period, there were still Indonesian Islamic banks that had a high liquidity risk, so it was necessary to find efforts to mitigate them. In this study, the factors used include liquid assets in non- core deposits, size of the bank, capital adequacy, return on equity, gross domestic product, and inflation. To see this relationship, this study calculates the ratio based on the financial statements of nine samples of Islamic banking in Indonesia in the period 2013 to 2019. Which is then processed using panel data regression to analyze the factors that affect liquidity risk. The results of this study indicate that the high level of liquidity risk is largely influenced by financing, income quality based on equity and capital adequacy. The results of this study indicate that Islamic banking in Indonesia is able to reduce liquidity risk if they always maintain financing, level of profitability based on equity, and their capital adequacy.
Keyword: Capital Adequacy, Return on Equity, Non Performing Financing, Liquidity Risk, Islamic Banking, Data Panel.
1. Introduction
The bank functions as a financial intermediary institution (Casu et al, 2015) and financial institution (Hubbard and O'brien, 2018). In Indonesia, a bank is specifically defined and functioned as a national financial instrument based on democracy and its main function is to collect public funds in the form of savings and channel these public funds into credit or other forms that are aimed solely at improving the standard of living of the people at large (Law of Republic of Indonesia No.7 of 1992 concerning banking).
Banking in Indonesia adopts two different banking systems, the first is general banking which adopts the conventional system as previously described by the above law, then the second is general banking which adopts the Islamic financial system.
Islamic banking in Indonesia is regulated by the OJK (Financial Services Authority) in the Sharia Banking Law of 2008, which is a bank (as referred to in Law No.7 above) which carries out its banking activities based on Islamic principles based on fatwas issued by institutions that have the authority to stipulation of fatwas in the field of sharia. Based on the provisions of the law above, the main difference between conventional general banking and Islamic general banking is the avoidance of carrying out ribawi activities (such as bank interest) in carrying out the functions of receiving deposits of funds, borrowing money, and money transfer services (Rodoni, 2008). This is because Islamic general banking prioritizes fair activities and prioritizes the concept of cooperation in profit-sharing schemes for both profit and loss (Aulina et al, 2018).
In carrying out its functions, both Islamic and conventional commercial banking have the same business risks. But in various literatures, Islamic banks are claimed to be more stable in the face of crises (Ali, 2007) (Parashar & Venkatesh, 2010) (Rahim, Hassan, & Zakaria, 2012) (Odeduntan, Adewale, & Hamisu, 2016). Sharia banks are also more liquid than Conventional Banks in stable financial conditions (Haddad, Ammari, & Bouri, 2020). This study focuses on Islamic banking. When carrying out its function as a financial institution in Indonesia, Islamic banking cannot escape liquidity risk.
Liquidity Risk in Islamic Banking in Indonesia itself experiences fluctuating conditions as illustrated in diagram 1.1. AL/NCD (liquid assets / non-core deposits) is the level of liquidity risk used in this study (Surjaningsih et al, 2014). The higher the AL/NCD value, the lower the level of liquidity risk experienced by the bank. The safe limit for AL/NCD is if it is still above 50% (OJK, 2015).
Diagram 1.1
Source: Financial Services Authority of Indonesia (OJK)
From the diagram above, it is found that during the study period, the majority of Islamic banks included in this study were above the safe limit. But there are also several banks that have a high liquidity risk, which is below 50% in 2019. Based on the above background, we decided to conduct a research entitled Analysis of Factors that Affect the Liquidity Risk of Islamic Banking in Indonesia.
2. Literature Review
Zhu (2005) stated that as a financial intermediary institution, banks are naturally very vulnerable to liquidity risk. Where this is caused by an imbalance in the level of liquidity between the assets and liabilities of a bank. Liquidity risk is the risk that a bank cannot have enough cash to carry out day-to-day operations. Provision of adequate liquidity in banks is essential because a lack of liquidity in meeting commitments to other banks and financial institutions can have a serious impact on a bank's reputation and the price of bank bonds on the money market. (Khandelwal, 2019). This risk occurs because of a gap between funding sources, which are generally short term and assets that are generally long term. If the gap is large enough, it will reduce the bank's ability to meet its obligations at maturity (Bani and Yaya, 2015).
Fathurrahman and Rusdi (2019) in their research analyzed liquidity risk in Islamic banking in Indonesia using the VECM (Vector Error Correction Model) method. From research using FDR as a proxy for liquidity risk, Fathurrahman and Rusdi concluded that CAR has a significant positive effect on FDR. ROA has a negative effect on the liquidity of Islamic banks due to management inefficiency and affects financial performance.
0.00 1.00 2.00 3.00 4.00
Mar-13 Jun-13 Sep-13 Dec-13 Mar-14 Jun-14 Sep-14 Dec-14 Mar-15 Jun-15 Sep-15 Dec-15 Mar-16 Jun-16 Sep-16 Dec-16 Mar-17 Jun-17 Sep-17 Dec-17 Mar-18 Jun-18 Sep-18 Dec-18 Mar-19 Jun-19 Sep-19 Dec-19
Rate of Islamic Bank's AL/NCD Quartal I-2013 to Quartal IV-2019
AL/NCD
In line with that, İncekara and Çetinkaya (2019) in their research state that there is a significant negative relationship of the dependent variable on liquid assets, gross domestic product and inflation at the 99% confidence level in Islamic Banks and NPLs have a significant positive effect. The results of this study are that the ROE and ROA variables do not have a significant result in explaining liquidity risk in Islamic banking.
The same results were also obtained from the results of research conducted by Pamungkas et al (2018) conducting research on the effect of capital adequacy and credit risk on liquidity in Islamic banking in Indonesia. Based on the results of this study, Pamungkas found that capital adequacy had a positive effect on liquidity, according to him, because bank capital was increasing compared to changes in bank RWA which had decreased. And credit risk affects liquidity risk significantly positively because of its relationship with lack of income due to bad credit.
The research above is also supported by research by Aulina et al (2018) which conducted research on ROE, ROA, and CAR on liquidity risk. Which is where the results of his research are that only ROE and CAR have an influence on liquidity risk. Where ROE negatively affects the amount of capital invested, and CAR positively affects liquidity risk in terms of capital management.
Then the same results were also obtained from the results of research by Milić and Soleša in 2017. They conducted an analysis and identification of what and how variations in macroeconomic factors affect banks that finance in the agricultural sector. The study results confirm the influence of the inflation rate, unemployment rate and gross domestic product as indirect determinants of banking sector liquidity. The research concludes that the motivation for banks to start financing the agricultural sector can run if the liquidity conditions of the banking sector are met.
This is also supported by the results of research by Effendi and Disman (2017). In a study involving 20 conventional banks and 12 Islamic banks in Albania, Saudi Arabia, Bahrain, Malaysia, Dubai, Qatar, and Indonesia using the dependent variable LR, it was concluded that CAR has a significant effect on Islamic banking. ROA has no effect on Islamic banking but has a significant positive effect on conventional banking.
2.1 Problem Statement
1. Knowing how minimum capital adequacy, return on equity, company size, non- performing finance, gross domestic product, and inflation affect liquidity risk in Indonesian Islamic banking.
2. Find the causes of the high liquidity risk in Islamic banking in Indonesia based on this research.
3. Knowing what efforts can be made to mitigate liquidity risk in Islamic banking in Indonesia based on the findings of this study.
3. Method
This section will explain how the stages of this research are carried out, which aims to investigate research problems and the procedures and techniques used to identify, select populations and samples, process and interpret the results of the study.
3.1 Materials
Here is how we determine the population, from which samples are obtained and prepared for research purposes
3.1.1 Samples
The population in this study is all data on Islamic Commercial Banks in Indonesia that report financial reports and financial ratio reports from 2013 to 2019 from the Financial Services Authority (OJK), an independent institution that regulates and supervises financial service activities in the banking sector, the market. capital, and the non-bank financial industry in Indonesia. Observations in this study were carried out on a sample of 9 Islamic commercial banks for 28 quarters (Q1 2013 - Q4 2019). The sample was determined by purposive sampling method to adjust the availability of data to the research objectives.
3.1.2 Site
The data used in this study are secondary in nature and are taken and processed from the OJK website (ojk.go.id) as the official website of an independent authority that regulates all financial activities both by banks and non-banks in Indonesia and to OJK all registered financial institutions in Indonesia. report their financial reports. Another website that is the source of data processing in this study, especially for obtaining inflation and GDP variables, is the Central Statistics Agency (BPS) at bps.go.id. BPS is a non-governmental organization that is responsible for providing statistical data to meet research needs for both government and society.
3.1.3 Procedures
The first step in conducting research is skimming the problems in Islamic banking to identify problems within the scope of the research, then collecting literacy studies to find the variables to be used. After the variables used are obtained, then secondary data is collected from the website of the financial services authority, the website of the Central Statistics Agency, or the bank concerned.
This research was carried out guided by modifying the model in previous research conducted by Santoso et al (2012) and Milic and Solesa (2017) to analyze the influence of the variables of gross domestic product, inflation, bank size, return on equity, capital adequacy ratio and adding research variables. from Pamungkas et al (2018), namely non-performing financing for its impact on the liquidity of Islamic banking in Indonesia. The following are the specifications of the model in this study:
𝑌𝑖,𝑡= 𝛼0+ 𝛽1𝑡𝑋1,𝑡+ 𝛽2𝑡𝑋2,𝑡+ 𝛽3𝑡𝑋3,𝑡+ 𝛽𝑡𝐿𝑜𝑔𝑋4,𝑡+ 𝛽5𝑡𝐿𝑜𝑔𝑋5,𝑡+ 𝛽6𝑡𝐿𝑜𝑔𝑋6,𝑡+ 𝜀𝑖,𝑡 Explanation:
Y = Liquid Asset/Non-Core Deposit X1 = Capital Adequacy Ratio X2 = Return On Equity
X3 = Non Performing Financing X4 = Bank Size
X5 = Gross Domestic Product (GDP) X6 = Inflation (IHK)
ε = Error
Research Hypothesis
In this research, several hypotheses will be formulated related to the research questions and objectives which aim to clarify the research objectives and provide direction to the research.
i. The effect of the capital adequacy ratio on liquidity risk
CAR has an influence on liquidity risk due to an increase in the level of capital adequacy due to increased bank capital compared to changes in the decreasing RWA. With increased capital coupled with an increase in deposits, banks can channel more financing, but banks are encouraged to reduce RWA which makes banks more careful in channeling their financing. Funds that should be channeled for financing are kept into cash by the bank because banks have to reduce credit defaults, with financing that is not maximal, the risk of bank liquidity decreases and bank liquidity increases. This hypothesis is supported by the findings of Pamungkas et al (2018) and Iqbal (2012), which conclude that CAR shows a negative relationship with liquidity risk.
The research hypothesis can be written as follows:
Hypothesis 1 : Capital Adequacy Ratio will affect Liquidity Risk in Islamic banking in Indonesia
In statistical testing, the hypothesis is written as:
H0 : Increasing the Capital Adequacy Ratio has no effect on Liquidity Risk in Islamic banking in Indonesia
H1 : Increased Capital Adequacy Ratio negatively affects Liquidity Risk in Islamic banking in Indonesia
ii. The effect of return on equity on liquidity risk
The higher ROE indicates the efficiency owned by the bank in increasing net income.
With the higher return, higher dividends distributed or retained earnings, the reinvested profit that is invested into retained earnings will be a source of liquidity, the more liquid a bank is, the less risk it faces. This hypothesis as has been proven in Iqbal's research (2012) in his research suggests that there is a positive significance of the independent variable ROE on the Liquidity Risk Dependent Variable at a significance level of 99%.
Aulina et al (2018) on liquidity management also found a significant negative relationship between ROE and liquidity risk.
The research hypothesis can be written as follows:
Hypothesis 1 : Return on Equity will affect Liquidity Risk in Islamic banking in Indonesia
In statistical testing, the hypothesis is written as:
H0 : Increasing Return on Equity has no effect on Liquidity Risk in Islamic banking in Indonesia
H1 : Increasing Return on Equity has a negative effect on Liquidity Risk in Islamic banking in Indonesia
iii. The effect of credit risk on liquidity risk
High credit risk indicates bad financing, which means it affects the amount of money received by banks and is a signal of liquidity risk in banks. This hypothesis is supported by the findings of Pamungkas et al (2018) and Leon and Ericson (2006) which found that credit risk and liquidity risk have a significant positive relationship, where the greater the credit risk, the more vulnerable a bank's liquidity level will be due to the large credit score or financing that fails to pay, and this will reduce the level of liquidity.
The research hypothesis can be written as follows:
Hypothesis 1 : Credit Risk will affect Liquidity Risk in Islamic banking in Indonesia In statistical testing, the hypothesis is written as:
H0 : Increased Credit Risk does not affect Liquidity Risk in Islamic banking in Indonesia
H1 : Increased Credit Risk has a positive effect on Liquidity Risk in Islamic banking in Indonesia
iv. The effect of bank size on liquidity risk
Bank size is the total assets in Islamic banking. The larger the size of the bank will affect liquidity risk because the bigger the assets held, especially liquid assets, the greater the ability to reduce liquidity risk. This hypothesis is supported by the research results of Iqbal (2012), Ramzan and Zafar (2014) that bank size has a significant negative effect on liquidity risk.
The research hypothesis can be written as follows:
Hypothesis 1 : Bank size will affect liquidity risk in Islamic banking in Indonesia In statistical testing, the hypothesis is written as:
H0 : Increasing Bank Size has no effect on Liquidity Risk in Islamic banking in Indonesia
H1 : Increasing Bank Size has a negative effect on Liquidity Risk in Islamic banking in Indonesia
v. The effect of inflation on liquidity risk
The growth of the money supply has various effects on bank liquidity. In the short term, bank cash liquidity increases with money growth. This is basically true because banks have to increase their liquidity reserves if deposits increase. In the long term, however, bank investment declines as the money supply increases, which in turn translates into more liquidity for the bank. This hypothesis is supported by the findings of Rashid (2017) in his research which concluded that the inflation rate has a positive effect on bank liquidity risk even though it is not significant. The inflation rate positively affects the investment-to-asset ratio, which means that higher inflation will ultimately reduce bank liquidity. İncekara and Çetinkaya (2019) concluded that inflation is one of the variables that has a significant positive effect on liquidity.
The research hypothesis can be written as follows:
Hypothesis 1 : Inflation will affect liquidity risk in Islamic banking in Indonesia
In statistical testing, the hypothesis is written as:
H0 : Increased inflation has no effect on Liquidity Risk in Islamic banking in Indonesia
H1 : Increased inflation has a positive effect on liquidity risk in Islamic banking in Indonesia
vi. The effect of gross domestic product on liquidity risk
A higher GDP growth rate reduces short-term liquidity (cash cash) due to high public spending but helps increase the long-term liquidity of banks. This hypothesis is supported by Rashid (2017) and research by İncekara and Çetinkaya (2019).
The research hypothesis can be written as follows:
Hypothesis 1 : Gross Domestic Product will affect Liquidity Risk in Islamic banking in Indonesia
In statistical testing, the hypothesis is written as:
H0 : The increase in Gross Domestic Product has no effect on Liquidity Risk in Islamic banking in Indonesia
H1 : The increase in Gross Domestic Product has a negative effect on Liquidity Risk in Islamic banking in Indonesia
Measurement of variables carried out in this study are as follows:
Table 3.1: Operationalization of Research Variables
Variable Definition Measurement Reference
AL/NCD Liquidity Risk 𝑨𝑳/𝑵𝑪𝑫 = 𝑳𝒊𝒒𝒖𝒊𝒅 𝑨𝒔𝒔𝒆𝒕
𝟏𝟎% 𝑮𝒊𝒓𝒐 + 𝟏𝟎% 𝑺𝒂𝒗𝒊𝒏𝒈 + 𝟑𝟎% 𝑫𝒆𝒑𝒐𝒔𝒊𝒕
Surjaningsih et al, 2014
CAR
Capital Adequacy Ratio
𝐶𝐴𝑅 =𝑪𝒂𝒑𝒊𝒕𝒂𝒍 𝑨𝑻𝑴𝑹
Widowati &
Yudono (2015), Ramzan & Zafar (2014), Santoso dan Sukihanjani (2013)
ROE Return on
Equity
𝑹𝑶𝑬 = 𝑵𝒆𝒕 𝑰𝒏𝒄𝒐𝒎𝒆 𝑻𝒐𝒕𝒂𝒍 𝑬𝒒𝒖𝒊𝒕𝒚
Widowati &
Yudono (2015), Santoso dan Sukihanjani (2013), Iqbal (2012)
NPF Credit Risk 𝑵𝑷𝑭 =𝑷𝒓𝒐𝒃𝒍𝒆𝒎𝒂𝒕𝒊𝒄 𝑭𝒊𝒏𝒂𝒏𝒄𝒊𝒏𝒈
(𝑻𝒐𝒕𝒂𝒍 𝑭𝒊𝒏𝒂𝒏𝒄𝒊𝒏𝒈)
Pamungkas et al (2018)
LSIZE Bank’s SIze 𝑳𝑺𝑰𝒁𝑬 = 𝑳𝒐𝒈 (𝑻𝒐𝒕𝒂𝒍 𝑨𝒔𝒔𝒆𝒕)
Aulina et al (2018), Effendi dan Disman (2017
LIHK Inflation 𝑳𝑰𝑯𝑲 = 𝑳𝒐𝒈 (𝑪𝒐𝒏𝒔𝒖𝒎𝒆𝒓 𝑷𝒓𝒊𝒄𝒆 𝑰𝒏𝒅𝒆𝒙)
Al-Homaidi et al (2019), İncekara dan Çetinkaya (2019), Rashid (2017)
LGDP
Produk Domestik Bruto
𝑳𝑮𝑫𝑷 = 𝑳𝒐𝒈 (𝑮𝒓𝒐𝒔𝒔 𝑫𝒐𝒎𝒆𝒔𝒕𝒊𝒄 𝑩𝒓𝒖𝒕𝒐)
İncekara &
Çetinkaya (2019), Al-Homaidi et al (2019), Mazreku (2019)
Observations in this study were carried out on a sample, namely Islamic commercial banks for 28 quarters (Q1 2013 - Q4 2019). The sample was determined by purposive sampling method to adjust the availability of data to the research objectives. Sharia banking which is the sample of this study is 9 of 11 active sharia banks and provides financial reports to the OJK from the beginning to the end of the research period, namely 2013 to 2019 which represents more than 80% of the population of Islamic banking in Indonesia.
3.2 Measurement
This study uses panel data to see the effect of income structure diversification on the risk and profitability of Islamic banks. One way to analyze this effect is using regression techniques. In simple terms, the regression technique is divided into three as follows (Gujarati, 2009):
1. Common Effect Model
It is the simplest model of a regression technique, which collects cross section and time series data into one and is estimated using ordinary least square (OLS) to obtain parameter values (Baltagi, 2005). The weakness of this technique is that the intercept in the data does not necessarily have the same value, which can lead to misinterpretation.
2. Fixed Effect Model (FEM)
This second model is a CEM model that is added with a dummy variable in its estimation so that it is also known as the least square dummy variable (LSDV).
Assuming that the intercept is owned by each sample and the slope remains the same value for each sample. The weakness of this model is that there are too many dummy variables, making it difficult to perform statistical analysis.
3. Random Effect Model (REM)
The main assumption of this model is that the sample intercept values vary to accommodate individual samples and time. This assumption solves the problem of FEM, which raises many dummy variables, thus giving the model uncertainty used.
This causes the error of the model to have a mean value of zero.
3.3 Data Analysis
Below is the analysis method used to interpret the regression results.
1. Test the coefficient of determination
Is a method to find out how much influence the independent variables simultaneously have on the dependent variable in the regression model.
2. Model Feasibility Test (F)
This method aims to determine whether the panel data regression model obtained is feasible to explain the effect of the independent variable on the dependent variable. If the obtained F-statistic is smaller than the α level which in this study is 5% (0.05), it can be concluded that the regression model is feasible to explain the effect of the independent variable on the dependent variable. But if it's bigger then it's not feasible.
3. T-statistical test
The t test is a test whether each independent variable is able to explain the independent variable one by one.
3.3.1 Validity and Reliability
Based on the explanation above, liquidity risk is a risk that can be experienced by any financial institution, especially banks, which incidentally have a wide number of assets and scope. This research is basically conducted because it wants to make a contribution to the progress of Islamic banking, especially in Indonesia. Because from the introduction above, it can still be seen that there are still Islamic banks that have a high liquidity risk and can be influenced by internal and external factors. This study uses reliable data sources and is processed using the method the researcher feels is the best.
4. Results and Discussion 4.1 Model Determination
In a research method that uses panel data regression, before regression is carried out, the appropriate regression model must first be carried out to choose from the three models. (PLS, Fixed Effect Model, or Random Effect Model) which will be used in this research. Model testing can be done by three testing methods, namely, the Chow Test (Likelihood Ratio), the Hausman Test, and the LM Test (Breush Pagan - Lagrange Multiplier Test). The following are the test results on the model:
Table 4.1: Result of Panel Regression Model Determination
Model Chow Hausman BP-LM Pilihan
Model 1 1.05 4.16 0.03 Pooled Least Square
Source: prepared by the author
In table 4.1, you can see a summary of the best model tests for the panel. From the results of the Chow test (model 1), the P-Value for all models is at the level of significance F (1.05) <F table (1.9384), so there is sufficient evidence to accept H0 so that PLS is chosen as the model.
Then for the Hausman test obtained Chi Square (4.16) <Chi Square table (15.507). The same result is also shown in the BP-LM test where Chi Square (0.03) <Chi Square table (13.362) so that the PLS model is chosen.
4.2 Classic Assumption Test
This is a test used to check whether the parameters in the model are BLUE ((Best, Linear, Unbiased, and Estimator).
Table 4.2: Classical Assumption Test Results Multicolinearity Test
(Mean VIF)
(Modified Wald) Heteroscedasticity Test
(Wooldridge) Autocorrelation Test
1.08 1.7427 0.1568
Source: prepared by the author
4.2.1 Multicollinearity Test
Multicollinearity test in this study uses Variance Inflation Factors (VIF). Which serves to show how much the variance of the estimated coefficient is increased by the rule of thumb multicollinearity factor of the VIF value is 10 to say there is no multicollinearity symptom in the dependent variable. In table 4.2, it can be seen that the multicollinearity test results have an average of 1.08.
4.2.2 Autocorrelation Test
Because the data used are time series and cross-section data, it is necessary to test autocorrelation to prove that the model is free from autocorrelation symptoms. Test serial correlation in the idiosyncratic errors of a linear panel-data model by Wooldridge (2002).
Which in table 4.4 results obtained 0.1568, which means (Prob> chi2)> α = (0.1568)> 0.05 so it can be concluded that there are no autocorrelation symptoms.
4.2.3 Heteroscedasticity Test
This test is used for unbiased estimation on OLS because the estimator result will have a patterned error movement. Because the estimate made by OLS no longer has a minimum variation and is efficient, it is no longer BLUE. Heteroscedasticity testing will use the Modified WaId test. Table 4.3 shows that the test result is 1.7427, which is greater than α 0.05, so it can be concluded that there are no symptoms of heteroscedasticity.
4.3 Research Result
Table 4.3: Regression Results
Dependent Variable Coefficient Std. Error P>|t|
ROE 0.0397672 *** (0.0030979) 0.000
NPF -0.338886 *** (0.1887556) 0.004
CAR 0.2369113 *** (0.1764946) 0.001
LNSIZE 0.0009935 0.0073221 0.892
LGDP -.0.730552 0.078234 0.128
LIHK 0 .0854864 0 .1108387 0.441
Constant 4.163702 1.557319 0.748
Prob>F 0,000
R-Squared 0.7351
Number Obs. 252
Model Pooled Least Square
Note: ***, **, and * indicate significance levels at the 1%, 5%, and 10% levels, respectively, the significance level used in the study = 95%
4.3.1 Determination Coefficient Test
From the estimation results, it is found that the value of R Squared is 0.7351, which means that the independent variables (company size, minimum capital requirement, non-performing financing, return on equity, gross domestic product, and inflation) are able to explain the dependent variable (liquidity risk) of 73,51% which means the other 26.49% are explained by
4.3.2 F-test
In this study, the results of the F-statistic probability test were 0.000 smaller than the α level, which is 5%. So it can be seen that this regression model is feasible to describe the dependent variable on the independent variable.
4.3.3 T-Statistic Test
The t test results can be seen in the table above. If the t-statistic probability value is lower than the significance level α (0.05), it can be said that the independent variable can explain the dependent variable. From the results of data processing, it is found that ROE, NPF, and CAR.
lower than the significance level α (0.05) rejects H0 and accepts H1. And the LSIZE, LGDP, and LIHK variables above exceed the significance level of α (0.05) accepting H0 and rejecting H1, so it can be said that the independent variable cannot explain the dependent variable.
Interpretation of Research findings
Regression Equations Analysis of Factors that Affect Liquidity Risk in Islamic Banking Based on the findings in table 4.3, it can be described in the following equation:
AL/NCD = 4,16+ 0,039(ROE) + 0,237(CAR) - 0.339 (NPF)
ROE has a positive coefficient value in the above model, which means that the greater the ROE in a bank, the larger the AL/NCD value, which indicates that the liquidity risk faced by banks is getting smaller. This is because ROE is a reflection of the level of profitability of a bank. In this study, ROE has a regression result with a value of 0.039, which means that each addition of one ROE point will reduce the AL/NCD liquidity risk by 0.039. The higher the ROE value of a bank, the lower the liquidity risk. The higher ROE indicates the efficiency owned by the bank in increasing net income. With the higher return, higher dividends distributed or retained earnings, the reinvested profit that is invested into retained earnings will be a source of liquidity, the more liquid a bank is, the less risk it faces. Islamic banks that use the profit sharing principle provide solutions or alternatives to the banking system that make the relationship between the bank and the public profitable, and prioritize aspects of fairness during transactions, ethical investment and avoid speculative activities. This study has similarities with Anjum Iqbal's research results on banking in Pakistan (Iqbal 2012), Muharam and Kurnia (2012), and Bani and Yaya (2015) where the higher the level of equity addition, the smaller the liquidity risk experienced by the bank.
CAR in the model shows a high significance at the 0.05 level, the variable has a positive value on the model. The CAR value of 0.237 indicates that for each addition of one CAR point by one point, it will reduce liquidity risk by 0.237. This means that the greater the CAR value of a bank, the higher the AL/NCD at the bank, which means that liquidity will be higher and liquidity risk has been successfully suppressed. CAR is a ratio that shows a bank's ability to provide funds for business investment and accommodate adequate liquidity. When the CAR level is higher, it shows that the bank can fulfill its activities and contribute to the bank's profitability level. The higher the CAR means the better the bank's ability to anticipate risks, including its productive assets. A Sharia bank with sufficient CAR will certainly be more resilient to face liquidity pressures from the market. In addition, CAR is needed to build strong infrastructure and ensure the best service in hostile conditions. Increasing CAR can be done in various ways, including increasing capital injection from investors, issuing new shares, or by increasing the company's retained earnings. Which results are similar to the results of research by Pamungkas et al (2018) and Buchory (2004) and Bani and Yaya (2015) which state that the higher the capital adequacy, the liquidity risk experienced by banks will be depressed because the intermediation function can be carried out optimally.
NPF in the model has a significant negative value at the level of confidence level of 0.05. The NPF value of -0.339 indicates that for each addition of NPF points by one, it will reduce AL/NCD by 0.339, this variable has a negative value in each which means, the smaller the NPF level, the higher the AL/NCD level as an indicator of liquidity risk - because The greater the AL/NCD points the safer it is from liquidity, which concludes that the greater the credit risk, the worse the level of liquidity risk at the bank because it indicates the smaller the liquidity capacity of a bank. The cause of this high NPF could be due to bank failure in managing third party funds (DPK) which triggers the emergence of credit risk which in this study is indicated by the NPF ratio. This failure occurs when Islamic banks have weak analytical skills in the sharia business and their inability to choose prospective NPF debtors is the ratio of problematic financing to the resulting financing, high credit risk indicates that Islamic banks are not careful in channeling funds so that the quality of financing what the bank does is getting worse, this bad financing indicates unproductive financing means that it is at risk of reducing the amount of money received by the bank and is a signal of high liquidity risk to banks. Another alternative that can be given is to increase equity based financing. Equity Based Financing is the main breath in a true Islamic economy because of the principle of profit and loss sharing contained therein. However, this product requires more effort from the side of the bank because it has to consider various things including the assessment of the debtor profile, and sufficient knowledge related to the industry. However, when Islamic banks are increasingly bold in using equity financing-based contracts, and are more willing to enter industries that were previously untouched, of course the cumulative knowledge of these banks will be better at doing business appraisal. Equity based financing, apart from having the potential to increase the profit from the bank, also benefits both parties due to the sharing of risks contained in the contract. These results are similar to the results of research by Pamungkas et al (2018) and Ericson and Renault (2007), where the greater the credit risk, the more vulnerable the level of liquidity of a bank is due to the too large credit score or the default financing, and this will reduce level of liquidity and solvency of the bank concerned.
5. Conclusion
From the results of this study it can be concluded that liquidity risk in Islamic banking for the 2013-2019 period is influenced by ROE, CAR, and NPF factors. Where mitigation can be done, among others, is to prioritize aspects of fairness and transparency in conducting transactions and investments so that the ROE of Islamic banking can be maintained. Then optimize income to increase the value of capital and increase investment to stabilize capital adequacy in Islamic banking. As well as improving the financing disbursement system so that financing becomes more targeted, thereby reducing the occurrence of credit risk in Islamic banking.
6. Acknowledgement
My thanks go to Tika Arundina Aswin, PhD, who has helped in the editorial of this research.
To the ranks of the University of Indonesia who have facilitated the author so that this research can be carried out until the end. And to the family of researchers who have supported the author in his efforts to complete this research.
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