Function of Non-Performing Loans In The Capital Adequacy Ratio Model of the Banking Sector

Research is being done in order to address the discrepancies in the findings of earlier investigations as well as the phenomenon of non-performing loans (NPL), which cannot account for the effect it has on the CARR. As a result, the researchers used different time series and cross-sectional data in their subsequent studies. Using multiple regression analysis, this type of descriptive quantitative research examines panel data from 22 samples of banking sector firms over a seven-year period. This formula uses the research object, which are companies in the banking sector listed on the Indonesia Stock Exchange, and uses NPL as an intervening variable to maximize the CAR value. Two research models are combined in one model, and each model is subjected to the Chow Test, Hausman Test, Lagrange Multiplier Test, and model selection test stages. Findings in the first model indicate that LDR's negative connection with NPL can be used to explain its impact. These outcomes differ from the relevant theory. Similar to the first study model, where the results are not as relevant as the theory, the results in the second model are also just LDR and may directly explain its effect on CAR with a negative association. It is hoped that these findings would give banking sector managers the best possible direction

in loan quality and additional financial system instability-is supported by Zhang et al. (2016)'s research findings in the Chinese banking industry.According to research conducted in the Turkish banking industry by Kjosevski et al. (2019), ineffective management was a major contributing factor to the rise in non-performing loans (NPLs).Additionally, they disclosed that ownership structure affects efficiency, which has ramifications for Turkey's banking industry.Tarchouna et al.'s (2018) study of the US banking industry found that possess strong corporate governance frameworks that enable them to cut down on bad loans.However, notably during the global financial crisis, corporate governance was unable to stop US mid-and large-sized commercial banks from taking excessive risks that lowered the quality of their loans and even resulted in significant losses.
Corporate governance was introduced with the intention of making company management more transparent and accountable in every aspect, because management works for maximum utilization of shareholder investments.Several empirical studies such as It has been demonstrated by Tarchouna et al. (2018), Love & Rachinsky (2015), O'Sullivan et al. (2016), and Liang et al. (2013) that bank corporate governance affects the performance and quality of loans.Effective corporate governance standards are crucial for the banking industry because they prevent serious banking instability and substantial losses caused by excessive risk-taking and weak corporate governance (Zagorchev & Gao, 2015;Zhang et al., 2016;Tarchouna et al.., 2018).As a result, a substantial body of research has been done on the subject of corporate governance's efficacy in financial institutions during the crisis.According to Tarchouna et al. (2018), there are two methods you can use to assess the caliber of corporate governance.First, it makes use of a variety of ownership and monitoring arrangements, including share ownership and board features.Secondly, employing just one corporate governance tool that assesses the corporate governance framework as a whole.In this study, the first optionusing ownership structure and board characteristics-is used to assess the caliber of bank corporate governance.The size of the board of directors, the percentage of independent boards, and the percentage of female board directors serve as stand-ins for key board characteristics.Institutional ownership serves as a standin for the share ownership structure of the various banking sector companies, each will have different policies when managing risk and have different systems for distributing credit, because basically banks implement strategies that are adapted to the conditions of each bank.These differences in conditions mean that the credit risk borne by the bank is not the same, this can be assessed from the operational activities carried out by the bank.Until now, Bank Indonesia as the central bank has established regulations that bank performance is considered good if the Non-Performing Loan ratio does not exceed 5%.If the Non-Performing Loan exceeds the predetermined limit, the bank is considered to have poor performance, especially in credit management.The rise and fall and high ratio of Non-Performing Loans can be influenced by internal bank factors including institutional ownership, operational performance such as BOPO and loan to deposit ratio (LDR).

LITERATURE REVIEW
The level of bank efficiency (BOPO) has a significant effect and a positive correlation to NPL, according to research by Koju et al. (2018) with commercial banks in Nepal as the research object.This means that the NPL ratio will decrease the more efficiently you manage your banking business, or the lower the BOPO level.The same results were also found in Ekanayake and Azeez (2015), Iksan Adisaputra (2012).
: There is an influence of BOPO on Non-Performing Loans (NPL).
Studies by Juniarmita A. S. and Salam S. ( 2023) demonstrate that nonperforming loans (NPL) are significantly impacted by the loan to deposit ratio (LDR).According to various research findings by Dewi and Ramantha (2015) and Malik, A. (2020), there is little correlation between the Loan to Deposit Ratio (LDR) and Non-Performing Loans (NPL).  : There is an influence of the Loan to Deposit Ratio (LDR) on Non-Performing Loans (NPL).
According to research by Bukian & Sudiartha (2016), bank efficiency (BOPO) significantly affects CAR and has a negative link with it.The results of the opposing studies, by Chiu et al. (2008) and Ismaulina et al. (2020), demonstrate that bank efficiency (BOPO) has a positive association with CAR and a significant impact.Aside from the aforementioned second finding, Fitrianto and Mawardi's (2006) research indicates that bank efficiency (BOPO) has no bearing on CAR.  : There is an influence of BOPO on the Capital Adequacy Ratio (CAR).
According to Ansary & Hafez's (2015) research findings, there is a favorable association and a noteworthy impact between the Loan to Deposit Ratio (LDR) and the Capital Adequacy Ratio (CAR).The findings from the same study in Yokoyama & Mahardika (2019), Rianto & Salim (2020), and Andini & Yunita (2015).Putri & Dana (2018) found quite different research results, indicating that the Capital Adequacy Ratio (CAR) was not significantly impacted by the Loan to Deposit Ratio (LDR).  : There is an influence of the Loan to Deposit Ratio (LDR) on the Capital Adequacy Ratio (CAR).
According to Romdhane (2012), the second study model's exogenous variable, non-performing loans (NPL), explains the research findings showing a substantial positive association between NPL and the capital adequacy ratio (CAR).Septiani & Lestari (2016) found different outcomes.Swandewi & Purnawati (2021) found that NPL has a substantial effect and a negative association with CAR, which is another research outcome with differing results.
Other studies show that NPL has a negligible impact on CAR (Murtiyanti et al., 2015;Nugroho et al., 2021).
: There is an influence of Non Performing Loans (NPL) on the Capital Adequacy Ratio (CAR).
Figure 1.Research Model Framework

METHODOLOGY
The descriptive, qualitative, and quantitative methodologies of this study make use of time series and cross-section data.The analysis method used is panel data regression, which integrates cross-section data from publicly traded banks on the Indonesia Stock Exchange (IDX).
With time series data for the period 2015 to 2021, or for 7 years.Purposive sampling and the criteria for selecting the research sample will be used to take the population size as a research sample.
Two research models employ four research variables conceptually which are divided into the first model using the endogenous variable Non Performing Loan (NPL) and the second model using the endogenous variable Capital Adequacy Ratio (CAR).By using the purposive sampling method as a research sampling method, 22 banking sector companies were produced as research samples.Then, utilizing the three fundamental analyses mentioned above, you may do the following three model appropriateness testing processes to choose the optimal panel data multiple regression model:

Chow Test
In this test, F-statistics are used to choose between the Fixed Effect Model (FEM) and the Common Effect Model (CEM).Acceptance or rejection of the hypothesis depends on the level α = 5% in the null hypothesis (H_0) and alternative hypothesis (H_a).In technical terms, based on these two models, it can be concluded that the null hypothesis (H_0) can be accepted and the alternative hypothesis (H_a) can be rejected if the test findings have a probability level of more than or equal to 5%.This situation calls for the application of the Common Effect Model (CEM), which states that the null hypothesis (H_0) will be rejected in the event that the test results have a probability level of less than or equal to 5%.
The acceptance of the alternative hypothesis (H_a).that the appropriate model that can be used is the Fixed Effect Model (FEM).In this test, F-statistics are used to choose between the Fixed Effect Model (FEM) and the Common Effect Model (CEM).Acceptance or rejection of the hypothesis depends on the level α = 5% in the null hypothesis (H_0) and alternative hypothesis (H_a).Theoretically, one may conclude from these two models that the alternative hypothesis (H_a) can be rejected and the null hypothesis (H_0) can be accepted if the test findings have a probability level of greater than 5%.In this instance, the Common Effect Model (CEM) is the suitable model to apply; should the test findings have a The Fixed Effect Model (FEM) is the appropriate model to use since a probability level of less than 5%, on the other hand, will accept the alternative hypothesis (H_a) and reject the null hypothesis (H_0).Test Criteria: Probability level test results >5% = H 0 Accepted (CEM) Probability level test results <5% = H 0 Rejected (FEM)

Hausman Test
Hausman testing will be used to decide between the Fixed Effect Model (FEM) and the Random Effect Model (REM).This Hausman test uses the Chi-Square statistical distribution with k degrees of freedom to identify the number of exogenous variables.Apply a probability level instead, which is established by the level α = 5%.To assess the hypothesis, use the Hausman test.If the results are the opposite and you reject the alternative hypothesis (H_a) and accept the null hypothesis (H_0), the Random Effect Model (REM) will be applied.If the results are the opposite, however, the Fixed Effect Model (FEM) will be applied.
Test Criteria: Probability level test results >5% = H 0 Accepted (REM) Probability level test results <5% = H 0 Rejected (FEM) Uji Lagrange Multiplier (LM) Testing the Lagrange Multiplier (LM) aims to determine the optimal match between the Random Effect Model (REM) and the Common Effect Model (CEM).This LM test is based on the Chi-Squares distribution, which has a degree of freedom equal to the number of exogenous variables.This test needs to be carried out if the findings of the Hausman Test and the Chow Test lead to different conclusions.In case the LM statistical value is greater than the critical value of the Chi-Squares statistic, the alternative hypothesis (H_a) will be accepted and the null hypothesis (H_0) will be rejected.This suggests that the Random Effect Model is being used in the fit estimate.On the other hand, if the LM statistic's value is below the critical threshold of The Since the Chi-Squares statistic will accept the null hypothesis (H_0) and reject the alternative hypothesis (H_a), the Common Effect Model should be utilized instead.Apply a probability level instead, which is established by the level α = 5%.
Test Criteria: Probability level test results >5% = H 0 Accepted (REM) Probability level test results <5% = H 0 Rejected (FEM) Carrying out the model suitability test as explained above can be simplified by looking at Figure 2    Research Models 1 and 2's Chow-test results demonstrate that statistical hypotheses are generated by the F test statistics and chi-square test, which reject the null hypothesis (H_0) and accept the alternative hypothesis (Ha) at the α = 5% level.This could mean that the Fixed Effect Model will be applied more successfully than the Common Effect Model.(Table 3)

Testing the Intervening Variable NPL Function
The Intervening Variable NPL is unable to act as a mediator between the Loan to Deposit Ratio (LDR) and the Capital Adequacy Ratio (CAR), which is 0.97613894 > 0.05, at the α = 5% level.(Refer to Table 8) below.

Loan (NPL) and Capital Adequacy Ratio (CAR) as Endogenous Variables in Testing the Suitability of Research ModelsTable 3 .
Chow Test