195 American Journal of Economic and Management Business p-ISSN: XXXX-XXXX e-ISSN: 2835-5199 Vol. 3 No. 7 July 2024 The Effect of Competition and Bank Size on Bank Stability in ASEAN-5 Countries Maudy1*, Isni Andriana2, Mu’izzuddin3, Marlina Widiyanti4 Sriwijaya University, Palembang, South Sumatera, Indonesia1,2,3,4 Email: maudiody2013@gmail.com1*, Isniandriana@unsri.ac.id2, muizzudin@unsri.ac.id3, marlinawidiyanti@fe.unsri.ac.id4 Abstract This study aims to examine and analyze the influence of competition and bank size on bank stability in ASEAN-5 countries. Utilizing a descriptive quantitative approach with panel data estimation, this research employs archival data analysis techniques. The data were sourced from Moody's Analytics Bank Focus, and the sample was selected using purposive sampling, resulting in 133 commercial banks from ASEAN-5 countries for the period 2013-2023.The findings reveal that competition, as measured by the Lerner index, has a positive and significant effect on bank stability, both when stability is measured by the z-score and by Non-Performing Loans (NPLs). Conversely, bank size has a negative and significant effect on bank stability, again both in terms of z-score and NPLs. These insights are valuable for banking institutions, debtors, customers, and investors, guiding their capital investments in the banking sector. Future research should consider additional factors such as efficiency, GDP, and interest rates on bank stability, and expand the scope of the study to include a broader range of observations. Keywords: Z-SCORE; Non-Performing Loan; Lerner; Size. This article is licensed under a Creative Commons Attribution-ShareAlike 4.0 International INTRODUCTION The global economic crisis that occurred in 2008 was an event that taught lessons about the importance of reducing systemic risk and maintaining financial system stability (Fauziah et al., 2020) and ((Rahmah, 2018). Significant changes to the legislative and supervisory framework in the banking industry of ASEAN countries are caused by the crisis (Noman et al., 2017) and (Kim et al., 2020). There is a complicated relationship between financial stability and macroeconomic stability. Therefore, financial stability analysis cannot be ignored (Amanda, 2023). Stability in the financial system can prevent financial crises and play an important role in boosting economic growth (Sri Setiawati, 2020) (Ahi & Laidroo, 2019; Ahmad et al., 2022; Shahriar et al., 2023). (Phan et al., 2019) and (Wai Peng Wong, 2016) There are several specific reasons for the rapid development of the domestic banking industry mailto:maudiody2013@gmail.com1* mailto:Isniandriana@unsri.ac.id2 mailto:muizzudin@unsri.ac.id3 mailto:marlinawidiyanti@fe.unsri.ac.id4 https://creativecommons.org/licenses/by-sa/4.0/ https://creativecommons.org/licenses/by-sa/4.0/ https://creativecommons.org/licenses/by-sa/4.0/ American Journal of Economic and Management Business Vol. 3 No. 7 July 2024 196 in ASEAN, including ASEAN is the fourth trading region that causes fierce competition, the existence of the ASEAN Economic Community (AEC) and an integrated banking sector. The bank overcomes various obstacles and threats to its stability. Competition and bank size are among the determinants of stability in several studies related to banking stability (Amanda, 2023). According to (Phan et al., 2019); (Hertina et al., 2019; T. P. T. Nguyen & Nghiem, 2020). Competition is a state in which various parties compete with each other to achieve a certain goal. Banking institutions are accelerating the consolidation process to maintain their market power in light of increasing competition, which raises concerns about the number of large banks and the level of concentration (Dutta & Saha, 2021). Furthermore, regarding the size of the bank, it is theoretically stated that the larger the bank's size, the better its risk management capabilities, infrastructure and human resources will be, which will have a positive impact on its stability (Ahmad Fatoni, 2022). The following is the ranking of the top 5 banks in the ASEAN-5 region. Table 1. Size of the 5th Largest Banks in ASEAN-By Total Assets It Bank Name Country Total Assets US$ 1 DBS Bank Singapore 491,9 2 OCBC Bank Singapore 394,5 3 United Overseas Bank Singapore 326,7 4 Malayan Banking Malaysia 213 5 CIMB Bank Malaysia 149,7 Source: Forbes, 2021 Table 1. shows the ranking of the largest banks in ASEAN-5 by total assets in 2021. The top 5 largest ASEAN-5 banks are occupied by Singapore and Malaysian banks. Singaporean banks still dominate ASEAN banking by occupying the top three rankings, namely DBS, OCBC, and UOB. It is followed by Malaysian banks at 4 to 5. The problem of limiting the size of banks has become increasingly prominent since the global financial crisis of 2007-2008. This is because of the abundance of evidence that shows that the big banks were responsible for the crisis that caused significant damage to many economies around the world (Adusei, 2019). Some studies show different results (Kanas et al., 2019). (Lee et al., 2023); (Ibrahim et al., 2019) stated that bank competition has a negative and significant effect on bank stability. The bank's level of stability supports the view that higher competition leads to instability and vice versa. This is also in line with the research conducted by (Arping, 2019) and (Leroy & Lucotte, 2019), which states that competition has a negative effect on the stability of the banking industry. However, it is different from the research conducted by (Pessarossi et al., 2020) and (Nasim et al., 2023) (Sanderson et al., 2019) have a positive and significant effect on bank stability, this result, in line with research conducted by (Shamshur & Weill, 2019) and (Huizhi & Xianghua, 2023) said that competition affects the stability of banks because banks will continue to innovate to maintain their market strength (Sanderson et al., 2019)and (Takahashi & Vasconcelos, 2024). Maudy, Isni Andriana, Mu’izzuddin, Marlina Widiyanti 197 Based on existing research related to the influence of competition and bank size on stability, we have not reached a consistent conclusion, possibly due to differences in region and sample period. Therefore, the researcher is interested in continuing and testing existing research and will specifically analyze the mechanism of the influence of competition and bank size on bank stability using data from the ASEAN-5 banking industry for the 2013-2023 period. Therefore, this study was compiled with the title "The Influence of Competition and Bank Size on Bank Stability in ASEAN-5 Countries". RESEARCH METHODS This study focuses on the variables of competition and bank size on bank stability. Competition is measured by the lerner index, the size of the bank is measured from the natural logarithm of total assets and stability is measured by Z-Score and NPL. The object of this study is to observe banks in ASEAN-5 countries for the period 2013 to 2023. Data collection techniques through archival data analysis. This study uses a descriptive quantitative approach. The data source in this study is a dataset obtained from Moody's Analytics Bank Focus. The sampling technique uses the purposive sampling method consisting of 133 commercial banks in ASEAN-5. Panel data analysis techniques were used in this research. The calculation of the panel data model in this study uses approaches such as the Fixed Effect Model (FEM), Random Effect Model (REM) and Generalized Least Squares (GLS) (Adusei, 2019; Ijaz et al., 2020; Q. K. Nguyen & Dang, 2022); (Anggraini et al., 2023; Muizzuddin et al., 2021). The regression model of panel data in this study is as follows: STABit = ++ πœΆπœ·πŸπ‘°π‘³π’Šπ’•πœ·πŸπ‘Ίπ‘°π’π‘¬π’Šπ’• + Ζπ’Šπ’• 𝛽1𝐼𝐿𝑖𝑑 shows the competition measured by the value of the bank lerrner index at bank s at time t, to measure the size of the bank proxied by the size of bank s at time t, and Ɛit is the standard or residual error. 𝛽2𝑆𝑖𝑧𝑒𝑖𝑑 Table 2. Variable Operational Definition Variable Definition Formula Dependent Variables Z-score Financial indicators of a banking health institution, which are calculated as the sum of return on assets (ROA) and the ratio of capital to assets, then divided by the standard deviation of ROA (Erward, 1968) ZSCOREit = 𝑅𝑂𝐴𝑖𝑑 + 𝐸𝑄𝑇𝐴𝑖𝑑 πœŽπ‘…π‘‚π΄π‘–π‘‘ NPL The ratio is used to measure the ability of banks to protect the risk of default of credit repayment by debtors (Kasmir, 2016) 𝑁𝑃𝐿 = Kredit Bermasalah Total Kredit Γ— 100% Independent Variables American Journal of Economic and Management Business Vol. 3 No. 7 July 2024 198 Variable Definition Formula Lerner Index Non-structural measures to estimate the level of banking competition (Weill, 2011). Lernerit = 𝑃𝑠𝑑 βˆ’ 𝑀𝐢𝑠𝑑 𝑃𝑠𝑑 Bank Size The bank's ability to distribute funds is seen in terms of assets owned (World Bank, 2020) 𝐿𝑛 (π‘‡π‘œπ‘‘π‘Žπ‘™ 𝐴𝑠𝑠𝑒𝑑 π΅π‘Žπ‘›π‘˜) RESULT AND DISCUSSION Descriptive Statistics Table 3. Descriptive statistics Variable Obs Average Std. Dev. Min. Max. Dependent Variables Z-SCORE 10,58 2,232 .487 -0,06 4,007 NPL 10,58 3,445 5,118 0 68,841 Independent Variables LERNER 10,58 0,291 0,178 -.38 0,616 SIZE 10,58 68,217 269,117 15,843 8,756.463 Source: STATA-17 Data Processing Results Table 3 presents descriptive statistics, including the number of observations, average values, standard deviations, minimums and maximums of all research variables. The competition that was proxied with lerner showed a value of 0.291, Size showed an average value of 68.217, while bank stability measured by z-score and npl showed a value of 2.232 and 3.445, respectively. The higher the variable value indicates the lower a bank faces Financial distress (Muizzuddin et al., 2021). However, a minimum value of -0.06 indicates that some banks are vulnerable to Financial distress (Risfandy et al., 2022). The average, minimum and maximum values of each variable in each country can be explained in (table 4, table 5 and table 6). Table 4. The average value of variables in each Country Country Z- SCORE NPL LERNE R SIZE Indonesia n 2,292 4,046 0,234 14,393 Malaysia 2,151 2,249 0,350 16,072 Philippin es 2,106 3,844 0,323 15,417 Singapor e 1,528 0, 678 0,405 17,79 Thailand 2,299 2,869 0,387 16,106 Maudy, Isni Andriana, Mu’izzuddin, Marlina Widiyanti 199 Source: STATA-17 Data Processing Results Table 5. Maximum value of variables in each Country Country Z- SCORE NPL LERNE R SIZE Indonesia n 4,049 68,731 0,616 18,595 Malaysia 4,007 34,89 0,616 18,679 Philippine s 3,145 31,78 0,615 18,156 Singapore 2,051 1,66 0,616 19,618 Thailand 4,092 25,525 0,616 18,559 Source: STATA-17 Data Processing Results Table 6. Variable minimum values in each Country Source: STATA-17 Data Processing Results Multicollinearity Test Table 7. Paired correlation matrix Variable (1) (2) (3) (4) (1) Z- SCORE 1,000 (2) NPL 0,058 1,000 (3) LERNER -0,053 -0,085* 1,000 (4) SIZE -0,480* -0,160* 0,607* 1.000 Source: STATA-17 Data Processing Results The multicoloniality test aims to test whether there is a relationship or correlation between dependent variables. By pairwise correlation matrix, If the coefficient between independent variables is more than >0.8, it means that there is a multicollinearity problem in the model (Adam et al., 2023). Table 4.5 shows that there is no multicollinearity problem between variables in this study, and diagnostic tests show that there is no multicollinearity issue. Panel Data Regression Table 8. Bank Stability Baseline Regression Results (Z-SCORE) Country Z- SCORE NPL LERNE R SIZE Indonesia n -0,008 0 -0,380 9,831 Malaysia 1,287 0,003 -0,380 11,506 Philippine s 0,643 0,256 -0,140 12,052 Singapore 1,13 0,022 0,124 16,071 Thailand 1,298 0,009 -0,380 12,656 American Journal of Economic and Management Business Vol. 3 No. 7 July 2024 200 Dependent Variable = Z-SCORE OLS FEM BRAKE GLS Independent Variables LERNER 1,0599*** 0,4385*** 0,5640*** 0,5597*** (0,0794) (0,1754) (0,1762) (0,0400) Control Variables SIZE -0,1931*** -0,0449*** -0,1218*** -0,1637*** (0,0077) (0,0603) (0,0291) (0,0057) Constant 4,8875*** 2,8112*** 3,9498*** 4,5447*** (0,1054) (0,9070) (0,4344) (0,0,0914) Number of observations 1,369 1,369 1,369 1,369 Number of banks 133 133 133 133 R-squared 0,3174 0,0265 0,3141 Hausman Test 0,0000 Heteroscedasticity Test 0,0000 Autocorrelation Test 0,0000 Source: STATA-17 Data Processing Results Table 8 presents the results of the panel data estimation that tests the influence of competition proxied with lerner and bank size measured by size on stability proxied by z-score. Based on the value of the hausman test, it shows a result of 0.0000<0.05 so it can be concluded that the FEM model is the right model in this study. However, there are problems of heteroscedasticity and autocorrelation in the regression of the FEM model, therefore, the Generalized Least Squared (GLS) model is used to correct errors in the analysis (Erfan et al., 2023). The regression results of the panel data are still used as a comparison between four estimation models, namely OLS, FEM, REM, and GLS. Table 9. Bank Stability Base Regression (NPL) Results Dependent Variable = Z-SCORE OLS FEM BRAKE GLS Independent Variables LERNER 0,5942*** 0,5769 0,2698 0,3400*** (1,0585) (0,9774) (1,3072) (0,2358) Control Variables SIZE -0,4735*** 0,3700*** -0,3508*** -0,3096*** (0,1014) (0,6312) (0,1636) (0,0416) Constant 10.4414** * -1,9925*** 8,6213*** 7,7361*** (1,3745) (9,1067) (2,3561) (0,6521) Number of observations 1,140 1,140 1,140 1,140 Number of banks 133 133 133 133 R-squared 0,0260 0,0010 0,2590 Maudy, Isni Andriana, Mu’izzuddin, Marlina Widiyanti 201 Hausman Test 0,0822 Heteroscedasticity Test 0,0000 Autocorrelation Test 0,0000 Source: STATA-17 Data Processing Results Table 9 presents the results of the panel data estimation that tests the effect of competition proxied with lerner and bank size measured by size on stability proxied by z-score. The Hausman test compares the two models, between the random effect model and the Fixed Effect Model and showing the results that Crosssection random has a value of 0.0822 > 0.05, so the test results Hausman Choosing a model BRAKE than FEM. However, there are problems of heteroscedasticity and autocorrelation in the regression of the FEM model, so the Generalized Least Squared (GLS) model is an alternative to overcome the problem of heteroscedasticity assumption and autocorrelation (Martaningtyas et al., 2024) and (Kosmaryati et al., 2019). Discussion Competition for bank stability Table 8 shows that competition has a positive and significant effect on stability as measured by z-score. The competition variable showed a significance level of 0.000<0.01 and a coefficient of 0.5597 on bank stability (z-score). This means that if the level of competition increases, it will have an effect on increasing stability (z-score) by 56%. Furthermore, in Table 9, the competition also has a positive and significant effect on stability as measured by NPLs, with a competition significance level (learner) of 0.149>0.01 and a coefficient of 0.3400*** on bank stability (NPL). This means that if the increase in competition increases, it will have an effect on increasing stability (NPL) by 34%. The results show that the increase in market strength supports banking stability in ASEAN-5, which means that banks have a small risk of bankruptcy and high stability of bank companies (Violeta Ketaren & Mulyo Haryanto, 2020). This is supported by the efficiency theory put forward by Drucker (1974), who said that all activities carried out by a company must be efficient in order to obtain maximum output from the inputs it has related to this,This research is in line with previous research conducted by (Sanderson et al., 2019), (Gumanica, 2022) and (Sanderson et al., 2019) who said that competition has a positive effect on the stability of banks. The size of the bank against the stability of the bank Table 8 shows that bank size has a negative and significant effect on stability as measured by z-score. The bank size variable showed a significance level of 0.000<0.01 and a coefficient of -0.1637*** to bank stability (z-score). This means that if the bank size level increases by 1%, it will have an effect on a decrease in stability (z-score) of 16%. Furthermore, in Table 9, bank size also has a negative and significant effect on stability as measured by NPLs, with a significance level of 0.00<0.01 and a coefficient of -0.3096*** to bank stability (NPL). This means that every 1% increase in bank size will reduce the bank's stability level by 31%. The results show that the larger the size of the bank, the more risky or unstable the bank will be. These results are in line with research (Sanjaya and Badjuri 2022) and (Shahriar, Mehzabin, and Azad 2023) who said that the size of the bank has a negative and significant effect on the stability of the bank. CONCLUSION American Journal of Economic and Management Business Vol. 3 No. 7 July 2024 202 This study provides information on the relationship between competition and bank size to banking stability, especially in banking companies in ASEAN-5 countries. The results of the study with the generalized least squared (GLS) model show that competition measured by learner has a positive and significant effect on stability measured by z-score and NPL. However, bank size has a negative and significant effect on stability, as measured by z-score and NPL. The findings suggest that there is more diversity of assets and funds, which will weaken bank stability. Banks need to be careful in managing their assets. This research makes a significant contribution to the literature on bank stability. This study has several limitations. First, it only uses a sample of ASEAN-5 countries. Future research is expected to expand the research object to all countries in Southeast Asia or the Asian continent. These two studies use 2 independent variables, such as competition and bank size; further research needs to be expected to pay attention to other variables that can affect banking stability. REFERENCES Adam, M., Sahriman, S., & Sirajang, N. (2023). Penerapan Metode Linearized Ridge Regression pada Data yang Mengandung Multikolinearitas. 4(1), 122–129. https://doi.org/10.20956/ejsa.vi.19081 Adusei, M. (2019). The impact of bank size and funding risk on bank stability. Cogent Economics and Finance, 3(1). https://doi.org/10.1080/23322039.2015.1111489 Ahi, K., & Laidroo, L. (2019). Banking market competition in Europeβ€”financial stability or fragility enhancing? Quantitative Finance and Economics, 3(2), 257–285. https://doi.org/10.3934/qfe.2019.2.257 Ahmad Fatoni. (2022). Pengaruh Kebijakan Restrukturisasi Pembiayaan, Ukuran Bank, Non Performing Financing, Dan Produk Domestik Bruto Terhadap Stabilitas Perbankan Syariah Di Indonesia: Bukti Empiris Di Tengah Pandemi Covid 19. Jurnal Ilmiah Ekonomi Dan Bisnis, 19(2), 140–148. https://doi.org/10.31849/jieb.v19i2.7124 Ahmad, S., Wan Ahmad, W. M., & Shaharuddin, S. S. (2022). Is excess of everything bad? Ramifications of excess liquidity on bank stability: Evidence from the dual banking system. Borsa Istanbul Review, 22, S92–S107. https://doi.org/10.1016/j.bir.2022.09.008 Amanda, C. (2023). Rural banking spatial competition and stability. Economic Analysis and Policy, 78, 492–504. https://doi.org/10.1016/j.eap.2023.03.021 Anggraini, F., Taufik, T., Muizzuddin, M., & Andriana, I. (2023). Analisis Stabilitas Perbankan Syariah dan Konvensional di Negara-Negara Kawasan MENA. Al-Kharajβ€―: Jurnal Ekonomi, Keuangan & Bisnis Syariah, 6(2), 609–621. https://doi.org/10.47467/alkharaj.v6i2.3801 Arping, S. (2019). Competition and risk taking in bankingβ€―: The charter value hypothesis revisited R. 107. https://doi.org/10.1016/j.jbankfin.2019.105609 Dutta, K. D., & Saha, M. (2021). Do competition and efficiency lead to bank stability? Evidence from Bangladesh. Future Business Journal, 7(1). https://doi.org/10.1186/s43093-020-00047-4 Erfan, R., Muizzuddin, & Taufik. (2023). Loan Growth and Bank Risk; Empirical Study on Commercial Banks in Asia-Pacific. The 8th Indonesian Finance Association International Conference, 1, 278–303. Maudy, Isni Andriana, Mu’izzuddin, Marlina Widiyanti 203 Erward, A. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. He Journal of Finance, 22(4), 589–609. Fauziah, Ayu, F., & Hidayatin, N. N. (2020). Inklusi Keuangan dan Stabilitas Sistem Keuangan ( Bank Z-Score ) di Asia Jurusan Administrasi Niaga , Politeknik Negeri Malang , Indonesia. Journal Ekonomi Dan Kewirausahaan, 14(1), 30–47. Gumanica, M. (2022). Analisis Pengaruh Kompetisi, Capital Buffer,Inklusi Keuangan,Dan Ukuran Bank Terhadap Stabilitas Perbankandi Indonesia. Contemporary Studies in Economic, 1(2), 283–296. Hertina, U., Astuti, W., Mahardika, P., & Saputra, A. (2019). Efficiency and Competition in Banking Industryβ€―: Case for ASEAN-5 Countries. 66(2), 141–152. https://doi.org/10.2478/saeb-2019-0011 Huizhi, L., & Xianghua, Y. (2023). Jo ur na l P re r oo f. HELIYON, e21378. https://doi.org/10.1016/j.heliyon.2023.e21378 Ibrahim, M. H., Salim, K., Abojeib, M., & Wee, L. (2019). Structural changes , competition and bank stability in Malaysia ’ s dual banking system. Economic Systems, 43(1), 111– 129. https://doi.org/10.1016/j.ecosys.2018.09.001 Ijaz, S., Hassan, A., Tarazi, A., & Fraz, A. (2020). Linking bank competition, financial stability, and economic growth. Journal of Business Economics and Management, 21(1), 200–221. https://doi.org/10.3846/jbem.2020.11761 Kanas, A., Hassan Al-Tamimi, H. A., Albaity, M., & Mallek, R. S. (2019). Bank competition, stability, and intervention quality. International Journal of Finance and Economics, 24(1), 568–587. https://doi.org/10.1002/ijfe.1680 Kasmir. (2016). Analisis Laporan Keuangan (1st ed.). Raja Grafindo Persada. Kim, H., Batten, J. A., & Ryu, D. (2020). Financial crisis, bank diversification, and financial stability: OECD countries. International Review of Economics and Finance, 65(January 2019), 94–104. https://doi.org/10.1016/j.iref.2019.08.009 Kosmaryati, K., Handayani, C. A., Isfahani, R. N., & Widodo, E. (2019). Faktor-Faktor yang Mempengaruhi Kriminalitas di Indonesia Tahun 2011-2016 dengan Regresi Data Panel. Indonesian Journal of Applied Statistics, 2(1), 10. https://doi.org/10.13057/ijas.v2i1.27932 Lee, C., Ni, W., & Zhang, X. (2023). FinTech development and commercial bank efficiency in China. Global Finance Journal, 57(November 2022), 100850. https://doi.org/10.1016/j.gfj.2023.100850 Leroy, A., & Lucotte, Y. (2019). Competition and credit procyclicality in European banking R. 99, 237–251. https://doi.org/10.1016/j.jbankfin.2018.12.004 Martaningtyas, N. U., Septiyaningrum, E. A., & Maulana, Z. (2024). Dampak Pelanggaran Asumsi Klasikterhadap Kesalahan Inferensi Dalam Analisis Ekonometrika. SYNERGYJurnal Ilmiah Multidisiplin, 1(4), 255–265. Muizzuddin, Tandelilin, E., Hanafi, M. M., & Setiyono, B. (2021). Does Institutional Quality Matter in the Relationship Between Competition and Bank Stability? Evidence From Asia. Journal of Indonesian Economy and Business, 36(3), 283–301. https://doi.org/10.22146/jieb.v36i3.1428 American Journal of Economic and Management Business Vol. 3 No. 7 July 2024 204 Nasim, A., Ullah, S., Ryong, J., & Hameed, A. (2023). Energy shocks and bank efficiency in emerging economies. Energy Economics, 126(July), 107005. https://doi.org/10.1016/j.eneco.2023.107005 Nguyen, Q. K., & Dang, V. C. (2022). The impact of risk governance structure on bank risk management effectiveness: evidence from ASEAN countries. Heliyon, 8(10), e11192. https://doi.org/10.1016/j.heliyon.2022.e11192 Nguyen, T. P. T., & Nghiem, S. O. N. H. (2020). THE EFFECTS of COMPETITION on EFFICIENCY: The VIETNAMESE BANKING INDUSTRY EXPERIENCE. Singapore Economic Review, 65(6), 1507–1536. https://doi.org/10.1142/S0217590817500114 Noman, A. H., Gee, C. S., & Isa, C. R. (2017). Does competition improve financial stability of the banking sector in ASEAN countriesβ€―? An empirical analysis. PLuS ONE, 12(5), 1– 27. https://doi.org/https://doi.org/10.1371/journal.pone.0176546 M Pessarossi, P., Thevenon, J., & Weill, L. (2020). Research in International Business and Finance Does high profitability improve stability for European banksβ€―? ⋆. Research in International Business and Finance, 53(February), 101220. https://doi.org/10.1016/j.ribaf.2020.101220 Phan, H. T., Anwar, S., Alexander, W. R. J., & Phan, H. T. M. (2019). Competition, efficiency and stability: An empirical study of East Asian commercial banks. North American Journal of Economics and Finance, 50(October 2018), 100990. https://doi.org/10.1016/j.najef.2019.100990 Rahmah, L. P. (2018). Analisis Hubungan Independensi Bank Sentral dan Variabel Makroekonomi terhadap Stabilitas Sistem Keuangan di ASEAN. Jurnal Ilmiah, 6(1), 1– 13. Risfandy, T., Tarazi, A., & Trinugroho, I. (2022). Competition in dual markets: Implications for banking system stability. Global Finance Journal, 52(December 2019), 100579. https://doi.org/10.1016/j.gfj.2020.100579 Sanderson, A., Roux, P. Le, & Mutandwa, L. (2019). Competition and bank stability. Journal of Financial Intermediation, 35(3), 57–69. https://doi.org/10.1016/j.jfi.2017.06.001 Sanjaya, S. A. K., & Badjuri, A. (2022). PENGARUH UKURAN BANK, KREDIT, KREDIT BERMASALAH, MODAL DAN PRODUK DOMESTIK BRUTO TERHADAP PENGAMBILAN RISIKO BANK. Jurnal Ilmiah MEA (Manajemen, Ekonomi, Dan Akuntansi), 6(1), 595. Shahriar, A., Mehzabin, S., & Azad, M. A. K. (2023). Diversification and bank stability in the MENA region. Social Sciences and Humanities Open, 8(1), 100520. https://doi.org/10.1016/j.ssaho.2023.100520 Shamshur, A., & Weill, L. (2019). Does bank efficiency influence the cost of creditβ€―? R. Journal of Banking and Finance, 105, 62–73. https://doi.org/10.1016/j.jbankfin.2019.05.002 Sri Setiawati, R. I. (2020). Analisis Pengaruh Faktor-Faktor Fundamental Kinerja Bank Dan Makro Ekonomi Terhadap Stabilitas Perbankan Di Indonesia. Jurnal Ilmiah Bisnis Dan Ekonomi Asia, 14(2), 123–132. https://doi.org/10.32812/jibeka.v14i2.194 Maudy, Isni Andriana, Mu’izzuddin, Marlina Widiyanti 205 Takahashi, L., & Vasconcelos, M. R. (2024). Bank efficiency and undesirable outputβ€―: An analysis of non-performing loans in the Brazilian banking sector. 59(November 2023), 1–10. https://doi.org/10.1016/j.frl.2023.104651 Violeta Ketaren, E., & Mulyo Haryanto, A. (2020). PENGARUH KINERJA KEUANGAN TERHADAP STABILITAS PERBANKAN YANG TERDAFTAR DI BURSA EFEK INDONESIA (Studi Kasus pada Bank yang Terdaftar di BEI Tahun 2014-2018). Diponegoro Journal of Management, 9(2), 1–13. Wai Peng Wong, Q. D. (2016). Efficiency analysis of banks in ASEAN countries. Benchmarking: An International Journal, 23(7), 1798–1817. https://doi.org/https://doi.org/10.1108/BIJ-11-2013-0102 Weill, L. (2011). Do Islamic banks have greater market power? Comparative Economic Studies, 53(2), 291–306. https://doi.org/10.1057/ces.2011.1 World Bank. (2020). Financial Stability. ttps://www.worldbank.org/en/ publication/gfdr/gfdr%022016/background/financial-stability Copyright holders: Maudy, Isni Andriana, Mu’izzuddin, Marlina Widiyanti (2024) First publication right: AJEMB – American Journal of Economic and Management Business