Almana : Jurnal Manajemen dan Bisnis Volume 9 No. 2/ August 2025: 388-400 p-ISSN: 2579-4892/ e-ISSN: 2655-8327 DOI: 10.36555/almana.v9i2.2892 The Influence of Specific, Macroeconomic and ESG Factors on Banking Liquidity Risk in Indonesia Ridhan Azka Hani Fanu*1, Elsa Agustine1, Henny Setyo Lestari1 Universitas Trisakti, Indonesia1 *Coresponding Email: 122012401055@std.trisakti.ac.id Abstract: Liquidity risk remains a critical challenge for the Article History: banking sector, as it affects financial stability and the ability Submitted: July 21, 2025 to meet short-term obligations. In Indonesia, banking liquidity Revised: August 11, 2025 is influenced by internal performance, macroeconomic Accepted: August 13, 2025 conditions, and the growing emphasis on Environmental, Published: 27 August, 2025 Social, and Governance (ESG) practices. This study aims to examine the impact of bank-specific factors, macroeconomic indicators, and ESG scores on banking liquidity risk in Keywords: Indonesia. Using panel data from Indonesian banks, liquidity Banking Liquidity Risk risk is measured through two proxies: the liquid asset ratio Bank-Specific Factors (LC1) and the loan-to-total-assets ratio (LC2). The findings Macroeconomic Indicators show that bank performance, represented by Net Interest ESG Margin (NIM), positively affects LC1, while inflation has a negative impact. Bank capital and size are positively related to LC2, whereas income diversification and bank performance reduce LC2. ESG scores do not exhibit significant effects on liquidity risk. These results provide important implications for policymakers, bank managers, and researchers in designing strategies to enhance liquidity resilience and guide future studies on ESG’s role in liquidity management. Fanu, R. A. H., Agustine, E., Lestari, H. S (2025). The Influence of Specific, Macroeconomic and ESG Factors on Banking Liquidity Risk in Indonesia. Almana : Jurnal Manajemen dan Bisnis, 9(2), 388-400. https://doi.org/10.36555/almana.v9i2.2892 INTRODUCTION In the midst of the dynamics of the global financial system, liquidity risk is a major concern for banks, considering its nature that can bring systemic instability if not managed properly. In Indonesia, the role of the banking sector as the main driver of financial intermediation makes it an important highlight, especially in anticipating internal and external challenges (Bank Indonesia, 2024). Bank-specific factors such as bank size, profitability, funding structure, and asset quality (NPL) have proven to play a significant role (Ahmed et al., 2021). For example, research by Mohamad (2024) shows that ROA, total assets, and NPLs have different influences on LCR and NSFR ratios during the COVID19 pandemic (Mohamad, 2024). Specifically, in Indonesia, a study by Panjaitan & Lisdiono (2024) confirms that liquidity risk management in accordance with OJK standards contributes to bank resilience. On the macro side, variables such as GDP growth, inflation, interest rates (BIRate), as well as foreign exchange reserves and the rupiah exchange rate clearly affect banking liquidity (Justiro & Irawati, 2023). At the end of 2024 and into mid-2025, Bank Indonesia implemented accommodative monetary measures, among them lowering the secondary reserve requirement from 5% to 4%, effective June. This action released approximately Rp78.45 trillion in additional liquidity flexibility for banks (Reuters, 2025). At the same time, BI's interest rate is maintained at the level of 5.50% on June 18, 2025, although room for downside is still open with inflation indicators flat at 1.6% and credit growth slowing to the This work is licensed under a Creative Commons Attribution-NonCommercialNoDerivatives 4.0 International License. https://creativecommons.org/licenses/by-nc-nd/4.0/ 388 Almana : Jurnal Manajemen dan Bisnis p-ISSN: 2579-4892 e-ISSN: 2655-8327 range of 8–9% (Departemen Komunikasi Bank Indonesia, 2025). Macroprudential policies such as KLM, PLM, and RPLN have also been strengthened to stimulate credit disbursement to priority sectors and green economies (Agung & Harun, 2021). In addition, the emergence of ESG (Environmental, Social, Governance) as an important dimension in the banking world has captured the attention of academics and practitioners. An empirical study by Sutopo (2025) found that bank size and CAR factors are positively correlated with ESG scores, although short-term profitability does not have a significant effect (Sutopo, 2025). More broadly, Ameliawati Mulyawan (2023) analysis shows that the social and governance pillars of ESG reduce banking risk (measured through NPLs, ZScore, RWA) in Asia, but the environmental pillar has not had a significant effect. In Indonesia, although research that explicitly focuses on ESG is still limited, OJK promotes green financial inclusion and the implementation of green banking, in line with global trends and international regulations. Departing from these conditions, a research gap arises there has been no study that comprehensively examines the simultaneous influence of bank-specific factors, macroeconomic conditions, and ESG scores on banking liquidity risk in Indonesia. In fact, the combination of these three elements is expected to be able to provide a more complete and relevant understanding of the latest conditions. Phenomena such as the relaxation of mandatory reserves, green credit stimulus, the placement of funds in priority sectors, accompanied by evolving ESG practices, provide a rich empirical background to analyze from the 2020–2024 period. The research subject is the banking sector in Indonesia listed on the Indonesia Stock Exchange (IDX), focusing on conventional commercial banks that have consistently published annual financial reports during the research period. The study examines three main groups of factors influencing liquidity risk, namely bank-specific, macroeconomic, and ESG-related factors. Bank-specific factors include revenue diversification (DIV) to measure the extent to which a bank's revenue comes from various lines of business (Susanto et al., 2024), bank capitalization (EQTA) which shows the ratio of equity to total assets, bank size which describes the scale of operations (Al-Sharkas & Al-Sharkas, 2022), and bank performance which is measured by net interest margin (NIM) (Sukmadewi, 2020). Macroeconomic factors consist of gross domestic product (GDP) growth rate and inflation rate as indicators of national economic conditions (Mukhamediyev & Temerbulatova, 2020). Meanwhile, ESG-related factors are represented by ESG rating scores that reflect the bank's commitment to sustainability practices (Ramadhani & Andriani, 2025). Liquidity risk, measured using the LC1 and LC2 ratios, is a dependent variable for assessing the bank's ability to manage liquidity effectively (Snjawi & Essa, 2021). These variables are selected to comprehensively capture internal bank characteristics, external economic conditions, and sustainability practices that may affect banks’ ability to manage liquidity risk effectively in Indonesia. Based on these selected variables, this study aims to fill the identified gaps by using a panel data approach on banks in Indonesia, namely liquidity risk measured through two proxies: the liquid asset ratio (LC1) and the loan-to-total-assets ratio (LC2). Moreover, the analysis examines bank-specific indicators (ROA, NPL, CAR, total assets), macroeconomic indicators (GDP, inflation, bank interest rates, foreign exchange rates), and ESG indices obtained from sustainability reports by the OJK, Sustainalytics, and Bloomberg. This study examines the impact of bank-specific factors, macroeconomic indicators, and ESG scores on banking liquidity risk in Indonesia, with the hope that these findings will enrich academic literature and provide practical policy implications for regulators and banking practitioners in maintaining financial stability and promoting sustainable banking practices. Based on the above background, the hypothesis of this study is as follows: H1 : There is influence Bank Capital against Liquidity Risk 1 H2 : There is influence Bank Capital against Liquidity Risk 2 H3 : There is influence Bank Size against Liquidity Risk 1 The Influence of Specific, Macroeconomic and ESG Factors on Banking Liquidity Risk in Indonesia Ridhan Azka Hani Fanu*1, Elsa Agustine1, Henny Setyo Lestari1 389 Almana : Jurnal Manajemen dan Bisnis Volume 9 No. 2/ August 2025: 388-400 H4 H5 H6 H7 H8 H9 H10 H11 H12 H13 H14 : There is influence Bank Size against Liquidity Risk 2 : There is influence Bank Performance against Liquidity Risk 1 : There is influence Bank Performance against Liquidity Risk 2 : There is influence Income Diversification against Liquidity Risk 1 : There is influence Income Diversification against Liquidity Risk 2 : There is influence GDP against Liquidity Risk 1 : There is influence GDP against Liquidity Risk 2 : There is influence Inflation Rate against Liquidity Risk 1 : There is influence Inflation Rate against Liquidity Risk 2 : There is an influence of ESG Score on Liquidity Risk 1 : There is an influence of ESG Score on Liquidity Risk 2 METHODS This study is a quantitative research with a descriptive-verificative approach. The quantitative method is employed to measure the relationships and effects between variables using numerical data analyzed through statistical techniques. The research subject is the banking sector in Indonesia listed on the Indonesia Stock Exchange (IDX), focusing on conventional commercial banks that have consistently published annual financial reports during the research period. The sampling technique employed was purposive sampling, selecting banks that met the predetermined criteria of being conventional commercial banks listed on the IDX and consistently publishing annual financial statements during the observation period. The research examines several variables, namely liquidity risk measured by the liquid asset ratio (LC1) and the loan-to-total-assets ratio (LC2), income diversification (DIV), bank capital (EQTA), bank size, bank performance represented by Net Interest Margin (NIM), gross domestic product (GDP), inflation rate, and ESG rating scores. The research was conducted from February to July 2025, with the observation period covering 2020–2024. The study took place in Indonesia, and the secondary data were collected from the official website of the Indonesia Stock Exchange (www.idx.co.id) and other authorized publications. The type of data used in this study is secondary data, obtained from processing audited financial statements of banks listed on the IDX, macroeconomic data from Statistics Indonesia (BPS) and Bank Indonesia, and ESG data from banks’ sustainability reports. Data collection was carried out through documentation, which involved downloading audited annual financial statements, annual reports, sustainability reports, and relevant macroeconomic publications. A literature review was conducted by examining textbooks, academic journals, and lecture notes to obtain a theoretical foundation on Liquidity Risk, Income Diversification, Bank Capital, Bank Size, Bank Performance, GDP, Inflation, and ESG. Data were analyzed using panel data regression to test the effect of independent variables on the dependent variable. The selection of the panel regression model (Common Effect, Fixed Effect, or Random Effect) was based on the Chow test, Hausman test, and Lagrange Multiplier test. Prior to hypothesis testing, classical assumption tests were conducted, including normality, multicollinearity, heteroscedasticity, and autocorrelation tests. The data processing was performed using EViews software. RESULTS AND DISCUSSION This section presents the results of data analysis and the interpretation of findings. Model 1 dan 2 aims to explain the relationship between the independent variables and the dependent variable by using panel data regression. Before conducting hypothesis testing, a model specification test is performed to determine the most appropriate estimation model, Common Effect Model (CEM), Fixed Effect Model (FEM), or Random Effect Model (REM). Website: http://journalfeb.unla.ac.id/index.php/almana/article/view /2892 390 Almana : Jurnal Manajemen dan Bisnis p-ISSN: 2579-4892 e-ISSN: 2655-8327 The selection is based on the results of the Chow Test, Hausman Test, and Lagrange Multiplier (LM) Test. MODEL 1 Model Specification Test Table 1. Results of Chow Test for Model 1 Effects Test Statistic d.f. Cross-section F 3.922119 (9,33) Cross-section Chi-square 36.369430 9 Source: EViews Output, 2025 Prob. 0.0018 0.0000 The probability value obtained from the Chow Test is 0.0000, which is lower than the significance level of 0.05. This result indicates that there is a statistically significant difference between the Common Effect Model (CEM) and the Fixed Effect Model (FEM). Table 2. Results of Hausman Test for Model 1 Chi-Sq. Test Summary Statistic Chi-Sq. d.f. Cross-section random 0.000000 7 Source: EViews Output, 2025 Prob. 1.0000 The probability value obtained from the Hausman Test is 1.0000, which is greater than the significance level of 0.05. This means that there is no statistically significant difference between the coefficients estimated using the Fixed Effect Model (FEM) and those estimated using the Random Effect Model (REM). Table 3. Results of Lagrange Multiplier Test for Model 1 Crosssection Time Both Breusch-Pagan 0.082086 1.628030 1.710116 (0.7745) (0.2020) (0.1910) Honda 0.286507 -1.275943 -0.699637 (0.3872) (0.8990) (0.7579) King-Wu 0.286507 -1.275943 -0.902723 (0.3872) (0.8990) (0.8167) Standardized Honda 1.191158 -0.529704 -3.227423 (0.1168) (0.7018) (0.9994) Standardized King-Wu 1.191158 -0.529704 -3.360624 (0.1168) (0.7018) (0.9996) Gourieroux, et al. --0.082086 (0.6272) Source: EViews Output, 2025 The Breusch-Pagan Lagrange Multiplier (LM) test produces a probability value of 0.7745, which is greater than the significance level of 0.05. This result means that we fail to reject the null hypothesis, which states that the Common Effect Model (CEM) is more appropriate than the Random Effect Model (REM). In other words, the test suggests that there is no significant variance in the data attributable to cross-sectional differences (for example, differences between entities such as banks) that would justify the use of REM. Classic Assumption Test The classical assumption test aims to test the condition of research data in the form of data processing. The classical assumption test in this study includes the normality test, the multicollinearity test, the heterogeneity test and the autocorrelation test. The Influence of Specific, Macroeconomic and ESG Factors on Banking Liquidity Risk in Indonesia Ridhan Azka Hani Fanu*1, Elsa Agustine1, Henny Setyo Lestari1 391 Almana : Jurnal Manajemen dan Bisnis Volume 9 No. 2/ August 2025: 388-400 1. Normality Test The normality test can be done by several methods, namely residual histogram, kolmogrov smirnov, kurtosius skewness and jarquebera. The normality test in this study used the JB or jarquebera test in the form of a histogram graph. Figure 1. Histogram of Residual Normality Test Using Jarque-Bera Source: EViews Output, 2025 Based on the above normality test, the significance value of jarquebera was obtained of 0.000 < 0.05, so it can be concluded that the data in this study is not normally distributed, which means that the normality assumption test is not fulfilled. X1 X2 X3 X4 X5 X6 X7 X1 1 -0.8088793... 0.64048781... -0.6233297... -0.0122335... -0.0110460... 0.54557478... X2 -0.8088793... 1 -0.5968427... 0.75566123... 0.10079476... 0.03007467... -0.7409348... X3 0.64048781... -0.5968427... 1 -0.7949053... 0.00739497... 0.04916082... 0.41246263... X4 -0.6233297... 0.75566123... -0.7949053... 1 -0.0907266... -0.0767506... -0.5929651... X5 -0.0122335... 0.10079476... 0.00739497... -0.0907266... 1 0.43437319... -0.0055852... X6 -0.0110460... 0.03007467... 0.04916082... -0.0767506... 0.43437319... 1 0.09588982... X7 0.54557478... -0.7409348... 0.41246263... -0.5929651... -0.0055852... 0.09588982... 1 Figure 2. Multicollinearity Test Source: EViews Output, 2025 Based on the multicollinearity test above, it was found that all correlation values between variables were free < 0.85 so that it could be concluded that there was no problem of multicollinearity. Figure 3. Heteroscedasticity Test Source: EViews Output, 2025 Website: http://journalfeb.unla.ac.id/index.php/almana/article/view /2892 392 Almana : Jurnal Manajemen dan Bisnis p-ISSN: 2579-4892 e-ISSN: 2655-8327 From the residual graph it can be seen that it does not cross the limit (500 and 500), meaning that the residual variant is the same. Therefore, there are no symptoms of heteroscedasticity or passing the heteroscedasticity test. Based on the model specification tests (Chow Test, Hausman Test, and Lagrange Multiplier Test), the Common Effect Model (CEM) was selected as the most appropriate estimation method for Model 1. The regression results using the CEM approach are presented in Table 4 below. Table 4. Results of Regressed Data Panel Variable Coefficient Std. Error t-Statistic Liquidity Risk -4.128870 3.518646 -1.173425 Income Diversification (DIV) -1.106713 1.475221 -0.750201 Bank Capital (EQTA) 0.230186 0.155988 1.475667 Bank Size 26.50938 4.350679 6.093161 Bank Performance (NIM) 0.722980 1.769787 0.408512 Gross Domestic Product (GDP) -0.092163 0.043520 -2.117708 Inflation Rate -0.107995 0.162541 -0.664413 ESG Rating Scores 0.008242 0.035631 0.231309 Source: EViews Output, 2025 Prob. 0.2472 0.4573 0.1475 0.0000 0.6850 0.0402 0.5101 0.8182 Based on the results of the regression data panel on, the regression model can be written as follows: Y1 = -4.13 - 1.11*X1 + 0.23*X2 + 26.51*X3 + 0.72*X4 - 0.09*X5 - 0.11*X6 + 0.01*X7 Hypothesis Partial tests (t-tests) are used to test whether each independent variable individually has a significant effect on the dependent variable (Y) in the regression model. Table 5. Results of Partial Test Variable Coefficient Std. Error Liquidity Risk -4.128870 3.518646 Income Diversification (DIV) -1.106713 1.475221 Bank Capital (EQTA) 0.230186 0.155988 Bank Size 26.50938 4.350679 Bank Performance (NIM) 0.722980 1.769787 Gross Domestic Product (GDP) -0.092163 0.043520 Inflation Rate -0.107995 0.162541 ESG Rating Scores 0.008242 0.035631 Source: EViews Output, 2025 t-Statistic -1.173425 -0.750201 1.475667 6.093161 0.408512 -2.117708 -0.664413 0.231309 Prob. 0.2472 0.4573 0.1475 0.0000 0.6850 0.0402 0.5101 0.8182 Based on the t-test table above, the following decisions can be made: Hipotesis: H₀ : Independent variables have no significant effect on dependent variables partially. H₁ : Independent variables have a significant effect on dependent variables partially. Party Signifikansi: α = 5% Decision Criteria: 1. Subtract H₀ if the p-value is < 0.05 2. Accept H₀ if the p-value is > 0.05 Results: Table 6. 1 Test Results t Variable Prob. 0.4573 Income Diversification (DIV) 0.1475 Bank Capital (EQTA) 0.0000 Bank Size 0.6850 Bank Performance (NIM) Results H₀ Accepted H₀ Accepted H₀ rejected H₀ Accepted The Influence of Specific, Macroeconomic and ESG Factors on Banking Liquidity Risk in Indonesia Ridhan Azka Hani Fanu*1, Elsa Agustine1, Henny Setyo Lestari1 393 Almana : Jurnal Manajemen dan Bisnis Volume 9 No. 2/ August 2025: 388-400 Gross Domestic Product 0.0402 (GDP) 0.5101 Inflation Rate 0.8182 ESG Rating Scores Source: EViews Output, 2025 H₀ rejected H₀ Accepted H₀ Accepted Conclusion: At the significance level, it was obtained that the variables X3 and X5 had a significant effect on Y1 while the variables X1, X2, X4, X6, and X7 did not have a significant effect on Y1.α = 5%. 1. Simultaneous Test (F Test) Simultaneous tests (F tests) are used to test whether simultaneously independent variables have a significant effect on dependent variables (Y) in regression models. Table 7. Results of Simulation Test Root MSE 0.673859R-squared Mean dependent var 1.448200Adjusted R-squared S.D. dependent var 1.149837S.E. of regression Akaike info criterion 2.368407Sum squared reside Schwarz criterion 2.674331Log likelihood Hannan-Quinn critter. 2.484905F-statistic Durbin-Watson stat 0.840968Prob(F-statistic) Source: EViews Output, 2025 0.649540 0.591130 0.735240 22.70428 -51.21018 11.12034 0.000000 Based on the F test table above, the following decisions can be made: Hipotesis: H₀ : Independent variables have no significant effect on dependent variables simultaneously. H₁ : Independent variables have a significant effect on dependent variables simultaneously. Party Signifikansi: α = 5% Decision Criteria: 1. Subtract H₀ if the p-value is < 0.05 2. Accept H₀ if the p-value is > 0.05 Results: P-Value 0.000 < 0.05 so that H₀ is subtracted Conclusion: At the significance level, the results were obtained that independent variables had a significant effect on Y1 simultaneously so that the regression model was feasible to use.α = 5%. The determination coefficient serves to measure how much the ability of independent variables to explain the dependent variables of a study. This study uses the adjusted R square value to evaluate the regression model. The following are the results of the determination coefficient proxied through Adjusted R2: Table 8. Results of Coefficient of Determination Root MSE 0.673859R-squared 0.649540 Mean dependent var 1.448200Adjusted R-squared 0.591130 S.D. dependent var 1.149837S.E. of regression 0.735240 Akaike info criterion 2.368407Sum squared reside 22.70428 Schwarz criterion 2.674331Log likelihood -51.21018 Hannan-Quinn critter. 2.484905F-statistic 11.12034 Durbin-Watson stat 0.840968Prob(F-statistic) 0.000000 Source: EViews Output, 2025 Website: http://journalfeb.unla.ac.id/index.php/almana/article/view /2892 394 Almana : Jurnal Manajemen dan Bisnis p-ISSN: 2579-4892 e-ISSN: 2655-8327 Based on the table of determination coefficients, the Adjusted R-squared value of 0.59 or 59% was obtained, so it can be concluded that the Y1 variable can be explained by an independent variable of 59% while the remaining 41% is explained by other variables that are not included in this study. MODEL 2 Model Specification Test Table 9. Results of Chow Test Effects Test Cross-section F Cross-section Chi-square Source: EViews Output, 2025 Statistic 7.811945 57.060121 d.f. (9,33) 9 Prob. 0.0000 0.0000 The Prob value is 0.000 < 0.05, then the FEM model is selected. Table 10. Results of Hausman Test Chi-Sq. Test Summary Statistic Chi-Sq. d.f. Cross-section random 0.000000 7 Source: EViews Output, 2025 Prob. 1.0000 The Prob value is 1.0000 > 0.05, then the REM model is selected. Table 11. Results of Lagrange Test Hypothesis Test Crosssection Time Both Breusch-Pagan 0.052209 0.145681 0.197890 (0.8193) (0.7027) (0.6564) Honda 0.228492 -0.381682 -0.108321 (0.4096) (0.6487) (0.5431) King-Wu 0.228492 -0.381682 -0.190834 (0.4096) (0.6487) (0.5757) Standardized Honda 1.124296 0.658372 -2.463256 (0.1304) (0.2551) (0.9931) Standardized King-Wu 1.124296 0.658372 -2.415742 (0.1304) (0.2551) (0.9921) Gourieroux, et al. --0.052209 (0.6532) Source: EViews Output, 2025 The Prob value is 0.8193 > 0.05, then the CEM model is selected. Based on the results of the Chow Test, Hasuman, and LM Test, the best model is the CEM model. Classic Assumption Test The classical assumption test aims to test the condition of research data in the form of data processing. The classical assumption test in this study includes the normality test, the multicollinearity test, the heterogeneity test and the autocorrelation test. The normality test can be done by several methods, namely residual histogram, kolmogrov smirnov, kurtosius skewness and jarquebera. The normality test in this study used the JB or jarquebera test in the form of a histogram graph. The Influence of Specific, Macroeconomic and ESG Factors on Banking Liquidity Risk in Indonesia Ridhan Azka Hani Fanu*1, Elsa Agustine1, Henny Setyo Lestari1 395 Almana : Jurnal Manajemen dan Bisnis Volume 9 No. 2/ August 2025: 388-400 Figure 4. Histogram of Residual Normality Test Using Jarque-Bera Source: EViews Output, 2025 Based on the above normality test, the significance value of jarquebera was obtained of 0.000 < 0.05, so it can be concluded that the data in this study is not normally distributed, which means that the normality assumption test is not fulfilled. X1 X2 X3 X4 X5 X6 X7 X1 1 -0.8088793... 0.64048781... -0.6233297... -0.0122335... -0.0110460... 0.54557478... X2 -0.8088793... 1 -0.5968427... 0.75566123... 0.10079476... 0.03007467... -0.7409348... X3 0.64048781... -0.5968427... 1 -0.7949053... 0.00739497... 0.04916082... 0.41246263... X4 -0.6233297... 0.75566123... -0.7949053... 1 -0.0907266... -0.0767506... -0.5929651... X5 -0.0122335... 0.10079476... 0.00739497... -0.0907266... 1 0.43437319... -0.0055852... X6 -0.0110460... 0.03007467... 0.04916082... -0.0767506... 0.43437319... 1 0.09588982... X7 0.54557478... -0.7409348... 0.41246263... -0.5929651... -0.0055852... 0.09588982... 1 Figure 5. Multicollinearity Test Source: EViews Output, 2025 Based on the multicollinearity test above, it was found that all correlation values between variables were free < 0.85 so that it could be concluded that there was no problem of multicollinearity. Figure 6. Heteroscedasticity Test Source: EViews Output, 2025 Website: http://journalfeb.unla.ac.id/index.php/almana/article/view /2892 396 Almana : Jurnal Manajemen dan Bisnis p-ISSN: 2579-4892 e-ISSN: 2655-8327 From the residual graph it can be seen that it does not cross the limit (500 and 500), meaning that the residual variant is the same. Therefore, there are no symptoms of heteroscedasticity or passing the heteroscedasticity test. Table 12. Results of Regressed Data Panel Variable Coefficient Std. Error t-Statistic Liquidity Risk -394.5494 175.0378 -2.254081 Income Diversification (DIV) 349.8614 73.38603 4.767411 Bank Capital (EQTA) 26.21387 7.759751 3.378185 Bank Size -885.3325 216.4279 -4.090658 Bank Performance (NIM) -197.1862 88.03943 -2.239749 Gross Domestic Product (GDP) -0.413294 2.164952 -0.190902 Inflation Rate -7.464464 8.085742 -0.923164 ESG Rating Scores 0.468333 1.772495 0.264222 Source: EViews Output, 2025 Prob. 0.0295 0.0000 0.0016 0.0002 0.0305 0.8495 0.3612 0.7929 Based on the results of the regression data panel on, the regression model can be written as follows: Y2 = -394.55 + 349.86*X1 + 26.21*X2 - 885.33*X3 - 197.19*X4 - 0.41*X5 - 7.46*X6 + 0.47*X7 Hypothesis Partial tests (t-tests) are used to test whether each independent variable individually has a significant effect on the dependent variable in the regression model. Table 13. Results of Partial Test Variable Coefficient Std. Error Liquidity Risk -394.5494 175.0378 Income Diversification (DIV) 349.8614 73.38603 Bank Capital (EQTA) 26.21387 7.759751 Bank Size -885.3325 216.4279 Bank Performance (NIM) -197.1862 88.03943 Gross Domestic Product (GDP) -0.413294 2.164952 Inflation Rate -7.464464 8.085742 ESG Rating Scores 0.468333 1.772495 Source: EViews Output, 2025 t-Statistic -2.254081 4.767411 3.378185 -4.090658 -2.239749 -0.190902 -0.923164 0.264222 Prob. 0.0295 0.0000 0.0016 0.0002 0.0305 0.8495 0.3612 0.7929 Based on the t-test table above, the following decisions can be made: Hipotesis: H₀ : Independent variables have no significant effect on dependent variables partially. H₁ : Independent variables have a significant effect on dependent variables partially. Party Signifikansi: α = 5% Decision Criteria: 2. Subtract H₀ if the p-value is < 0.05 3. Accept H₀ if the p-value is > 0.05 Results: Table 14. Results t Test Variable Prob. 0.0000 Income Diversification (DIV) 0.0016 Bank Capital (EQTA) 0.0002 Bank Size 0.0305 Bank Performance (NIM) Gross Domestic Product 0.8495 (GDP) 0.3612 Inflation Rate Results H₀ rejected H₀ rejected H₀ rejected H₀ rejected H₀ Accepted H₀ Accepted The Influence of Specific, Macroeconomic and ESG Factors on Banking Liquidity Risk in Indonesia Ridhan Azka Hani Fanu*1, Elsa Agustine1, Henny Setyo Lestari1 397 Almana : Jurnal Manajemen dan Bisnis Volume 9 No. 2/ August 2025: 388-400 0.7929 ESG Rating Scores Source: EViews Output, 2025 H₀ Accepted Conclusion: At the significance level, the results were obtained that the variables X1, X2, X3, and X4 had a significant effect on Y2 while the variables X5 X6, and X7 did not have a significant effect on Y2.α = 5%. Simultaneous tests (F tests) are used to test whether simultaneously independent variables have a significant effect on dependent variables in regression models. Table 15. Results of Simultaneous Test Root MSE 33.52163R-squared Mean dependent var 72.62500Adjusted R-squared S.D. dependent var 45.28310S.E. of regression Akaike info criterion 10.18226Sum squared reside Schwarz criterion 10.48818Log likelihood Hannan-Quinn critter. 10.29876F-statistic Durbin-Watson stat 2.531490Prob(F-statistic) Source: EViews Output, 2025 0.440820 0.347623 36.57509 56184.97 -246.5565 4.729993 0.000553 Based on the F test table above, the following decisions can be made: Hipotesis: H₀ : Independent variables have no significant effect on dependent variables simultaneously. H₁ : Independent variables have a significant effect on dependent variables simultaneously. Party Signifikansi: α = 5% Decision Criteria: 3. Subtract H₀ if the p-value is < 0.05 4. Accept H₀ if the p-value is > 0.05 Results: P-Value 0.000 < 0.05 so that H₀ is subtracted Conclusion: At the level of significance, the results were obtained that independent variables had a significant effect on Y2 simultaneously so that the regression model was feasible to use.α = 5%. The determination coefficient serves to measure how much the ability of independent variables to explain the dependent variables of a study. This study uses the adjusted R square value to evaluate the regression model. The following are the results of the determination coefficient proxied through Adjusted R2. Table 16. Results of Coefficient of Determination Root MSE 33.52163R-squared 0.440820 Mean dependent var 72.62500Adjusted R-squared 0.347623 S.D. dependent var 45.28310S.E. of regression 36.57509 Akaike info criterion 10.18226Sum squared reside 56184.97 Schwarz criterion 10.48818Log likelihood -246.5565 Hannan-Quinn critter. 10.29876F-statistic 4.729993 Durbin-Watson stat 2.531490Prob(F-statistic) 0.000553 Source: EViews Output, 2025 Based on the table of determination coefficients, the Adjusted R-squared value of 0.347 or 34.7% was obtained, so it can be concluded that the Y2 variable can be explained by an independent variable of 34.7% while the remaining 65.3% is explained by other variables that are not included in this study. Website: http://journalfeb.unla.ac.id/index.php/almana/article/view /2892 398 Almana : Jurnal Manajemen dan Bisnis p-ISSN: 2579-4892 e-ISSN: 2655-8327 CONCLUSION This study finds that bank-specific factors, particularly profitability measured by the Net Interest Margin (NIM) and capital strength, play a crucial role in shaping liquidity risk in Indonesian banks. Stronger bank performance is associated with greater liquidity reserves, while inflationary pressures tend to erode banks’ capacity to maintain liquidity. Large and well-capitalized banks are generally more aggressive in lending, which can influence their liquidity position, whereas strategies such as income diversification can help strengthen liquidity. Meanwhile, macroeconomic variables like GDP growth and the ESG score did not show a significant direct impact during the study period, although the inclusion of ESG factors provides an important empirical insight for future research. 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The Influence of Specific, Macroeconomic and ESG Factors on Banking Liquidity Risk in Indonesia Ridhan Azka Hani Fanu*1, Elsa Agustine1, Henny Setyo Lestari1 399 Almana : Jurnal Manajemen dan Bisnis Volume 9 No. 2/ August 2025: 388-400 Sukmadewi, R. (2020). The Effect of Capital Adequacy Ratio, Loan to Deposit Ratio, Operating-Income Ratio, Non Performing Loans, Net Interest Margin on Banking Financial Performance. ECo-Buss, 2(2), 1–10. Susanto, F. O., Adib, N., & Prastiwi, A. (2024). The Impact Of Revenue Diversification On Bank Profitability And Stability: Evidence From Indonesia Banking Industry. Jurnal Reviu Akuntansi Dan Keuangan, 14(4), 989–1006. Sutopo, B. (2025). Apakah Pengungkapan ESG Berpengaruh terhadap Kinerja Perbankan: Studi Empiris Perbankan di Indonesia. Monex: Journal of Accounting Research, 14(1), 82–98. Website: http://journalfeb.unla.ac.id/index.php/almana/article/view /2892 400