Internal Financial Factors Affecting the Profitability of Banks in Laos

Internal Financial Factors Affecting the Profitability of Banks in Laos | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Internal Financial Factors Affecting the Profitability of Banks in Laos Ploypailin Kijkasiwat, Anawat Phoemsanam, Long Yang, Chuanchen Bi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8943064/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines the relationship between internal financial factors and the profitability of banks in Laos. Data were collected from 27 commercial banks that publicly disclosed their annual financial statements over a five-year period from 2019 to 2023. The study employs summary statistics and multiple regression analysis. The independent variables include loan growth rate, deposit growth rate, total asset growth rate, bank age, bank size, and loan-to-deposit ratio, while the control variables are Laos’ GDP growth rate and the US dollar/kip exchange rate. The findings indicate that the total asset growth rate has a significant positive effect on Net Profit Margin (NPM) and Return on Assets (ROA), while bank size and the loan-to-deposit ratio have significant negative effects on both NPM and ROA. However, none of the examined variables had a statistically significant effect on Return on Equity (ROE). Bank profitability Commercial banks Laos Multiple regression analysis Figures Figure 1 1 Introduction The banking sector plays a pivotal role in facilitating economic activities and fostering national development. Commercial banks, as key financial intermediaries, mobilize savings, extend credit to businesses, and facilitate payment transactions. By performing these functions, they contribute to the smooth operation of business activities and overall economic growth, including in the Lao People’s Democratic Republic (Lao PDR). Like other profit-oriented enterprises, commercial banks seek to maximize returns. Their profitability is shaped by a combination of internal and external factors. While external factors lie largely beyond managerial control, the effective management of internal, controllable factors is essential to enhancing operational efficiency. Efficient resource allocation and sound internal management practices are critical for sustaining profitability enabling banks to meet operating expenses, service debt obligations, and distribute dividends to shareholders (Dangsiri et al., 2020 ). Although numerous studies have investigated the determinants of bank profitability in various countries (Athanasoglou et al., 2008 ; Dietrich & Wanzenried, 2011 ; Sufian & Habibullah, 2009 ), much of the existing literature has focused on larger or more developed banking systems, such as those in Thailand, Malaysia, and other ASEAN economies. Empirical research on the Lao banking sector remains limited, particularly in examining the influence of internal financial factors under managerial control. Laos presents a distinctive case due to its relatively small banking market, developing financial infrastructure, and transitional economy, where banking operations are shaped by unique structural, regulatory, and macroeconomic conditions. Furthermore, the country’s integration into the ASEAN Economic Community (AEC) increases competitive pressures and underscores the need for evidence-based strategies tailored to its domestic context. Given these differences, it is unclear whether the determinants identified in other economies are applicable to Laos, making a focused investigation both timely and necessary for theory and practice. Against this backdrop, the present study investigates the internal financial factors influencing the profitability of commercial banks in Laos, with a particular focus on variables within managerial control. The findings aim to provide policymakers with evidence to guide performance improvement strategies, strengthen competitiveness, and support sustainable profitability. They will also offer investors and analysts insights for informed decision-making, while assisting bank executives in refining operations, aligning policies with profitability objectives, and fostering long-term value creation. The analysis covers 27 of the 39 officially operating banks in Laos, selected based on the availability of complete financial data for the five-year period from 2019 to 2023. It examines internal financial indicators such as loan growth rate, total asset growth rate, deposit growth rate, income growth rate, bank age, bank size, and loan-to-deposit ratio, alongside macroeconomic controls including GDP growth rate and the USD/LAK exchange rate. For the purposes of this study, a commercial bank is defined as a profit-oriented financial institution that accepts deposits payable on demand or at a specified term at a lower interest rate and deploys these funds through lending at higher rates or foreign exchange trading, serving individuals, small businesses, and medium-sized enterprises (Commercial Banking Act (No. 2), 1979; Srivastav, 2022). Internal financial factors refer to bank-specific, controllable variables that directly influence financial performance and decision-making, measured here using quantifiable financial indicators. 2 Literature Review and Hypothesis Development 2.1 Profitability Concept Profitability is a core measure of performance for all types of businesses, including commercial banks, whose primary goal is to generate profit. It reflects an institution’s ability to deploy resources effectively to generate returns Bampennerkit (1995). Profitability can be measured through various ratios, but this study adopts three widely used indicators: Net Profit Margin (NPM) Measures the proportion of net profit generated from total revenue after deducting all expenses Net Profit Margin =( Net Profit/ Total Revenue) ×100% Return on Assets (ROA) Assesses a bank’s ability to generate profit from its total assets Return on Assets =( Net Profit/ Total Assets) ×100% Return on Equity (ROE) Evaluates profitability relative to shareholders’ equity, indicating returns per 100 units of equity Return on Equity =( Net Profit/ Equity) ×100% 2.2 Internal Financial Factors in Banking Internal financial factors refer to controllable elements within an organization that influence financial performance and strategic decision-making. In banking, these variables capture operational efficiency and are critical to sustaining profitability. This study groups internal financial factors into three main categories. Growth and Performance Indicators Previous research shows that bank growth metrics, such as loan, deposit, asset, and income growth, are significant predictors of profitability. For instance, Sufian and Habibullah ( 2009 ) found in Malaysian banks that higher loan and deposit growth enhance profitability through increased interest income. Similarly, Athanasoglou et al. ( 2008 ) reported that asset growth improves performance in European banks by enabling economies of scale. In developing economies, Nguyen et al. (2021) highlighted that rapid income growth in Vietnamese banks positively correlates with profitability due to improved operational efficiency. In this study, the following growth-related variables are considered: Loan Growth Rate (LGR) : LGR = (Loan n - Loan n-1 )/ Loan n-1 Deposit Growth Rate (DGR) : DGR=(Deposit​ n −Deposit n−1 ​)/Deposit n−1 ​ Total Asset Growth Rate (TAGR) : TAGR=(TotalAsset n ​−TotalAsset n−1 ​)/TotalAsset n−1​ Income Growth Rate (IGR) : IGR=(Income n ​−Income n−1 ​)/Income n−1​ Bank size is an important determinant of profitability and risk. Larger banks may benefit from economies of scale, stronger capital reserves, and better access to technology, enabling them to compete more effectively (Anawatchakul ( 2010 ); Demirgüç-Kunt & Huizinga, 1999 ). However, Vennet (1998) cautioned that excessively large banks may face diseconomies of scale, increasing operational inefficiency. In ASEAN studies, Almazari ( 2014 ) found that larger banks in Jordan achieved higher ROA, while Sufian (2011) observed that bank size positively influenced profitability in the Philippines. This study measures size as the natural logarithm of total assets: SIZE = log (Total Asset) Liquidity management is critical for banking stability. The Loan-to-Deposit Ratio (LDR) reflects the extent to which deposits fund loans, with higher ratios suggesting lower liquidity. Phattanachai (2016) observed that an excessively high LDR increases funding costs and financial risk. In Indonesia, Angbazo (1997) found that banks with optimal liquidity levels achieved higher profitability, while those with overextension of loans relative to deposits experienced reduced margins. The LDR is calculated as: LDR= (Loans to Customers n ​)/ (Customer Deposits n ​) 2.3 Hypothesis Development Drawing from the above literature, this study examines whether internal financial factors; loan growth rate, deposit growth rate, total asset growth rate, income growth rate, bank age, bank size, and loan-to-deposit ratio significantly influence bank profitability, measured by NPM, ROA, and ROE. Prior studies in ASEAN and other developing markets suggest that these variables may have varying effects depending on market maturity, regulatory environment, and competitive dynamics. Growth indicators such as loan and income expansion directly influence interest and fee-based revenues, thereby impacting margins Sufian, F., & Habibullah, M. S. (2009). Similarly, optimal liquidity and larger bank size have been linked to improved cost efficiency, enhancing net margins (Demirgüç-Kunt & Huizinga, 1999 ). Therefore, the first hypothesis is developed: H1: Internal financial factors have a statistically significant relationship with NPM. ROA captures the bank’s efficiency in using its total assets to generate profits. Studies in Malaysia and Vietnam have shown that higher asset growth, adequate liquidity, and larger size often improve ROA by allowing banks to leverage resources more effectively (Nguyen, 2021 ; Almazari, 2014 ). Therefore, the second hypothesis is developed: H2: Internal financial factors have a statistically significant relationship with ROA. ROE reflects returns to shareholders, which are sensitive to leverage, asset allocation, and income generation capacity. Prior evidence from Thailand (Anawatchakul, 2010 ) and the Philippines (Sufian, 2011) suggests that both growth performance and liquidity management can enhance shareholder returns, while excessive leverage or liquidity constraints can erode them. Therefore, the third hypothesis is developed: H3: Internal financial factors have a statistically significant relationship with ROE. Based on theoretical reasoning and prior empirical findings, the conceptual framework (Fig. 1 ) illustrates the relationship between internal financial factors (independent variables) and profitability measures (dependent variables), with GDP growth and the exchange rate (USD/LAK) as control variables. 3 Research Methodology 3.1 Population and sample The population of this research comprises 39 officially operating banks in Laos. The data analysis was based on financial statements and accompanying notes that were publicly disclosed for at least two years within a five-year period. However, 12 banks in Laos did not disclose sufficient financial details publicly during the years 2019–2021, which were necessary for analysis. As a result, these banks were excluded from the sample. Thus, the final sample for this study includes 27 banks in Laos that provided annual financial statement data from 2019 to 2023, covering a total period of five years. The sample details are shown in Table 1 below. Table 1 Sample group of banks in Laos No. Name of Bank Abbreviation 1 BANQUE POUR LE COMMERCE EXTERIEUR LAO PUBLIC BCEL 2 Lao Development Bank Co., Ltd LDB 3 Agricultural Promotion Bank Co., Ltd. APB 4 Lao-Viet Bank Co., Ltd LVB 5 Banque Franco-Lao Ltd. BFL 6 Lao China Bank Co.,Ltd LCNB 7 Joint Development Bank Co., Ltd. JDB 8 PHONGSAVANH BANK LTD PSVB 9 INDOCHINA BANK LTD IB 10 Booyoung Lao Bank Ltd BLB 11 MARUHAN Japan Bank Lao Co., Ltd. MJB 12 BIC Bank Lao Co., Ltd. BIC 13 ST Bank Ltd. STB 14 ACLEDA BANK LAO.,LTD ACLEDA 15 RHB Bank Lao Sole Co., Ltd. RHB 16 KASIKORNTHAI Bank Sole Ltd. K Laos 17 Saigon-Hanoi Bank Lao Ltd. SHB 18 Canadia Bank Lao Co., Ltd. CBL 19 Public Bank Lao Ltd. Vientiane Branch PBLVientiane 20 Public Bank Lao Ltd. Savannakhet Branch PBL Savannakhet 21 Public Bank Lao Ltd. Wattay Branch PBL Wattay 22 Public Bank Lao Ltd. Pakse Branch PBL Pakse 23 Saigon Thuong Tin Bank Lao Co., Ltd STTB 24 ICBC Limited Vientiane Branch ICBC 25 Cathay united bank Vientiane Capital Branch Cub 26 Bank of China Limited, Vientiane branch BCL 27 First Commercial Bank LTD, Vientiane Branch FCB Source: Bank of the Lao PDR 3.2 Variables and Data Based on theoretical concepts and relevant literature, the independent variables include the loan growth rate, deposit growth rate, total asset growth rate, bank age, bank size, and loan-to-deposit ratio. These variables are based on the study by Paukmongkol (2021) which examined key internal factors influencing the profitability of Thai commercial banks. Dependent variables include the net profit margin, return on assets (ROA), and return on equity (ROE). These indicators are used to measure profitability, as applied in the study on profitability and its impact on profit growth in automotive industry companies listed on the Stock Exchange of Thailand (Rattanawongchaiyo, T. 2024). Additionally, the control variables include the GDP growth rate (%) and the exchange rate (USD/LAK). These were applied similarly in Paukmongkol (2021) study to account for external macroeconomic factors that may influence bank performance. 3.3 Data Collection This research utilizes secondary data, specifically the published financial statements of 27 banks over a period of five years, from 2019 to 2023, comprising a total of 128 data sets. The data were collected from the banks' official websites and publicly accessible online databases. 3.4 Data Analysis The objective of this study is to examine the relationship between internal factors and the profitability of banks in Laos. Accordingly, the data analysis is divided into two main parts: Descriptive Statistical Analysis and Multiple Regression Analysis. Descriptive Statistical Analysis is used to describe the characteristics of the dataset, including the mean, median, maximum, minimum, and standard deviation of each variable. Multiple Regression Analysis is used to examine the relationship between one dependent variable and two or more independent variables, based on the following multiple regression model (Chancharoen, 2013). y = β 0 + β 1 x 1 + β 2 x 2 + ... + β n x n + ε Where y = The observed value of the dependent variable at observation i x i = The observed value of the independent variables at observation i, from variable 1 to n β 0 = Constant (intercept) β i = Regression coefficient of the independent variable \(\:\:{x}_{1}\) n = Number of independent variables in the regression equation ε = Error term (residual) The study proposes three regression models: a model for studying the relationship between internal factors and net profit margin, a model for studying the relationship between internal factors and return on total assets, and a model for studying the relationship between internal factors and return on equity, which are detailed as follows: Model 1 An Analysis of the Relationship Between Internal Financial Factors and Net Profit Margin NPM i,t = β 0 + β 1 LG i,t + β 2 AG i,t + β 3 DG i,t + β 4 IG i,t + β 5 Age i,t + β 6 Size i,t + β 7 LDR i,t + β 8 GDP i,t + β 9 EXR i,t + ε Model 2 An Analysis of the Relationship Between Internal Financial Factors and Return on Total Assets ROA i,t = β 0 + β 1 LG i,t + β 2 AG i,t + β 3 DG i,t + β 4 IG i,t + β 5 Age i,t + β 6 Size i,t + β 7 LDR i,t + β 8 GDP i,t + β 9 EXR i,t + ε Model 3 An Analysis of the Relationship Between Internal Financial Factors and Return on Equity ROE i,t = β 0 + β 1 LG i,t + β 2 AG i,t + β 3 DG i,t + β 4 IG i,t + β 5 Age i,t + β 6 Size i,t + β 7 LDR i,t + β 8 GDP i,t + β 9 EXR i,t + ε Where NPM i,t denotes the net profit margin of bank i in year t. ROA i,t denotes the return on assets (ROA) of bank i in year t. ROE i,t denotes the return on equity (ROE) of bank i in year t. LG i,t denotes the loan growth rate of bank i in year t. AG i,t denotes the total asset growth rate of bank i in year t. DG i,t denotes the deposit growth rate of bank i in year t. IG i,t denotes the income growth rate of bank i in year t. Age i,t denotes the age of bank i in year t. Size i,t denotes the size of bank i in year t. LDR i,t denotes the loan-to-deposit ratio of bank i in year t. GDP i,t denotes the GDP growth rate of Laos in year t. EXR i,t denotes the USD/LAK exchange rate in year t. β 0 denotes the constant term β 1−9 denote coefficients of the independent variables. ε denotes the error term (residual). i denotes the 27 banks operating in Laos. t denotes the period from 2019 to 2023, covering a total of 5 years. 4 Empirical findings From the study on internal financial factors affecting the profitability of banks in Laos, annual financial statement data were collected from 27 banks over a 5-year period (2019–2023), totaling 128 data sets. The data was processed and analyzed using statistical software EViews 12. 4.1 Descriptive Statistics The research results are based on data from 27 banks during 2019–2023. Table 2 presents descriptive statistics including the median, maximum, mean, and standard deviation of the key variables. Table 2 Descriptive statistics: median, maximum, mean, and standard deviation Variables Mean Median Maximum Minimum Standard Deviation NPM 14.90% 18.88% 73.84% -645.58% 87.56% ROA 1.14% 1.24% 13.41% -54.23% 7.46% ROE 6.47% 6.47% 51.28% -177.13% 27.65% LG 40.81% 22.05% 15.64% 163.01% -53.05% AG 21.50% 20.64% 136.55% -54.86% 25.62% DG 29.43% 24.28% 179.35% -100.00% 34.80% IG 26.61% 22.76% 200.22% -48.68% 39.87% Age 15 12 47 3 8.75 Size 28.7 28.4 32.5 25.9 1.39381 LDR 291% 112% 2436% 20% 475% GDP 2.98 2.71 5.46 0.50 1.62 EXR 13,421.88 11,154.18 20,451.35 8,868.40 4,650.14 From the data analysis in Table 2 , it was found that some variables exhibit outliers that significantly deviate from the mean, particularly NPM, ROE, and LDR. These variables show extreme maximum and minimum values that differ greatly from the average, which may reflect differences in the nature of each bank's operations such as strategic approaches or the influence of external factors. These outliers may impact the regression coefficients, potentially resulting in some variables being statistically insignificant and leading to a lower explanatory power of the model (Adjusted R²). Nevertheless, the results can still provide useful preliminary insights into the trends and relationships among the variables. 4.2 Checking Variables Before Multiple Regression Analysis Multicollinearity Check assesses the degree of correlation among independent variables, multicollinearity is examined. High correlation among independent variables can reduce the accuracy of coefficient estimates in multiple regression analysis. Pearson’s correlation coefficient (ranging from − 1.0 to + 1.0) is used to assess the relationship between independent variables. A pairwise correlation exceeding 0.95 indicates a potential multicollinearity problem. The results from EViews 12 are shown in Table 3, Table 3 shows the correlation coefficients of all varia From Table 3, the correlation coefficients among the independent variables; Loan Growth Rate (LG), Total Asset Growth Rate (AG), Deposit Growth Rate (DG), Income Growth Rate (IG), Bank Age (Age), Bank Size (Size), and Loan-to-Deposit Ratio (LDR) range from − 0.23619 to + 0.58195. Notably, the correlation between AG and DG is + 0.58195, which may indicate a moderate to high positive relationship. Although this value is not excessively high, to ensure that multicollinearity does not affect the regression analysis, it is necessary to further examine the Variance Inflation Factor (VIF) for a more detailed assessment of the relationships among the independent variables. The Variance Inflation Factor (VIF) is also used to measure the severity of multicollinearity. Generally, a VIF value exceeding 10 indicates a serious multicollinearity problem. When the VIF values for the independent variables used in this study were calculated using the EViews 12 software, the results were displayed as shown in Table 4 below. Table 4 presents the VIF values of the independent and control variables. Variables LG AG DG IG Age Size LDR GDP EXR VIF 1.6105 2.6661 1.5881 1.7381 1.1502 1.3381 1.2186 1.0788 1.3264 From Table 4 , the VIF values show no independent variable exceeded 10, indicating no significant multicollinearity among the variables (Wanichbancha, 2009). Autocorrelation Problem Investigation Before conducting multiple regression analysis, it is important to test for autocorrelation among the error terms. If the error terms are correlated, the regression estimates remain unbiased but become inefficient due to increased variance. This violates the BLUE (Best Linear Unbiased Estimator) assumption of the OLS method. Moreover, it may lead to underestimation of error variance and inflated t-statistics, potentially resulting in incorrect conclusions (Anthong, 2007 ) Table 5 Results of the Multiple Regression Analysis Variables NPM ROA ROE Coefficient t-Statistic Prob. Coefficient t-Statistic Prob. Coefficient t-Statistic Prob. Constant 4.2678 3.5711 0.0005 0.4219 4.2472 0.0000 -0.5328 -1.2322 0.2203 LG -0.0386 -0.1894 0.8501 -0.0025 -0.1491 0.8817 0.0564 0.7659 0.4453 AG 0.9202 2.3957 0.0182** 0.0695 2.1769 0.0315** 0.1052 0.7568 0.4507 DG -0.2524 -1.1331 0.2595 -0.0221 -1.1949 0.2346 -0.0235 -0.2915 0.7712 IG 0.1680 0.8723 0.3848 0.0152 0.9521 0.3430 0.0561 0.8049 0.4225 Age 0.0043 0.7377 0.4622 0.0007 1.4011 0.1638 -0.0015 -0.7393 0.4612 Size -0.1566 -3.7319 0.0003* -0.0158 -4.5381 1.38e-05 * 0.0187 1.2344 0.2195 LDR -0.0324 -2.4167 0.0172** -0.0025 -2.2387 0.0271** -0.0022 -0.4598 0.6465 GDP 0.0472 1.3709 0.1730 0.0039 1.3553 0.1779 0.0120 0.9624 0.3379 EXR 7.44E-06 0.5639 0.5739 1.26E-06 1.1448 0.2546 8.48E-07 0.1778 0.8592 F-statistic 3.572795 0.2476 1.5504 Prob (F-statistic) 0.0006 0.0001 0.1388 R-squared 0.2156 0.2476 0.1066 Adjusted R-squared 0.1552 0.1898 0.0378 Durbin-Watson 1.9373 2.0493 1.6123 Note: *, ** indicate significance at the 0.01 and 0.05 levels, respectively. Table 5 shows that the Durbin-Watson statistics for the three models are 1.9373, 2.0493, and 1.6123, respectively all within the acceptable range of 1.5 to 2.5. This indicates that none of the regression models suffer from autocorrelation. 4.3 Multiple regression analysis results The results of the multiple regression analysis, aimed at studying the relationship between internal financial factors and the profitability of banks in Laos, are divided into 3 models shown in Table 5 . When analyzing profitability using the Net Profit Margin (NPM), it was found that the independent variable affecting NPM was the Total Asset Growth rate (AG), with a regression coefficient of 0.9202 at a 0.05 statistical significance level. In contrast, the Loan to Deposit Ratio (LDR) and Bank Size (Size) variables had regression coefficients of -0.0324 and − 0.1566, at a 0.05 and 0.01 statistical significance level, respectively. Additionally, no variables were found to significantly affect the Net Profit Margin. The results of the analysis were then used to create the following multiple regression equation for forecasting the NPM: NPM i,t = (4.2678) + (0.9202)AG i,t + (-0.1566)Size i,t + (-0.0324)LDR i,t + ε The multiple regression analysis revealed that the model has an R-squared value of 0.2156, meaning that the independent variables; total asset growth rate (AG), bank size (Size), and loan-to-deposit ratio (LDR) which explain 21.56% of the variation in net profit margin, while other factors not included in the model account for the remaining 78.44%. In analyzing profitability using the return on assets (ROA) ratio, it was found that the independent variable affecting ROA was the growth rate of total assets (AG), with a regression coefficient of 0.0695 at a statistical significance level of 0.05. Conversely, the loan-to-deposit ratio (LDR) and bank size (Size) had regression coefficients of 0.0025 and 0.0158 at a significance level of 0.05 and 0.01, respectively. Additionally, no variables had a statistically significant impact on the ROA ratio. The results were used to create a multiple regression equation for forecasting net profit margin, as follows: ROA i,t = (0.4219)+ (0.0695)AG i,t + (-0.0158)Size i,t + \(\:(-0.0025)\) LDR i,t + ε The multiple regression analysis revealed that this model has an R-squared value of 0.2476, meaning that the independent variables; growth rate of total assets (AG), bank size (Size), and loan-to-deposit ratio (LDR) accounting for 24.76% of the variation in return on total assets (ROA), with other factors not considered in this model potentially contributing to the remaining 75.24%. In analyzing profitability using return on equity (ROE), no variable in the model was found to have a statistically significant effect on the net profit margin. The multiple regression equation used to forecast net profit margin is as follows: ROE i,t = + ε The multiple regression analysis showed an R-squared value of 0.1065, indicating that none of the independent variables in this model have a clear effect on return on equity (ROE). 5 Discussion and Conclusion This study examines the relationship between internal financial factors and the profitability of banks in Laos using a multiple regression analysis model, with net profit margin (NPM), return on assets (ROA), and return on equity (ROE) as the dependent variables. The results indicate that, for NPM, the total asset growth rate has a statistically significant positive effect, whereas bank size and the loan-to-deposit ratio have significant negative effects, with the model explaining 21.56% of the variance. For ROA, the total asset growth rate again exerts a positive influence, while bank size and the loan-to-deposit ratio negatively affect performance, accounting for 24.76% of the variance. In contrast, none of the independent variables significantly affect ROE, and the model explains only 10.65% of its variance. The analysis of internal financial factors affecting the profitability of banks in Laos reveals that the asset growth rate has a positive effect on both net profit margin (NPM) and return on assets (ROA), consistent with the theory that asset expansion enhances profitability and in line with Wachanasarikalakul, L. (1984), who reported a positive correlation between total asset growth and net profit growth. Conversely, bank size exhibits a negative effect on NPM and ROA, suggesting that larger banks may incur higher operating costs, a finding that contrasts with studies in Thailand by Anawatchakul ( 2010 ) and Maruset (2024), which supported a positive size–profitability relationship consistent with economies of scale. The loan-to-deposit ratio is negatively associated with NPM and ROA, indicating that higher ratios may increase liquidity risk and borrowing costs, thereby reducing profitability; this aligns with Sunaryo ( 2020 ). findings for Southeast Asian commercial banks during 2012–2018. Loan growth rate shows no significant relationship with any profitability measure, contradicting Nguyen (2022), who found that higher lending activity tends to enhance profitability. Similarly, deposit growth rate is unrelated to profitability ratios, in contrast to Putra & Vidyantari, ( 2023 ), who reported a marginal positive impact of deposit and loan growth on profit growth. Finally, bank age has no significant effect on profitability, differing from Işık, and Ersoy ( 2022 ) who found a significant relationship between bank age and profitability. The findings of this study suggest several policy and management implications as well as directions for future research. First, banks should promote sustainable asset growth by focusing on improving the quality and efficient utilization of existing assets rather than pursuing purely quantitative expansion. Second, larger banks need to manage costs effectively to mitigate the potential negative effects of scale on profitability, ensuring that operational growth does not erode financial performance. Third, maintaining an optimal loan-to-deposit ratio is essential to balancing lending and deposit mobilization, thereby reducing liquidity risk, lowering borrowing costs, and enhancing profitability. For future research, incorporating additional independent variables such as the operating expense ratio, non-performing loan (NPL) ratio, and interest rate measures could provide a more comprehensive analysis of profitability determinants. Improving data quality through the identification and treatment of outliers, expanding the sample size, and using standardized data sources would further strengthen analytical reliability. Moreover, examining external macroeconomic factors such as monetary policy and import–export values, alongside additional control variables including inflation rates and financial market indicators, could offer deeper insights into how both internal and external influences shape bank profitability. Declarations Funding Declaration No funding was received for this research. Author Contribution All co-authors contributed equally to the work Data Availability The data used in this study were obtained from publicly available annual financial statements of commercial banks in Laos for the period 2019–2023. These reports are accessible through the official websites of the respective banks and regulatory authorities. The compiled dataset used for analysis is available from the corresponding author upon reasonable request. References Almazari AR (2014) Impact of internal factors on bank profitability: Comparative study between Saudi Arabia and Jordan. J Appl Finance Bank 4:125–140 Anawatchakul H (2010) Factors affecting the profitability of Thai commercial banks (Master’s thesis). Thammasat University, Bangkok, Thailand Anthong A (2007) EViews & basic econometrics. Chiang Mai University Social Research Institute Athanasoglou P, Brissimis SN, Delis MD (2008) Bank-specific, industry-specific and macroeconomic determinants of bank profitability. 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Department of Statistics, Faculty of Commerce and Accountancy, Chulalongkorn University Vatchanasarigkul L (1984) A study on the relationship between growth rates of total assets and net profits of Thai commercial banks (Master’s thesis). Chulalongkorn University Vennet RV (2002) Cost and profit efficiency of financial conglomerates and universal banks in Europe. J Money Credit Bank 34(1):254–282 WallStreetMojo (2022), February 3 Commercial bank . https://www.wallstreetmojo.com/commercial-bank/ Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8943064","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":606180913,"identity":"27ad8cf1-7333-4da5-b1c6-442566c78930","order_by":0,"name":"Ploypailin Kijkasiwat","email":"","orcid":"","institution":"Khon Kaen University","correspondingAuthor":false,"prefix":"","firstName":"Ploypailin","middleName":"","lastName":"Kijkasiwat","suffix":""},{"id":606180914,"identity":"90bd34ff-db1f-4d6c-9b66-11e1d85ececb","order_by":1,"name":"Anawat Phoemsanam","email":"","orcid":"","institution":"Khon Kaen University","correspondingAuthor":false,"prefix":"","firstName":"Anawat","middleName":"","lastName":"Phoemsanam","suffix":""},{"id":606180915,"identity":"fd15577d-2ec4-42f1-933b-4f3342a91529","order_by":2,"name":"Long Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYBACPghlYyABFUggqIUNQqXBtBgQreUwKVrYew+/5qk5byw5I4Hxww+GP3mEtfCcS7PmOXbbTFoigVmyh8GgmLAWiRwzYx622zZyEgkM0kCHJTYQp+XfOZAW5t/EajF+zNt2AOQwNiJt4Tljxji3L9lYsudhm2WPgTFhLfzsPcYf3nyzM5xxPPnwjR8VcoS1gCyS4gHTjEDFBkSoBwLmjz+IUzgKRsEoGAUjFQAASQgzXk48dgUAAAAASUVORK5CYII=","orcid":"","institution":"Khon Kaen University","correspondingAuthor":true,"prefix":"","firstName":"Long","middleName":"","lastName":"Yang","suffix":""},{"id":606180916,"identity":"dd51dbbd-ff68-439a-bba5-ef8718e24ada","order_by":3,"name":"Chuanchen Bi","email":"","orcid":"","institution":"Khon Kaen University","correspondingAuthor":false,"prefix":"","firstName":"Chuanchen","middleName":"","lastName":"Bi","suffix":""}],"badges":[],"createdAt":"2026-02-23 05:23:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8943064/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8943064/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104724172,"identity":"cc36b064-f08c-49ec-92af-4a662e0a8692","added_by":"auto","created_at":"2026-03-16 13:08:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":94585,"visible":true,"origin":"","legend":"\u003cp\u003eResearch conceptual framework\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8943064/v1/60bd9d0819cbf2c8ad8f37e3.png"},{"id":105180203,"identity":"59ca25b2-3a5f-4d79-85d8-62e943a21c14","added_by":"auto","created_at":"2026-03-23 07:12:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1116805,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8943064/v1/ca68cca3-c48c-4230-b3f1-f4d0b208eeba.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Internal Financial Factors Affecting the Profitability of Banks in Laos","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe banking sector plays a pivotal role in facilitating economic activities and fostering national development. Commercial banks, as key financial intermediaries, mobilize savings, extend credit to businesses, and facilitate payment transactions. By performing these functions, they contribute to the smooth operation of business activities and overall economic growth, including in the Lao People\u0026rsquo;s Democratic Republic (Lao PDR).\u003c/p\u003e \u003cp\u003eLike other profit-oriented enterprises, commercial banks seek to maximize returns. Their profitability is shaped by a combination of internal and external factors. While external factors lie largely beyond managerial control, the effective management of internal, controllable factors is essential to enhancing operational efficiency. Efficient resource allocation and sound internal management practices are critical for sustaining profitability enabling banks to meet operating expenses, service debt obligations, and distribute dividends to shareholders (Dangsiri et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough numerous studies have investigated the determinants of bank profitability in various countries (Athanasoglou et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Dietrich \u0026amp; Wanzenried, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sufian \u0026amp; Habibullah, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), much of the existing literature has focused on larger or more developed banking systems, such as those in Thailand, Malaysia, and other ASEAN economies. Empirical research on the Lao banking sector remains limited, particularly in examining the influence of internal financial factors under managerial control. Laos presents a distinctive case due to its relatively small banking market, developing financial infrastructure, and transitional economy, where banking operations are shaped by unique structural, regulatory, and macroeconomic conditions. Furthermore, the country\u0026rsquo;s integration into the ASEAN Economic Community (AEC) increases competitive pressures and underscores the need for evidence-based strategies tailored to its domestic context. Given these differences, it is unclear whether the determinants identified in other economies are applicable to Laos, making a focused investigation both timely and necessary for theory and practice.\u003c/p\u003e \u003cp\u003eAgainst this backdrop, the present study investigates the internal financial factors influencing the profitability of commercial banks in Laos, with a particular focus on variables within managerial control. The findings aim to provide policymakers with evidence to guide performance improvement strategies, strengthen competitiveness, and support sustainable profitability. They will also offer investors and analysts insights for informed decision-making, while assisting bank executives in refining operations, aligning policies with profitability objectives, and fostering long-term value creation.\u003c/p\u003e \u003cp\u003eThe analysis covers 27 of the 39 officially operating banks in Laos, selected based on the availability of complete financial data for the five-year period from 2019 to 2023. It examines internal financial indicators such as loan growth rate, total asset growth rate, deposit growth rate, income growth rate, bank age, bank size, and loan-to-deposit ratio, alongside macroeconomic controls including GDP growth rate and the USD/LAK exchange rate. For the purposes of this study, a commercial bank is defined as a profit-oriented financial institution that accepts deposits payable on demand or at a specified term at a lower interest rate and deploys these funds through lending at higher rates or foreign exchange trading, serving individuals, small businesses, and medium-sized enterprises (Commercial Banking Act (No. 2), 1979; Srivastav, 2022). Internal financial factors refer to bank-specific, controllable variables that directly influence financial performance and decision-making, measured here using quantifiable financial indicators.\u003c/p\u003e"},{"header":"2 Literature Review and Hypothesis Development","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Profitability Concept\u003c/h2\u003e \u003cp\u003eProfitability is a core measure of performance for all types of businesses, including commercial banks, whose primary goal is to generate profit. It reflects an institution\u0026rsquo;s ability to deploy resources effectively to generate returns Bampennerkit (1995). Profitability can be measured through various ratios, but this study adopts three widely used indicators:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNet Profit Margin (NPM)\u003c/strong\u003e \u003cp\u003e \u003cem\u003eMeasures the proportion of net profit generated from total revenue after deducting all expenses\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eNet Profit Margin =( Net Profit/ Total Revenue) \u0026times;100%\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eReturn on Assets (ROA)\u003c/strong\u003e \u003cp\u003e \u003cem\u003eAssesses a bank\u0026rsquo;s ability to generate profit from its total assets\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eReturn on Assets =( Net Profit/ Total Assets) \u0026times;100%\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eReturn on Equity (ROE)\u003c/strong\u003e \u003cp\u003e \u003cem\u003eEvaluates profitability relative to shareholders\u0026rsquo; equity, indicating returns per 100 units of equity\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eReturn on Equity =( Net Profit/ Equity) \u0026times;100%\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Internal Financial Factors in Banking\u003c/h2\u003e \u003cp\u003eInternal financial factors refer to controllable elements within an organization that influence financial performance and strategic decision-making. In banking, these variables capture operational efficiency and are critical to sustaining profitability. This study groups internal financial factors into three main categories.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGrowth and Performance Indicators\u003c/b\u003e Previous research shows that bank growth metrics, such as loan, deposit, asset, and income growth, are significant predictors of profitability. For instance, Sufian and Habibullah (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) found in Malaysian banks that higher loan and deposit growth enhance profitability through increased interest income. Similarly, Athanasoglou et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) reported that asset growth improves performance in European banks by enabling economies of scale. In developing economies, Nguyen et al. (2021) highlighted that rapid income growth in Vietnamese banks positively correlates with profitability due to improved operational efficiency. In this study, the following growth-related variables are considered:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cb\u003eLoan Growth Rate (LGR)\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e \u003cem\u003eLGR = (Loan\u003c/em\u003e \u003csub\u003e \u003cem\u003en\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e- Loan\u003c/em\u003e \u003csub\u003e \u003cem\u003en-1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e)/ Loan\u003c/em\u003e \u003csub\u003e \u003cem\u003en-1\u003c/em\u003e \u003c/sub\u003e \u003c/p\u003e\u003cp\u003e \u003cb\u003eDeposit Growth Rate (DGR)\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e \u003cem\u003eDGR=(Deposit​\u003c/em\u003e \u003csub\u003e \u003cem\u003en\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e\u0026minus;Deposit\u003c/em\u003e \u003csub\u003e \u003cem\u003en\u0026minus;1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e​)/Deposit\u003c/em\u003e \u003csub\u003e \u003cem\u003en\u0026minus;1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e​\u003c/em\u003e \u003c/p\u003e\u003cp\u003e \u003cb\u003eTotal Asset Growth Rate (TAGR)\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e \u003cem\u003eTAGR=(TotalAsset\u003c/em\u003e \u003csub\u003e \u003cem\u003en\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e​\u0026minus;TotalAsset\u003c/em\u003e \u003csub\u003e \u003cem\u003en\u0026minus;1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e​)/TotalAsset\u003c/em\u003e \u003csub\u003e \u003cem\u003en\u0026minus;1​\u003c/em\u003e \u003c/sub\u003e \u003c/p\u003e\u003cp\u003e \u003cb\u003eIncome Growth Rate (IGR)\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e \u003cem\u003eIGR=(Income\u003c/em\u003e \u003csub\u003e \u003cem\u003en\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e​\u0026minus;Income\u003c/em\u003e \u003csub\u003e \u003cem\u003en\u0026minus;1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e​)/Income\u003c/em\u003e \u003csub\u003e \u003cem\u003en\u0026minus;1​\u003c/em\u003e \u003c/sub\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eBank size is an important determinant of profitability and risk. Larger banks may benefit from economies of scale, stronger capital reserves, and better access to technology, enabling them to compete more effectively (Anawatchakul (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e); Demirg\u0026uuml;\u0026ccedil;-Kunt \u0026amp; Huizinga, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). However, Vennet (1998) cautioned that excessively large banks may face diseconomies of scale, increasing operational inefficiency. In ASEAN studies, Almazari (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found that larger banks in Jordan achieved higher ROA, while Sufian (2011) observed that bank size positively influenced profitability in the Philippines. This study measures size as the natural logarithm of total assets:\u003c/p\u003e \u003cp\u003e \u003cem\u003eSIZE\u0026thinsp;=\u0026thinsp;log (Total Asset)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eLiquidity management is critical for banking stability. The Loan-to-Deposit Ratio (LDR) reflects the extent to which deposits fund loans, with higher ratios suggesting lower liquidity. Phattanachai (2016) observed that an excessively high LDR increases funding costs and financial risk. In Indonesia, Angbazo (1997) found that banks with optimal liquidity levels achieved higher profitability, while those with overextension of loans relative to deposits experienced reduced margins. The LDR is calculated as:\u003c/p\u003e \u003cp\u003eLDR= (Loans to Customers\u003csub\u003en\u003c/sub\u003e​)/ (Customer Deposits\u003csub\u003en\u003c/sub\u003e​)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Hypothesis Development\u003c/h2\u003e \u003cp\u003eDrawing from the above literature, this study examines whether internal financial factors; loan growth rate, deposit growth rate, total asset growth rate, income growth rate, bank age, bank size, and loan-to-deposit ratio significantly influence bank profitability, measured by NPM, ROA, and ROE. Prior studies in ASEAN and other developing markets suggest that these variables may have varying effects depending on market maturity, regulatory environment, and competitive dynamics.\u003c/p\u003e \u003cp\u003eGrowth indicators such as loan and income expansion directly influence interest and fee-based revenues, thereby impacting margins Sufian, F., \u0026amp; Habibullah, M. S. (2009). Similarly, optimal liquidity and larger bank size have been linked to improved cost efficiency, enhancing net margins (Demirg\u0026uuml;\u0026ccedil;-Kunt \u0026amp; Huizinga, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Therefore, the first hypothesis is developed:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH1: Internal financial factors have a statistically significant relationship with NPM.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eROA captures the bank\u0026rsquo;s efficiency in using its total assets to generate profits. Studies in Malaysia and Vietnam have shown that higher asset growth, adequate liquidity, and larger size often improve ROA by allowing banks to leverage resources more effectively (Nguyen, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Almazari, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Therefore, the second hypothesis is developed:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH2: Internal financial factors have a statistically significant relationship with ROA.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eROE reflects returns to shareholders, which are sensitive to leverage, asset allocation, and income generation capacity. Prior evidence from Thailand (Anawatchakul, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and the Philippines (Sufian, 2011) suggests that both growth performance and liquidity management can enhance shareholder returns, while excessive leverage or liquidity constraints can erode them. Therefore, the third hypothesis is developed:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH3: Internal financial factors have a statistically significant relationship with ROE.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eBased on theoretical reasoning and prior empirical findings, the conceptual framework (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) illustrates the relationship between internal financial factors (independent variables) and profitability measures (dependent variables), with GDP growth and the exchange rate (USD/LAK) as control variables.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3 Research Methodology","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Population and sample\u003c/h2\u003e \u003cp\u003eThe population of this research comprises 39 officially operating banks in Laos. The data analysis was based on financial statements and accompanying notes that were publicly disclosed for at least two years within a five-year period.\u003c/p\u003e \u003cp\u003eHowever, 12 banks in Laos did not disclose sufficient financial details publicly during the years 2019\u0026ndash;2021, which were necessary for analysis. As a result, these banks were excluded from the sample. Thus, the final sample for this study includes 27 banks in Laos that provided annual financial statement data from 2019 to 2023, covering a total period of five years. The sample details are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample group of banks in Laos\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName of Bank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbbreviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBANQUE POUR LE COMMERCE EXTERIEUR LAO PUBLIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBCEL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLao Development Bank Co., Ltd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLDB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgricultural Promotion Bank Co., Ltd.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAPB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLao-Viet Bank Co., Ltd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLVB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBanque Franco-Lao Ltd.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBFL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLao China Bank Co.,Ltd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLCNB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJoint Development Bank Co., Ltd.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJDB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePHONGSAVANH BANK LTD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePSVB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINDOCHINA BANK LTD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBooyoung Lao Bank Ltd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBLB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMARUHAN Japan Bank Lao Co., Ltd.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMJB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIC Bank Lao Co., Ltd.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eST Bank Ltd.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSTB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACLEDA BANK LAO.,LTD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACLEDA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRHB Bank Lao Sole Co., Ltd.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRHB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKASIKORNTHAI Bank Sole Ltd.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eK Laos\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSaigon-Hanoi Bank Lao Ltd.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSHB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCanadia Bank Lao Co., Ltd.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCBL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic Bank Lao Ltd. Vientiane Branch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePBLVientiane\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic Bank Lao Ltd. Savannakhet Branch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePBL Savannakhet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic Bank Lao Ltd. Wattay Branch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePBL Wattay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic Bank Lao Ltd. Pakse Branch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePBL Pakse\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSaigon Thuong Tin Bank Lao Co., Ltd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSTTB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICBC Limited Vientiane Branch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eICBC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCathay united bank Vientiane Capital Branch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCub\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBank of China Limited, Vientiane branch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBCL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFirst Commercial Bank LTD, Vientiane Branch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFCB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eSource: Bank of the Lao PDR\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Variables and Data\u003c/h2\u003e \u003cp\u003eBased on theoretical concepts and relevant literature, the independent variables include the loan growth rate, deposit growth rate, total asset growth rate, bank age, bank size, and loan-to-deposit ratio. These variables are based on the study by Paukmongkol (2021) which examined key internal factors influencing the profitability of Thai commercial banks. Dependent variables include the net profit margin, return on assets (ROA), and return on equity (ROE). These indicators are used to measure profitability, as applied in the study on profitability and its impact on profit growth in automotive industry companies listed on the Stock Exchange of Thailand (Rattanawongchaiyo, T. 2024). Additionally, the control variables include the GDP growth rate (%) and the exchange rate (USD/LAK). These were applied similarly in Paukmongkol (2021) study to account for external macroeconomic factors that may influence bank performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Data Collection\u003c/h2\u003e \u003cp\u003eThis research utilizes secondary data, specifically the published financial statements of 27 banks over a period of five years, from 2019 to 2023, comprising a total of 128 data sets. The data were collected from the banks' official websites and publicly accessible online databases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Data Analysis\u003c/h2\u003e \u003cp\u003eThe objective of this study is to examine the relationship between internal factors and the profitability of banks in Laos. Accordingly, the data analysis is divided into two main parts: Descriptive Statistical Analysis and Multiple Regression Analysis. Descriptive Statistical Analysis is used to describe the characteristics of the dataset, including the mean, median, maximum, minimum, and standard deviation of each variable. Multiple Regression Analysis is used to examine the relationship between one dependent variable and two or more independent variables, based on the following multiple regression model (Chancharoen, 2013).\u003c/p\u003e \u003cp\u003e \u003cem\u003ey\u0026thinsp;=\u0026thinsp;β\u003c/em\u003e \u003csub\u003e \u003cem\u003e0\u003c/em\u003e \u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ ...\u0026thinsp;+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003en\u003c/em\u003e\u003c/sub\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003en\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;ε\u003c/em\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003ey\u003c/em\u003e\u0026thinsp;=\u0026thinsp;The observed value of the dependent variable at observation i\u003c/p\u003e \u003cp\u003e \u003cem\u003ex\u003c/em\u003e \u003csub\u003ei\u003c/sub\u003e = The observed value of the independent variables at observation i, from variable 1 to n\u003c/p\u003e \u003cp\u003e \u003cem\u003eβ\u003c/em\u003e \u003csub\u003e \u003cem\u003e0\u003c/em\u003e \u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Constant (intercept)\u003c/p\u003e \u003cp\u003e \u003cem\u003eβ\u003c/em\u003e \u003csub\u003ei\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Regression coefficient of the independent variable\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{x}_{1}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003en\u003c/em\u003e= Number of independent variables in the regression equation\u003c/p\u003e \u003cp\u003e \u003cem\u003eε\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Error term (residual)\u003c/p\u003e \u003cp\u003eThe study proposes three regression models: a model for studying the relationship between internal factors and net profit margin, a model for studying the relationship between internal factors and return on total assets, and a model for studying the relationship between internal factors and return on equity, which are detailed as follows:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eModel 1\u003c/strong\u003e \u003cp\u003eAn Analysis of the Relationship Between Internal Financial Factors and Net Profit Margin\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eNPM\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u003c/em\u003e \u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eLG\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eAG\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eDG\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eIG\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e5\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eAge\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e6\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eSize\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e7\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eLDR\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e8\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eGDP\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e9\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eEXR\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;ε\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eModel 2\u003c/strong\u003e \u003cp\u003eAn Analysis of the Relationship Between Internal Financial Factors and Return on Total Assets\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eROA\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u003c/em\u003e \u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eLG\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eAG\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eDG\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eIG\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e5\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eAge\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e6\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eSize\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e7\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eLDR\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e8\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eGDP\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e9\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eEXR\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;ε\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eModel 3\u003c/strong\u003e \u003cp\u003eAn Analysis of the Relationship Between Internal Financial Factors and Return on Equity\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eROE\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u003c/em\u003e \u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eLG\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eAG\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eDG\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eIG\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e5\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eAge\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e6\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eSize\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e7\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eLDR\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e8\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eGDP\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;β\u003c/em\u003e\u003csub\u003e\u003cem\u003e9\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eEXR\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;ε\u003c/em\u003e\u003c/p\u003e \u003cp\u003eWhere NPM\u003csub\u003ei,t\u003c/sub\u003e denotes the net profit margin of bank i in year t.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eROA\u003csub\u003ei,t\u003c/sub\u003e denotes the return on assets (ROA) of bank i in year t.\u003c/p\u003e\u003cp\u003eROE\u003csub\u003ei,t\u003c/sub\u003e denotes the return on equity (ROE) of bank i in year t.\u003c/p\u003e\u003cp\u003eLG\u003csub\u003ei,t\u003c/sub\u003e denotes the loan growth rate of bank i in year t.\u003c/p\u003e\u003cp\u003eAG\u003csub\u003ei,t\u003c/sub\u003e denotes the total asset growth rate of bank i in year t.\u003c/p\u003e\u003cp\u003eDG\u003csub\u003ei,t\u003c/sub\u003e denotes the deposit growth rate of bank i in year t.\u003c/p\u003e\u003cp\u003eIG\u003csub\u003ei,t\u003c/sub\u003e denotes the income growth rate of bank i in year t.\u003c/p\u003e\u003cp\u003eAge\u003csub\u003ei,t\u003c/sub\u003e denotes the age of bank i in year t.\u003c/p\u003e\u003cp\u003eSize\u003csub\u003ei,t\u003c/sub\u003e denotes the size of bank i in year t.\u003c/p\u003e\u003cp\u003eLDR\u003csub\u003ei,t\u003c/sub\u003e denotes the loan-to-deposit ratio of bank i in year t.\u003c/p\u003e\u003cp\u003eGDP\u003csub\u003ei,t\u003c/sub\u003e denotes the GDP growth rate of Laos in year t.\u003c/p\u003e\u003cp\u003eEXR\u003csub\u003ei,t\u003c/sub\u003e denotes the USD/LAK exchange rate in year t.\u003c/p\u003e\u003cp\u003eβ\u003csub\u003e0\u003c/sub\u003e denotes the constant term\u003c/p\u003e\u003cp\u003eβ\u003csub\u003e1\u0026minus;9\u003c/sub\u003e denote coefficients of the independent variables.\u003c/p\u003e\u003cp\u003eε denotes the error term (residual).\u003c/p\u003e\u003cp\u003ei denotes the 27 banks operating in Laos.\u003c/p\u003e\u003cp\u003et denotes the period from 2019 to 2023, covering a total of 5 years.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Empirical findings","content":"\u003cp\u003eFrom the study on internal financial factors affecting the profitability of banks in Laos, annual financial statement data were collected from 27 banks over a 5-year period (2019\u0026ndash;2023), totaling 128 data sets. The data was processed and analyzed using statistical software EViews 12.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Descriptive Statistics\u003c/h2\u003e \u003cp\u003eThe research results are based on data from 27 banks during 2019\u0026ndash;2023. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents descriptive statistics including the median, maximum, mean, and standard deviation of the key variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics: median, maximum, mean, and standard deviation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-645.58%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87.56%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.41%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-54.23%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.46%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-177.13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.65%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.64%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e163.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-53.05%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.64%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136.55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-54.86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.62%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-100.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.80%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e200.22%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-48.68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.87%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.39381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e291%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2436%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e475%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEXR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,421.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,154.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20,451.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,868.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,650.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFrom the data analysis in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, it was found that some variables exhibit outliers that significantly deviate from the mean, particularly NPM, ROE, and LDR. These variables show extreme maximum and minimum values that differ greatly from the average, which may reflect differences in the nature of each bank's operations such as strategic approaches or the influence of external factors. These outliers may impact the regression coefficients, potentially resulting in some variables being statistically insignificant and leading to a lower explanatory power of the model (Adjusted R\u0026sup2;). Nevertheless, the results can still provide useful preliminary insights into the trends and relationships among the variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Checking Variables Before Multiple Regression Analysis\u003c/h2\u003e \u003cp\u003eMulticollinearity Check assesses the degree of correlation among independent variables, multicollinearity is examined. High correlation among independent variables can reduce the accuracy of coefficient estimates in multiple regression analysis. Pearson\u0026rsquo;s correlation coefficient (ranging from \u0026minus;\u0026thinsp;1.0 to +\u0026thinsp;1.0) is used to assess the relationship between independent variables. A pairwise correlation exceeding 0.95 indicates a potential multicollinearity problem. The results from EViews 12 are shown in Table\u0026nbsp;3,\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable 3\u003c/b\u003e shows the correlation coefficients of all varia\u003c/p\u003e \u003cp\u003e\u003cimg 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\" width=\"609\" height=\"438\"\u003e\u003c/p\u003e\u003cp\u003eFrom Table\u0026nbsp;3, the correlation coefficients among the independent variables; Loan Growth Rate (LG), Total Asset Growth Rate (AG), Deposit Growth Rate (DG), Income Growth Rate (IG), Bank Age (Age), Bank Size (Size), and Loan-to-Deposit Ratio (LDR) range from \u0026minus;\u0026thinsp;0.23619 to +\u0026thinsp;0.58195. Notably, the correlation between AG and DG is +\u0026thinsp;0.58195, which may indicate a moderate to high positive relationship. Although this value is not excessively high, to ensure that multicollinearity does not affect the regression analysis, it is necessary to further examine the Variance Inflation Factor (VIF) for a more detailed assessment of the relationships among the independent variables.\u003c/p\u003e \u003cp\u003eThe Variance Inflation Factor (VIF) is also used to measure the severity of multicollinearity. Generally, a VIF value exceeding 10 indicates a serious multicollinearity problem. When the VIF values for the independent variables used in this study were calculated using the EViews 12 software, the results were displayed as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003epresents the VIF values of the independent and control variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEXR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.7381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.1502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.3381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.2186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.3264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFrom Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the VIF values show no independent variable exceeded 10, indicating no significant multicollinearity among the variables (Wanichbancha, 2009).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAutocorrelation Problem Investigation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBefore conducting multiple regression analysis, it is important to test for autocorrelation among the error terms. If the error terms are correlated, the regression estimates remain unbiased but become inefficient due to increased variance. This violates the BLUE (Best Linear Unbiased Estimator) assumption of the OLS method. Moreover, it may lead to underestimation of error variance and inflated t-statistics, potentially resulting in incorrect conclusions (Anthong, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2007\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the Multiple Regression Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eNPM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eROE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003et-Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003et-Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003et-Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.2678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.2472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.5328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.2322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.2203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.1491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.8817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.7659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.4453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0182**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.1769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0315**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.7568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.4507\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.1331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.1949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.2915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.7712\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.8049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.4225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.4011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.7393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.4612\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.7319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0003*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.5381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.38e-05 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.2344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.2195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.4167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0172**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.2387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0271**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.4598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.6465\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.3553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.9624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.3379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEXR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.44E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.26E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.1448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.48E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.8592\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e3.572795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.2476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e1.5504\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProb (F-statistic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.1388\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.2156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.2476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.1066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.1552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.1898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.0378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDurbin-Watson\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e1.9373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e2.0493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e1.6123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote: *, ** indicate significance at the 0.01 and 0.05 levels, respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that the Durbin-Watson statistics for the three models are 1.9373, 2.0493, and 1.6123, respectively all within the acceptable range of 1.5 to 2.5. This indicates that none of the regression models suffer from autocorrelation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Multiple regression analysis results\u003c/h2\u003e \u003cp\u003eThe results of the multiple regression analysis, aimed at studying the relationship between internal financial factors and the profitability of banks in Laos, are divided into 3 models shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eWhen analyzing profitability using the Net Profit Margin (NPM), it was found that the independent variable affecting NPM was the Total Asset Growth rate (AG), with a regression coefficient of 0.9202 at a 0.05 statistical significance level. In contrast, the Loan to Deposit Ratio (LDR) and Bank Size (Size) variables had regression coefficients of -0.0324 and \u0026minus;\u0026thinsp;0.1566, at a 0.05 and 0.01 statistical significance level, respectively. Additionally, no variables were found to significantly affect the Net Profit Margin. The results of the analysis were then used to create the following multiple regression equation for forecasting the NPM:\u003c/p\u003e \u003cp\u003e \u003cem\u003eNPM\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e= (4.2678) + (0.9202)AG\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ (-0.1566)Size\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ (-0.0324)LDR\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;ε\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe multiple regression analysis revealed that the model has an R-squared value of 0.2156, meaning that the independent variables; total asset growth rate (AG), bank size (Size), and loan-to-deposit ratio (LDR) which explain 21.56% of the variation in net profit margin, while other factors not included in the model account for the remaining 78.44%.\u003c/p\u003e \u003cp\u003eIn analyzing profitability using the return on assets (ROA) ratio, it was found that the independent variable affecting ROA was the growth rate of total assets (AG), with a regression coefficient of 0.0695 at a statistical significance level of 0.05. Conversely, the loan-to-deposit ratio (LDR) and bank size (Size) had regression coefficients of 0.0025 and 0.0158 at a significance level of 0.05 and 0.01, respectively. Additionally, no variables had a statistically significant impact on the ROA ratio. The results were used to create a multiple regression equation for forecasting net profit margin, as follows:\u003c/p\u003e \u003cp\u003e \u003cem\u003eROA\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e= (0.4219)+ (0.0695)AG\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e+ (-0.0158)Size\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e+\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:(-0.0025)\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003eLDR\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;ε\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe multiple regression analysis revealed that this model has an R-squared value of 0.2476, meaning that the independent variables; growth rate of total assets (AG), bank size (Size), and loan-to-deposit ratio (LDR) accounting for 24.76% of the variation in return on total assets (ROA), with other factors not considered in this model potentially contributing to the remaining 75.24%. In analyzing profitability using return on equity (ROE), no variable in the model was found to have a statistically significant effect on the net profit margin. The multiple regression equation used to forecast net profit margin is as follows:\u003c/p\u003e \u003cp\u003e \u003cem\u003eROE\u003c/em\u003e \u003csub\u003e \u003cem\u003ei,t\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e= + ε\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe multiple regression analysis showed an R-squared value of 0.1065, indicating that none of the independent variables in this model have a clear effect on return on equity (ROE).\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Discussion and Conclusion","content":"\u003cp\u003eThis study examines the relationship between internal financial factors and the profitability of banks in Laos using a multiple regression analysis model, with net profit margin (NPM), return on assets (ROA), and return on equity (ROE) as the dependent variables. The results indicate that, for NPM, the total asset growth rate has a statistically significant positive effect, whereas bank size and the loan-to-deposit ratio have significant negative effects, with the model explaining 21.56% of the variance. For ROA, the total asset growth rate again exerts a positive influence, while bank size and the loan-to-deposit ratio negatively affect performance, accounting for 24.76% of the variance. In contrast, none of the independent variables significantly affect ROE, and the model explains only 10.65% of its variance.\u003c/p\u003e \u003cp\u003eThe analysis of internal financial factors affecting the profitability of banks in Laos reveals that the asset growth rate has a positive effect on both net profit margin (NPM) and return on assets (ROA), consistent with the theory that asset expansion enhances profitability and in line with Wachanasarikalakul, L. (1984), who reported a positive correlation between total asset growth and net profit growth. Conversely, bank size exhibits a negative effect on NPM and ROA, suggesting that larger banks may incur higher operating costs, a finding that contrasts with studies in Thailand by Anawatchakul (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Maruset (2024), which supported a positive size\u0026ndash;profitability relationship consistent with economies of scale. The loan-to-deposit ratio is negatively associated with NPM and ROA, indicating that higher ratios may increase liquidity risk and borrowing costs, thereby reducing profitability; this aligns with Sunaryo (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). findings for Southeast Asian commercial banks during 2012\u0026ndash;2018. Loan growth rate shows no significant relationship with any profitability measure, contradicting Nguyen (2022), who found that higher lending activity tends to enhance profitability. Similarly, deposit growth rate is unrelated to profitability ratios, in contrast to Putra \u0026amp; Vidyantari, (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who reported a marginal positive impact of deposit and loan growth on profit growth. Finally, bank age has no significant effect on profitability, differing from Işık, and Ersoy (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) who found a significant relationship between bank age and profitability.\u003c/p\u003e \u003cp\u003eThe findings of this study suggest several policy and management implications as well as directions for future research. First, banks should promote sustainable asset growth by focusing on improving the quality and efficient utilization of existing assets rather than pursuing purely quantitative expansion. Second, larger banks need to manage costs effectively to mitigate the potential negative effects of scale on profitability, ensuring that operational growth does not erode financial performance. Third, maintaining an optimal loan-to-deposit ratio is essential to balancing lending and deposit mobilization, thereby reducing liquidity risk, lowering borrowing costs, and enhancing profitability. For future research, incorporating additional independent variables such as the operating expense ratio, non-performing loan (NPL) ratio, and interest rate measures could provide a more comprehensive analysis of profitability determinants. Improving data quality through the identification and treatment of outliers, expanding the sample size, and using standardized data sources would further strengthen analytical reliability. Moreover, examining external macroeconomic factors such as monetary policy and import\u0026ndash;export values, alongside additional control variables including inflation rates and financial market indicators, could offer deeper insights into how both internal and external influences shape bank profitability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding Declaration\u003c/h2\u003e\n\u003cp\u003eNo funding was received for this research.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eAll co-authors contributed equally to the work\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data used in this study were obtained from publicly available annual financial statements of commercial banks in Laos for the period 2019\u0026ndash;2023. These reports are accessible through the official websites of the respective banks and regulatory authorities. The compiled dataset used for analysis is available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlmazari AR (2014) Impact of internal factors on bank profitability: Comparative study between Saudi Arabia and Jordan. J Appl Finance Bank 4:125\u0026ndash;140\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnawatchakul H (2010) \u003cem\u003eFactors affecting the profitability of Thai commercial banks\u003c/em\u003e (Master\u0026rsquo;s thesis). Thammasat University, Bangkok, Thailand\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnthong A (2007) EViews \u0026amp; basic econometrics. Chiang Mai University Social Research Institute\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAthanasoglou P, Brissimis SN, Delis MD (2008) Bank-specific, industry-specific and macroeconomic determinants of bank profitability. 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J Money Credit Bank 34(1):254\u0026ndash;282\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWallStreetMojo (2022), February 3 \u003cem\u003eCommercial bank\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wallstreetmojo.com/commercial-bank/\u003c/span\u003e\u003cspan address=\"https://www.wallstreetmojo.com/commercial-bank/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bank profitability, Commercial banks, Laos, Multiple regression analysis","lastPublishedDoi":"10.21203/rs.3.rs-8943064/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8943064/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the relationship between internal financial factors and the profitability of banks in Laos. Data were collected from 27 commercial banks that publicly disclosed their annual financial statements over a five-year period from 2019 to 2023. The study employs summary statistics and multiple regression analysis. The independent variables include loan growth rate, deposit growth rate, total asset growth rate, bank age, bank size, and loan-to-deposit ratio, while the control variables are Laos\u0026rsquo; GDP growth rate and the US dollar/kip exchange rate. The findings indicate that the total asset growth rate has a significant positive effect on Net Profit Margin (NPM) and Return on Assets (ROA), while bank size and the loan-to-deposit ratio have significant negative effects on both NPM and ROA. However, none of the examined variables had a statistically significant effect on Return on Equity (ROE).\u003c/p\u003e","manuscriptTitle":"Internal Financial Factors Affecting the Profitability of Banks in Laos","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-16 13:08:05","doi":"10.21203/rs.3.rs-8943064/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b9085f4f-bc2f-4de6-9d80-231b15b2a371","owner":[],"postedDate":"March 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T07:11:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-16 13:08:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8943064","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8943064","identity":"rs-8943064","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}