Factor affecting bank profitability of commercial banks in vietnam from 2008 to 2020

INTRODUCTION

THE NECESSITY OF THE RESEARCH

Banks serve as financial intermediaries by collecting funds from surplus units and lending them to deficit units, profiting from the interest rate differential Like all businesses, their primary objectives are to maximize profits while minimizing costs and risks However, since Vietnam's accession to the World Trade Organization (WTO) in 2007, domestic banks have faced increased competition from foreign banks, impacting their profitability The rise of financial institutions and fintech companies has also expanded the available credit in the financial system Additionally, Vietnamese authorities are liberalizing the banking sector by removing interest rate ceilings, phasing out direct lending, and gradually opening capital accounts.

The ongoing banking restructuring process in Vietnam presents challenges in assessing its impact on the banking system's performance However, sustainable profitability is a key indicator of financial stability and potential crises, as noted by Demirgüç-Kunt & Detragiache (2000) This article aims to identify the determinants of bank profitability in Vietnam, which is crucial for banking regulators in conducting prudential analysis By analyzing the factors influencing bank profitability, this study seeks to enhance the understanding of fundamental banking operations in the context of emerging markets.

RESEARCH OBJECTIVES

This study aims to analyze the factors influencing the profitability of commercial banks in Vietnam Based on the research findings, the thesis offers recommendations to enhance the profitability of these banks.

To achieve the general objective, the thesis conducts these specific objectives following:

Find out the factors that can affect the profitability of commercial banks The research assesses the factors affect the profitability of commercial bank in Vietnam

The research suggests some recommendations to improve banking profitability.

RESEARCH QUESTIONS

Suggests the following research questions:

What are the factors affecting the profitability of commercial banks in Vietnam? How is the impact level and direction of the factors on the profitability of commercial bank?

What recommendations are there to improve the profitability of commercial banks in Vietnam?

SUBJECTS AND THE SCOPE OF THE RESEARCH

The thesis analyzes the factors affecting profitability of commercial banks in Vietnam

Vietnam has 31 commercial banks, but only 25 provide sufficient data for analysis According to the State Bank's statistics as of December 31, 2020, these 25 banks represent over 80% of the total assets in the banking system, ensuring a comprehensive overview of the sector.

This thesis analyzes the efficiency of commercial banks' performance from 2008 to 2020, a period marked by intense competition for profits among these financial institutions.

Vietnam This was also the period when the banking systems had many changes with the projects implemented by the government such as restructuring the system of credit institutions during 2011 - 2015

The project to enhance access to banking services for the economy

Decision on the approval on proposal for development of banking sector towards 2008 and orientation towards 2020.

DATA AND RESEARCH METHODOLOGY

The research utilizes a comprehensive dataset that encompasses macroeconomic variables such as economic growth, inflation rates, and interest rates sourced from the General Statistics Office of Vietnam and the State Bank of Vietnam Additionally, bank-specific data is gathered from the income statements and balance sheets available on the Fiinpro and State Bank of Vietnam websites.

This study was investigated by quantitative method and for each research question, there will be a different methodological explanation:

This article explores the factors influencing the profitability of commercial banks in Vietnam by examining three key perspectives: net interest margin (NIM), return on equity (ROE), and return on total assets (ROA) These serve as the dependent variables in the analysis Additionally, the study incorporates independent variables, including Bank Specific Factors and Macroeconomic Factors, to assess their correlation with the profitability metrics.

To evaluate the impact of various factors on the profitability of commercial banks in Vietnam, this study employs a panel data regression technique using STATA software 14 The analysis includes methods such as Pooled OLS, Fixed Effects Model (FEM), and Random Effects Model (REM), followed by tests to identify the most suitable regression model If the selected model exhibits issues like heteroskedasticity and autocorrelation, the research utilizes the Feasible Generalized Least Squares (FGLS) estimator to address these challenges.

To enhance profitability for commercial banks in Vietnam, it is essential to implement targeted strategies based on prior analyses Key recommendations include optimizing operational efficiency, diversifying financial products, and leveraging technology for better customer service Additionally, focusing on risk management and enhancing regulatory compliance can further strengthen financial performance By adopting these solutions, commercial banks can achieve sustainable growth and improved profitability in a competitive market.

THE THESIS CONTRIBUTIONS

The research provides valuable insights into the profitability assessment of commercial banks in Vietnam, offering practical results that can be compared with similar studies using different methodologies.

Secondly, the thesis proposes a number of new recommendations based on limitations and the reasons that commercial banks in Vietnam has faced when trying to improve its profitability

This study offers empirical evidence from Vietnam regarding the factors influencing the profitability of commercial banks, while also organizing existing theories and prior research related to this topic.

STRUCTURE OF THE THESIS

Chapter 1 outlines the significance of the thesis in relation to its economic necessity, while also providing a comprehensive overview of its objectives, methodologies, and contributions.

THEORETICAL BASIC AND LITERATURE REVIEW

THEORETICAL FRAMEWORK

The financial system is critical to a country's economic growth and development

A robust financial system is essential for a country's economic growth, as it supports the development of profitable and efficient industries that lay the groundwork for future production (Tadesse Wubie Abate and Enyew Alemaw Mesfin, 2019) Moreover, the primary role of the financial system extends beyond merely transferring funds from savers to investors; it is crucial in directing capital to the economy's most critical sectors.

Financial institutions contribute significantly to financial stability and economic growth by mobilizing financial resources across the economy (Masood and Ashraf,

Banks serve as essential financial intermediaries in the economy, facilitating the transfer of financial resources from surplus economic units to those with deficits This role is particularly vital for emerging economies, as it supports economic growth and stability.

Raghuram G Rajan (1998) argues that the traditional commercial bank, which accepts demand deposits and provides loans, has become obsolete However, banking remains essential for driving development activities and fostering economic growth (Goddard, Molyneux, & Wilson, 2004) The key function of banks—accepting deposits on demand and lending those funds—plays a crucial role in ensuring liquidity and facilitating quick access to cash.

The U.S Banking Act of 1971 defines commercial banks as institutions that accept demand deposits and issue loans These banks are characterized by their regulatory framework, which includes government oversight, guarantees, and the role of a public lender of last resort, ensuring stability and trust in the banking system.

Commercial banks play a dual role in economic development, both facilitating and hindering progress (Mongid et al., 2012) They operate under a country's monetary policies, primarily managing cash flow in relation to anticipated returns and emissions (Enna & Lace, 2013).

In summary, the commercial bank used in this study refers to a financial organization that holds deposits, issues credit to various economic sectors, and helps a country's economic development

Banks serve as financial intermediaries, channeling funds from individuals with surplus money to those in need They facilitate essential financial services, including accepting deposits, offering checking accounts, and providing various loans such as mortgages, auto loans, business loans, and personal loans Additionally, commercial banks enhance payment processing and international trade through their robust infrastructure and services.

Developing countries are characterized by low per capita income, leading to reduced savings and investment rates, as noted by Dao Binh Thi Thanh and Kieu Anh Nguyen (2020) Additionally, these nations often face societal challenges due to a large population coupled with high unemployment rates Their economies primarily rely on the non-industrial sector and raw material exports Furthermore, emerging nations exhibit an immature financial structure that is vulnerable during economic fluctuations and heavily dependent on government intervention (McConnell, Brue, and Flynn, 2015).

Vietnam is classified as a developing economy, and in response to the pandemic, its banks have introduced non-physical payment card products that offer money transfer and payment capabilities, akin to debit cards.

Profitability is a key indicator of a bank's performance, typically assessed over a twelve-month period (Kieu Anh Nguyen and Binh Thi Thanh Dao, 2020) It serves as a fundamental metric for evaluating a bank's overall effectiveness and is essential for fostering a creative, productive, and efficient banking sector (Chen and Liao, 2011).

Bank profitability can be assessed through various metrics, including the net interest margin (NIM), which measures net interest revenue relative to interest-bearing assets, and the return on assets (ROA), which reflects net earnings against total assets However, calculating bank profitability using these measures presents challenges, particularly due to endogeneity; more profitable banks often have greater access to equity through retained earnings This complicates the assumption of perfect capital markets, as increased capital can enhance projected profitability Additionally, unobserved heterogeneity among banks, especially in the context of China, may significantly impact results due to variations in corporate governance.

Tan, Yong, and Christos Floros (2011) highlight the limitations of using bank profitability indicators like ROA and NIM, particularly issues of endogeneity and unobserved bank heterogeneity, especially in the context of China's varying corporate governance ROA, which measures returns on assets, is frequently utilized to assess banks' operational efficiency Historical analyses by Hart and Jaffee (1974), Blair and Heggestad (1978), and Edwards and Heggestad (1979) have focused on banks' decision-making regarding returns on assets and equity, with ROA previously serving as an industry benchmark Additionally, return on investment (ROI) remains a critical metric, while recent trends indicate that regulators and financial markets are increasingly prioritizing capital ratios, as noted by Brewer and Lee (1986).

Borroni and Rossi's (2016) study, on the other hand, defines profitability as an economic entity's capacity to achieve a positive balance between revenues and costs

Profitability can be assessed monthly, quarterly, or annually, and is often averaged over extended periods When combined with other indices, it generates valuable market data for analysts, researchers, and publicly traded companies This research will focus on three key indicators of bank profitability: Return on Assets (ROA), Return on Equity (ROE), and Net Interest Margin (NIM) These indicators are favored for their accessibility, ease of calculation, and straightforward interpretation Additionally, the stringent controls surrounding the publication of a bank's balance sheet enhance the reliability and appeal of these metrics for researchers and analysts alike.

EMPIRICAL STUDIES REVIEW

Elisa Menicucci and Guido Paolucci (2016) emphasize the importance of evaluating bank profitability from both micro and macroeconomic perspectives At the micro level, business outcomes are essential for assessing a bank's financial health and competitiveness in a challenging market Conversely, a robust banking industry at the macro level enhances resilience to economic shocks and strengthens overall financial system stability The authors define "profitability" as a business's ability to consistently generate profits, which reflects effective management and serves as a key performance indicator for investors.

Research utilizing Generalized Method of Moments (GMM) reveals significant insights into the banking sector's performance amid inflationary pressures Yong Tan (2012) analyzed the profitability of 101 Chinese banks from 2003 to 2009, finding that inflation positively influences bank profitability, although non-traditional activities and high taxes can adversely affect it Meanwhile, Anna P I Vong (2009) assessed the impact of macroeconomic factors and financial structure on bank performance in Macao, using Return on Assets (ROA) as a key performance indicator Her study, which spanned 1993 to 2007 and included data from five major commercial banks representing approximately 75% of Macao's banking assets, indicated that capital strength is crucial for profitability, with well-capitalized banks being less risky and more profitable Furthermore, the research concluded that banks with extensive deposit-taking networks do not necessarily achieve higher profits compared to those with limited networks, underscoring the significant influence of inflation on overall bank performance.

In his 2020 study, Mohammad Sofie Abdul Hasan focused on the size and moderating variables of banks in Indonesia, uniquely selecting net interest margin (NIM) as an independent variable rather than a dependent one The findings revealed that NIM significantly impacts the bank's return on equity (ROE), alongside other factors such as the ratio of operational expenses to operational profit, capital adequacy ratio (CAR), and loan-to-deposit ratio Furthermore, the study indicated that return on assets (ROA) is influenced by the Fed Rate, cement consumption, CAR, and the ratio of operational expenses to operational profit (BOPO).

A study analyzing commercial banks in Vietnam, Thailand, and Malaysia from 2012 to 2016 reveals significant insights into banking profitability The research, conducted by Binh Thi Thanh Dao and Dung Phuong Nguyen (2020), highlights a strong negative correlation between operational risk and banking profitability across the three nations Additionally, while bank size negatively impacts profitability in Vietnam and Thailand, it shows no significant effect in Malaysia Notably, the study also uncovers a contentious negative relationship between the Capital Adequacy Ratio (CAR) and profitability, alongside a positive correlation between credit risk and bank profitability.

Interrelationships between liquidity production, regulatory capital, and bank profitability in the United States of America (Vuong Thao Tran, Chien-Ting Lin, Hoa

The model by Nguyen (2016) highlights a positive correlation between regulatory capital and liquidity, which is especially relevant for small banks in stable economic periods However, banks that generate substantial liquidity often experience lower profit margins Additionally, the relationship between regulatory capital and bank performance is nonlinear and varies based on the bank's capitalization level.

Tu DQ.Le and Thanh Ngo (2020) analyze the factors influencing bank profitability across 23 countries using panel data and the generalized method of moments (GMM) estimator Their findings indicate that banking technology variables, including the number of issued cards, automated teller machines (ATMs), and point of sale (POS) machines, positively impact bank profitability, although they simultaneously detract from the bank's competitiveness Data on banking technology services were sourced from the Payment System Statistics (Bank for International Settlements, 2017), while banking information was obtained from the Financial Development and Structural dataset (Beck et al., 2000) Despite the comprehensive global data, the study does not address the relationship between bank ownership structure and profitability.

Tu Le (2017) analyzed the profitability of Vietnamese commercial banks over a decade from 2005 to 2015, employing the GMM method proposed by Arellano and Bover (1995) The findings revealed that banks with a focus on lending specialization, lower liquidity risk, diversified portfolios, smaller sizes, and those that are publicly listed tend to be more profitable Furthermore, the research indicates that profitability can be enhanced in a less concentrated banking sector while also being influenced by inflation and economic growth, reflecting the current state of the Vietnamese market, which notably lacks a wholesale bank.

In a study by Duong Thuy Nguyen (2017), an empirical analysis was conducted on 13 commercial banks from 2006 to 2015 using Regression Analysis for Panel Data The results revealed that factors such as state ownership, asset size, and macroeconomic variables, including GDP and inflation, were statistically insignificant.

On the other hand, foreign ownership, cost of income, and credit risk all have a detrimental effect on the profitability of Vietnamese commercial banks

A study on the impact of online banking on bank performance is seldom discussed in Vietnam Research conducted by Van Dinh (2015) examines three critical factors: profitability ratios, noninterest operational expenditures, and earnings This analysis encompasses twenty banks that represent nearly 70% of the total assets within the Vietnamese banking sector.

Between 2009 and 2014, a study utilized random effect models (REM) and fixed effect models (FEM) to analyze correlations between various variables The results revealed that online banking has a minimal impact on bank profitability, with effects lagging over three years compared to previous research findings.

RESEARCH GAP

The earlier studies faced significant limitations due to the selection of variables in their research models, primarily focusing on GDP growth and inflation rates while neglecting other crucial macroeconomic factors like exchange rates, credit to GDP, and credit growth rates Additionally, local researchers did not analyze all commercial banks in Vietnam, largely due to restricted data sources that hindered access to comprehensive financial information from every institution.

This chapter explores commercial banking and its profitability, defining a commercial bank as a financial institution that pays interest to depositors and provides credit across various economic sectors Profitability is characterized by total interest income and its role in facilitating a country's economic development Bank interest revenue is generated through deposit and lending activities, along with additional financial services Key metrics for assessing bank profitability include Return on Assets (ROA), Return on Equity (ROE), and Net Interest Margin (NIM) The chapter also reviews empirical research on the factors influencing commercial bank profitability, drawing insights from both domestic and international studies to enhance understanding of existing research methodologies.

DATA AND METHODOLOGY

RESEARCH METHODOLOGY

Quantitative research uses physical sciences-based approaches that are intended to assure impartiality, generalizability, and, most crucially, dependability (Baradie,

Quantitative research involves unbiased and random selection methods, as noted by Weerd-Nederhof (2001) Bryman and Bell (2003) emphasize that it utilizes scientific methodologies to develop research topics and sampling strategies grounded in robust theoretical frameworks Research questions are formulated as hypotheses, which are then tested through estimation models.

Regression analysis identifies the relationship between the variations of two variables and defines the nature of this relationship (Sekaran and Bougie, 2010; Cooper and Schindler, 2003) It assigns weights to each independent variable, known as regression coefficients, which indicate the relative contribution of these variables to the overall prediction model, enhancing the clarity of each variable's impact on the prediction process (Easa, 2012; Hair et al.).

Ordinary Least Squares (OLS) is a method used to determine the best-fitting line by minimizing the sum of squared deviations, which helps explain variations in a dependent variable through changes in independent variables while accounting for unexpected measurement variations This approach captures both the systematic and random components of variation in the target variable, making it suitable for investigating hypotheses through multiple regression analysis In contrast, panel data models can be estimated using fixed effect or random effect methods According to Judge et al (1988), when there is a large amount of time series data and a small number of cross-sectional units, the differences in parameter estimates between these two models are minimal Judge (1982) and Biorn (2017) provide insights on how to choose between Fixed Effect Models (FEM) and Random Effect Models (REM).

When T (number of time series data) is big and N (number of cross-sectional units) is modest, FEM may be better

The Fixed Effects Model (FEM) is suitable for scenarios where the time variable (T) is small and the number of cross-sectional units (N) is large, especially when we know that the individual units in our sample are not randomly selected from a larger population Conversely, the Random Effects Model (REM) is appropriate when the cross-sectional units are considered random samples, allowing for broader generalizations.

When the individual error component I and one or more regressors are correlated, FEM is an unbiased estimator

When N is big and T is small, and the underlying assumptions of REM hold, REM estimators are more efficient than FEM estimators

This research is based on a limited dataset, comprising only 25 cross-sectional units and 12 years of data As a result, the study will utilize efficiency and statistical significance indicators to determine the appropriate method for analysis, selecting between Fixed Effects Model (FEM), Random Effects Model (REM), or Ordinary Least Squares (OLS).

RESEARCH MODELS

Financial institutions prioritize profit generation, with performance typically defined by economic indicators such as accounting expenses and profitability While some literature suggests that pricing indices for banking services are inadequate performance metrics, others, like Heggestad (1977), advocate for using profitability as a comprehensive measure of performance.

Profitability plays a crucial role in building and maintaining public trust in banks, serving as a key metric for investors evaluating an institution's current and future reliability and creditworthiness According to Gilbert (1984), bank profit rates are the most dependable indicators of financial performance, while Rhoades (1982, 1985) and Evanoff and Fortier (1988) support the notion that profitability metrics are essential for assessing overall bank performance.

The main drawback of accounting performance metrics is that they rely on the carrying value of assets, liabilities, and equity, which remain unchanged until sold or otherwise disposed of, failing to reflect current market values Many financial firms provide only accounting data, lacking essential market data for comprehensive analysis To accurately evaluate a bank's financial performance, it is crucial to break down income and expenses, which helps assess the quality of earnings and understand year-over-year profit fluctuations Key components of the income statement typically include interest revenue, fees, operational expenses, credit loss charges, taxes, and net income Important performance measures include average return on equity, average return on assets, net profit margin, and expense-to-income ratio.

This research aims to investigate the internal and external factors affecting bank profitability, utilizing size as a moderating variable.

Profitabilityit = β0 + β1SIZEit + β2CAPit + β3LOANit + β4DEPit + β5GDPt + β6INFt + β7CGRit + εit

Where: Profitability is ROE, ROA, NIM β0 : constant term β1 ,…, β7 : Regression coefficients of independent variables i: bank; t: year of observation

The return on equity (ROE) model, developed by Cole in 1971, serves as a vital tool for analyzing financial statements This method allows analysts to assess the origin and magnitude of bank profits in relation to the specific risks taken ROE is calculated by dividing a company's net profit by its total assets, providing insights into financial performance and risk management.

Aggregate bank profitability is commonly assessed using return on equity (ROE) and return on assets (ROA) To analyze ROE, four key accounting metrics are necessary: net income, total operating income, average assets, and average equity The first two metrics are flow variables from the bank's income statement, while the latter two are stock variables from the balance sheet ROE is calculated by dividing net income by total or average equity, reflecting the percentage return on each pound of shareholders' equity A higher ROE signifies greater returns for shareholders, allowing banks to enhance retained earnings and dividend payouts Consequently, total equity capital serves as a short-term indicator of long-term value maximization, focusing on shareholder returns.

Return on Equity (ROE) is a comprehensive metric for assessing the profitability of commercial banks, as noted by Goddard et al (2014) While ROE can effectively evaluate bank performance, Elisa and Guido (2015) highlight that it is often understated due to low leverage Therefore, our research incorporates Return on Assets (ROA)—the ratio of net profits to total assets—as a secondary profitability indicator, including it as a dependent variable in our regression model.

In the financial sector, Return on Assets (ROA) is often analyzed alongside Return on Equity (ROE) to assess a company's performance While ROE evaluates the returns generated for shareholders, ROA focuses on the efficiency of asset utilization in generating profits.

Numerous writers have utilized this profitability metric, including Boyd and Runkle (1993), Woosley and Baer (1995), and Berger (1995) Borroni and Rossi

Return on Assets (ROA) is a crucial metric in banking, reflecting how effectively management generates profits from available assets, influenced by various factors such as management decisions, risk appetite, and the bank's strategic plan (2019) It is shaped not only by the bank's policy actions but also by external economic and regulatory conditions (Mulenga Chaile, 2017) ROA is essential in demonstrating management's ability to acquire deposits at reasonable costs and invest them profitably (Badreldin, 2009) Consequently, it has become the preferred measure for investors and analysts (Rosly & Abu Bakar, 2003), leading me to identify ROA as a dependent variable in my research.

Net Interest Margin (NIM) is a crucial performance metric for commercial banks, as it focuses solely on income and costs related to core banking operations, excluding other expenses and revenues This specificity allows for a deeper analysis of a bank's profitability While Return on Assets (ROA) reflects how effectively management uses real investments, NIM highlights the profit generated from interest-related activities As a key component of a bank's net income, NIM significantly influences other performance measures such as Return on Equity (ROE) and ROA.

While the Net Interest Margin (NIM) offers several advantages, it also has notable drawbacks, primarily its focus on net interest income, which can overlook other significant revenue sources for banks As highlighted by Borroni and Rossi (2019), even though net interest income is the main profit driver for commercial banks, non-interest income can account for over 30%–40% of operational revenue Consequently, relying solely on NIM may provide an unclear picture of a bank's overall revenue-generating potential To address the strengths and weaknesses of various financial metrics, this study incorporates Return on Assets (ROA), Return on Equity (ROE), and NIM as dependent variables to better represent a bank's profitability.

Through seven parameters, we attempted to capture and examine the determining elements that may affect banks' profitability as independent variables

We categorized our independent variables into two groups based on existing literature: internal factors, which include bank-specific features, and external factors, encompassing macroeconomic and industry-specific characteristics In this initial analysis, we examined seven distinct factors.

Bank size is quantified using the natural logarithm of total assets, as established by Athanasoglou et al (2006) and Naceur (2003) It serves as a key factor in analyzing size-related economies and diseconomies of scale Although a bank's size is a result of its strategic choices, it does not guarantee increased profitability Furthermore, the size ratio is associated with variations in costs, product diversity, and risk diversification, yet there remains a lack of consensus regarding its overall impact.

Economies of scale and scope contribute to cost reductions for major banks, as highlighted by Akhavein et al (1997) and Goddard et al (2004) However, Boyd and Runkle (1993) reveal an inverse relationship between bank size and profitability, suggesting that smaller banks tend to operate more profitably, a finding supported by Redmond and Bohnsack (2007).

Eichengreen and Gibson (2001) suggest that a bank's size can positively impact its profitability, but only up to a certain limit Beyond this threshold, increased size may lead to negative consequences due to bureaucracy and other factors Consequently, the relationship between size and profitability is likely non-linear, indicating that larger banks may experience diminishing returns on profitability as they grow.

The capital ratio, which is calculated as total equity divided by total asset value, is often regarded as a leading indication of capital strength (Z Adali and M Uysal,

The majority of authors underlined the relevance of capitalization in stimulating profitability through lower finance costs, cautious lending, and the necessity of creditworthiness (Molyneux 1993; Bourke 1989; Haron and Azmi 2004; Bashir

EMPIRICAL RESULTS

DATA DESCRIPTION

This section presents descriptive statistics, including the mean and standard deviation for each variable in the study The research model identifies Return on Assets (ROA) as the dependent variable, while independent variables are categorized into three groups: bank-specific variables (bank size, capital ratio, liquidity, loan ratio, and deposit ratio), industry-specific variables (credit growth rate), and macroeconomic variables (GDP and inflation rate) The dataset comprises 300 observations from 25 commercial banks in Vietnam, with the results of the descriptive statistics detailed below.

Table 4.1 Statistical table describing the variables

Variable Obs Mean Std Dev Min Max

Source: Result are calculated by Stata, detailed in Appendix 03 – Picture 1

This research presents the descriptive statistics of the variable, as illustrated in Table 1 The Average Return on Assets (ROA) is recorded at 0.9%, with a standard deviation of 0.7% Although these figures may appear modest, the small standard deviation indicates a consistent performance in ROA.

The average Return on Equity (ROE) for banks stands at 10.03%, with a standard deviation of 8.2% In comparison, the average Net Interest Margin (NIM) is 3.1%, accompanied by a standard deviation of 1.1% Although this NIM is relatively high compared to previous studies, the fluctuations among banks are minimal, indicating a highly competitive market environment.

The capital ratio (CAP) averages 8.5% with a standard deviation of 5.7%, indicating a healthy position for banks as it exceeds regulatory requirements Maintaining this ratio is crucial for improving operational efficiency and ensuring compliance with regulations.

The mean bank size (SIZE) in Vietnam is 18.2, with a standard deviation of 1.2, indicating that banks tend to have similar sizes The maximum and minimum values are 21.1 and 14.6, respectively, suggesting a limited range in bank sizes Notably, banks with larger capital sources predominantly exhibit state ownership.

The high DEP and LOAN ratios of 62.5% and 54.9% respectively demonstrate that banks primarily focus on capital raising and lending activities However, the relatively moderate lending to assets figure of 54.9% indicates that Vietnamese banks are also prioritizing business diversification to mitigate risks Additionally, the medium standard deviations of these variables suggest that the data series are fairly homogeneous and stable.

This research examined the impact of macroeconomic variables on bank profitability, maintaining GDP and inflation rates at 5.9% and 7.2%, respectively, due to low standard deviations indicative of a stable economy As a developing country with a large population, Vietnam experiences high credit demand, reflected in an average credit growth rate (CGR) of 29% However, the relatively high standard deviation of 63.4% suggests significant disparities within Vietnam's credit market.

CORRELATION MATRIX

This section presents the Pearson correlation coefficients for each pair of variables utilized in the research model A statistical test, accompanied by a p-value, is employed to determine the statistical significance of the relationships between the two variables The resulting Pearson correlation values are detailed below.

ROA ROE NIM SIZE CAP LOAN DEP

The Pearson correlation matrix illustrates the relationships between variables, with significance tested alongside the coefficients As shown in Table 4.2, Return on Assets (ROA) is statistically significant at the 10% level when analyzed with Inflation (INF) and Capital Growth Rate (CGR), while exhibiting a negative correlation with Debt (DEP) and Gross Domestic Product (GDP) Conversely, ROA shows a positive correlation with Size (SIZE), Capital (CAP), Loans (LOAN), INF, and CGR The connections between ROA and other variables are generally weak to moderate, with the strongest positive Pearson coefficient of 0.1711 observed between ROA and CGR.

The relationship between Return on Equity (ROE), company size (SIZE), and loans (LOAN) is statistically significant at the 10% level Notably, ROE shows an inverse relationship with capital (CAP) and gross domestic product (GDP), while it positively correlates with SIZE, LOAN, deposits (DEP), inflation (INF), and capital growth rate (CGR) Among these, the correlation between ROE and SIZE is the strongest, with a Pearson coefficient of 0.4093.

The analysis reveals that the Net Interest Margin (NIM) shows a significant correlation with Capital Adequacy Ratio (CAP), which stands at 16.03 Conversely, factors such as SIZE, DEP, and GDP negatively impact NIM, while CAP, LOAN, Inflation (INF), and Credit Growth Rate (CGR) contribute positively to its performance.

REGRESSION MODELS

4.3.1.1 Fixed effect model (FEM) of ROE

Table 4.3 Fixed effect model Overall R-square = 0.2637; F-test = 10.89; P-value = 0.0000

4.3.1.2 Random effect model (REM) of ROE

Table 4.4 Random effect model (REM) Overall R-square = 0.2809; Wald-test = 95.16; P-value = 0.0000

4.3.1.3 Model choice beetwen FEM and REM

Hausman test is conducted to choose between FEM and REM Obtained result is presented as below:

H0: there is no correlation between regressors and individual effects, which means REM model is more appropriate

H1: there is corelation between regressors and individual effects, which means FEM model is more appropriate

Table 4.5 Chi-test valued of FEM and REM of ROE

Conclusion REM is more appropriate

The Chi-square test yielded a value of 9.57, with a p-value of 0.2144, which exceeds the 0.05 threshold Consequently, we reject the null hypothesis (H0), indicating that the Random Effects Model (REM) is more suitable than the Fixed Effects Model (FEM).

The Wooldridge test for autocorrelation in panel data was conducted to assess the null hypothesis of no autocorrelation The findings indicate that there is no first-order autocorrelation present in the data, as summarized in the accompanying table.

H0: there is no first-order autocorrelation

H1: there is first-order autocorrelation

Table 4.6 Autocorrelation diagnostics of ROE

The Wooldridge test results indicate a value of 5.314, with a P-value of 0.0301, which is below the 0.05 threshold Consequently, we reject the null hypothesis (Ho), confirming the presence of autocorrelation issues within the dataset.

Second, multicollincarity is ruled out by the Variance Inflation computation (VIF) The following table summarizes the obtained result:

Table 4.7 Multicollinearity diagnostics of ROE

The results, calculated using Stata and detailed in Appendix 03 – Picture 8, indicate that none of the variables exhibit a Variance Inflation Factor (VIF) greater than 10.0, suggesting that there are no issues with multicollinearity among the variables.

Finally, the author used Wald test to check heteroskedasticity The result is presented as below:

H0: the error variances are all equal

H1: the error variances are not equal

Table 4.8 Heteroskedasticity diagnostics of ROE

As seen in the table above, p-value = 0.0000 < α = 0.05 Hence, FEM regression models do encounter the issue of heteroskedasticity

To fix autocorrelation and heteroskedasticity the author conducts cross- sectional time-series FGLS regression Obtained result is presented as below:

Table 4.9 Model fix of ROE Wald-test = 236.07; P-value = 0.0000

Source: Result are calculated by Stata, detailed in Appendix 03 – Picture 10 ROE = -0.3882 + 0.0277*SIZE - 0 1792*CAP + 0.1633*LOAN - 0.1875*DEP + 0.3161*INF + 0.0476*CGR

Table 4.10 Fixed effect model (FEM) of ROA Overall R-square = 0.1255; F-test = 4.00; P-value = 0.0000

Table 4.11 Random effect model (REM) of ROA Overall R-square = 0.1351; Wald-test = 69.16; P-value = 0.0000

Finally, Hausman test is conducted to choose between FEM and REM Obtained result is presented as below:

Table 4.12 Model choice beetwen FEM and REM of ROA

Conclusion REM is more appropriate

The results, calculated using Stata and detailed in Appendix 03 – Picture 13, indicate that the Chi-square test yielded a value of 12.48 with a p-value of 0.0858 Since this p-value exceeds 0.05, we reject the null hypothesis (H0), suggesting that the Random Effects Model (REM) is more suitable than the Fixed Effects Model (FEM).

The Wooldridge test for autocorrelation in panel data is utilized to assess the null hypothesis of no autocorrelation The findings indicate that there is no first-order autocorrelation present in the data.

Table 4.13 Autocorrelation diagnostics of ROA

The Wooldridge test results indicate a value of 10.996, and since the P-value is below 0.05, we reject the null hypothesis (Ho) This suggests that there is a significant issue with autocorrelation in the dataset.

Variance Inflation is used to test for multicollincarity (VIF) The following table summarizes the obtained result:

Table 4.14 Multicollinearity diagnostics of ROA

The analysis presented in Appendix 03 – Picture 8 reveals that all variables have a Variance Inflation Factor (VIF) below 10.0, confirming the absence of multicollinearity among them Consequently, it can be concluded that these variables do not demonstrate multicollinearity.

Table 4.15 Heteroskedasticity diagnostics of ROA

As seen in the table above, p-value = 0.0000 < α = 0.05 Hence, FEM regression models do encounter the issue of heteroskedasticity

Autocorrelation and heteroskedasticity must be fixed The author performs cross-sectional FGLS regression using time series data The following table summarizes the obtained result:

Table 4.16 Model fix of ROA Wald-test = 130.41; P-value = 0.0000

Source: Result are calculated by Stata, detailed in Appendix 03 – Picture 16 ROA = -0.0173 + 0.0015*SIZE + 0.0126*LOAN - 0.0167*DEP + 0.0250*INF + 0.0059*CGR

Table 4.17 Fixed effect model (FEM) of NIM Overall R-square = 0.0471; F-test = 5.56; P-value = 0.0000

Table 4.18 Random effect model (REM) of NIM Overall R-square = 0.0683; Wald-test = 35.32; P-value = 0.0000

Finally, Hausman test is conducted to choose between FEM and REM Obtained result is presented as below:

Table 4.19 Model choice beetwen FEM and REM of NIM

Conclusion FEM is more appropriate

The Chi-square test results indicate a value of 19.56, with a p-value of 0.0066, which is below the 0.05 significance level Consequently, we reject the null hypothesis (H0) and conclude that the Fixed Effects Model (FEM) is more suitable than the Random Effects Model (REM).

The Wooldridge test for autocorrelation in panel data is employed to identify autocorrelation, specifically testing the null hypothesis that there is no first-order autocorrelation present The results of this test are detailed below.

Table 4.20 Autocorrelation diagnostics of NIM

Table above shows Wooldridge -test valued at 117.024 P-value is 0.0000 and it is lower than 0.05, hypothesis Ho is rejected Thus there is the issue related to autocorrelation in the dataset

Second, multicollincarity is checked through the calculation of Variance Inflation (VIF) Obtained result is presented as below:

Table 4.21 Multicollinearity diagnostics of NIM

The analysis conducted using Stata, as detailed in Appendix 03 – Picture 8, reveals that none of the variables exhibit a Variance Inflation Factor (VIF) exceeding 10.0, indicating the absence of multicollinearity issues among the variables.

Table 4.22 Heteroskedasticity diagnostics of NIM

Source: Result are calculated by Stata, detailed in Appendix 03 – Picture 20 Table above shows that p-value = 0.0000 < α = 0.05, Therefore FEM regression models does face up with heteroskedasticity issue

To fix autocorrelation and heteroskedasticity the author conducts cross- sectional time-series FGLS regression Obtained result is presented as below:

Table 4.23 Model fix of NIM Wald-test i.93; P-value = 0.0000

Source: Result are calculated by Stata, detailed in Appendix 03 – Picture 22 NIM = 0.0101 + 0.0268*LOAN - 0.0123*DEP + 0.0344*INF

CONCLUSIONS AND RECOMMENDATIONS

CONCLUSIONS

This study aims to identify the factors affecting the operational efficiency of commercial banks in Vietnam from 2008 to 2020 The results indicate that when using Return on Assets (ROA) and Return on Equity (ROE) as dependent variables, the Random Effects Model (REM) is optimal, while the Fixed Effects Model (FEM) is better suited for Net Interest Margin (NIM) However, the REM shows issues of heteroskedasticity and autocorrelation, which are addressed using the Feasible Generalized Least Squares (FGLS) method Key findings reveal that bank size, liquidity, inflation rate, and credit growth rate positively influence banking performance, suggesting that enhancing these elements can lead to greater financial stability and operational efficiency Consequently, banks are encouraged to improve their asset management and lending practices to increase profitability Additionally, effective monetary policies by the State Bank of Vietnam have contributed to improved operational efficiency among commercial banks during this period.

Studies reveal a negative correlation between deposit (DEP) and capital ratio (CAP), while no significant relationship exists between GDP and banking efficiency When banks aggressively seek deposits, often through competitive interest rates, it can lead to increased capital mobilization costs, subsequently lowering net income and overall efficiency Additionally, banks that extend more loans than they attract in deposits risk liquidity issues Non-performing loans adversely affect profitability, as delayed repayments force banks to allocate alternative resources to manage debt, further diminishing efficiency Ultimately, when operational costs surpass revenue, banks struggle to maintain efficiency.

RECOMMENDATIONS

According to research, bank size has a beneficial impact on bank profitability

The size of a bank significantly impacts its competitiveness by enhancing its capital sources through branch development and operational expansion This growth not only increases funding for commercial activities but also helps the bank build a reputation for attracting and mobilizing deposits at lower costs.

Commercial banks are increasingly considering mergers to enhance their branch networks and overall market presence Merging large banks with smaller ones or consolidating smaller banks can significantly expand their reach To effectively navigate potential challenges and fulfill liquidity requirements, these banks must prioritize asset quality and maintain a balanced structure of assets and capital, ensuring compliance with the State Bank of Vietnam's minimum capital adequacy ratio By establishing a sound asset and capital framework, commercial banks can optimize their earnings while implementing stringent risk management practices.

Research indicates that increasing the loan ratio can significantly enhance commercial bank profits To improve lending rates, banks should implement attractive interest rates across various loan terms and develop effective marketing programs Additionally, boosting consumer loans, which are vital for the economy, is essential Expanding suitable lending packages to meet the diverse needs of different client segments will help attract more customers through targeted marketing and advertising strategies Furthermore, deposit operations are crucial for commercial banks, as they generate substantial profits; thus, any changes in deposit scale will notably influence overall bank profitability.

The study indicates that GDP positively influences commercial bank ROA, highlighting the need for government agencies to implement policies that promote and sustain GDP growth for a more effective business environment Key strategies include promptly shutting down unprofitable ventures, addressing bad debts, and removing barriers to production to enhance purchasing power and consumption Additionally, efforts should focus on increasing labor productivity and operational efficiency, emphasizing the adoption of high technology to boost industry competitiveness During economic downturns, it is crucial to explore ways to minimize promotional services in the market.

RESEARCH LIMITATIONS AND THE NEXT SUGGEST STUDY 49

Despite thorough efforts, the study faces inherent limitations due to insufficient information and documentation during the research period Consequently, it does not provide a comprehensive overview of the profitability of the banking industry in Vietnam.

This study focuses exclusively on 25 commercial banks within the Vietnamese banking system, which encompasses various types of institutions, including state-owned, joint-stock, fully foreign-owned, joint venture banks, social policy banks, and cooperative banks.

In addition, future research should expand the sample to include the complete financial system in Vietnam in order to generalize its operation

On the other hand, there are alternative approaches that can be used to measure performance using market-driven profitability metrics to provide a more multi- dimensional view

In the future, GMM can be used to overcome the limitations of regression models with larger variable sizes, allowing for more complex and in-depth studies of variables

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APPENDIX 01 List of commercial banks selected for the research

1 ABB An Binh Commercial Joint Stock Bank

2 ACB Asia Commercial Joint Stock Bank

3 BID Joint Stock Commercial Bank for Investment and Development of Vietnam

5 CTG Vietnam Joint Stock Commercial Bank for Industry and Trade

6 EIB Vietnam Commercial Joint Stock Export Import Bank

7 BVB Viet Capital Commercial Joint Stock Bank

8 HDB Ho Chi Minh City Development Joint Stock Commercial Bank

9 KLB Kien Long Commercial Joint Stock Bank

10 LPB Lien Viet Post Joint Stock Commercial Bank

11 MBB Military Commercial Joint Stock Bank

12 MSB Vietnam Maritime Commercial Join Stock Bank

13 NAB Nam A Commercial Joint Stock Bank

14 NVB National Citizen Commercial Joint Stock Bank

15 OCB Orient Commercial Joint Stock Bank

16 PGB Petrolimex Group Commercial Joint Stock Bank

18 SSB Southeast Asia Commercial Joint Stock Bank

19 SGB Saigon Bank For Industry And Trade

20 SHB Saigon Hanoi Commercial Joint Stock Bank

21 STB Sai Gon Thuong Tin Commercial Joint Stock Bank

22 TCB Vietnam Technological and Commercial Joint Stock Bank

23 TPB Tien Phong Commercial Joint Stock Bank

24 VAB Vietnam - Asia Commercial Joint Stock Bank

25 VCB Bank for Foreign Trade of Vietnam

26 VIB Vietnam International Commercial Joint Stock Bank

27 VPB Vietnam Prosperity Joint Stock Commercial Bank

CGR 325 2900471 6342109 -.6095 10.5886 INF 325 0721538 0639153 006 231 GDP 325 0593077 0105511 029 071 DEP 325 6257929 1272956 1851 8937 LOAN 325 5491628 1360206 1139 8517 CAP 325 0856415 0576206 026 4428 SIZE 325 18.26195 1.287053 14.6987 21.1398 NIM 325 0311145 0110026 -.0089 0709 ROE 325 1002911 0822761 -.5633 315 ROA 325 0092942 0079371 -.0599 0557 Variable Obs Mean Std Dev Min Max

ROA ROE NIM SIZE CAP LOAN DEP

_cons -.4591299 1000707 -4.59 0.000 -.6560165 -.2622432 CGR 0213132 0063758 3.34 0.001 0087689 0338575 INF 3205965 0734477 4.36 0.000 17609 4651031 GDP -.3335051 3719643 -0.90 0.371 -1.065336 3983256 DEP -.136861 0425838 -3.21 0.001 -.2206436 -.0530783 LOAN 1459075 0361122 4.04 0.000 0748576 2169574 CAP -.1254584 1057794 -1.19 0.236 -.3335768 0826599 SIZE 0310016 004807 6.45 0.000 0215439 0404594 ROE Coef Std Err t P>|t| [95% Conf Interval]

Total 2.19327195 324 006769358 Root MSE = 07018 Adj R-squared = 0.2724 Residual 1.56134202 317 004925369 R-squared = 0.2881 Model 631929928 7 090275704 Prob > F = 0.0000 F(7, 317) = 18.33 Source SS df MS Number of obs = 325

F test that all u_i=0: F(24, 293) = 3.87 Prob > F = 0.0000 rho 25431268 (fraction of variance due to u_i) sigma_e 06361185 sigma_u 0371487

_cons -.3151853 1840024 -1.71 0.088 -.6773192 0469486 CGR 0195122 0063596 3.07 0.002 0069959 0320285 INF 2248669 0870874 2.58 0.010 0534708 3962631 GDP -.3868463 3383491 -1.14 0.254 -1.052749 2790563 DEP -.2585065 046143 -5.60 0.000 -.3493202 -.1676927 LOAN 2154085 0452698 4.76 0.000 1263132 3045037 CAP -.1625919 1126663 -1.44 0.150 -.3843298 059146 SIZE 0259521 0093057 2.79 0.006 0076376 0442667 ROE Coef Std Err t P>|t| [95% Conf Interval] corr(u_i, Xb) = -0.0535 Prob > F = 0.0000 F(7,293) = 10.89 overall = 0.2637 max = 13 between = 0.3876 avg = 13.0 within = 0.2065 min = 13 R-sq: Obs per group:

Group variable: BANK Number of groups = 25 Fixed-effects (within) regression Number of obs = 325

Picture 5 Model choice between OLS and REM (ROE)

Picture 6 Model choice between FEM and REM (ROE)

Variables: fitted values of ROE

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity

Test: Ho: difference in coefficients not systematic

B = inconsistent under Ha, efficient under Ho; obtained from xtreg b = consistent under Ho and Ha; obtained from xtreg CGR 0195122 0207386 -.0012264 0018254 INF 2248669 2745254 -.0496585 0450644 GDP -.3868463 -.3609264 -.0259199 DEP -.2585065 -.2137377 -.0447688 0149775 LOAN 2154085 1870345 028374 020571 CAP -.1625919 -.1373529 -.025239 0403508 SIZE 0259521 0303493 -.0043971 0068371 fem rem Difference S.E.

Wooldridge test for autocorrelation in panel data

H0: sigma(i)^2 = sigma^2 for all i in fixed effect regression model

Modified Wald test for groupwise heteroskedasticity

_cons -.3882183 0699943 -5.55 0.000 -.5254046 -.2510319 CGR 0476268 0087799 5.42 0.000 0304186 064835 INF 3161654 0547084 5.78 0.000 2089389 4233919 GDP -.3368676 2582159 -1.30 0.192 -.8429614 1692262 DEP -.1875255 030367 -6.18 0.000 -.2470438 -.1280073 LOAN 1633088 0258959 6.31 0.000 1125537 2140639 CAP -.1792932 0697838 -2.57 0.010 -.3160668 -.0425195 SIZE 0277758 0033824 8.21 0.000 0211464 0344053 ROE Coef Std Err z P>|z| [95% Conf Interval]

Prob > chi2 = 0.0000 Wald chi2(7) = 236.07 Estimated coefficients = 8 Time periods = 13 Estimated autocorrelations = 0 Number of groups = 25 Estimated covariances = 25 Number of obs = 325

Cross-sectional time-series FGLS regression

_cons -.0096394 0194157 -0.50 0.620 -.0478514 0285726 CGR 0027691 0006711 4.13 0.000 0014484 0040898 INF 0115604 0091894 1.26 0.209 -.0065251 0296459 GDP -.0625815 0357022 -1.75 0.081 -.1328469 0076839 DEP -.0285028 004869 -5.85 0.000 -.0380854 -.0189203 LOAN 0200341 0047768 4.19 0.000 0106329 0294354 CAP 0168425 0118884 1.42 0.158 -.006555 0402401 SIZE 0014456 0009819 1.47 0.142 -.0004869 0033782 ROA Coef Std Err t P>|t| [95% Conf Interval] corr(u_i, Xb) = -0.2387 Prob > F = 0.0000 F(7,293) = 11.02 overall = 0.1255 max = 13 between = 0.0011 avg = 13.0 within = 0.2084 min = 13 R-sq: Obs per group:

Group variable: BANK Number of groups = 25 Fixed-effects (within) regression Number of obs = 325 rho 15101671 (fraction of variance due to u_i) sigma_e 00671226 sigma_u 00283095

The analysis reveals several significant factors affecting Return on Assets (ROA) The coefficient for Capital Growth Rate (CGR) is 0.0027, indicating a strong positive relationship with ROA, supported by a p-value of 0.000 Inflation (INF) also shows a positive impact with a coefficient of 0.0162 and a p-value of 0.038 Conversely, Gross Domestic Product (GDP) has a negative coefficient of -0.0599, although it is not statistically significant at the 0.096 level Debt (DEP) significantly negatively influences ROA with a coefficient of -0.0231 and a p-value of 0.000 Additionally, Loans (LOAN) positively affect ROA with a coefficient of 0.0183 and a p-value of 0.000 Capital (CAP) shows a coefficient of 0.0192, nearing significance with a p-value of 0.084 Finally, Size (SIZE) positively correlates with ROA, with a coefficient of 0.0016 and a p-value of 0.012 The overall model is statistically significant, with a Wald chi-square of 69.16 and a probability greater than chi-square of 0.0000.

Group variable: BANK Number of groups = 25Random-effects GLS regression Number of obs = 325

H0: sigma(i)^2 = sigma^2 for all i in fixed effect regression model

Modified Wald test for groupwise heteroskedasticity

B = inconsistent under Ha, efficient under Ho; obtained from xtreg b = consistent under Ho and Ha; obtained from xtreg

Picture 13 Model choice between FEM and REM (ROA)

Wooldridge test for autocorrelation in panel data

ROA Coef Std Err z P>|z| [95% Conf Interval]

Estimated autocorrelations = 0 Number of groups = 25

Estimated covariances = 25 Number of obs = 325

Cross-sectional time-series FGLS regression

NIM Coef Std Err t P>|t| [95% Conf Interval] corr(u_i, Xb) = -0.1832 Prob > F = 0.0000

F(7,293) = 5.56 overall = 0.0471 max = 13 between = 0.0158 avg = 13.0 within = 0.1172 min = 13

Group variable: BANK Number of groups = 25

Fixed-effects (within) regression Number of obs = 325

Picture 19 Model choice between FEM and REM (NIM) rho 23036937 (fraction of variance due to u_i) sigma_e 00906116 sigma_u 00495741

The analysis presents various coefficients and statistical metrics related to key economic indicators Notably, the variable DEP shows a significant negative coefficient of -0.0256492 with a p-value of 0.000, indicating a strong impact on the dependent variable In contrast, LOAN has a positive coefficient of 0.0197104 and a p-value of 0.001, suggesting a significant positive relationship Other variables such as CGR, INF, GDP, CAP, and SIZE exhibit coefficients that do not reach conventional significance levels, with p-values above 0.05 The overall model demonstrates a Wald chi-squared statistic of 35.32, with a probability of chi-squared greater than zero at 0.0000, indicating that the model is statistically significant The R-squared values suggest limited explanatory power, with an overall R-sq of 0.0683 and within-group R-sq of 0.1130.

Group variable: BANK Number of groups = 25 Random-effects GLS regression Number of obs = 325

B = inconsistent under Ha, efficient under Ho; obtained from xtreg b = consistent under Ho and Ha; obtained from xtreg CGR 0014895 0013456 0001439 0002097 INF 0133162 0147658 -.0014496 0058118 GDP -.0258413 -.0239667 -.0018746 DEP -.0299318 -.0256492 -.0042826 0016904 LOAN 0165152 0197104 -.0031951 0024979 CAP 0203431 0212743 -.0009312 004858 SIZE 0015348 000985 0005498 0009025 fem rem Difference S.E.

Tiêu đề	Factors Affecting Bank Profitability Of Commercial Banks In Vietnam From 2008 To 2020
Tác giả	Tieu Quoc Phong
Người hướng dẫn	Ph.D. Nguyen Thi Nhu Quynh
Trường học	Banking University of Ho Chi Minh City
Chuyên ngành	Finance – Banking
Thể loại	Bachelor Thesis
Năm xuất bản	2021
Thành phố	HCMC

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