INTRODUCTION
Rationale
The banking system serves as the vital circulatory system of the economy, channeling funds from savers to borrowers Its significance cannot be overstated, as both microeconomic and macroeconomic activities heavily rely on it Profitability is a key objective for banks, and their financial success directly contributes to overall economic stability Understanding the factors that influence bank profitability is crucial for effective management and strategic planning, making this topic a focal point for scholars and economists alike.
Bank profitability is influenced by three primary categories: bank-specific factors, industry-specific factors, and macroeconomic factors Key indicators for bank-specific factors include total assets, equity ratio, loan ratio, deposit ratio, and liquidity ratio Market concentration serves as the main indicator for industry-specific factors, while macroeconomic factors are represented by inflation rate, economic growth, unemployment rate, and interest rate Common measures of bank profitability include Return on Assets (ROA), Return on Equity (ROE), and Net Interest Margin (NIM).
Numerous empirical studies have explored the factors influencing bank profitability For instance, Yılmaz (2013) conducted research focusing on emerging countries, while Petria et al examined multiple nations simultaneously.
Research on European countries by 3 et al (2015) and on South Asian countries by Islam & Nishiyama (2016) highlights varying findings across different regions Some studies focus on individual markets, such as Obamuyi (2013) in Nigeria, Serwadda (2018) in Hungary, and Tam et al (2017) in Vietnam, revealing diverse results For instance, Yılmaz's findings further illustrate these discrepancies in research outcomes.
Determinants of bank profitability vary across studies, with key factors identified by different researchers According to a 2013 study, these factors include capital adequacy, operating expense management, credit risk, bank size, and inflation rate In contrast, Tam et al (2017) highlight size, asset growth rate, GDP growth rate, and interest rate as significant determinants Overall, research findings differ not only across various target markets but also within the same market over different time periods.
This research aims to investigate the determinants of bank profitability using the cases of Vietnamese banks during five most recent years, from 2015 to 2019.
Research questions
This paper aims to find answers for two below research questions:
(1) What are potential determinants of profitability for the bank?
This research aims to identify the key determinants of bank profitability by analyzing existing theories and empirical studies It will then focus on specific factors to examine their impact on the profitability of Vietnamese banks.
(2) What are the determinants of profitability for Vietnamese banks?
The potential determinants identified in the initial research question will be empirically examined in the context of Vietnamese banks using a range of statistical tools The study will conclude with insights into the level and direction of the influencing relationships.
4 addition, the research will aim to construct regression models, which are used to explain the bank’s profitability indicators by included dependent variables.
Research scope
The research focused exclusively on Vietnamese listed banks, examining both the Hanoi Stock Exchange (HNX) and the Hochiminh Stock Exchange (HoSE) The study analyzed data from the most recent five-year period, spanning from 2015 to 2019.
Overview about the research methodology
This study utilizes a quantitative design and various statistical tools to analyze the relationship between variables The researcher aims to gather secondary data from all thirteen listed banks over the past five years, from 2015 to 2019, resulting in a total of 65 observations The dependent variables chosen to assess bank profitability include Return on Assets (ROA), Return on Equity (ROE), and Net Interest Margin (NIM), while the independent variables consist of specific bank factors and macroeconomic indicators.
Research structure
There are five chapters in this paper:
Chapter IV: Analyses and Findings
Overview about the Vietnamese banking system
Vietnamese bank system is bank-based financial system, where the financial sources for companies mainly come from the bank system
Prior to the Doi Moi Revolution in 1986, Vietnam's financial system was centrally controlled by the State Bank of Vietnam (SBV), serving primarily as a government tool for budget management However, following the economic reforms initiated during the Revolution, the Vietnamese banking system transitioned to a market-oriented approach, leading to significant improvements and development in the sector (Thanh, 2010).
The Vietnamese economy has successfully transitioned from a centrally planned to a market-oriented system, leading to significant developments, particularly in its banking sector Since 1990, the banking system has evolved from a one-tier to a two-tier structure, allowing for the emergence of various banks alongside the State Bank of Vietnam (SBV) By the end of 2019, the total assets of the Vietnamese banking system reached 12,146,226 billion Vietnam Dong, with a charter capital of 617,473 billion Vietnam Dong, comprising state commercial banks, policy banks, commercial joint stock banks, foreign banks, and a cooperative bank However, despite these advancements, the market capitalization of the Vietnamese banking system remained low, accounting for only 33% of GDP in 2016.
6 government (accounting more than 90%) and the bond market was worth 15% GDP (Vuong, 2018).
Theoretical reviews
Profit and profitability, though distinct concepts, are interconnected as they both assess earning capacity Profit refers to the absolute monetary figure, while profitability indicates the relative measure of earnings.
The classic definition of profit by Hicks (1946) views it as the maximum distribution to shareholders while maintaining the economic value of the organization's net assets Barker (2010) proposed that profit equals the difference between income and expenses, although this is not always practiced According to Nimalathasan (2009), generating profit is the primary goal of a business In financial reports, the profit earned by a company during a specific period is reflected in the income statement, calculated as total revenue minus total expenses.
Profitability, unlike profit which is an absolute figure, is a relative measure that assesses a business's capacity to generate and sustain profits over time It reflects management efficiency and serves as a crucial factor for investors in their decision-making processes (Menicucci & Paolucci, 2016).
Key metrics for assessing a firm's profitability include net profit margin (NPM), return on assets (ROA), return on equity (ROE), return on investment (ROI), and net interest margin (NIM), the latter being a specific profitability indicator for banks.
NPM indicates the remaining percentage of revenue after deducting all operating expenses and interest, taxes and dividends paid to preferred stockholders (Weygandt, et al., 2013) Its formula is as below:
Return on Assets (ROA) measures the percentage growth rate of profit generated from a company's assets, reflecting its profitability in relation to total owned assets The formula for calculating ROA is as follows:
Total assets are crucial in assessing a company's profitability, as the Return on Equity (ROE) reflects the percentage growth rate of profit generated from shareholder equity This metric provides insight into how effectively a company utilizes its equity to generate profits, highlighting its financial performance in relation to shareholders.
2013) Its formula is as below:
Shareholder's equity is a crucial metric that reflects the percentage of return a company generates from its total investment It demonstrates the relationship between earnings and costs, highlighting the company's efficiency in utilizing its investments The formula for calculating return on investment (ROI) is essential for understanding this financial performance indicator.
The Net Interest Margin (NIM) is a key profitability metric for banks, used to assess their operational efficiency and effectiveness The NIM ratio indicates the amount of interest income generated per unit of interest expenses (Saksonova, 2014) The formula for calculating NIM is as follows:
2.2 Theories about the profits and profitability
2.2.1 The rent theory suggested by Walker (1887)
Walker (1887) proposed a rent theory that equates profit to the rent derived from superior abilities, suggesting that entrepreneurs with exceptional skills can earn profits similar to how superior land generates rent He categorized entrepreneurs into two groups: those with superior abilities, who can earn profits, and those without, who cannot According to Walker, profit is akin to rent, as both represent a differential surplus; just as superior land yields rent over inferior land, only the first group of entrepreneurs can reap profits due to their unique capabilities, while the second group, akin to no-rent land, remains inefficient and unable to generate profit.
2.2.2 The risk theory suggested by Hawley (1893)
Hawley's risk theory (1893) emphasizes that profits are the rewards for entrepreneurs who take risks, contrasting with Walker's rent theory (1887), which overlooked the role of risk-taking According to Hawley, entrepreneurs would not willingly expose their businesses to risk if they could only achieve normal returns.
(1893) believed that the profit is the actual value of risk He suggested that the
Riskier companies anticipate higher profit rates as compensation for the risks they undertake However, this theory has been criticized for focusing solely on profits without considering other factors, such as the business's capabilities, which are highlighted in Walker's rent theory (1887).
2.2.3 The wage theory of profit suggested by Taussig (1910)
According to Taussig's wage theory (1910), profit is viewed as a form of wage awarded to entrepreneurs for their services, contrasting with Walker's perspective that likens profit to rent Taussig emphasizes the parallels between labor wages and entrepreneurial profits, suggesting that both laborers and entrepreneurs are compensated for their contributions However, this theory faces criticism for overlooking the fundamental differences between labor and entrepreneurship Notably, entrepreneurs bear risks that laborers do not, and while laborers consistently receive wages, entrepreneurs may face losses instead of profits.
2.2.4 The uncertainty-bearing theory suggested by Knight (1921)
Knight's uncertainty-bearing theory (1921) builds upon Hawley's risk theory, asserting that profit serves as compensation for businesses that undertake non-insurable risks and uncertainties Unlike Hawley, Knight categorizes immeasurable risks as uncertainties, which he views as genuine threats to a company's stability He defines pure profit as a temporary reward that varies with the degree of uncertainty faced by the business.
10 theory are that it is not realistic and offers difficulties in allocating profits Besides, it does not account the monopoly position
2.2.5 The innovation theory suggested by Schumpeter (1934)
According to Schumpeter's innovation theory (1934), innovation is crucial for generating profits and serves as a reward for entrepreneurial efforts He posited that to achieve profitability, entrepreneurs must embrace innovation, which can take five forms: (1) introducing a new product or a unique variation of existing products, (2) discovering or implementing new production or sales methods, (3) penetrating new markets, (4) securing new sources of raw materials or semi-finished goods, and (5) restructuring industries, such as dismantling monopolies However, Schumpeter's theory has faced criticism for overlooking the uncertainties and risks inherent in business operations.
2.2.6 The dynamic theory of profits suggested by Clark (1990)
According to Clark's dynamic theory (1990), profit exists in a dynamic economy but not in a static one, where conditions remain unchanged He identifies five sources of change in a dynamic economy: shifts in tastes and preferences, advancements in production methods, variations in capital formation, alterations in business structure, and changes in population size and income These factors influence the demand and supply of goods and services, leading to fluctuations in business profits Consequently, companies that can swiftly and effectively adapt to these changes are more likely to succeed.
Empirical reviews
The research will be reviewed first is the research on emerging countries of Yılmaz (2013) The researcher used the panel data set of 195 banks collected from
The study analyzed the profitability of banks in nine countries, including Turkey, Poland, Hungary, Czech Republic, Malaysia, Taiwan, Brazil, Mexico, and South Africa, using Return on Assets (ROA) and Net Interest Margin (NIM) as dependent variables Key independent variables included liquidity ratio, operating expenses to total assets ratio, capital adequacy, non-performing loans to total loans ratio, bank size (measured by the natural logarithm of total assets), and dummy variables for mergers and acquisitions and ownership structure Additionally, three control variables were incorporated: country dummies, annual GDP growth rate, and inflation rate The analysis utilized the Hausman test over a six-year period from 2005 to 2010, revealing that the main determinants of bank profitability were capital adequacy, efficiency in managing operating expenses, credit risk, bank size, and inflation rate.
Petria et al (2015) conducted research to investigate the factors influencing bank profitability across 27 European countries over an 8-year period from 2004 to 2011, utilizing various proxies as dependent variables.
12 bank profitability, which are ROE and ROA Independent variables were from three groups as below:
Key bank-specific factors include bank size, assessed through the logarithm of total assets, and capital adequacy, indicated by the ratio of equity to total assets Additionally, credit risk is evaluated by the proportion of impaired loans to gross loans, while management efficiency is measured by the cost-to-income ratio Liquidity risk is determined by the ratio of loans to customer deposits, alongside a business mix indicator that reflects the bank's operational diversity.
Industry-specific factors: market concentration (measured by the Herfindhal-Hirschman Index)
Macroeconomic factors: inflation and economic growth (indicated by GDP per capital growth)
The research identified key factors influencing the profitability of European banks, including credit risk, liquidity risk, business mix, market concentration, and GDP growth Although bank size showed a minimal impact on Return on Assets (ROA), it had no effect on Return on Equity (ROE) Consequently, the researchers recommended that bank supervisors focus on managing credit and liquidity risks while fostering competition to enhance profitability.
Menicucci & Paolucci (2016) conducted a study to explore the determinants of bank profitability within the European banking system, focusing specifically on bank-specific factors Utilizing regression analysis, the research analyzed a dataset comprising 175 observations from 35 leading European banks over a five-year period, from 2009 to 2014.
2013 Three indicators of profitability, including ROA, ROE and NIM, were employed as dependent variables Selected independent variables included bank
The research examined various factors affecting the profitability of the European banking sector, including size, capital adequacy, loan ratio, deposit ratio, and loan loss provisions The findings revealed that all independent variables significantly influenced profitability, though the strength of these relationships varied Notably, size and capital ratio demonstrated significant connections, while loan loss provisions, deposit ratio, and loan ratio were found to have insignificant effects Based on these results, the researchers offered recommendations for bank regulators.
In contrast to previous studies, the research conducted by Islam and Nishiyama (2016) analyzed 259 commercial banks across four South Asian countries—Bangladesh, India, Nepal, and Pakistan—over a five-year period.
From 1997 to 2012, the research analyzed the dependent variables of Return on Assets (ROA) and Return on Equity (ROE) The independent variables were classified into three distinct groups, with further details provided below.
Key bank-specific variables include the equity to total assets ratio, non-performing loan ratio, liquidity ratio, cost of fund ratio, productivity ratio, recurring earning power, and growth of total deposits Additionally, factors such as bank size, loan to deposit ratio, interest income to total loan ratio, and off-balance sheet income ratio play a crucial role in assessing a bank's financial health and performance.
Industry-specific variables: Hirschman-Herfindahl index
Macroeconomic-specific variables: term structure of interest rate, inflation and economic growth rate (shown by GDP)
Research findings revealed a positive correlation between the equity ratio and recurring earning power with bank profitability Conversely, significant negative relationships were identified with the liquidity ratio, funding gap, cost of funds, and productivity ratio Other variables showed no connection to the bank's profitability.
Ahmad et al (2016) conducted a study on 70 East Asian banks and 89 Latin American banks over twelve years, utilizing an unbalanced panel data set of 1,160 observations Unlike previous research, they measured profitability using risk-adjusted return on assets rather than ROE or NIM The study identified bank-specific factors such as capital adequacy, cost management efficiency, liquidity ratio, credit risk ratio, and bank size, alongside macroeconomic factors like real GDP growth, inflation rate, and concentration ratio The findings revealed that East Asian banks' profitability was primarily influenced by bank-specific factors, while Latin American banks' profitability was more significantly affected by macroeconomic factors.
Developing countries receive many attentions of researchers
Obamuyi (2013) conducted research on Nigerian banks using panel secondary data from 20 banks over a seven-year period, from 2006 to 2012, resulting in 980 observations The use of panel data was justified as it helps to address the unique characteristics of each bank The study focused on two widely recognized profitability indicators: Return on Assets (ROA) and Return on Equity (ROE).
This research examines the impact of both business-specific and macroeconomic determinants on the profitability of Nigerian banks Key business-specific indicators include capital adequacy, bank size (measured by the natural logarithm of total assets), and expense management (assessed by the ratio of operating expenses to total assets) Macroeconomic indicators considered are interest rates and real GDP growth The findings reveal that capital adequacy, expense management efficiency, interest rates, and GDP significantly influence bank profitability Enhancing capital adequacy, interest income, and cost management efficiency is essential for improving the performance and growth of banks in Nigeria Consequently, it is recommended that Nigerian policymakers promote capital expansion and create a supportive economic environment for banks.
Pakistan, a developing country in South Asia, was the focus of a study by Yao et al (2018) that examined how different business-specific, industry-specific, and macroeconomic factors influence bank profitability.
This study analyzed data from 28 banks across various sectors, including private, Islamic, foreign, government-owned, and specialized banks, resulting in 276 observations over a 10-year period from 2007 to 2016 The research focused on key dependent variables such as Return on Assets (ROA), Return on Equity (ROE), and Net Interest Margin (NIM), while independent variables included bank size, solvency, credit quality, liquidity, and several others Utilizing the two-step generalized method of moments, the findings revealed that positive determinants of profitability for Pakistani banks included bank size, solvency ratio, operating expenses, economic growth, financial structure, market power, and labor productivity Conversely, factors such as inflation, credit quality, operational management efficiency, banking industry development, and industry concentration were found to have a significant negative impact on profitability, with government transition also adversely affecting bank performance.
METHODOLOGY
Research design
A research paper could use quantitative design or qualitative design or hybrid of both two types
The qualitative design utilizes inductive reasoning to explore various perspectives on a topic, revealing diverse thoughts and emotions of individuals Typically involving fewer than 50 samples, qualitative research often employs unstructured data collection methods, such as open interviews and focus group discussions.
Quantitative research focuses on investigating trends, statistical measurements, and relationships between variables by analyzing large datasets, often comprising hundreds or thousands of observations Data is typically collected through questionnaires, reports, or surveys and is examined using various mathematical techniques, including descriptive statistics, scatter plots, correlation analysis, ANOVA, Chi-square tests, and regression analysis.
With hybrid design, both qualitative and quantitative approaches will be employed at the same time
Among three mentioned above types, this paper employs the quantitative design to determine factors influencing the profitability of observed banks.
Collection of data and sampling techniques
There are two main types of research data: primary and secondary Primary data is generated specifically for a research project and has not been published elsewhere, while secondary data consists of previously collected information from various sources such as the internet, newspapers, reports, or magazines This paper focuses on secondary data, utilizing publicly available information from the internet and annual reports of selected banks The research aims to gather data from reliable online sources, including https://cophieu68.vn, https://finance.vietstock.vn/, and the official website of the General Statistics of Vietnam, along with the annual financial reports of the banks involved.
This research includes two stock exchange markets in Vietnam, specifically focusing on three banks listed on the HNX and ten banks on the HoSE A total of thirteen banks are analyzed, with financial data collected over the past five years, from 2015 to 2019, resulting in 65 observations.
The sample profile is provided in the table below:
Table 2: Banks included in the research
JSC Bank for Investment and Development of
2 CTG Vietnam Joint Stock Commercial Bank for HoSe
Ho Chi Minh City Development Joint Stock
4 MBB Military Commercial Joint Stock Bank HoSe
Sai Gon Thuong Tin Commercial Joint Stock
6 TPB Tien Phong Commercial Joint Stock Bank HoSe
7 VPB Vietnam Prosperity Joint Stock Commercial Bank HoSe
Vietnam Commercial Joint Stock Export Import
9 VCB Bank for Foreign Trade of Vietnam HoSe
Vietnam Technological and Commercial Joint
11 ACB Asia Commercial Bank HNX
12 NVB National Citizen Commercial Joint Stock Bank HNX
13 SHB Saigon Hanoi Commercial Joint Stock Bank HNX
Variables used in the research
The research targets two groups of potential determinants: the bank-specific factors and the macroeconomic factors Selected dependent and independent variables are described in the following table:
Table 3: Description of variables used in the research
ROA Return on assets ROE Return on Equity NIM Net interest margin
SIZE Bank size (Ln of assets) CAP Capital adequacy (Equity to Assets) LOAN Net loans to total asset ratio
DEP Deposit to total asset ratio
RDI Re-discount interest rate INF Inflation rate
Hypotheses
Based on results from previous researches reviewed in chapter II, the hypotheses about the determinants of bank’s profitability are constructed as below:
H1: The size of bank is negatively related to the profitability
H2: The capital adequacy is positively related to the profitability
H3: The loan ratio is positively related to the profitability
H4: The deposit ratio is positively related to the profitability
H5: The bad debt rate is negatively related to the profitability
H6: The inflation rate is negatively related to the profitability
H7: The re-discount interest rate is negatively related to the profitability
Analysing techniques
The research uses SPSS program to analyse input data Tools will be used are descriptive statistics, correlation and regression method
The descriptive statistics is used to discover descriptive characteristics of observations, such as maximum, minimum, range and standard deviation
The correlation technique is employed to analyze the relationship between a dependent variable and an independent variable, ultimately identifying the determinants of bank profitability The results of the correlation will provide insights into the strength and direction of these relationships In this analysis, the Pearson coefficient will be utilized instead of the Spearman correlation method.
The regression method is employed to construct regression model to explain each dependent variable from all independent variables Three regression models aimed to construct as followings:
ANALYZES AND FINDINGS
Descriptive results
Descriptive statistics offer a summary of the collected data set, highlighting the minimum, maximum, average values, and standard deviation for each variable The results of these descriptive statistics are presented in Table 4 below.
N Range Minimum Maximum Mean Std Deviation
Between 2015 and 2019, all banks analyzed demonstrated positive profitability, as indicated by their positive ROE, ROA, and NIM ratios Among these, ROE exhibited the greatest variability, with a range of 27.53 In comparison, ROA and NIM had ranges of 2.93 and 7.85, respectively The highest ROE recorded was 27.73, achieved by ACB in 2018, while the lowest was a mere 0.2, attributed to NCB in 2015.
ROA, its largest value is 2.95 (TCB in 2019) and its lowest value is 0.02 (NCB in
2015) Lastly, regard to NIM, its highest value is 9.41 (VPB in 2019) and its smallest value is 1.56 (STB in 2016)
The analysis of independent variables reveals that total assets exhibit the highest dispersion, with a range of 1,441,727,291 million Vietnam Dong The maximum total assets, amounting to 1,489,957,293 million Vietnam Dong, are attributed to BIDV in 2019, while the lowest total assets of 48,230,002 million Vietnam Dong belong to NCB in 2015 In contrast, the other independent variables—CAP, LOAN, DEP, BD, INF, and RDI—show less dispersion, with ranges of 0.12, 0.38, 0.41, 0.0614, 2.91, and 0.030050, respectively Their maximum values are 0.16, 0.74, 0.89, 0.0668, 3.54, and 0.045, while the minimum values are 0.04, 0.36, 0.48, 0.0054, 0.63, and 0.04 On average, Vietnamese listed banks report a capital adequacy ratio (CAP) of 0.737, a loan ratio (LOAN) of 0.6094, a deposit ratio (DEP) of 0.7047, a bad debt ratio (BD) of 0.018, a return on deposits (RDI) of 0.043, and an inflation rate (INF) of 2.63 Notably, there is a missing observation for BD, as HD Bank did not disclose its bad debt rate for 2019, providing only a general amount.
Correlation results
The correlation analysis is used to discover the relationship of one pair between one dependent variable, or indicator of profitability, and one independent variable
In this paper, the Pearson correlation method is employed with the help of SPSS program Results are provided in the table below:
ROE ROA NIM SIZE CAP LOAN DEP BD INF RDI
** Correlation is significant at the 0.01 level (2-tailed)
* Correlation is significant at the 0.05 level (2-tailed)
To interpret the results, it is essential to consider both the correlation coefficient (r) and the significance level (p) There are four scenarios to evaluate: (1) no connection exists when the absolute value of r (|𝑟|) is less than 0.2 and/or p exceeds 0.05; (2) a weak connection is indicated when |𝑟| is greater than 0.2 but less than 0.4, with p not exceeding 0.05; (3) a moderate connection is present when |𝑟| is greater than 0.4 but less than 0.5, and p is also not greater than 0.05; and (4) a strong connection is identified when |𝑟| is greater than 0.5.
A significant connection is established when the p-value is less than or equal to 0.05 The nature of this relationship is determined by the sign of the correlation coefficient, r; a negative r indicates a negative relationship, while a positive r signifies a positive connection.
Applying to the results table 5, some noticeable connections are discovered as below:
The analysis identifies six key relationships concerning Return on Equity (ROE) Among these, three demonstrate positive moderate correlations: ROE is positively associated with SIZE (r = 0.384), CAP (r = 0.252), and INF (r = 0.266) Conversely, three relationships exhibit moderate negative correlations, with ROE negatively related to DEP (r = -0.420), BD (r = -0.265), and RDI (r = -0.297) Notably, all correlations are statistically significant, with significance levels below 0.05.
The analysis reveals two key relationships concerning Return on Assets (ROA) Firstly, a strong positive correlation exists between ROA and Capital (CAP), with a correlation coefficient of \$r = 0.649\$ and a significance level of \$p < 0.0005\$ Conversely, a moderate negative relationship is observed between ROA and Debt to Equity Ratio (DEP), indicated by a correlation coefficient of \$r = -0.484\$ and a significance level of \$p < 0.0005\$.
In the analysis of Net Interest Margin (NIM), two distinct relationships are identified: one positive and one negative A moderate positive correlation exists between Capital Adequacy Ratio (CAP) and NIM, with a correlation coefficient of \$r = 0.483\$ and a significance level of \$p < 0.0005\$ Conversely, a moderate negative relationship is observed between Deposit (DEP) and NIM, indicated by a correlation coefficient of \$r = -0.474\$ and a significance level of \$p < 0.0005\$.
The analysis reveals that a connection between selected independent variables and profitability is established when an independent variable demonstrates at least two significant relationships with profitability metrics Notably, Capital (CAP) emerges as a positive determinant, exhibiting favorable relationships with all three profitability variables, while Debt (DEP) acts as a negative determinant, showing adverse relationships with the same profitability metrics These findings regarding CAP align with previous research conducted by Yılmaz (2013) and Menicucci & Paolucci.
(2016), and Obamuyi (2013) However, the conclusion about DEP as a negative influence is different from conclusion of Menicucci & Paolucci (2016)’s research, which found that DEP has positive impacts on bank profitability
Therefore, the acceptance of pre-provided hypotheses is represented in the table below:
H1: The size of bank is negatively related to the profitability
Reject Reason: No relation H2: The capital adequacy is positively related to the profitability
H3: The loan ratio is positively related to the profitability
H4: The deposit ratio is positively related to the profitability
Reject Reason: Negative relation H5: The bad debt rate is negatively related to the profitability
Reject Reason: No relation H6: The inflation rate is negatively related to the profitability
Reject Reason: No relation H7: The re-discount interest rate is negatively related to the profitability
Regression results
LOAN, DEP, INF and RDI, (b)
Enter a Dependent Variable: ROA b All requested variables entered
As provided in the table above, the independent variables are SIZE, CAP, BD, LOAN, DEP, INF and RDI while the dependent variable is ROA
Std Error of the Estimsate
1 851 a 724 689 40909 a Predictors: (Constant), SIZE, CAP, BD, LOAN, DEP, INF and RDI
The R square value from the model summary in Table 8 indicates the proportion of the dependent variable that can be predicted by the independent variables A higher R square signifies a better model fit for explaining the dependent variable In Model 1, the dependent variable is Return on Assets (ROA), with predictors including constant, SIZE, CAP, BD, LOAN, DEP, INF, and RDI The R square for Model 1 is 0.724, indicating that 72.4% of ROA can be explained by the constructed regression model, which is considered a significant percentage.
Total 33.902 63 a Dependent Variable: ROA b Predictors: (Constant), SIZE, CAP, BD, LOAN, DEP, INF and RDI
In Model 1, the sum of squares for regression, residual, and total are 24.530, 9.372, and 33.902, respectively, with a total degrees of freedom (dF) of 63, including 7 dF from regression predictors and 56 dF from residuals The significance value (p) indicates the reliability of the independent variables in predicting the dependent variables, with a smaller p value signifying greater reliability Since the significance level is below the alpha threshold of 0.05, it can be concluded that the predictors—Constant, UEP, DEP, CAP, SIZE, INF, and LOAN—are reliable in predicting ROA.
Based on the results of B under unstandardized coefficient column shown in the table 10, the model 1 could be details constructed as below:
The estimates reveal the relationship between independent variables and the predicted dependent variable through the direction of the B value Specifically, increases in SIZE, CAP, and INF lead to higher ROA, while increases in LOAN, DEP, BD, and RDI result in lower ROA Furthermore, the estimates quantify the change in predicted science scores for a one-unit increase or decrease in each predictor, assuming all other variables remain constant For instance, a one-unit increase in SIZE corresponds to a 0.310-unit increase in the predicted science score when other predictors are held constant.
The p values in the final column are utilized to evaluate the null hypothesis, which posits that the coefficient is equal to 0 in a two-tailed test The results of the null hypothesis testing for the seven predictors are presented below based on these p values.
The LOAN, BD, RDI and INF are not statistically significantly different from 0 because their significance values are 0.556, 0.345, 0.120 and 0.489 respectively and they are higher than alpha level of 0.05
The SIZE, CAP, and DEP are statistically significantly different from 0 because their significance values are lower than 0.05
Model Variables Entered Variables Removed Method
Enter a Dependent Variable: ROE b All requested variables entered
In the model 2, seven independent variables of INF, CAP, BD, LOAN, DEP, RDI, and SIZE are trying to predict ROE- the dependent variable
Model R R Square Adjusted R Square Std Error of the Estimate
2 706 a 498 435 5.80884 a Predictors: (Constant), INF, CAP, BD, LOAN, DEP, RDI, and SIZE
The R square value reveals that 49.8% of the Return on Equity (ROE) can be predicted by seven variables: Inflation (INF), Capital (CAP), Bank Deposits (BD), Loans (LOAN), Deposits (DEP), Retail Deposits Index (RDI), and Size (SIZE) This percentage is lower compared to the results from model 1.
Squares df Mean Square F Sig
Total 3764.121 63 a Dependent Variable: ROE b Predictors: (Constant), INF, CAP, BD, LOAN, DEP, RDI, and SIZE
In Model 2, the sum of squares for regression, residual, and total are 1874.531, 1889.590, and 3764.121, respectively The degrees of freedom (dF) for the total is 63, with 7 dF from regression predictors and 56 dF from the residual Additionally, the significance level of the model is below the alpha threshold of 0.05 Consequently, it can be concluded that the predictors, including (Constant), INF, and CAP, are statistically significant.
BD, LOAN, DEP, RDI, and SIZE will reliably predict ROE
Based on the results of B under unstandardized coefficient column shown in the table 14, the model 2 could be details constructed as below:
The increases of SIZE, CAP, LOAN and INF will result increases of ROE while the increases of DEP, BD and RDI will results decreases of ROE
According to the p values, the results of null hypothesis testing for seven predictors are provided as below:
The LOAN, BD, RDI and INF are not statistically significantly different from 0 because their significance values are 0.925, 0.131, 0.169, and 0.725 respectively and they are higher than alpha level of 0.05
The SIZE, CAP, and DEP are statistically significantly different from 0 because their significance values are 0.001, 0.010 and 0.001 respectively, which are lower than 0.05
Enter a Dependent Variable: NIM b All requested variables entered
In the model 3, seven independent variables of (Constant), INF, CAP, BD,
LOAN, DEP, RDI, and SIZE are trying to predict NIM- the dependent variable Table 16: Model Summary
Std Error of the Estimate
3 716 a 513 452 1.22298 a Predictors: (Constant), INF, CAP, BD, LOAN, DEP, RDI, and SIZE
The R square value reveals that 51.3% of the Net Interest Margin (NIM) can be predicted by seven variables: Constant, INF, CAP, BD, LOAN, DEP, RDI, and SIZE This prediction capability is lower than that of model 1 but exceeds that of model 2.
Squares df Mean Square F Sig
Total 171.973 63 a Dependent Variable: NIM b Predictors: (Constant), INF, CAP, BD, LOAN, DEP, RDI, and SIZE
In Model 3, the sum of squares for regression, residual, and total are 88.214, 83.759, and 171.973, respectively, with a total degrees of freedom (dF) of 63, including 7 dF from regression predictors and 56 dF from the residual The model demonstrates significance below the alpha level of 0.05, indicating that the predictors—Constant, INF, CAP, BD, LOAN, DEP, RDI, and SIZE—are reliable in predicting NIM.
Based on the results of B under unstandardized coefficient column shown in the table 10, the model 3 could be details constructed as below:
The increases of SIZE, CAP, LOAN, and BD will result increases of NIM while the increases of DEP, BD, RDI and INF will result decreases of NIM
According to the p values, the results of null hypothesis testing for seven predictors are provided as below:
The SIZE, RDI and INF are not statistically significantly different from 0 because their significance values are 0.904, 0.6399 and 0.256 respectively and they are higher than alpha level of 0.05
The CAP, LOAN, DEP and BD are statistically significantly different from 0 because their significance values are 0.000, 0.036, 0.000, and 0.036 respectively
CONCLUSIONS
Conclusion
The banking system functions as a vital conduit in the economy, channeling funds from savers to borrowers while aiming for profitability as a core objective Key determinants of bank profitability can be categorized into bank-specific factors, such as size, capital adequacy, management efficiency, liquidity, and deposit ratios; industry-specific factors, including market concentration; and macroeconomic factors like inflation, unemployment, and economic growth rates This research focuses on the profitability determinants of Vietnamese listed banks over a five-year period from 2015 to 2019 The World Bank identifies the banking sector as the largest component of Vietnam's financial system, which has evolved through market-oriented reforms since the economic revolution (Thanh, 2010) As of 2019, Vietnam has 47 banks, with only 13 listed on the HNX and HoSE stock exchanges The study analyzes 64 valid observations using SPSS software to draw conclusions about the factors influencing bank profitability.
This paper employs quantitative design with the help of various statistical mathematical tools to discover the relationship between variables Dependent
The research identifies 45 variables that serve as indicators of a bank's profitability, with independent variables including bank-specific factors such as size, capital adequacy, loan ratio, deposit ratio, and bad debt expenses, alongside macroeconomic factors like the inflation rate and re-discount interest rate The hypotheses regarding the determinants of bank profitability are formulated accordingly.
H1: The size of bank is negatively related to the profitability
H2: The capital adequacy is positively related to the profitability
H3: The loan ratio is positively related to the profitability
H4: The deposit ratio is positively related to the profitability
H5: The bad debt rate is negatively related to the profitability
H6: The inflation rate is negatively related to the profitability
H7: The re-discount interest rate is negatively related to the profitability
Descriptive statistics, Pearson correlation and regression are three techniques used in this research Their results are as following
In descriptive statistics, it is concluded that total assets and ROE are two disperse variables while other variables are concentrated
In Pearson correlation, some noticeable relationships are concluded They are:
Strong positive relationship between ROA and CAP;
Moderate positive relationships between ROE and SIZE, ROE and CAP, ROE and INF, CAP and NIM; and
Moderate negative relationships between ROE and DEP, ROE and BD, ROE and RDI, ROA and DEP, and NIM and DEP
The conclusion about the connection between selected independent variables and the profitability will be recognized if the independent variable has at least two
46 considerable connections with the profitability variables Therefore, the conclusion about bank profitability’s determinants are:
CAP is the determinant with positive influence This result is same as the conclusion of Yılmaz (2013), Menicucci & Paolucci (2016), and Obamuyi
DEP is determinants with negative influences This result is different from conclusion of Menicucci & Paolucci (2016)’s research, which found that DEP has positive impacts on bank profitability
The reject and acceptance of pre-provided hypotheses are:
In regression method, three models are constructed as below:
The predictors of model 1 could predict 72.4% the dependent variable, which is ROA Whereas, it is 49.8% in model 2 and 51.3% in the model 3.
Limitations and recommendations for the future researches
This study has several limitations, including its focus on only two groups of potential determinants: bank-specific and macroeconomic factors, while excluding industry-specific indicators Additionally, the research is limited to a five-year period from 2015 to 2019, which may restrict the applicability of its findings to other economic contexts Furthermore, the analysis is confined to listed banks, potentially compromising the representativeness of the results for the unlisted banking sector.
Regardless of above limitations, this paper could be used as a useful reference for future researches with the similar topics
Future researchers may find this paper a valuable reference for broadening their research scope to include unlisted banks or extending the study period Additionally, examining countries with similar economic conditions, such as Thailand, Malaysia, or the Philippines, could provide insightful comparisons.
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