INTRODUCTION
THE IMPORTANCE OF THE TOPIC
The research findings will offer essential insights for current shareholders and prospective investors, enhancing their understanding of the factors influencing bank profitability and serving as a foundation for informed investment decisions in bank stocks.
Research findings serve as a valuable resource for administrators of joint-stock commercial banks, aiding managers in decision-making, risk mitigation, and the formulation of effective development policies These insights are grounded in the understanding of both the positive and negative impacts of various factors on profitability.
The research findings provide a scientific foundation for the Government and the State Bank of Vietnam to evaluate the factors influencing the profitability of joint stock commercial banks This enables the formulation of timely and rational macro policies aimed at fostering a sustainable banking system, ensuring healthy and effective operations, and promoting overall economic development.
MOTIVATION
In the context of globalization and integration, our country is undergoing significant transformation, with its economy increasingly aligned with market mechanisms amid substantial regulatory challenges For commercial banks to thrive, enhancing profitability has become paramount, as it serves as a key indicator of operational efficiency Profitability not only provides financial resources for production expansion but also plays a crucial role in meeting government obligations, boosting national income, and fostering employee commitment to their work.
Analyzing and measuring profitability is crucial for assessing the performance of commercial banks in Vietnam, especially in the context of regional and global economic integration As key players in the national economy, most commercial banks are profitable, yet they face the challenge of enhancing their operations Profitability analysis serves as an effective economic management tool, enabling bank managers to optimize their activities This study aims to evaluate the factors influencing the profitability of Vietnamese commercial banks and provide recommendations for regulators to improve their performance.
The determinants of banking profitability is a compelling topic that attracts numerous researchers; however, studies focusing on the Vietnam stock market are limited compared to more established markets like the London and New York Stock Exchanges, as Vietnam remains an emerging market in a developing nation This article presents a quantitative analysis of the factors influencing the profitability of Vietnam's listed commercial banks on the stock exchange, aiming to enhance knowledge and provide valuable insights for future research.
STEP PROGRESS AND OBJECTIVES
This study aims to identify the key factors influencing the profitability of commercial banks in Vietnam It analyzes data from the financial statements of over 20 Vietnamese commercial banks from 2012 to 2019 The research offers recommendations to enhance the operational efficiency of these banks, ultimately improving their profitability.
This study begins by exploring the theoretical foundations of profitability and the factors influencing it in commercial banks It then analyzes the current profitability landscape of Vietnamese commercial banks, assessing the impact of various determinants on their financial performance The ultimate goal is to provide actionable recommendations for bank managers to enhance operational efficiency Finally, the dissertation concludes with proposed solutions aimed at improving profitability within the Vietnamese commercial banking sector.
This study aims to guide investors in selecting the most promising commercial banks listed in Vietnam for profitability By considering company information alongside the macroeconomic landscape, businesses can formulate effective policies and strategies crucial for their survival and success Understanding the influence of macroeconomic factors will also assist policymakers in developing monetary and financial strategies Ultimately, this research identifies the key determinants of banking profitability in the Vietnamese stock market and serves as a valuable reference for investors assessing share price fluctuations before making investment decisions.
OBJECT AND SCALE OF RESEARCH
This research examines the factors influencing profitability in Vietnamese commercial banks, focusing on 24 institutions with varying asset sizes from 2012 to 2019 The analysis utilizes audited financial statements from over 20 listed banks during this period Additionally, external variables are derived from macroeconomic data sourced from the World Bank, providing a comprehensive overview of the elements affecting bank profitability in Vietnam.
RESEARCH APPROACHES
We use qualitative and quantitative methods:
- Qualitative method: by the table of data, we analyze the situation of profitability and influencing factors drawn from previous research documents
- Quantitative method: using the regression estimation method with table data to analyze internal and external factors affecting profitability at Vietnamese commercial
Descriptive statistical analysis involves calculating mean values, standard deviations, and identifying maximum and minimum values for each study variable This process is essential for summarizing the fundamental characteristics of data gathered from empirical research, offering a comprehensive overview of the sample.
Correlation analysis to examine the relationship between independent and dependent variables
Regression analysis is a statistical method used to evaluate the significant or insignificant effects of independent variables on a dependent variable This technique helps to determine the direction and strength of the impact that each independent variable has on the dependent variable.
The results of the model were tested and compared to find the most suitable model in studying the factors affecting profitability at joint stock commercial banks in Vietnam.
STRUCTURE OF DISSERTATION
The thesis consists of 5 chapters:
- Chapter III: Data and research methodology
- Chapter IV: Results from empirical analysis of banking profitability in Vietnamese listed commercial banks
- Chapter V: Conclusion and solutions to improve profitability at Vietnamese commercial banks
Profitability is a key indicator of a bank's performance and is essential for sustainable competition and growth Enhancing profitability must be closely aligned with ensuring safety in banking operations.
Empirical studies investigate the factors influencing the profitability of commercial banks across different countries, categorizing these factors into external influences, such as economic growth and inflation, and internal elements, including capital ownership, bank size, customer deposits, credit balances, provisions for credit losses, operating expenses, and ownership structure This research enhances our understanding of how these various factors relate to the profitability of commercial banks.
The impact of influencing factors and the nature of connections varies across countries and specific study periods To conduct a thorough analysis and provide accurate recommendations, it is essential to understand the fundamental concepts related to bank profitability and its determinants, both internal and external This discussion will be elaborated on by the author in the following chapter.
LITERATURE REVIEW
LITERATURE REVIEW
Most empirical studies around the world use linear regression models to study the factors affecting the profitability of commercial banks, namely:
A study conducted by Muhammad Bilal, Asif Saeed, Ammar Ali Gull, and Toquer Akram (2013) analyzed the impact of internal and macroeconomic factors on the profitability of 25 commercial banks in Pakistan from 2007 to 2011 The research utilized the ratios of income to total assets (ROA) and income to equity (ROE) as dependent variables, while independent variables included bank size, equity, bad debt, customer deposits, and net interest income ratio for internal factors, and inflation, industrial production growth rate, and real GDP growth rate for macroeconomic factors Findings revealed a positive correlation between bank size and profitability, while bad debt showed no significant correlation with ROA and a negative correlation with ROE Customer deposits had a positive but insignificant correlation with both ROA and ROE, and equity significantly influenced ROE but had a negligible effect on ROA The net interest income ratio was significantly positively correlated with bank profitability, and macroeconomic factors like GDP growth positively impacted profitability, whereas inflation had a negligible positive correlation with ROE and a strong negative correlation with ROA.
Nesrine Ayadi and Younès Boujelbene (2012) conducted a study using a regression model to analyze the factors influencing the profitability of Tunisian Deposit Banks, measured by return on assets (ROA) The research identified intrinsic bank characteristics such as liquidity risk, credit risk, equity, and bank size as independent variables Additionally, financial structure variables included concentration, total bank assets relative to GDP, and market capitalization metrics Macroeconomic factors considered were the real GDP growth rate and inflation rate The study analyzed data from 12 Tunisian deposit banks over the period from 1995 to 2005.
Research indicates a positive correlation between bank size and equity with profitability, while credit risk also shows a positive relationship Conversely, liquidity risk does not significantly affect return on assets (ROA) Furthermore, the ratio of total assets to gross national product is negatively correlated with bank profitability, whereas concentration positively influences profitability Notably, market capitalization relative to total assets and GDP is significantly negatively correlated with bank profitability Lastly, both the real GDP growth rate and inflation rate exhibit an inverse negative correlation with bank profitability.
The linear regression model was also used by Fadzlan Sufian and Royfaizal Razali Chong
A study conducted in 2008 examined the factors influencing bank profitability in the Philippines, focusing on return on assets (ROA) and return on equity (ROE) as measures of profitability The research identified key intrinsic bank characteristics, such as bank size, bad debt levels, book value of equity, non-interest income relative to total assets, and management costs compared to total assets Additionally, macroeconomic factors like GDP growth rate, money supply rate, inflation rate, and market capitalization were analyzed The study utilized data from 24 commercial banks in the Philippines covering the period from 1990 to 2005.
Research indicates that a bank's size, bad debt levels, and total management expenses relative to total assets negatively impact its profitability Conversely, non-interest income as a proportion of total assets shows a strong positive correlation with profitability Additionally, macroeconomic factors, including the real GDP growth rate, money supply rate, and market capitalization, also positively influence bank profitability, with the exception of inflation.
In their 2011 study, Sehrish Gul, Faiza Irshad, and Khalid Zaman employed the Pooled OLS regression model to analyze the factors influencing bank profitability in Pakistan They assessed profitability through four key metrics: the ratio of income to total assets (ROA), the ratio of income to equity (ROE), the net interest income margin (NIM), and the ratio of income to capital employed (ROCE) The study identified independent variables related to the banks' intrinsic characteristics, including bank size, equity, customer loans, and customer deposits, alongside macroeconomic factors such as the actual growth rate of GDP, inflation, and market capitalization The research focused on data from the top 15 commercial banks in Pakistan during the period from 2005 to 2009.
Research indicates that bank size, customer loans, and customer deposits positively correlate with ROA and ROE, while they negatively correlate with ROCE and NIM Conversely, equity shows an inverse correlation with all four performance indicators: ROA, ROE, ROCE, and NIM Additionally, real GDP growth positively influences ROA, ROE, and ROCE but negatively affects NIM Inflation demonstrates a positive correlation with ROA, ROE, ROCE, and NIM, whereas market capitalization negatively correlates with ROA, ROE, and ROCE, but positively correlates with NIM.
In addition to the regression model, various researchers globally have employed Fixed Effects and Random Effects models to analyze the factors influencing bank profitability.
In their 2005 study, Panayiotis Athanasoglou, Sophocles Brissimis, and Matthaios Delis examined the financial, industry, and macroeconomic factors influencing the profitability of Greek banks from 1985 to 2001 They measured bank profitability using the return on assets (ROA) and return on equity (ROE) ratios The independent variables were categorized into three groups: financial indicators specific to each bank, including bank capital (EA), credit risk (PL), labor productivity (PR), operating cost management (EXP), and bank size (S); industry indicators, which encompassed ownership and concentration; and macroeconomic indicators, notably inflation (IR) and cyclical output.
Research indicates a significant positive correlation between bank capital, productivity growth, and profitability, while credit risk and operating cost management negatively impact profitability Interestingly, bank size does not significantly influence profitability, and factors such as ownership variables and industry concentration have minimal effects Additionally, macroeconomic indicators, including inflation and production cycles, play a crucial role in shaping the profitability of the banking sector.
Samy Ben Naceur (2003) conducted a study on the factors influencing the profitability of Tunisian banks from 1980 to 2000, utilizing Fixed Effects and Random Effects models The research analyzed data from ten commercial banks during this period, focusing on the dependent variables of return on assets (ROA) and net interest margin (NIM) Key independent variables reflecting the banks' internal characteristics included bank size (LNSIZE), equity (CAP), customer loans (BLOAN), and total management expenses relative to total assets (OVERHEAD) Additionally, macroeconomic factors examined in the study comprised the growth rate of gross domestic product (GROWTH), inflation (INF), market concentration (CONC), market capitalization relative to GDP (MCAP), and market capitalization relative to total assets (RSIZE).
Research indicates a positive correlation between a bank's profitability and factors such as equity, customer loans, and total management expenses relative to total assets Conversely, the size of the bank shows a negative correlation with profitability Additionally, macroeconomic variables, including market capitalization relative to GDP and total assets, also positively influence bank profitability Furthermore, market concentration plays a significant role in this relationship.
A study by Mamatzakis and Remoundos (2003) investigates the factors influencing the profitability of commercial banks in Greece The research indicates that the ratio of loans to total assets, equity ownership relative to total assets, the Athenian stock index, and market structure positively impact bank profits Conversely, the cost of human resources as a proportion of total assets negatively affects the profitability of these banks.
Research by Anna P.I Vong and Hoi Si Chao (2008) highlights the significant impact of a bank's capital potential on its profitability in Macao, indicating that higher capital levels are associated with lower risk and increased profits Conversely, asset quality has a detrimental effect on bank profitability Interestingly, banks with extensive deposit networks do not necessarily experience greater returns compared to those with smaller networks Additionally, among macroeconomic factors, only the inflation rate demonstrates a significant correlation with bank profitability.
A study by Antonina (2010) revealed that in Ukraine, factors such as the capital coefficient, GDP, inflation, and exchange rates positively influence bank profitability Conversely, administration expenses relative to total assets, liquidity, and deposits negatively impact profits Notably, foreign ownership significantly detracts from the profits of Ukrainian banks, despite the perceived efficiency and expertise of foreign institutions This unexpected outcome suggests that local banks may enhance their profitability through means beyond mere technical efficiency.
AN OVERVIEW OF BANKING PROFITABILITY
Profitability is defined as an international investment's capacity to boost business efficiency and enhance profits (Harward and Upton, 1961) It reflects how effectively resources are managed to generate profit (Amico, Vita, and Pappalardo, 2011) However, a bank exhibiting high profitability may not be inherently favorable, as achieving such profitability could involve accepting a high-risk asset structure.
Profitability is essential for the survival and success of any business, making it crucial to assess historical, current, and future profitability metrics Understanding these financial indicators helps businesses navigate challenges and ensure long-term viability.
Bank profitability stems from effectively utilizing both physical and financial assets, reflecting the economic capital maintained by the institution It represents the bank's capacity to generate profit across all business operations while considering associated risks.
Profitability in banking is assessed through various metrics, including return on assets (ROA) and return on equity (ROE) To enhance profitability, banks should focus on diversifying income streams, optimizing operating costs, and implementing effective risk management strategies These measures are essential for ensuring the safety and stability of individual banks and the overall banking system.
Joint stock commercial banks, like any business, aim to maximize profitability and growth, which are crucial indicators of their performance and future direction Profitability not only reflects the bank's operational success but also serves as a foundation for making informed business decisions.
A highly profitable bank generates diversified and substantial capital, forming the foundation for developing profitable assets Additionally, enhancing profitability is essential for State-Owned Commercial Banks (SOCBs) to maintain their capital, enabling them to grow their lending market and invest in technological innovations to attract more customers.
There exists a trade-off between profitability and risk in commercial banking, where increased profitability often entails higher risk Consequently, bank managers must continuously balance these trade-offs while evaluating the profitability and gain ability ratios of their institutions.
Banks play a crucial role in the economy, as their profitability serves as both motivation and economic leverage for society When banks operate efficiently, they promote financial stability and growth, leading to a healthier financial sector This, in turn, contributes to monetary stability, helps curb inflation, and stimulates economic growth.
In a globally competitive landscape, enhancing the profitability of individual banks is essential for fostering sustainable growth within the banking system, which in turn supports national economic development and bolsters the reputation of national credit.
2.2.3 Factors defining the banking profitability
To measure profitability, banks need to consider the level of profitability, the ability to offset costs for losses occurring Bank's profitability is usually measured by the following criteria:
Return on Assets (ROA) is a crucial financial ratio that assesses the profitability of a bank's assets, highlighting the relationship between the bank's profitability and its asset management This key indicator is essential for evaluating a bank's performance, as it reflects how effectively the bank utilizes its assets to generate profits.
The ROA ratio is calculated by dividing the net profit by the average total assets of each period:
The profit-to-assets ratio, as defined by Phan Duc Dung (2008), is a key financial metric that assesses an enterprise's profitability relative to its assets This ratio is determined by dividing the net profit or profit after tax for a specific reporting period, sourced from the income statement, by the total asset value obtained from the balance sheet.
Return on equity measures the return on capital of ordinary shareholders (Sehrish Gul, Faiza Irshad and Khalid Zaman, 2011)
The index effectively gauges an individual's spending and savings habits Return on equity (ROE), defined as the ratio of net income to total equity, serves as a crucial financial metric (Fraker, G.T., 2006) ROE is calculated using a specific formula that reflects a company's profitability relative to shareholder equity.
According to Nguyen Thi Ngoc Trang and Nguyen Thi Lien Hoa (2007), a positive ratio indicates enterprise profitability, with a higher ratio reflecting greater business effectiveness Conversely, a negative ratio signifies unprofitability This profit or loss is expressed as a percentage of the total assets' value, highlighting the management and utilization of assets to generate income The profit-to-assets ratio varies by business season and industry, making it essential for corporate finance analysts to compare this ratio among businesses within the same industry and for the same time period.
The net interest margin (NIM) is a key indicator of a bank's profitability, reflecting its ability to generate income from its banking operations based on profitable assets (Ben Naceur, S Goaied, M., 2008).
NIM is calculated using the following formula:
Interest income for banks is derived from lending activities and investments in securities, while interest expenses encompass the costs associated with deposits and other liabilities Profitable assets include customer loans, investments, interbank loans, and deposits held at the central bank, all contributing to the bank's overall profitability.
Higher NIM is an important sign that the bank is succeeding in managing assets and debt Conversely, a low NIM would indicate that the bank is having difficulty making a profit.
DETERMINANTS OF BANKING PROFITABILITY
The profitability of a bank is influenced by both internal and external factors Internal factors include the bank's management decisions and policies, which encompass aspects such as bank size, equity, customer deposits, customer loans, liquidity, bad debt, and credit risk.
The profitability of banks is significantly influenced by external factors, including inflation rates, real annual GDP growth, market capitalization of assets, and real interest rates.
Equity represents the capital that a bank requires to function, consisting of funds contributed by the owner as well as retained earnings generated through its business operations.
Numerous studies, including those by Bashir (2000), Abreu and Mendes (2002), Kosmidou (2005), and Alper and Anbar (2011), have identified the equity ratio as a key independent variable in analyzing factors influencing bank profitability Their findings consistently indicate a positive correlation between capital levels and the profitability of banks.
Equity is an important factor determining the existence and development of a bank This indicator helps assess the solvency of the bank in case the bank suffers a loss b Deposit – DP
The deposit ratio, representing the proportion of customer deposits to total assets, serves as a key indicator of a bank's efficiency According to Vong and Chan (2009), customer deposits are a primary and cost-effective source of financing for banks, positively impacting operations when there is sufficient demand for loans However, in scenarios where borrowing demand is low, an increase in deposits can lead to reduced income, making deposits costly, especially when banks must expand their networks to attract more funds.
Top-performing banks enhance labor and capital productivity while maintaining a strong ratio of deposit accounts to assets By increasing the total deposits relative to total assets, these banks boost their available capital, enabling profitable investments and lending opportunities.
Banks with a high ratio of time and savings deposits may face increased financing costs, potentially leading to reduced profitability, which contrasts with the positive effect hypothesis.
Lending plays a crucial role in banking, representing a significant portion of a bank's total assets and serving as the primary source of profit through interest income However, this sector also poses substantial risks, as the growth in loans often outpaces the implementation of quality control measures.
Research has shown mixed results regarding the relationship between customer loan rates and bank profitability Athanasoglou et al (2006) and Gul & Zaman (2011) identified a positive correlation, indicating that higher customer loan rates can enhance bank profitability Conversely, Alper and Anbar (2011) found a negative correlation, suggesting that an increased ratio of customer loans may adversely affect bank profitability Additionally, the provision for credit risk, specifically Loan Loss Provisions (LLP), plays a crucial role in this dynamic.
The impact of asset quality on profitability is reflected in the provisions for credit losses on total outstanding loans, serving as an indicator of both capital risk and credit quality This metric assesses bank managers' capability to evaluate credit risk effectively In risky operating environments, banks that lack the necessary expertise in managing lending practices may face increased provisioning rates for credit losses.
A higher ratio comes with a lower credit quality and lower profitability Some previous studies showed a negative relationship with profitability
A study by Vong and Chan (2009) examined the influence of bank characteristics and external factors on bank profitability in Macao from 1993 to 2007 The findings indicated that asset quality, particularly credit risk, significantly negatively affects bank profitability The authors noted that although increased lending operations can enhance profitability, the low credit quality of loan portfolios in Macao necessitates a substantial provision for credit risk.
According to the new Basel III banking regulations, setting interest rates based on associated risks can lead to higher profit margins from riskier loans, positively impacting bank profitability However, improved loan quality often necessitates increased resources for credit evaluation and monitoring, which can elevate operational costs This presents a contrasting hypothesis: credit risk may positively influence a bank's profitability, suggesting a favorable relationship between risk and return under the "risk-return" hypothesis.
Salaries and personnel costs are the primary operating expenses for most banks, as highlighted by Peter Rose (2002) These expenses have surged due to competitive pressures Additionally, operating costs encompass depreciation expenses, housing and banking equipment, legal fees, and other essential documentation.
The bank's operating cost ratio, which compares operating expenses to total profits—excluding bad debt losses—serves as a key indicator of cost management efficiency This metric reflects how effectively a bank controls its costs, with research indicating that a higher cost-to-return ratio negatively affects profitability (Dietrich and Wanzenried, 2009).
Reducing operating costs typically leads to increased efficiency, while higher costs often correlate with decreased benefits Numerous studies have established this inverse relationship between cost and benefit.
DATA AND RESEARCH METHODLOGY
RESEARCH MODEL BRIEF INTRODUCTION
The research data encompasses the year-end consolidated financial statements of 24 commercial banks in Vietnam from 2012 to 2019 These financial statements were sourced directly from the banks' websites The dataset is characterized as unbalanced due to the emergence of new banks, mergers, and incomplete financial disclosures from some institutions during this period Additionally, macroeconomic data was obtained from the World Bank's website to complement the analysis.
VARIABLES DEFINITIONS AND MEASURING
This research paper evaluates bank profitability using key dependent variables: return on total assets (ROA), return on equity (ROE), and net interest margin (NIM) ROA assesses the profitability of a bank's assets, while ROE reflects the earnings generated for ordinary shareholders relative to their equity NIM indicates the difference between interest income and interest expenses, highlighting a bank's efficiency in managing profitable assets and minimizing funding costs These metrics are widely recognized as essential criteria in global research on bank profitability.
The Return on Assets (ROA) is a key performance metric that evaluates how effectively a bank manages its assets to generate profits A higher ROA indicates greater efficiency in asset utilization and superior management performance, while a lower ROA signifies ineffective asset use Thus, ROA serves as a crucial indicator of a bank's profitability and operational effectiveness.
Numerous studies have utilized Return on Assets (ROA) as a key indicator of profitability, as it effectively assesses the efficiency of bank assets Notable research by Vong and Chan (2009), Bennaceur and Goaied (2008), and Nguyen Cong Tam (2012) supports the selection of ROA as a measure of bank profitability in this study.
Return on total assets (ROA) is determined by dividing net income after tax by average total assets, with after-tax profit reflecting the bank's performance over a specific period and average total assets derived from the balance sheet Since total assets are recorded at a specific point in time, they may not accurately represent the bank's financial health throughout the period Thus, using the average total assets in ROA calculations allows for a more precise analysis of profitability.
Return on Equity (ROE) is a crucial financial metric that measures the profitability of banks by indicating how effectively they generate returns from shareholders' equity investments It assesses how well banks utilize shareholder funds, making it an essential indicator of financial performance According to Dietrich and Wanzenried (2011) and Pham Thi Hang Nga (2011), ROE serves as a key measure of bank profitability In this study, the author employs ROE as a dependent variable to evaluate profitability, highlighting its importance in reflecting the efficient management of shareholder capital.
The return on equity (ROE) ratio is determined by dividing after-tax profit by the average total equity Like the return on assets (ROA) calculation, ROE utilizes average equity, with figures sourced from the bank's income statement and balance sheet Additionally, the net interest margin (NIM) is an important metric in assessing financial performance.
Net Interest Margin (NIM) is defined as net interest income divided by total earning assets, serving as a key indicator of the profitability of banks and financial institutions It reflects the difference between the interest income generated and the interest paid to lenders, relative to total assets As a fundamental metric for assessing banks' returns on loans, NIM plays a crucial role in profitability studies Research by Bennaceur and Goaied (2008) has highlighted NIM as a significant indicator of bank profitability, prompting its selection as a dependent variable in this study.
The net interest margin is calculated by dividing net interest income by total average earning assets, where net interest income is derived from interest income minus interest expenses This metric reflects the bank's core income sources, represented by profitable assets, which are determined by summing earning assets or subtracting non-profitable assets from total assets The denominator for this calculation is the average of the beginning and end balances from the bank's balance sheet.
3.2.2 Independent variables and hypothesis a Capital (CA)
Equity capital, initially contributed by the bank's owner and augmented through business operations, serves as a vital buffer against bankruptcy risk, fostering public confidence and assuring creditors of the bank's financial stability A larger equity base reduces reliance on external loans and lowers capital costs, ultimately enhancing profitability Research by Vong and Chan (2009) indicates that a higher equity ratio correlates with safer banking practices and provides a competitive edge for achieving greater profits.
The bank's equity ratio, a key variable in studies on bank profitability, is determined by the ratio of equity to total assets, as reported in the balance sheet.
The variable representing equity is used as the ratio of equity to total assets and is calculated as follows:
Previous research, including studies by Bashir (2000), Abreu and Mendes (2002), Kosmidou (2005), and Alper and Anbar (2011), indicates that the equity ratio significantly influences bank profitability These findings demonstrate a positive correlation between capital and a bank's profitability, leading to the formulation of the following hypothesis.
H 1 : Equity ratio has a positive impact (+) on the bank's profitability b Deposits (DP)
Customer deposits serve as the primary source of capital for commercial banks, significantly influencing their overall business capital A higher deposit size increases the likelihood of utilizing the bank's capital effectively Furthermore, a greater ratio of deposits to assets enhances the bank's capacity to fund credit operations, ultimately boosting profitability.
The deposit ratio is a key indicator of a bank's capital effectiveness, as banks primarily rely on public deposits to fund loans Deposits serve as the most affordable source of funding, positively influencing profitability during periods of high loan demand Conversely, when loan demand is low, an excess of deposits can diminish a bank's income due to elevated deposit rates and extended terms Thus, while a high deposit ratio can enhance profitability in favorable lending conditions, it can have the opposite effect when loan demand is weak.
Funding efficiency is measured by the ratio of deposits to total assets Deposits and total assets are collected on a bank's balance sheet
The variable representing deposits is used as the ratio of deposits and is calculated as follows:
On the basis of the previous research results of Vong and Chan (2009), the author proposes the following hypothesis:
The deposit ratio significantly influences a bank's profitability; it positively affects earnings during periods of strong loan demand, while a low loan demand leads to a negative impact on profitability.
Lending is a profitable activity for banks but also carries significant risks When managed effectively, it can cover capital mobilization costs and generate profits; however, poor lending practices can jeopardize liquidity and the overall safety of the banking system The effectiveness of lending is often assessed using the ratio of outstanding loans to total assets.
Asset composition is estimated by total loans divided by total assets Calculated data are taken in the balance sheets of banks
The formula for determining customer loans is as follows:
Asset composition is estimated by total loans to total assets Providing loans is the main means of income generation for commercial banks (Athanasoglou, Burki, Niazi, G.S.K.,,
DESCRIPTIVE STATISTICS OF QUANTIVIVE VARIABLES
Data for this analysis was gathered from the financial statements of 24 banks, sourced from their official websites and select securities companies, covering the period from 2012 to 2019, resulting in a total of 190 observations Additionally, macroeconomic data was obtained from reports published by the General Statistics Office.
The following table shows descriptive statistics for the minimum and maximum values as well as the mean and standard deviations of these variables
Table 3: Descriptive statistics of quantitative variables
2 The results regarding ownership structure of Dietrich and Wanzenried (2011) find empirical evidence that private owned banks are more profitable than state-owned banks in Switzerland.
3 the findings of Bennaceur and Goaied (2008) which also indicate that government owned banks exhibit a lower return variables DP 0.7527 0.8960 0.2940 0.0896 190
From above summary table, we have following discussion:
• ROA, ROE: The average value of ROA and ROE is 0.79% and 9.05%, respectively
The standard deviation of Return on Assets (ROA) is 0.0065, while Return on Equity (ROE) stands at 0.06890, both of which are considered acceptable Notably, the National Citizen Commercial Joint Stock Bank reported the lowest ROA and ROE at 0.01% and 0.07%, respectively, in 2013—a challenging year for banks marked by low overall profitability This bank's significantly lower profit compared to its peers during the research period, coupled with relatively stable total assets and equity, resulted in its diminished ROA and ROE.
The average Net Interest Margin (NIM) stands at 3.36%, with an acceptable standard deviation of 0.0226, highlighting significant variability among banks The NIM ranges from a maximum of 28.90% to a minimum of 0.99%, showcasing the diverse performance across the sector Notably, Southeast Asia Commercial Bank (SeABank) experienced the lowest marginal net interest income in 2015, attributed to a rise in bad debts and a sharp decline in interest income, which severely impacted its core business operations.
The equity to total assets ratio for banks averages 9.89%, with a standard deviation of 0.0633, indicating a stable financial environment Notably, An Binh Commercial Joint Stock Bank (ABBank) recorded the highest capital value at 47.94% in 2011, while the Vietnam Joint Stock Commercial Bank for Investment and Development (BIDV) had the lowest at 4.06% in 2018 This disparity arises from varying levels of financial leverage among banks, with some institutions maintaining significantly higher debt relative to their equity.
The deposit-to-total-assets ratio (DP) among banks remains relatively consistent, with an average of 75.27% and a standard deviation of 0.0896 In contrast, the lending-to-total-assets ratio (LN) exhibits significant variability, averaging 54.20%, with a maximum of 79.75% and a minimum of 8.47%, accompanied by a standard deviation of 0.1451.
The average loan loss provision (LLP) ratio among banks is 1.41% of the total outstanding loan balance, indicating a consistent provisioning level The maximum recorded ratio is 3.89%, while the minimum stands at 0.66%, with a standard deviation of 0.0055, reflecting a relatively uniform index across the banking sector.
The operating cost to total income ratio (CI) has a mean of 93.14% and a standard deviation of 0.3605, indicating a notable range with a maximum of 185.48% and a minimum of 30.62% Recent trends show a gradual decrease in this ratio, reflecting an improving ability of banks to manage their operating costs effectively.
The average logarithm of bank sizes is 18.4916, with a standard deviation of 1.1169 The largest bank size recorded is 20.9956, while the smallest is 16.3976 Additionally, there is a noticeable trend of increasing bank sizes year after year.
The Gross Domestic Product (GDP) remains stable with an average value of 6.13% and a standard deviation of 0.0066 In contrast, the inflation rate (INF) averages 5.87% with a standard deviation of 0.0486 Notably, in 2016, the inflation rate increased by 0.63%, marking the lowest inflation rate observed in several years.
• OWN: The thesis introduces statistics of ownership form as follows:
Table 4: Statistics on form of ownership
Based on the table above, we can see that most private owned banks with 83.68%, the rest
RESEARCH MODEL
This study employs a Panel data model that incorporates both temporal and spatial observable characteristics, offering numerous advantages for economic research The prevalent regression methods utilized with this tabular data include the Pool model, fixed effects regression (FEM), and random effects regression (REM) models.
The Ordinary Least Squares (OLS) method is often inadequate for Pool regression models due to incorrect estimation results and frequent autocorrelation in the data This misalignment can lead to a misunderstanding of the relationship between the dependent and independent variables As a solution, researchers commonly utilize Fixed Effects Regression (FEM) and Random Effects Regression (REM) models The FEM model focuses on differences in group intercepts while maintaining constant slopes and errors, whereas the REM model examines variance and error components, assuming constant intercepts with uniform slopes To determine the most appropriate model, the study will consider essential factors and employ the Hausman test to select between FEM and REM for implementation.
Previous studies on bank profitability typically categorize impact factors into internal and external variables This research will utilize the model proposed by the authors in the theoretical framework to identify relevant research variables while excluding certain factors to better align with Vietnam's economic conditions.
The profitability of banks is evaluated using key dependent variables such as return on total assets (ROA), return on equity (ROE), and net marginal interest income (NIM) To assess these factors, independent variables are also considered, including bank size (SIZE), which indicates economic efficiency, and the capital ratio (CA), representing equity strength through equity per total assets Additionally, the deposits per total assets ratio is analyzed to gauge the impact of deposits on bank performance.
The efficiency of funds in banks is assessed through various metrics, including deposit performance (DP), which evaluates lending against total assets, while loan (LN) reflects asset composition Additionally, credit risk reserves to total outstanding balance (LLP) indicate asset quality To measure cost management efficiency, the operating cost on income (CI) is analyzed, alongside the ownership structure (OWN) to differentiate between state-owned and private banks Furthermore, external factors such as economic growth, represented by GDP growth, and inflation, measured by the annual Consumer Price Index (CPI), are also considered in the proposed model.
To effectively assess the influence of independent variables on bank profitability, this study employs two distinct analytical models The first model incorporates key internal bank variables, including CA, DP, LN, LLP, and CI, alongside a dummy variable representing bank ownership (OWN) The second model introduces macroeconomic variables, GDP and INF, to analyze external factors impacting bank profitability, while excluding dummy variables to prevent multicollinearity The analysis is grounded in the econometric theory for tabular data as outlined by Mai Van Nam (2005).
𝑌1, 𝑌2 are the dependent variable of case 1, selected case 2 are ROA, ROE, NIM respectively
𝛼 is the constant of the model
𝜇 is remainder of the regression equation (representing errors and variables not appearing in the model) regression coefficient
This article examines the various internal and macroeconomic factors influencing the profitability of 24 banks, specifically focusing on the differences between state-owned commercial banks and joint stock commercial banks By analyzing different ownership models, the research aims to highlight how these factors uniquely impact the financial performance of each type of bank.
METHODS OF DATA COLLECTION AND DATA PROCESSING
Data were manually gathered from the annual and consolidated reports of 24 commercial banks and subsequently entered into Excel for calculations Specialized statistical analysis software, Eviews, was utilized to perform descriptive statistics, correlation analysis, and regression modeling on the dataset.
Data processing methods encompass descriptive statistics of observed variables, correlation analysis between variables, and the evaluation of regression model suitability Key tests include the Hausman test for model selection, assessments for multicollinearity, variable error variance, and the detection of unnecessary variables and self-correlation The subsequent sections will provide a detailed overview of the data survey process and the performance tests conducted.
The article presents data in a statistical table format, detailing each variable with key metrics including the variable name, mean value, minimum and maximum values, standard deviation, and the number of observations Additionally, it explores the correlation between variable pairs to analyze their relationships effectively.
To address multicollinearity in regression analysis, it is essential to establish a correlation coefficient matrix and examine the correlation pairs between variables High correlation coefficients, particularly those exceeding 0.8, indicate potential multicollinearity issues, as noted by Hoang Ngoc Nham (2008) Consequently, the study emphasizes the importance of analyzing the correlation matrix to identify and exclude variables that contribute to multicollinearity, ensuring a more robust regression model Additionally, the Hausman test is employed to further validate the model's reliability.
When deciding between the random effects model and the fixed effects model, it is essential to conduct a hypothesis test to determine the most suitable approach This test evaluates the appropriateness of each model, guiding researchers in selecting the one that best fits their data analysis needs.
H 0 : U i 4 and the independent variable are not correlated (Select REM 5 model for analysis)
H 1 : U i and the independent variable are correlated (Select FEM 6 model for analysis)
When the value of Prob > chi 2 is less than 0.05, we reject the null hypothesis (H0), indicating a correlation between Ui and the independent variable, which justifies the use of a fixed effects model Conversely, if Prob > chi 2 exceeds 0.05, we accept H0, suggesting no correlation between Ui and the independent variable, and thus a random effects model should be employed It is essential to evaluate the suitability of the regression model in both scenarios.
To assess the suitability of a research regression model, it is essential to determine if the estimated coefficients align with theoretical expectations and findings from prior studies Additionally, the statistical significance of these coefficients must be evaluated Furthermore, conducting model defect tests is crucial for a comprehensive analysis.
The correlation matrix of coefficients is utilized to assess multicollinearity within regression models Additionally, the study employs the Variance Inflation Factor (VIF) to evaluate this phenomenon further.
This study will examine the autocorrelation phenomenon by utilizing the Durbin-Watson statistic values found in the regression results table, along with the testing methodology outlined by Hoang Ngoc Nham (2008), to determine the presence of self-correlation in the regression model.
The study employs the Wald test to assess the significance of variables within the model It evaluates whether the regression coefficients of the non-zero variables hold meaningful interpretations.
Finally, for the variable variance of variance, use the White test to generalize the consistency of the variance
This chapter explores the factors influencing the profitability of commercial joint stock banks in Vietnam, categorizing them into internal and external elements The author develops a regression model tailored to the research topic and the collected data Subsequent calculations and discussions based on these models will be presented in the following chapter.
RESULTS OF EMPIRICAL ANALYSIS OF BANKING
CORRELATION ANALYSIS BETWEEN VARIABLES
The table illustrates the strength of linear relationships between pairs of variables in the observations, while also assessing the potential for multicollinearity between two independent variables.
Table 5: Correlation matrix between variables
ROA ROE NIM CA DP LN LLP CI SIZE OWN GDP INF
NIM 0.36* 0.37 * 1 *: statistics at the significance level 5%
The correlation matrix reveals that most correlations are statistically significant, exhibiting medium to low levels Additionally, the correlation coefficients among the variables do not exceed 0.8, indicating a low likelihood of multicollinearity in the regression model.
REGRESSION RESULTS
H 0 : U i and the independent variable are not correlated
H 1 : U i and the independent variable are correlated
When the p-value (Prob > chi²) is less than 0.05, we reject the null hypothesis (H0), indicating a correlation between Ui and the independent variable, which warrants the use of a fixed effects model Conversely, if the p-value is greater than 0.05, we accept the null hypothesis, suggesting no correlation between Ui and the independent variable, and thus, a random effects model should be employed.
The Hausman Test evaluates each variable—ROA, ROE, and NIM—across two distinct cases In Case 1, the analysis employs the bank's internal variables along with a dummy variable representing ownership structure, utilizing a random effects model Conversely, Case 2 adopts a fixed-effects model to assess both the bank's internal variables and random variables among the independent variables.
The summary results of Hausman Test for each dependent variable of each case are collected and run through Eviews, detailed data shown in appendix 2
Table 6 Summary result of Hausman test for variable ROA
ROA Case no.1 12.25 0.0564 Random Effect
ROA Case no.2 19.55 0.0122 Fixed Effect
According to the Hausman test results, if the Prob value is less than 0.05, the hypothesis that
When H0, Ui, and the independent variable are correlated, the fixed effects model is more suitable for analysis In case 2, the ROA model shows a probability of 0.0122, which is less than 0.05, leading us to reject the hypothesis of correlation and favor the fixed effects model Conversely, in remaining cases where the probability exceeds 0.05, we accept the hypothesis H0, indicating no correlation between Ui and the independent variable, thus necessitating the use of the random effects model for further analysis.
Table 7 Summary result of Hausman test for variable ROE
ROE Case no.1 11.71 0.0689 Random Effect
ROE Case no.2 25.59 0.0012 Fixed Effect
According to the Hausman test results, if the Prob value is less than 0.05, the hypothesis that
When H0, Ui, and the independent variable are correlated, the fixed effects model is more suitable for analysis In case 2 of the ROE model, with a probability of 0.0012 (which is less than 0.05), we reject the hypothesis of correlation and opt for the fixed effects model Conversely, in cases where the probability exceeds 0.05, we accept the hypothesis H0, indicating no correlation between Ui and the independent variable, leading us to utilize the random effects model for further analysis.
Table 8 Summary result of Hausman test for variable NIM
NIM Case no.1 2.85 0.8271 Random Effect
NIM Case no.2 3.14 0.9251 Fixed Effect
According to the Hausman test results, if the Prob value is less than 0.05, the hypothesis that
If there is a correlation between H0, Ui, and the independent variable, the fixed effects model will support the hypothesis H0, indicating that Ui and the independent variable are not correlated In this case, the random effects model will be employed for further analysis.
4.2.2 Regression outcomes a Regression result and variable estimation result of ROA
The regression results for the Return on Assets (ROA) variable are presented in two cases Case 1 employs the bank's internal variables along with a dummy variable for ownership structure, utilizing a random effects regression model In contrast, Case 2 applies a fixed-effects model to evaluate the independent variables, incorporating both the bank's internal variables and random variables.
Variable estimation result of ROA
The variable estimation results are collected from regression result table run through Eviews, detailed data shown in appendix 3
Table 9 Variable estimation result of ROA in both cases
(*,**,***: statistics at the significance level of 1%, 5%, 10%)
The regression analysis reveals three independent variables—CA, LN, and SIZE—that significantly influence ROA at the 1% and 5% levels, with CA exhibiting the strongest effect among them Additionally, the DP variable demonstrates an opposite impact on ROA, maintaining statistical significance at the 1% level in both scenarios.
The analysis indicates that the CI variable and LLP are not statistically significant in either model In Case 1, it is evident that state-owned banks yield lower returns compared to private banks Conversely, Case 3 reveals that inflation exerts a strong positive influence on profitability.
Based on each variable in the regression model in the cases with significance level 1%, 5%, the regression model is rewritten as follows:
∗ 𝑺𝑰𝒁𝑬 + 𝟎 𝟎𝟕𝟒𝟎 ∗ 𝑮𝑫𝑷 + 𝟎 𝟎𝟑𝟑𝟑 ∗ 𝑰𝑵𝑭 b Regression result and variable estimation result of other dependent variables (ROE, NIM)
The variable estimation results are collected from regression result table run through Eviews, detailed data shown in appendix 3
Variable estimation result of ROE
Table 10 Variable estimation result of ROE in both cases
No Variable Case No.1 Case No.2
(*,**,***: statistics at significance level of 1%, 5%, 10%)
The regression analysis indicates that the independent variables exert similar statistical significance on both Return on Equity (ROE) and Return on Assets (ROA), demonstrating comparable effects on profitability related to total assets and equity Notably, the impact of variables such as DP, LN, CI, SIZE, OWN, and INF is consistent with ROA, though these variables influence ROE more strongly While the variable CA lacks statistical significance in both models, the LLP variable shows the most substantial effect on ROE in case 1, achieving significance at the 10% level This distinction highlights a key difference between the two research models.
The regression model is rewritten as follows:
Variable estimation result of NIM
The variable estimation results are collected from regression result table run through Eviews, detailed data shown in appendix 3
Table 11 Variable estimation result of NIM in both cases
No Variable Case No.1 Case No.2
(*,**,***: statistics at significance level of 1%, 5%, 10%)
The regression analysis indicates that the variables CA, LN, and CI significantly influence bank profitability, as measured by NIM, ROA, and ROE Conversely, the variables DP, LLP, SIZE, OWN, GDP, and INF do not show statistical significance in this model, with GDP also lacking a notable effect in both the ROA and ROE analyses Based on these findings, the NIM model can be reformulated into a new equation.
𝟐 𝑵𝑰𝑴 = 𝟎 𝟎𝟖𝟖𝟕 ∗ 𝑪𝑨 + 𝟎 𝟎𝟒𝟎𝟎 ∗ 𝑳𝑵 c Regression result and variable estimation result of form of bank ownership
This chapter analyzes data from 24 commercial banks to regress various models based on ownership structure, aiming to further investigate the impact factors and highlight the differences in relationships among variables according to ownership forms Additionally, we evaluate the regression results and the estimation of variables, with calculations detailed below.
From the calculation obtained through Eviews of regression results in the appendix 4, we have summary table as follow:
Table 12 Variable estimation result of impact on ROA, ROE, NIM in different form of ownerships
State- owned commercial banks Private-owned commercial banks
ROA ROE NIM ROA ROE NIM
(*,**,***: statistics at significance level of 1%, 5%, 10%)
A comparison of factors influencing state-owned and privately-owned banks reveals that variables such as CA, DP, LN, CI, SIZE, and INF significantly affect joint stock commercial banks, mirroring the aggregate data model Given that the majority of banks in the sample are joint stock commercial banks, their influence extends across the entire banking sector Notably, GDP growth negatively impacts the Net Interest Margin (NIM) and is statistically significant within this group In contrast, only the inflation variable demonstrates a statistically significant impact on state-owned commercial banks.
The dissertation will analyze the impact of each independent variables as belows
Table 13 Regression results of factors affecting profitability
No Variables Assumption Conclusion Sign
1 CA Equity ratio has a positive impact on the bank's profitability Accept +
Deposit ratio has the same or opposite impact on the bank's profitability when the demand for borrowing is low
3 LN The debt balance ratio has the same impact on the profitability of the bank
Proportion of provision for credit losses has negative impact on the profitability of a bank
5 CI Operating expenses have a negative impact on the profitability of a bank
6 SIZE Bank size has a positive impact on the bank's profitability
7 OWN Ownership form affects the profitability of a bank
8 GDP Economic growth has a positive impact on the bank's profitability
9 INF Economic growth has a positive impact on the bank's profitability Accept +
(Source: Calculation of author) a Capital
The capital ratio demonstrates a statistically significant positive relationship with Return on Assets (ROA) and Net Interest Margin (NIM) at a 5% level or better, although it does not show significance with Return on Equity (ROE) This variable has the highest impact among internal factors, with coefficients of approximately 0.27 for ROA and 0.09 for NIM in the regression model Consistent with theoretical foundations, the capital ratio's influence on bank profitability is evident across all types of banks, including state-owned and joint-stock commercial banks Furthermore, the capital ratio serves as a key indicator of a bank's safety The findings align with similar research conducted in developing countries, such as Tunisia, as noted by Bennaceur and Goaied (2008), and corroborated by other studies, including those by Vong and Chan (2009) and Nguyen Viet Hung (2008), which also affirm the positive impact of capital strength on bank profitability.
The ratio of deposits to total assets negatively impacts ROA and ROE, with a statistically significant effect of 1%, while showing no significant correlation with NIM The influence of deposits on ROA and ROE is notably strong, with coefficients of 0.016 and 0.13, respectively These findings are surprising compared to prior research, although they are not entirely unprecedented in the literature.
Davydenko (2011) identifies a negative impact of deposits on Ukrainian banks, concluding that increased short-term deposits prevent banks from benefiting from deposit returns This competitive market environment hinders banks from lowering interest rates to cut costs and enhance profits The regression analysis reveals consistently negative results for the deposit ratio throughout the study period.
7 Banks with a high degree of equity can cut the cost of capital by providing a secure net for depositors and providing capital to the interbank market thereby increasing profits
EVALUATE THE SUITABILITY OF THE REGRESSION MODEL
This section will go into detail to evaluate the suitability of the regression model and examine the model's defects
4.3.1 Evaluate the suitability of the regression model
In our analysis, we evaluate the consistency of variable signs with established theories and findings from previous research Although numerous studies have identified various variables, the results have not always aligned In this study, some variables demonstrated a positive effect, whereas other studies reported negative or statistically insignificant effects This highlights the need for further research in this area.
State-owned banks tend to be less profitable than their privately owned counterparts due to ineffective utilization of their advantages This finding highlights the superior profitability of private banks, indicating a need for state-owned banks to improve their efficiency and competitiveness.
14 This positive result is generally due to the fact that Vietnamese banks in the study period have accurate
Vietnam will assess impact factors and compare them with prior research The author focuses on result variables that align with theoretical expectations and demonstrate statistical significance Many of the chosen impact variables show a statistical significance of 10% or less within the research model These findings align with similar studies conducted in various countries, providing a coherent explanation for the results.
The analysis of the correlation matrix indicates a low likelihood of multicollinearity among the surveyed variables To further investigate this issue, the study employs Variance Inflation Factor (VIF) magnification factors for testing According to Hoang Ngoc Nham (2008), a VIF value exceeding 10 suggests the presence of multicollinearity between the variable Xj and other independent variables.
We have the following results by running Eviews to do the multi-collinearity testing for each determinant (both internal and external ones) that impact on banking profitability
Table 14 Variance inflation factors (VIF) of the independent variables
(Source: Results from calculation of author through Eviews)
A Variance Inflation Factor (VIF) greater than 10 indicates the presence of multicollinearity among variables in research However, the analysis reveals that the VIF for all variables is below 10, suggesting that multicollinearity is not a concern in this study.
15 R 2 is collected from Tables of Appendix 5 variables in the study do not have multicollinearity phenomenon 16 This is a positive sign in testing and selecting an appropriate econometric model
The Durbin-Watson test is used to determine whether or not the correlation is in the model If
1