The ideal study model chosen for this study is the Random Effect Model, the Fixed Effect Model, and the General Least Square Model with six independent variables: bank size, leverage, lo
INTRODUCTION
THE NECESSITY OF THE RESEARCH
The essence of the business that commercial banks carry out is currency trading and banking services Banking performs an intermediary role in the economy because it is a field directly related to all sectors of socio-economic life To operate a business, commercial banks must have capital and financial autonomy Banks may hold at least the optimal level of capital in relation to the risk and risk of bank default Therefore, this incident requires authorities to regulate bank capital (Rime, 2001) The overall purpose of regulation is to reduce risk and increase the system's reliability by providing sufficient capital This will help the banking system continue as a mediator without interruption
Capital adequacy ratio is an important criterion, a measure of well-being and reliability for banks and financial institutions (Aspal and Nazneen, 2014) A bank's operations will be guaranteed when its capital is maintained at a safe level, and the bank may face losses and ensure the safety of fixed assets when the economy becomes unfavorable (Abusharba et al., 2013; Aspal and Nazneen, 2014) With this capital adequacy ratio, the investor can determine the bank's ability to pay due debts and risks This ratio is also used to warn depositors against bank risks (Aspal and Nazneen, 2014)
In recent years, Vietnamese commercial banks have gradually oriented their systems closer to international standards, taking that as a prerequisite to ensure the maximum reduction of systemic risks in the bank Vietnam still needs to have unified regulations on banks' capital adequacy ratios Banks need to maintain a minimum capital adequacy ratio of 8% if they comply with regulations on capital adequacy ratio according to Circular No 41/2016
By the end of 2023, more than 20 commercial banks are implementing Basel II at the State Bank of Vietnam (SBV) request in Circular No 41/2016, dated December 30,
2016, regulating the capital adequacy ratio for banks and foreign bank branches The State Bank issued Circular No 41/2016, regulating capital adequacy ratios for banks and foreign branches The banking system needs to meet Basel II standards, including three pillars: Pillar I focuses on determining the minimum capital adequacy ratio that a bank needs to maintain against market risks, credit risks, and operational risks; Pillar II is the risk management monitoring process of an organization; Pillar III is information disclosure by banks (Chen, 2023)
Being in a situation where the capital adequacy ratio is too high or too low is detrimental to the bank Suppose the capital adequacy ratio is too high In that case, banks cannot provide capital for investment projects and only invest in assets with a lower level of risk, leading to lower capital efficiency and lower profits On the contrary, when banks have low capital adequacy ratios, their ability to cope with crises and economic shocks will decrease Therefore, maintaining the capital adequacy ratio at an appropriate level by controlling factors that affect the capital adequacy ratio will help the bank use capital effectively and maintain safe banking operations (Lê Hồng Thái, 2020)
In this study, the factors that are the main influencing factors on the capital adequacy ratio of Vietnamese commercial banks in the period 2013-2022 will be studied Based on the results obtained from the research, several solutions are proposed for both micro (for commercial banks) and macro (for state banks) to contribute to the development of the Vietnamese economy—the stable development of the Joint Stock Commercial Bank in particular and the Vietnamese banking system in general.
THE OBJECTIVE OF THE RESEARCH
The general objective of the research is to examine the factors affecting Vietnamese commercial banks' capital adequacy ratio (CAR) From the results obtained, the study proposes implications and policies to promote development and ensure capital adequacy efficiency for Vietnamese commercial banks
To achieve the general objectives, the study undertakes the following specific objectives:
First, determining the influence of banking dimension factors on the CAR of Vietnamese commercial banks
Second, the degree and dimensions of the impact of factors on banks' capital adequacy ratios are measured
Finally, based on research results, the thesis proposes some implications and policies to promote capital adequacy efficiency for Vietnamese commercial banks.
RESEARCH QUESTION
To achieve these research objectives, the author will respond to the following research questions:
RQ1: What factors affect the capital adequacy ratio of Vietnamese commercial banks?
RQ2: How do these factors impact the capital adequacy ratio of Vietnamese commercial banks?
RQ3: What policy implications should be implemented to help Vietnamese commercial banks enhance efficiency and ensure capital adequacy ratio?
OBJECT AND SCOPE OF THE RESEARCH
Object of the research: factors affecting the capital adequacy ratio of Vietnamese commercial banks
Scope of space: Vietnam's banking system currently has a total of 49 banks including the State Bank, Joint Stock Commercial Bank, Joint Venture Bank, Foreign Bank Branches in Vietnam and 100% foreign-owned banks This study was conducted on 25 commercial banks in Vietnam, based on carefully considered criteria to ensure a representative sample Firstly, the banks included in the study have publicly disclosed their capital adequacy ratios Secondly, only banks with a charter capital of over 3000 billion VND were considered, guaranteeing that the study focuses on larger-scale banking institutions Lastly, these 25 banks represent approximately 83% of the total charter capital and 70% of the total number of commercial banks at the time of the study This significant proportion underscores the relevance and representativeness of the sample, allowing for a more accurate reflection of the overall situation regarding the capitalization and operations of major commercial banks in Vietnam
Scope of time: The study has a total of 250 observations The study data was measured between 2013 and 2022 This period encompasses a phase of robust economic growth and transformative events in the banking sector, such as the COVID-19 pandemic, offering rich insights into banks' responses to economic shocks Additionally, these years feature improved data availability and witness the impact of technological advancements in banking, making the analysis both relevant and comprehensive for current banking practices.
METHODOLOGY OF THE RESEARCH
The author uses a quantitative approach in this study, where quantitative research concentrates on measuring and evaluating cause-and-effect relationships between different variables (Hardani et al., 2020) Specifically, the research uses Pooled OLS Regression Model, Fixed Effects Model (FEM) and Random Effects Model (REM) to estimate the regression equations In addition, using the Hausman test to choose the suitable model The Breusch-Pagan test and Wooldridge test is used to verify defects in the study model Finally, the study will utilize FGLS (Feasible Generalized Least Squares) estimation to address these issues.
CONTRIBUTION OF THE RESEARCH
The thesis will not only strengthen the theory of factors affecting the capital adequacy ratio but will clarify the degree of impact of factors on the capital adequacy ratio
From the data from quantitative models, the thesis proposes some governance and policy implications in enhancing efficiency and ensuring capital adequacy efficiency for commercial banks in Vietnam
There have been many studies on factors affecting capital adequacy ratios, but the impact results are still inconsistent The thesis "Factors affecting capital adequacy ratio of commercial banks in Vietnam" provides additional empirical evidence on factors affecting capital adequacy ratios of commercial banks in Vietnam The topic is a tangible reference that provides information for interested subjects.
STRUCTURE OF THE RESEARCH
The thesis consists of 5 chapters with the following main contents:
To determine the feasibility and accuracy of the research topic, Chapter 1 sets out important contents such as the urgency of the topic, research questions to determine the research objectives, objects and scope of the topic, appropriate research methods, contribution of research and structure of research topic These contents help the reasoning behind the research become more rigorous and highly practical.
LITERATURE REVIEW
OVERVIEW OF CAPITAL ADEQUACY RATIO
In the mid-1970s, lending in banks was widely developed without any parallel increase in capital (total capital divided by total assets measures the ratio of capital) Therefore, the government sets a regulatory standard for capital adequacy ratios to control this situation (Al-Sabbagh, 2004)
Capital adequacy is said to be a platform for monitoring the safety of a bank The capital adequacy ratio (CAR) is used as an indicator for banks and investors to determine the risk level of each bank Banks must keep their entire equity to a minimum to avoid unexpected losses and the risk of bankruptcy (Aspal and Nazneen, 2014) Capital adequacy ratios are often used to signal depositors about bank risks and are also aimed at increasing the steadiness and efficiency of the commercial banking system The investor can determine the bank's ability to pay its debts and term risks with this capital adequacy ratio When the bank ensures that the capital adequacy ratio is maintained at a stable level, its ability to withstand financial shocks also becomes more vital than ever so that the bank can protect itself and its customers (Bateni et al., 2014)
The capital adequacy ratio serves as an essential indicator for evaluating a bank’s resilience against financial disturbances and unforeseen losses It plays a pivotal role in ensuring the overall stability and reliability of the banking system (Gharaibeh, 2023) Capital Adequacy Ratio is a coefficient that determines a bank's capacity to meet term liabilities and other risks such as credit risk, market risk, operational risks, and other risks This coefficient is calculated based on the ratio of equity capital to risk-weighted assets of the bank Therefore, regulatory agencies use the capital adequacy ratio as an important financial index to evaluate the "safety and soundness" of banks The purpose of the CAR coefficient is for commercial banks to operate stably, avoid the situation of banks chasing profits, and increase investment through lending activities in risky areas (Vu and Dang, 2020)
Each bank exists and operates in a different environment, and for each country, there will be different economic stability Therefore, bank management agencies in each country will have different regulations on this coefficient, but still must comply with the standards of the Basel committee, banks must ensure to maintain this coefficient according to national regulations aimed to stabilize the region's economy and contribute to the stability of the world economy In Vietnam, commercial banks comply with the minimum capital ratio of 8%, applying Basel II standards issued by the State Bank in Circular No 41/2016, dated December 30, 2016, regulating the capital adequacy ratio for banks and foreign bank branches
Capital Adequacy Ratio (CAR) is the ratio of a bank’s equity capital to its risk- weighted assets Central banks and banking regulators decide to prevent commercial banks from using excessive leverage and becoming insolvent (Vu and Dang, 2020) In this research, the Capital Adequacy Ratio is presented by the following formula:
LITERATURE REVIEW
The factors affecting the capital ratio of Indonesian stock market-listed banks were studied by Setiawan and Muchtar (2021) using secondary data from the financial statements of 42 selected banks for the period 2015-2019 The study used regression model analysis in three models: the Random Effect Model, Fixed Effect Model, and Common Effect Model, with five independent variables: bank size, loan loss reserves, return on equity, liquidity ratio, and loan ratio While bank size and return on equity factors positively impact the capital adequacy ratio, loan ratio negatively impacts the capital adequacy ratio On the other hand, loan loss reserve and liquidity do not affect the capital adequacy ratio
Also set against Indonesia's stock market backdrop, Usman et al (2019) conducted a similar study as Setiawan and Muchtar (2021) The difference is that the authors used secondary data taken from the financial statements of 27 conventional banks listed on the stock market from 2007 to the end of 2018 The ideal study model chosen for this study is the Random Effect Model, the Fixed Effect Model, and the General Least Square Model with six independent variables: bank size, leverage, loan loss reserves, net interest margin, loans to assets ratio, and liquidity ratio The results of the study show that liquidity does not have any effect on the capital ratio At the same time, the capital adequacy ratio has a positive relationship with leverage and net interest margin The opposite happens when capital ratio is negatively linked to bank size, loan loss reserve, and loan asset ratio
Masood and Ansari (2016) gathered data from the annual financial statements of
14 Pakistani regional banks to identify determinants of the capital adequacy ratio The Fixed Effect Model and the Random Effect Model are used to measure the research model of the impact of independent variables on CAR: return on assets, return to equity, non-performing loans, loan-to-asset ratio, loan loss reserves, deposit asset ratio, equity asset ratio, bank size, and ownership concentration The study found that loan loss reserves, deposit asset ratio, and equity asset ratio positively affect the capital adequacy ratio In contrast, loan-to-asset ratio and ownership concentration have the opposite effect of capital adequacy ratio Additionally, the remaining independent variables, including non-performing loans, bank size, and profitability indicators such as return on assets and return to equity, do not give results that explain the impact on capital adequacy ratios
Another study on the impact of factors on capital adequacy ratios in the Albanian banking system was conducted by Shingjergji and Hyseni (2015) The 31 banks’ data samples were collected from financial statements published on the official websites of Albanian banks Regression Model-Ordinary Least Squares (OLS) was applied to the study to find out the relationship between capital adequacy ratio and independent variables, containing return on equity, return on assets, non-performing loans, loans to deposit ratio, equity multiplier, and the natural logarithm of total assets Research results indicate that profitability indicators such as return on equity and return on assets are not statistically significant with the capital adequacy ratio Non-performing loans, loans-to- deposit ratio, and equity multiplier are negatively associated with the capital adequacy ratio On the other hand, the bank size positively correlates with the capital adequacy ratio
In the Federal Republic of Nigeria region, Olarewaju and Akande (2016) conducted a study comparing factors affecting capital adequacy ratios of 15 quoted banks between 2005 and 2014 The study applied the descriptive and fixed effect panel regression model to measure independent variables, including return on assets, bank size, credit risk, liquidity structure, deposit structure, and two macroeconomic variables, gross domestic product and inflation Finally, the paper found that return on assets and bank size positively correlated with capital adequacy ratios In contrast, credit risk, liquidity structure, and deposit structure negatively impacted the capital adequacy ratio In addition, factors such as gross domestic product and inflation are not statistically significant to capital ratios
In Ethiopia, Mekonnen (2015) conducted an empirical study of factors influencing the capital adequacy ratio of commercial banks The author gathered available data for eight banks for the period from 2004 to the end of 2013 The author proposes the Random Effect and Fixed Effect Model methods that measure factors influencing the capital ratio, including bank size, deposit ratio, loan to total asset, liquidity position, return on asset, return on equity, net interest margin and leverage The author gives the results: return on assets, deposit ratio and bank size positively correlate to capital adequacy ratio Return on equity and interest margin are negatively related to the capital adequacy ratio Other factors such as highly liquid position, loan to total assets ratio, and leverage ratio have no statistically significant impact on capital adequacy ratio
An experimental study using the Feasible Generalized Least Squares Model, Fixed Effects and Random Effects Model took data from the financial statements of 24 banks in Turkey conducted by Buyuksalvarci and Abdioglu (2011) for the period 2006-
2010 Bank size, deposits, loans, loan loss reserve, liquidity, profitability such as return on asset and return on equity, net interest margin and leverage were proposed by Ahmet Büyükşalvarcı (2011) as operative independent variables and capital adequacy ratio as dependent variables Finally, a negative effect on the capital adequacy ratio is caused by the loan ratio, return on equity, and leverage ratio Loan loss reserve and return on assets positively affect the capital adequacy ratio The capital adequacy ratio is unaffected by bank size, debt ratio, liquidity, and net profit margin
The topic of the capital adequacy ratio has been studied extensively in Vietnam in recent years Le et al (2022) report a model with independent variables, including bank size, leverage, loan loss reserve, customer deposits, loans to customers, liquidity, and profitability, affecting the dependent variable, which is the capital adequacy ratio The Pooled OLS Regression Model, Fixed Effects, and Random Effects Model measure data of 24 Vietnamese commercial banks over 11 years from 2009 to 2019 The paper found that financial leverage, client deposits, customer loans, liquidity, and profitability negatively affect the CAR Additionally, bank size and loan loss reserves do not correlate with capital adequacy ratios
The Feasible General Least Square (FGLS) Model was included in the research model by Vu and Dang (2020) to explain the impact of independent variables, including bank size, deposit ratio, loans to total assets, loan loss reserves, liquidity, profitability ratio, net interest margin, non-performing loan ratio and leverage on the dependent variable capital adequacy ratio The research data is taken from the annual financial reports of 31 commercial banks in Vietnam from 2011 to 2018 The author gives the final result that loan loss reserves, leverage and return on equity negatively impact the capital adequacy ratio The opposite situation is when only a return on assets positively correlates with the capital adequacy ratio The authors report no correlation between bank size, net interest margin, non-performing loan ratio, deposit ratio, loans to total assets, liquidity and capital adequacy ratio
Pham and Nguyen (2017), using a fixed effect model, analyzed factors affecting the capital adequacy ratio of 29 Vietnamese commercial banks from 2011-2015 The research model rescue includes assets with liquidity of cash and precious metals in total assets, net interest margin, bank size, loan loss reserve, leverage, and loan share Research results show that the net interest margin and liquidity have a positive effect on the capital adequacy ratio Meanwhile, the share of loan and loan loss reserve harm the capital adequacy ratio This study did not find the effects of the leverage ratio and the size of the bank on the capital adequacy ratio
The research conducted by Lu and Doan (2021), using the Pooled OLS Regression Model and Fixed Effects Model, identified factors influencing the capital adequacy of 20 joint-stock commercial banks in Vietnam The research findings that the rate of return on assets, deposit ratio, liquidity ratio, loan ratio, loan loss reserve, non- performing loan ratio, leverage ratio, bank size, board size, Independent members in the Board, Female members of the Board of Directors, The foreign member ratio in the Board, Educational attainment of the members of the Board, Consumer price index, Gross domestic product
Table 2.1: Summary of previous studies Authors Topic of research
Research methods and research data
The study used secondary data as a sample taken from the financial statements of
While bank size and return on equity factors have a positive impact on the capital adequacy
Ratio of Banks Listed in Indonesia Stock
24 selected banks during the year The period from 2006-2010 Using the Random Effect Model, Fixed Effect Model and Common Effect Model methods ratio, loan ratio negatively impacts the capital adequacy ratio
On the other hand, loan loss reserve and liquidity do not have any effect on the capital adequacy ratio
Determinants of capital adequacy ratio: A perspective from Pakistani banking sector
The study used data from the annual financial statements of 14 Pakistani regional banks
Using Fixed Effect Model and Random Effect Model
Loan loss reserves, deposit asset ratio, and equity asset ratio positively affect the capital adequacy ratio In contrast, loan-to-asset ratio and ownership concentration have the opposite effect of capital adequacy ratio In addition, the remaining independent variables, including non- performing loans, bank size, and profitability indicators such as return on assets and return to equity, do not give results that explain the impact on capital adequacy ratios
An Empirical Analysis of Capital
Adequacy Determinants in Nigerian Banking Sector
The study used data from
Exchange between 2005 and 2014 Using Cross- Sectional Specific fixed effect model
Return on assets and bank size positively correlated with capital adequacy ratios In contrast, credit risk, liquidity structure and deposit structure negatively impacted the capital adequacy ratio In addition, factors such as gross domestic product and inflation are not statistically significant to capital ratios
The factors affecting the capital adequacy ratio in the commercial banking system in Ethiopia in the period 2004-2013
The study used secondary data which is gathered from annual reports of eight commercial banks in Ethiopia in the period 2004-2013 Using the Random Effect Model and Fixed Effect Model
RESEARCH GAP
Many empirical studies have explored the factors that affect the capital adequacy ratio of Vietnamese and global banks, but they often come to different conclusions This inconsistency is because of factors such as short-term sample size and inadequate representation of banks Furthermore, they often do not fully consider external factors such as gross domestic product, inflation rate, etc
The study focuses on Vietnamese banking and uses the latest data from the most recent period to provide further empirical evidence on the determinants affecting the capital adequacy ratio and valuable insights into the unique context of the country Additionally, suggesting some policy implications by accurately identifying the most impactful factors.
METHODOLOGY
THE RESEARCH PROCESS
The process of the study will be carried out in steps as follows:
Step 1: Identify research questions and objectives for the thesis topic
Step 2: Collect and briefly summarize the theoretical basis and previous studies on the research topic within Vietnam and abroad
Step 3: Formulate relevant hypotheses and design the appropriate research model Step 4: Collect data from the financial statements of 25 commercial banks through Fiinpro's database
Step 5: Define research methods using specific analysis and estimation techniques such as Pooled OLS Regression Model (OLS), Fixed Effects Model (FEM), Random Effects Model (REM), descriptive statistics, correlation analysis, and regression analysis of panel data
Step 6: Analyze the achieved regression results and draw conclusions explaining the direction of impact of independent variables on the CAR-dependent variable
Step 7: This is the final step in the process, where, based on the regression results, write a research on the topic reviewed, draw conclusions and make relevant recommendations and policy implications to answer the research questions and address the proposed research objectives
RESEARCH MODELS
Based on empirical research conducted globally and in Vietnam, such as Le et al (2022), Vu and Dang (2020), Setiawan and Muchtar (2021), Usman et al (2019),…The author discovered that each country has various variables, measurement methods, and the direction in which the variables (or factors) affect capital in research papers
The author discovered that prior research had focused on some variables, including bank size, return on total assets, liquidity, net interest margin, loan loss reserves, loans, and the leverage ratio, that may affect the capital of the business These variables share the ability to collect data, economic significance, correlation, and an explanation of the study problem
Therefore, the research model was chosen to show variables that affect the capital of listed construction businesses in Vietnam, which is presented by the following formula:
CAR it = 𝜷 𝟎 + 𝜷 𝟏 ROA it + 𝜷 𝟐 SIZE it +𝜷 𝟑 NIM it + 𝜷 𝟒 LLR it + 𝜷 𝟓 LIQ it + 𝜷 𝟔 LOA it +
ROAit: Return on total assets
SIZEit: Size of commercial bank
LLRit: Loan loss reserves, the ratio of loan loss provision to total loans
LOAit: Loans to customers, the ratio of total loans to total assets
LIQit: Liquidity, the ratio of total liquid assets to total assets
LEVit: Leverage ratio of the commercial bank i: Bank Index t: Index showing the number of years observed εit: Random error.
MEASUREMENT OF RESEARCH VARIABLES AND DEVELOPMENT
The capital adequacy ratio is based on the Basel I Treaty The calculation of the capital adequacy ratio mainly focuses on the credit risk of capital Capital Adequacy
Ratio (CAR) is the ratio of a bank’s equity capital to its risk-weighted assets (Vu and Dang, 2020) In this research, the Capital Adequacy Ratio is presented by the following formula:
Research by Vu and Dang (2020) indicates that return on assets has a positive impact on capital adequacy ratios This ratio measures management efficiency in using available resources and its ability to realize revenues from funds or resources available from various financing resources; thus, it reflects the effect of the bank's financial and operational activities (Olarewaju & Akande, 2016); in the meantime, this ratio has been used as a measure of bank performance in Gropp and Heider (2007) study, and a direct relationship, between return on assets ratio and capital High returns on assets will make the bank more attractive in terms of capital growth
The return on total assets (ROA) ratio calculates how much money a corporation can make on each dollar of assets The formula for calculating this ratio is to divide earnings after tax after tax by the average asset value (Lu and Doan, 2021)
Hypothesis H 1 : The return on total assets (ROA) positively impacts the capital adequacy ratio
Bank size represents the bank's total assets and is one of the measures used to determine the bank's liquidity level According to Kultan (2016) and Mishu et al (2020), have demonstrated that large-sized banks have poor risk control and low safety since they invest in high-risk assets Large banks might sometimes have suboptimal risk control when investing in high-risk assets due to the pursuit of higher returns to satisfy shareholder expectations, which can lead to an increased tolerance for risk Their vast and complex operations can make managing and assessing risk difficult Furthermore, the test bank scale results show a negative effect on the capital adequacy ratio, which was researched by Bateni et al (2014)
By computing the logarithm of a bank's total assets, the SIZE factor, which measures both the capital and the bank's current total assets, is used to determine the size of a bank (Le et al., 2022)
Hypothesis H 2 : Bank size negatively impacts the capital adequacy ratio
Usman et al (2019) found that net interest margin significantly positively affects the bank capital of banks in the Indonesia Stock Exchange Financial institutions with substantial earnings can bolster their capital reserves by retaining profits, which sends a favorable message about the company's worth When banks report high-income figures, it simplifies the process for bank executives to administer the bank's equity capital effectively and reduces the inclination toward risk-taking (Iloska, 2014) Another consensus study by Pham and Nguyen (2017) showed a similar relationship between net interest margin and capital adequacy ratio
The net interest margin (NIM) measures the relationship between the net profit margin and the average earning assets Net interest income is the sum of the interest payments made on deposits less the interest received on loans (Usman et al., 2019)
𝐀𝐯𝐞𝐫𝐚𝐠𝐞 𝐄𝐚𝐫𝐧𝐢𝐧𝐠 𝐀𝐬𝐬𝐞𝐭𝐬 Hypothesis H 3 : Net interest margin positively impacts the capital adequacy ratio
A negative relationship can occur between the capital adequacy ratio and loss provision, as demonstrated by Pham and Nguyen (2017) During a financial crisis, when banks suffer loan losses, the impact hits their capital reserves- the funds set aside to cover potential losses As these reserves deplete due to non-repaying loans, the banks find it challenging to maintain the required levels of capital that regulators deem sufficient to absorb losses, known as capital adequacy ratios Essentially, it's harder for banks to stay financially healthy and meet regulatory standards when losing money on loans during a crisis (Usman et al., 2019) This view also agrees with research by Workneh (2014) showing that loan loss reserves have the opposite effect on capital adequacy ratios Announcing loan and impairment losses by banks will lead to a reduction in the capital adequacy ratio
Loan loss reserve (LLR), a cash reserve set aside by a bank to guard against prospective loan losses, is the ratio of loan loss provision to total loans Banks usually maintain adequate reserves to cover anticipated losses on their lending portfolios (Usman et al., 2019)
Hypothesis H 4 : The loan loss reserve negatively impacts the capital adequacy ratio
In the Indonesia Stock Exchange context, Setiawan & Muchtar (2021) found an inverse relationship between the capital adequacy ratio and loan assets ratio The higher this ratio indicates that a bank is loaned up and its liquidity is low-pitched The higher the loans-to-assets ratio, the more hazardous a bank may be to have higher defaults In Vietnam, Le et al (2022) also proved that the loan assets ratio conversely affects the capital adequacy ratio Lending is the primary source of income for commercial banks When a bank's ratio of loans given out to customers increases, it faces greater difficulty in covering its short-term obligations This is because it lacks sufficient liquid assets to fulfil the loan commitments it has promised to customers If too much of the bank's resources are tied up in customer loans, it may not have enough readily available funds to meet immediate financial liabilities (Workneh, 2014)
The ratio of total loans to total assets is known as the loan size (LOA) The most significant investment asset on a bank's balance sheet is its loan portfolio (Usman et al., 2019)
Hypothesis H 5 : The loan assets ratio negatively impacts the capital adequacy ratio
According to Olarewaju and Akande (2016), liquidity adversely influences the capital adequacy ratio When a bank increases its liquidity by storing more liquid assets, it also reduces its investment in profitable assets to generate profits By boosting liquidity by accumulating more easily convertible assets, a bank inevitably scales back its investments in higher-yield assets that drive profits This adjustment means that while the bank enhances its ability to meet financial obligations quickly, it simultaneously curtails its potential earnings from investments that offer higher returns but are less liquid The consensus study with the above hypothesis comes from Le et al (2022), the bank's income will decrease because the investment is less profitable, and the bank cannot afford to raise capital from that poor profit Of course, the bank's capital adequacy ratio will fall sharply accordingly
The ratio between the commercial bank's short-range assets and short-range liabilities measures the liquidity of assets (Le et al., 2022)
Hypothesis H 6 : The liquidity negatively impacts the capital adequacy ratio
Vu and Dang (2020) indicated that the capital adequacy ratio and leverage ratio are inversely related Adding more debt to a bank's financial framework can reduce its capital adequacy ratio This is because taking on extra debt introduces higher levels of risk Consequently, these increased risks can make borrowing more expensive for the bank, as lenders demand higher interest rates to compensate for the greater lending risk Furthermore, shareholders, recognizing the elevated risk, may expect higher investment returns This combination of factors can make it more challenging for the bank to attract additional equity as both the costs of debt and the expectations of equity holders rise (Aktaş & Unal, 2015) Buyuksalvarci and Abdioglu (2011) also proved an inverse relationship between leverage and capital adequacy ratio Compared to banks with low leverage, whose low leverage poses a danger to the bank, a bank with high leverage is frequently riskier Furthermore, banks with high leverage levels may start with less capital than those with low ones
A bank's leverage ratio (LEV) is the ratio of total liabilities to total assets (Le et al., 2022)
Hypothesis H 7 : The leverage ratio negatively impacts the capital adequacy ratio
Seven variables are included in the model, and the hypothesis is that they can affect the capital adequacy ratio of Vietnamese commercial banks The bank's specific variables are ROA, SIZE, NIM, LLR, LOA, LIQ, and LEV They are supposed to have positive or negative effects on the capital adequacy ratio of Vietnamese commercial banks and are recapped in Table 3.1 below
Table 3.2: Statistics of expected signs of variables in the model
Vu and Dang (2020), Le et al (2022) adjusted Total Assets
Vu and Dang, (2020) Bank size SIZE Natural logarithm of total assets
(2020), Bateni et al (2014), Gropp and Heider (2007), Shrieves and Dahl (1992), Võ Hồng Đức, Nguyễn Minh Vương and Đỗ Thành Trung (2014), Usman et al (2019)
Usman et al (2019), Do et al (2019), Bateni & Asghari (2014),
(2001), Pham and Nguyen (2017), Workneh (2014) Loan asset ratio
- Le et al (2022), Setiawan and Muchtar (2021), Masood and Ansari (2016), Pham and Nguyen (2017),
Usman et al (2019), Aspal and Nazneen (2014)
- Le et al (2022), Hewaidy and Alyousef (2018), Olarewaju and Akande (2016)
- Vu and Dang (2020), Aktaş and Unal (2015), Le et al (2022), Buyuksalvarci and Abdioglu (2011)
RESEARCH METHODS
To comprehend the variables influencing the capital of joint-stock commercial banks in Vietnam, the relationships between the variables, as well as the direction of the independent variables' impacts, the author uses STATA 17.0 software for three separate models to demonstrate three estimation techniques for the independent variable, namely Pooled Model, FEM Model and REM Model The order of detailed steps of the process that has been performed is as follows
Descriptive statistical methods are a data analysis technique in econometrics to describe, summarize, and understand data quantitatively A fundamental descriptive statistic will include statistical indicators to describe data, such as the number of variable observations (Obs), mean value (Mean), standard deviation (Sd), maximum value (Max), minimum value (Min), etc Descriptive statistical methods help analyze and understand economic data, helping economists make informed decisions and make accurate forecasts of economic variables
Step 2: Selection of the suitable regression model between Pooled OLS, FEM and REM
The least squared regression model, abbreviated as OLS (Ordinary Least Square), also known as the Pooled OLS regression model, is a particular case of the generalized least squares method (GLS) used to find the regression path closest to the continuous value of the dependent variable or in other words, how to sum up the minor noise grades or errors as the name of the method itself (Nguyễn Văn Nghiện, Dư Thị Lan Quỳnh and Đinh Ngọc Hân, 2020)
The following equation defines the Pooled OLS Regression Model:
𝑍: A set of variables that remain constant over time i: Bank Index t: Index showing the number of years observed
The FEM Model only cares about individual differences that contribute to the model, so there will be no self-correlation in the model The use of fixed factors to analyze the effect on the model is considered the same as an OLS model using pseudovariables, in which the pseudovariable acts as fixed factors However, this method has the disadvantage of reducing the degree of freedom of the model, especially when the number of pseudovariables is large (Nguyễn Văn Nghiện, Dư Thị Lan Quỳnh and Đinh Ngọc Hân, 2020)
The following equation defines the Fixed Effects Model:
𝑌 𝑖𝑡 : The value of the dependent variable of object i at time t
𝛽 1𝑖 : Individual fixed effects of each object i
𝑋 2,𝑖𝑡 , 𝑋 3,𝑖𝑡 , … , 𝑋 𝑘,𝑖𝑡 : The value of the corresponding independent variables of object i at time t
𝛽 2𝑖 , 𝛽 3𝑖 , … , 𝛽 𝑘𝑖 : The regression parameter corresponds to the independent variables
𝑢 𝑖𝑡 : Random error of object i at time t
The model of error components (ECM) or the model of random effects (REM) This model is concerned with the differences of the analyzed objects contributing to the model over time, so self-correlation is a potential problem that needs to be solved However, this method has the advantage of good elimination for variable variance factors (Nguyễn Văn Nghiện, Dư Thị Lan Quỳnh and Đinh Ngọc Hân, 2020)
The REM model is concerned with the differences of each object and has assumptions:
- Individual characteristics between subjects are assumed to be random and do not correlate with independent variables in the model
- The residuals of each entity (not correlated with the independent variable) are a new independent variable
If the objects in the model have their own characteristics In that case, 𝛽 1𝑖 is not fixed but assumes it is a random variable with a mean value of 𝛽 1 and the individual difference in the origin value of each object is reflected in the error term 𝜀 𝑖
𝛽 1𝑖 = 𝛽 1 + 𝜀 𝑖 (i = 1,2,…,N) From there, the Random Effects Model takes the equation in the form:
𝑌 𝑖𝑡 = 𝛽 1 + 𝜀 𝑖 + 𝛽 2𝑖 𝑋 2,𝑖𝑡 + 𝛽 3𝑖 𝑋 3,𝑖𝑡 + ⋯ + 𝛽 𝑘𝑖 𝑋 𝑘,𝑖𝑡 + 𝑢 𝑖𝑡 Where, 𝜀 𝑖 là unit cross-fault component
The thesis proceeded in the following sequel to get the optimal modeling: first estimate the Pooled OLS model, then estimate the Fixed Effect and Random Effect model groups To know between the Pooled OLS model and the FEM and REM model groups which model is more suitable, you can use the Redundant Fixed Effects tool to verify whether the impact factor of the regression functions of each row is different If there is no difference, we can choose Pooled OLS as the estimation model for the study; if, conversely, the FEM and REM model sets are suitable, we must perform a Hausman Test to choose between the FEM model and the REM model, see which model is most suitable for this study
Step 3: Methods for testing regression coefficients and model suitability
First, use the Breusch-Pagan Lagrange Test method to select the Pooled OLS model and the FEM model The Breusch-Pagan Lagrange Multiplier Test is a test method for checking the uniformity of errors in regression models The validation process is carried out by building a regression model, calculating errors, and checking the uniformity of errors in the model If the results show heteroskedasticity, the Fixed Effects Model (FEM) is considered more suitable Conversely, if the result shows homoskedasticity, we can use the Pooled OLS model as an appropriate model Next, use the Hausman Test method to select the FEM and REM models Hausman Test is a testing method to determine the fit between the Fixed Effects Model (FEM) and the Random Effects Model (REM) The verification process is done by comparing the estimates of the parameters in the FEM and REM models and assessing the differences between these estimates If these estimates differ significantly, the FEM model is considered more appropriate Conversely, the REM model is considered more appropriate if the estimates are similar Therefore, the Hausman Test is used to help researchers decide the most suitable model for their data
Step 4: Inspection of defects of the model
Linear regression analysis is used to check whether variables in the model have multicollinearity The author used the variance inflation factor (VIF) to assess the degree of multicollinearity of the variable If the VIF value of the variable is less than 10, then the variable does not cause multicollinearity in the regression model Conversely, if the VIF value is greater than 10, that variable causes multicollinearity and must be processed before a linear regression model is used
The proper test method for panel data is the Wooldridge test, which can be applied to the Pooled OLS model and is used to determine if variables are linearly correlated Suppose the Wooldridge Test shows a linear autocorrelation between the variables in the model In that case, we should consider removing the Pooled OLS model and choosing the FEM and REM model set Conversely, the Pooled OLS model is suitable if the test results show no autocorrelation
The Breusch-Pagan test, which can be applied to the Pooled OLS model or the FEM model, is used to check whether the variance of the independent variables has changed significantly during the study period If the Breusch-Pagan test shows heteroskedasticity between independent variables, the studied model can be overcome by re-estimating the selected model using the Generalized Least Squares (GLS) method This method can help adjust for variance changes and lessen its effect on the model's results
From the results obtained from the research model, the author will discuss the relevance of research results compared to reality and the results of previous studies.
RESEARCH RESULTS AND DISCUSSION
DESCRIPTIVE STATISTICS
The research applies a descriptive statistical method of study data through the SUMMARIZE function in STATA software to get an overview of study data, including total observations, mean value, standard deviation, minimum value, and maximum value Secondary data collected from 25 join-stock commercial banks in Vietnam between 2013 and 2022 is shown through the research variables in the following tables:
Variable Obs Mean Std Dev Min Max
The capital adequacy ratio (CAR) has an average value of 12.52% and a standard deviation of 3.09% The lowest value of the capital adequacy ratio (CAR) is 8.35% (LienViet Post Joint Stock Commercial Bank), while the highest is 27.98% (Orient Commercial Joint Stock Bank) The difference between CAR's maximum and minimum values shows a considerable difference in the capital adequacy ratio of Vietnamese commercial banks
Figure 4.1: The average CAR of Vietnamese commercial banks from 2013 to 2022
The capital adequacy ratio (CAR) decreases gradually from 2013 to 2022 This decrease may signal that banks are inclined to use more capital for investment and lending or increase leverage, which may increase profits and risks The chart above shows that the capital adequacy ratio of Vietnamese commercial banks reached the highest level in 2013, at 14.53%, and maintained a stable level from 2013 to 2015 After that, banks' capital adequacy ratio decreased continuously from 2016-2020, reaching the lowest level of 10.72% in 2020 In 2022, banks' capital adequacy ratio stabilized and rebounded to 11.76%
The return on total assets ratio (ROA) has a mean value of 0.94% and a standard deviation of 0.74% The lowest value of the return on total assets ratio (ROA) is 0% (National Citizen Bank), and the highest is 3.65% (Vietnam Technological And Commercial Joint Stock Bank)
Figure 4.2: The average ROA of Vietnamese commercial banks from 2013 to 2022
The chart above shows the return on total assets (ROA) change from 2013 to
2022 Specifically, the ROA of banks tends to grow markedly and stably over the years Banks are becoming more efficient in using assets to generate profits, enhance interest income and optimize costs Banks' return on total assets ratio was relatively low between
2013 and 2016, only 0.51% in 2015 However, banks' ROA ratio grew strongly from 2017-2022, reaching the highest rate in 2022 at 1.55% Both savings and loan interest rates are currently in the potential range At that point, the bank's ability to expand its operation scale will significantly increase
The size of the bank (SIZE) has an average statistical value of 1862.36 and a standard deviation of 162.09 The lowest size of the bank is 1202.33 (Asia Commercial Joint Stock Bank), while the highest value is 2147.50 (Joint Stock Commercial Bank for Investment and Development of Vietnam)
Figure 4.3: The average SIZE of Vietnamese commercial banks from 2013 to 2022
Bank size (SIZE) grew steadily from 2013-2022 There have been no sudden fluctuations or sharp declines over the years, which signals stability and predictability in banks' management and business strategies The chart above indicates the steady growth of the bank's size, with a low of 17.97 in 2013 and a high of 19.25 in 2022 The banking industry is in the digital transformation process, so commercial banks are also actively promoting investment in this form of technology Improved products and services bring satisfaction to customers, thereby expanding the scale of the bank
The net interest margin ratio (NIM) average is 3.13% and a standard deviation of 1.31%, with the lowest value (NIM) at 0.58% (Ho Chi Minh City Development Joint Stock Commercial Bank) and the highest at 9.45% (VietNam Prosperity Joint Stock Commercial Bank)
Figure 4.4: The average NIM of Vietnamese commercial banks from 2013 to 2022
The change in banks' net interest margin (NIM) fluctuated slightly from 2013 to
2022 Net interest margins show some fluctuations over the years but no extreme change, which signals a degree of stability in the difference in interest rates obtained from loans and interest paid on deposits The chart above shows a slight upward trend in NIM from
2013 to 2022, especially from 2019 onwards, and reached a peak rate of 3.67% in 2022 Stable NIM indicates that banks are capable of maintaining competitive interest rates for both loans and deposits, which is necessary for stability and forecasting profits in the long term
The loan loss reserve ratio (LLR) averaged 83.37%, and its standard deviation is 52.66%, with the highest value at 420.52% (Joint Stock Commercial Bank For Foreign
Trade Of Vietnam) and the lowest at 11.22% (National Citizen Bank), demonstrating that high lending activity results in a high reserve ratio
Figure 4.5: The average LLR of Vietnamese commercial banks from 2013 to 2022
From 2013 through 2022, the provision ratio for customer loans of commercial banks increased significantly It can be seen that the bank is strengthening its capital provision to protect against the risk of customers not being able to repay their debts The chart above illustrates that LLR increased from around 56.08% in 2013 to over 111.10% in 2022 The continuous increase, especially the jump from 2020, can be related to the impact of economic events such as the COVID-19 pandemic, making the bank strongly aware of lending risks, thereby increasing reserves to cope with the growth of bad loans and doubtful loans
The average value of the loans on assets ratio (LOA) is 58.62%, and the standard deviation is 10.55%, with the lowest at 22.01% (Vietnam Maritime Commercial Joint Stock Bank) and the highest at 78.81% (Joint Stock Commercial Bank for Investment and Development of Vietnam)
Figure 4.6: The average LOA of Vietnamese commercial banks from 2013 to 2022
The chart shows that the ratio of customer loans to total assets (LOA) from 2013 to 2022 has been fairly stable, around 45% to over 60% throughout the period indicated Banks maintain a balance between expanding lending operations and maintaining sufficient liquid assets to manage cash needs and meet fiat capital requirements Stability in this ratio can also indicate that the bank manages its risks effectively, not lending excessively relative to the capabilities of total assets
The liquidity ratio (LIQ) has a mean value of 16.76% and a standard deviation of 6.8% The lowest liquidity ratio (LIQ) is 4.52% (Sai Gon Thuong Tin Commercial Join Stock Bank), and the highest at 42.56% (Kien Long Commercial Joint Stock Bank)
Figure 4.7: The average LIQ of Vietnamese commercial banks from 2013 to 2022
RESEARCH RESULTS
The correlation matrix shows the correlation between two variables The correlation coefficient is from -1 to +1 A correlation coefficient close to 0 or 0 means that the two variables do not affect each other The two variables negatively correlate if the correlation coefficient's value is negative This study analyzes the correlation between the dependent variable CAR, which represents the capital of banks, with the research variables: return on assets, size of banks, net interest margin, loan loss reserve, liquidity of total assets, loans on assets, and leverage ratio
Table 4.2: Correlation coefficients between research variables
CAR ROA SIZE NIM LLR LOA LIQ LEV
According to the results in Table 4.2, since the absolute value of the correlation coefficients of the variables are all less than 0.8, this shows that the relationship between the variables is at an acceptable level The dependent variable CAR has a positive relationship with the independent variables NIM and LIQ, while the opposite happens with the variables ROA, SIZE, LLR, LOA and LEV
We rely on the variance exaggeration factor VIF through STATA software to check whether multicollinearity happens in the regression model If the variance inflation factor (VIF) is > 2, there is a sign of multicollinearity, which is undesirable If VIF > 10, then there is multicollinearity If VIF < 2, there is no multicollinearity
Table 4.3: Results of multicollinearity test
The results show that Mean VIF = 1.85 < 2, so this model has a non-serious multicollinearity phenomenon (although the ROA and NIM variables have VIF > 2, it can be considered that they can be ignored).
REGRESSION MODEL AND VALIDATION MODEL RESULTS
Table 4.4 below summarizes the regression results of the study model according to the three Pooled OLS, FEM and REM estimation methods
Table 4.4: Results of Pooled – OLS, FEM and REM Variable Pooled OLS FEM REM ROA -1.545*** -2.096*** -1.841***
The result of the Pooled OLS Model illustrates that three variables, ROA, SIZE and LEV, with a statistical significance of 1%, and two variables, with a statistical significance of 5%, NIM and LOA Also, the LLR and LIQ variables were not statistically significant in the model At the same time, the coefficient R-sq=0.5035 of the model shows that the independent variable can explain 50.35% of the data variability in the model
The result of the FEM Model shows that three variables, ROA, NIM and LEV, with a statistical significance of 1%, and LOA is with a statistical significance of 5% Also, the SIZE, LLR and LIQ variables were not statistically significant in the model At the same time, the coefficient R-sq=0.4663 of the model shows that the independent variable can explain 46,63% of the data variability in the model
The result of the REM Model demonstrates that three variables, ROA, LOA and LEV, with a statistical significance of 1%, and two variables with a statistical significance of 5%, SIZE and NIM Also, the LLR and LIQ variables were not statistically significant in the model At the same time, the coefficient R-sq=0.4560 of the model shows that the independent variable can explain 45,60% of the data variability in the model
Compare the FEM model and the REM model
The study applies the Hausman test to compare and choose between FEM and REM The study research hypothesis is:
H0: There is no correlation between the independent variables and the residual (The REM model is more suitable)
H1: There is a correlation between the independent variables and the residual (The FEM model is more suitable)
Table 4.5: Results of the Hausman test
Test: H 0 : Difference in coefficients not systematic
Based on the above results from STATA software, it illustrates that Prob > chi2 0,0000 < 5%, so rejecting hypothesis H0 and accepting hypothesis H1 means that the FEM model is more suitable
Compare Pooled OLS Model and Fixed Effects Model (FEM)
The Breusch-Pagan Test is used to choose between the Pooled OLS model and the FEM model The study research hypothesis is:
H0: The Pooled-OLS model is more suitable for the research variables
H1: The FEM model is more suitable for the research variables
Table 4.6: Results of Breusch and Pagan LM test
The table below shows the results of the Breusch-Pagan test that Prob > Chibar2(01) = 0.0000 < 5%, so the hypothesis H0 is rejected, which means the FEM model is more suitable for the research variables of the study
4.3.2 Inspection of Fixed Effects Model (FEM) defects
Test for the phenomenon of autocovariance change
After selecting the FEM model as the most suitable model for a research model, the study conduct a test to verify the phenomenon of changing the covariance through the following hypothesis:
H0: The model does not have variable variance
H1: The model occurs with variable variance
Table 4.7: Results of Modified Wald test
Modified Wald test for groupwise heteroskedasticity in fixed effect regression model
After choosing the appropriate research model as FEM, the study used the test of variance phenomenon to obtain the results Prob > F = 0.0000 < 5%, so hypothesis H0 was rejected This means that the phenomenon of variable variance has occurred with the model
Wooldridge Test is used to verify whether research models have an autocorrelation phenomenon The following hypothesis is proposed:
H0: The research model does not have autocorrelation
H1: The research model has autocorrelation
Table 4.8: Results of Wooldridge test
Wooldridge test for autocorrelation in panel data
The table below shows that the research model has Prob>F = 0.000 < 5%, which means hypothesis H0 is rejected, which means the research model has autocorrelation
From the results of the above two tables, the author concludes that the selection of the study according to the FEM model does not occur the multicollinearity However, the FEM model has heteroskedasticity and autocorrelation between variables, which makes the estimated results obtained from the regression model invalid; the tests are no longer valid To overcome the stated problem, the study uses the GLS method commonly used in previous studies so that the estimated results are stable and most effective
4.3.3 Overcoming the research model and GLS regression model method
Feasible Generalized Least Square Methods (FGLS) is used in the research model to overcome autocorrelation and variable variance
Table 4.9: Results of FGLS model troubleshooting Independent Variables Dependent Variable
Prob > chi2 0.0000 t statistics in brackets
The table shows the results of the model’s FGLS model troubleshooting Specifically, only two variables, LLR and LLQ, are not statistically significant and have not explained the impact of variables on the CAR change
Besides, ROA, SIZE, LOA, and LEV had the opposite effect on CAR and were statistically significant by 1% This means that when ROA, SIZE, LOA and LEV increase by 1%, CAR will decrease by 136.91%, 0.28%, 5.57% and 71.09%, respectively
On the other hand, the NIM variable has a synergistic effect with a significant level of 5% This clearly shows that the capital adequacy ratio will decrease by 41.41% when NIM increases
From the estimated results found, the model studies the factors affecting the capital of commercial banks in Vietnam in the period 2013 - 2022:
CAR it = 0.8659 - 1.3691.ROA it - 0.0028.SIZE it + 0.4141.NIM it - 0.0557.LOA it -
The study summarizes the final results of variables from the FGLS remediation model as follows
Table 4.10: Summary of estimated results Variables Reject H0 Expected Sign Estimated Sign Significant Level
CONCLUSION
CONCLUSION
This thesis aims to examine the factors that have impacted CAR, such as return on assets, bank size, net interest margin, loan-to-asset ratio, provision for loan losses, liquidity and leverage, in terms of financial stability at 25 Vietnamese commercial banks from 2013 to 2022
The regression model chosen for this research is the FGLS model According to this result, only the net interest margin positively affects the capital adequacy ratio Meanwhile, factors that negatively affect the bank's capital (CAR) are return on assets, bank size, loan-to-assets ratio and leverage The two variables, liquidity and loan loss reserve, have no statistical significance in the research model.
THEORETICAL IMPLICATIONS
The research findings elucidate that among various banking metrics, the net interest margin (NIM) positively impacts the capital adequacy ratio (CAR), a crucial indicator of a bank's financial health and ability to withstand potential losses This outcome highlights the significance of banks managing their interest-related income and expenses efficiently to enhance their profitability, which, in turn, bolsters their capital adequacy First, adopting dynamic interest rate management could allow banks to adjust the rates on loans and deposits based on market conditions, optimizing interest margins and enhancing profitability Additionally, banks should use stringent cost control measures to manage interest expenses more effectively This might include renegotiating terms with creditors or optimizing strategies for managing cash reserves Investing in advanced financial analytics is also advisable, enabling banks to predict and adapt to market trends that affect interest income and expenses efficiently Furthermore, developing and employing risk-based pricing models would ensure that the risks associated with loans are adequately reflected in their pricing, thereby contributing positively to the bank’s profitability
Conversely, several factors adversely affect the CAR, including the return on assets (ROA), suggesting that higher profitability through asset utilization may entail risk-taking, jeopardizing capital adequacy Practical policies that could be applied to commercial banks in Vietnam include the implementation of a robust risk management framework that scrutinizes the risk-return ratio of each asset class This would involve stricter due diligence and continuous asset performance monitoring to identify risks early and manage proactively Banks could also establish limits on the proportion of high-risk assets in their portfolios, thereby preventing excessive exposure to potentially volatile investments Additionally, incentivizing risk-adjusted performance among management and staff could align personal objectives with the bank’s long-term stability, further reinforcing prudent risk-taking behaviors
Furthermore, the negative correlation of bank size with CAR implies that larger banks might struggle with risk management across their extensive operations, impacting their capital ratio negatively Firstly, enhancing the granularity of risk management by segmenting operations into more manageable units could help closely monitor and control risks This division allows for more tailored risk management strategies appropriate for different operational areas' scales and specific risk profiles Secondly, implementing advanced technological solutions, such as sophisticated risk analytics and automated risk management systems, would help large banks detect and mitigate risks in real-time, improving responsiveness to potential threats Additionally, enhancing internal risk reporting and compliance structures could ensure that all levels of the organization are informed and accountable for managing risks effectively
The loan-to-assets ratio and leverage are also detrimental to CAR, indicating that a higher reliance on loans as assets and borrowed funds increases financial risk, undermining capital adequacy Commercial banks in Vietnam should consider implementing policies aimed at optimizing their balance sheets One practical policy could be the establishment of stricter lending criteria and loan approval processes to ensure that all issued loans meet high creditworthiness standards, thus reducing the likelihood of defaults Additionally, banks could enforce more conservative loan-to- assets and leverage ratios, setting internal limits that are stricter than regulatory requirements to maintain a safer buffer Moreover, diversifying the asset portfolio beyond loans to include more securities, cash, and other low-risk assets could decrease overall risk exposure.
LIMITATION AND FUTURE RESEARCH
The study gathered data from 25 joint stock commercial banks in Vietnam and produced specific results Notably, while gathering data from 25 joint stock commercial banks in Vietnam, the author could not locate data from a few banks Furthermore, the author cannot discover several variables in the financial accounts The research of 25 joint stock commercial banks in Vietnam over ten years is too small to generalize the banking environment From there, the author finds it impossible to assess the capital adequacy ratio objectively Finally, because the study focuses on Vietnamese joint stock commercial banks, other kinds of banks are not addressed; therefore, the research results are unsuitable for many other types of banks
The research topic covers 25 joint-stock commercial banks in Vietnam over a 10- year period from 2013 to 2022 The author must broaden the area of the research and forecast trends based on the research scope From there, we can see the growth and competitiveness of Vietnam's joint stock commercial banks
Chapter 5 summarizes the results of the research model on the impact of factors on the capital adequacy ratio of Vietnamese commercial banks Based on the above, the thesis proposes policies to develop and ensure capital ratio, such as solutions to increase business efficiency, credit growth solutions and solutions to ensure capital safety The limitations of this study are also mentioned, from which limitations build new foundations to guide future research
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Appendix 1 Results of statistical description
Appendix 3 Pooled OLS Regression Results
Appendix 5 FEM regression model results
Appendix 6 REM regression model results
Appendix 7 Results of Hausman test
Appendix 8 Results of Breusch-Pagan test
Appendix 9 Results of Modified Wald test
Appendix10 Results of Woolridge test
Appendix 11 FGLS regression model results
Appendix 12 Synthesis of results of 4 research models: Pooled OLS, FEM, REM and FGLS
Appendix 13 Name of Commercial Banks in Vietnam
No Stock Code Full name of commercial bank
1 ABB An Binh Commercial Joint Stock Bank
2 ACB Asia Commercial Joint Stock Bank
3 BAB Bac A Commercial Joint Stock Bank
4 BID Joint Stock Commercial Bank for Investment and Development of Vietnam
5 BVB Viet Capital Commercial Joint Stock Bank
6 CTG Vietnam Joint Stock Commercial Bank For Industry And Trade
7 EIB Vietnam Export Import Commercial Joint - Stock Bank
8 HDB Ho Chi Minh City Development Joint Stock Commercial Bank
9 KLB Kien Long Commercial Joint Stock Bank
10 LPB LienViet Post Joint Stock Commercial Bank
11 MBB Military Commercial Joint Stock Bank
12 MSB Vietnam Maritime Commercial Joint Stock Bank
13 NAB National Commercial Banking Corporation of Australia
14 NVB National Citizen Commercial Joint Stock Bank
15 OCB Orient Commercial Joint Stock Bank
16 PGB Prosperity and Growth Commercial Joint Stock Bank
17 SHB Saigon-Hanoi Commercial Joint Stock Bank
18 SSB Southeast Asia Commercial Joint Stock Bank
19 STB Sai Gon Thuong Tin Commercial Join Stock Bank
20 TCB Vietnam Technological And Commercial Joint Stock Bank
21 TPB TienPhong Commercial Joint Stock Bank
22 VAB Vietnam - Asia Commercial Joint Stock Bank