MINISTRY OF EDUCATION AND TRAINING THE STATE BANK OF VIETNAM HO CHI MINH UNIVERSITY OF BANKING ********************* HOCHIMINH UN1VERSITY OF BANKING NGUYEN DANG KHANH HUYEN FACTORS AFFECTING CAPITAL A[.]
INTRODUCTION
Reason for choosing the topic
The banking industry is regarded as the backbone of every economy and is crucial to the growth of the national economy Storage, mobilization, and allocation of currency are tasks performed by the bank in its capacity as a currency trading organization (Barbara Casu, Claudia Girardone, & Philip Molyneux, 2015). However, an underperforming bank might cause the failure of other banks and negatively impact the economy Oloo (2011) underlines that every incident has significant effects on the expansion of an economy if commercial banks dominate the financial sector This is because every bankruptcy in the banking sector has a spread effect on financial crises and other economic problems.
Since the banking crisis caused the financial crisis (Friedman & Schwartz,
1963), therefore, to safeguard depositors and prevent the banking system from collapsing, the regulatory authorities have concentrated on using the Capital Adequacy Ratio (CAR) based on the Basel standard to advance the stability and effectiveness of the financial system (Barbara Casu, Claudia Girardone, & Philip Molyneux, 2015).
In the face of the constantly changing and volatile situation of Vietnam's financial sector in general and the banking industry in particular, the study was conducted to determine the factors affecting the adequacy ratio of Vietnam's commercial banking system in the current context.
The level of capital adequacy has become a supervisory standard, a key factor for assessing the stability and transparency of the system, helping banks to withstand financial shocks, and protecting depositors and the bank itself (Hoggarth, Reis, & Saporta, 2002) The goal of this is to make sure that banks have enough cushion to absorb a reasonable amount of losses before they become insolvent and consequently lose depositors’ funds.
There have been several studies in the world and Vietnam on the factors
2 affecting the capital adequacy ratio of commercial banks Many empirical studies have been conducted in Hong Kong, the European Union, and some developing countries such as Turkey, Pakistan, and Nigeria However, the conclusions of these studies are still different and controversial, the suggested solutions of which may not be applicable to Vietnam's banking industry Therefore, the author has chosen to study the topic " Factors affecting capital adequacy ratio of commercial banks in
Vietnam " for the graduation thesis.
The research objectives
The study’s overall objective is to analyse the factors affecting the capital adequacy ratio of commercial banks in Vietnam, thereby providing implication recommendations to effectively manage the bank's capital adequacy ratio.
Based on the overall objectives, the author details the objectives as follows:
Firstly , determining the factors impacting the capital adequacy ratio of commercial banks in Vietnam.
Secondly , analysing the level of the impact of factors influencing the capital adequacy ratio of commercial banks in Vietnam.
Finally , proposing implications to manage the capital adequacy ratio of
The research question
Based on the particular objectives mentioned above, the thesis will in turn answer the following questions:
Question 1 : What factors affect the capital adequacy ratio of commercial banks in Vietnam?
Question 2 : What is the impact level and direction of these factors on the capital adequacy ratio of Vietnam's commercial banks?
Question 3 : What are suggestions that can manage the capital adequacy ratio of
Subject and scope of the research
Research subject : Factors influencing the capital adequacy ratio of commercial banks.
Research scope : Samples were collected on audited financial statements of 20
Vietnamese commercial banks which are listed on the Stock Exchange, in the period
2012 - 2021 The reason for choosing these banks is due to the publication of the capital adequacy ratio, and these banks have charter capital large enough to be able to represent other commercial banks The criteria needed for the study are shown in the bank’s audited financial statements and annual reports.
Methodology
In order to accomplish the research objectives mentioned above, the thesis uses the following research methodologies:
To solve question 1, the author uses the qualitative method Specifically, this graduation thesis collects, statistics, and synthesizes and analyzes the financial data of Vietnamese commercial banks In addition, the thesis also collects and synthesizes domestic and foreign research related to the research thesis to find out factors affecting the capital adequacy ratio.
To solve question 2, the author uses both qualitative and quantitative methods. Specifically, after collecting and synthesizing domestic and foreign research related to the research thesis, a summary table of the expectations of factors that may affect CAR will be drawn up, from which the author will hypothesize the impact of such factors on the CAR indicator Based on data collected from audited financial statements and annual reports of banks, the thesis uses commonly used estimation models for panel data: compound least squares model (POLS), fixed effects regression model (FEM), and random effects regression model (REM) In addition, there are tests and remedies for defects to establish the optimal regression model.
To solve question 3, the author uses the qualitative method Specifically, based on the established model, and other empirical studies in Vietnam, the author will suggest some possible solutions to help Vietnamese commercial banks manage capital adequacy.
Research content
The study analyzes and evaluates factors influencing the capital adequacy ratio of Vietnamese commercial banks in the period 2012 - 2021 From that, examines the impact level of these factors on the capital adequacy ratio of Vietnamese commercial banks through the use of econometric models to test the effect and significance level of this effect The assessment and explanation of the factors influencing the capital adequacy ratio of Vietnamese commercial banks based on the actual operation of the banking industry in the research period will be carried out, and finally proposed solutions for improving the efficiency and safety of Vietnamese commercial banks.
Contribution of the thesis
Theoretically, the thesis contributes to building an empirical research model inVietnam to understand the financial and macro factors affecting the capital adequacy ratio of commercial banks From the research results, the study will open up many new and more in-depth research directions.
Determining the factors affecting the capital adequacy ratio at Vietnamese commercial banks will show the relationship between these factors as well as the degree of negative or positive influence on the capital adequacy ratio of Vietnamese commercial banks Thereby proposing solutions and appropriate management policies to maintain a capital adequacy ratio of at least 8% at Vietnamese commercial banks.
The proposed layout of the thesis
This chapter introduces an overview of the urgency of the thesis, the purpose, and the scope of the research presenting domestic and international studies as well as a quick view outline of the research methods used.
THEORETICAL FRAMEWORK
The capital adequacy ratio of commercial banks
2.1.1 The concept of the capital adequacy ratio
The 1988 Accord established minimum capital requirements for globally active banks and included off-balance-sheet exposures and a risk-weighting methodology designed, in part, to prevent banks from being dissuaded from retaining low-risk assets (Bank for International Settlements, 1999) The Basel Committee recommended that the minimum capital requirement must be maintained at 8.0% of risk-weighted assets (RWA), therefore, the explanation of capital risk management must be clearly defined by representing the combination of many categories of risk that may have an impact on banking operations After a review of Basel II following the financial crisis of 2008, a revised standard (Basel III) was published in 2010. However, the revised standard does not completely replace the old one (Basel Committee on Banking Supervision, 2001).
The capital adequacy ratio (CAR), according to (Olalekan & Sokefun, 2013), has traditionally been regarded as a critical concern for financial firms It is defined as the percentage of a financial institution's primary capital to assets and is used to assess its financial strength and stability Moreover, the study of (Parvesh Kumar & Nazneen, 2014), the capital adequacy ratio is a ratio proposed by the regulatory authority in the banking industry to evaluate the health of the banking system and make sure that banks can bear a sufficient level of losses resulting from damages. The capital adequacy ratio reflects the bank's internal capacity to withstand losses during a crisis The stronger the bank is, and the greater the extent of investor protection, the higher the CAR ratio By using this ratio, banks are guaranteed to be able to meet their obligations and manage other risks like operational risk, credit risk, and market risk.
All things concerned, in this study, the capital adequacy ratio is understood as an economic indicator reflecting the relationship between the bank’s equity capital and its risk-weighted assets, which is considered an important criterion to reflect a bank's capacity in terms of solvency.
2.1.2 Meaning of the capital adequacy ratio
One crucial factor in evaluating how well commercial banks operate is their capital adequacy ratio, which is a key indicator of the bank's operations safety. Commercial banks face two major challenges in the increasingly rapid and diversified development of the financial industry in particular and the economy in general: competitiveness and potential risks in business activities In order to safeguard depositors and prevent the banking system from potential risks, banks are required to use the capital adequacy ratio (CAR) based on the Basel standard to advance their stability (Barbara Casu, Claudia Girardone, & Philip Molyneux, 2015).
To put it differently, by guaranteeing this ratio, the bank protected both itself and depositors by creating a buffer against financial shocks Consequently, suitable capital adequacy ratio implementation also contributes to the improvement of commercial banks' reputation and competitiveness This has a very specific significance to commercial banks' deposit business.
CAR is also a tool used by the State Bank to monitor the capital of commercial banks and establish a minimum capital adequacy ratio periodically to ensure that commercial banks are complying with standards The fact that banks follow capital adequacy ratio regulations helps to prevent insolvency from leading to bankruptcy,which puts the entire financial system of the entire nation in peril.
2.1.3 Measure the capital adequacy ratio
The capital adequacy ratio is a measure of a bank's capital adequacy According to (The State Bank of Vietnam, 2014), the ratio is calculated by the formula below:
Equity capital of a commercial bank is the monetary value created by the bank that belongs to the bank (Aktas, Acikalin, Bakin, & Celik, 2015) In the formula of calculated the CAR, equity capital is determined as follows:
Equity capital = Tier 1 Capital + Tier 2 Capital
Tier 1 capital includes the most reliable and most liquid types of financial resources, which primarily consist of shareholders' equity (common stock) and retained earnings According to (Bank for International Settlements, 2011), tier 1 capital must be at least 6.0% of risk-weighted assets at all times From a regulator's point of view, tier 1 capital is the core measure of a bank's financial strength. However, each nation's banking system regulator has its specific regulations on whether specific financial products can be included in tier 1 capital.
Tier 2 Capital includes: Undisclosed reserves, The added value of asset revaluation through asset revaluation provision, General provision and provision for general loss of debt collection, Mixed capital instruments, Preferential loans, and Investments in financial subsidiaries and other financial institutions.
Risk-weighted assets are the total amount of a bank's assets that have been
CAR = Equity capital weighed for credit risk in line with the law with a formula provided by the state authorities, typically the State Bank The Bank of International Settlements (BIS),which sets these weights, is used by the majority of State Banks.
RESEARCH METHODS
Analysis process
Step 1 : Review the theoretical basis and related previous studies in Vietnam and other countries, then discuss previous studies to identify research gaps and design directions for the research model.
Step 2 : Based on the theoretical basis and empirical evidence, the thesis designs a research model, predicts regression equations, explains variables, and builds research hypotheses.
Step 3 : Determine the research sample suitable for the research objectives as well as the object and scope of the research, then collect and process data according to the research model in step 2.
Step 4 : Identify methodology with specific analysis and estimation techniques namely descriptive statistics, correlation analysis, and regression analysis of panel data according to OLS, FEM, and REM.
Step 5 : After performing the estimation with 3 methods Pooled OLS, FEM, REM, the author conducts some tests including F - Test, Breusch – Pagan, and Hausman to choose the most suitable model.
Step 6 : Correct the model's defects such as variable variance or autocorrelation if the model has arisen.
Step 7 : This is the final step of the process based on the regression results; the topic conducts discussions, draws conclusions, and makes relevant suggestions and policy implications for answering the research questions as well as solving the research objectives set out.
Samples and research data
The research is conducted based on secondary data collected from audited financial statements and related documents from 2012 to 2021 of 20 commercial banks in Vietnam.
The topic uses secondary data to measure the dependent and independent variables belonging to the group of micro-factors belonging to commercial banks,collected from audited financial statements from 2012 to 2021 of 20 commercial banks in Vietnam, which are listed on the stock market in Vietnam Secondary data to measure the independent intrinsic variables collected from the banks’ audited financial statements from 2012 to 2021 Data sources for the independent variables in the group of macro factors were collected from the World Bank’s website (data.worldbank.org).
The data source for the dependent variable was gathered from the annual reports of those 20 banks from the period 2012 to 2021.
The results of measuring the impact of factors affecting the capital adequacy ratio of commercial banks in Vietnam are based on panel data with the support of Excel software and Stata 16.0 software.
Regression methods and tests
3.3.1 Ordinary least square method (OLS)
The Pooled OLS model is a regression model in which all coefficients do not change over time and in units (banks) In other words, the independent variables are considered the same, so the level of impact on the dependent variable is the same between commercial banks and does not change over time This is the simplest approach and the simplest model when it does not consider the space and time of the combined data but only estimates according to conventional OLS regression So this model can produce incomplete results and distort the reality of the relationship between independent variables and dependent variables.
Assuming that each unit has unique characteristics that can influence explanatory variables, FEM analyzes the correlation between each unit's residual and explanatory variables, thereby controlling and separating the effects of distinct characteristics(constant over time) from explanatory variables so that we can estimate the real effects (net effects) of variable interpretation up dependent variable.
The REM model determines the different intercepts for each cross-unit, the overall effect of the explanatory variables The intercepts of each cross-unit are derived from a common intercept that is constant over time and subject, and a random variable is a component of variable error by an object but does not change over time.
Research model and hypothesis
After the process of synthesizing the theoretical framework related to the capital adequacy ratio, examining studies related to factors affecting the capital adequacy ratio at commercial banks, and identifying research gaps, the author selected the model of Le Hoang, Nguyen Hoang, Le Thi Minh, & Cao Bich (2022) to inherit and expand However, this study is still incomplete to fill the research gaps, so the author will add some banks’ intrinsic factors and the macro variable which is the inflation rate Therefore, the thesis proposes a research model with the following equation:
CAR it = α + β1SIZE it + β2LOA it + β3ROA it + β4NPL it + β5DEP it + β6INF t + it
CAR it : Capital adequacy ratios of banks i year t
SIZEit : Size of commercial bank i in year t
LOA it : The loans ratio of commercial bank i in year t
ROAit : Return on assets of commercial bank i in year t
NPL it : Non-performing loans ratio of commercial bank i in year t
DEPit: Deposit ratio of commercial banks in year t
INF t : Inflation rate in year t
With i , it correspond to the bank and the survey year; α is the intercept factor; 1-6 are the slopes of the independent variables, and it is the statistical residual.
3.4.2 Description of variables and hypothesis
The natural logarithm of total assets is used as a representative of bank size (SIZE). The bank size is important because of its relationship with the financial markets for easy access to capital Larger banks have better access to the capital market at a lower cost since the larger capacity in paying back the lower bankruptcy risk to investors. However, bank size in Bhattarai (2020); Shingjergji, Xhuvani, & Hyseni (2015); Kalifa & Bektaş (2017); Bateni, Vakilifard, & Asghari (2014); Nadja Dreca (2014); Bahtiar, Henny Setyo, & Tiara (2019); Chi (2018); and Thoa, Anh, & Minh
(2020) give mixed results With opposite arguments, therefore, in this research paper, the author expects a negative relationship between SIZE and CAR, showing that the larger the bank’s capital is, the lower CAR is expected, as the results of the majority of previous Vietnamese studies:
H 1 : Bank size has a negative effect on the capital adequacy ratio.
Loans ratio (LOA) is the ratio of total loans to total assets On the one hand, it provides the majority of income to commercial banks, and on the other, it determines the amount of credit risk that a bank will have as a result of lending The risk and returns of the loan portfolio depend on the characteristics of the loans and the extent of portfolio diversification of a bank In general, the more loans extended, the higher the risk To hedge against the risk, a larger amount of capital will be needed.However, the loans ratio in Ahmet Büyükşalvarci (2011); Shingjergji, Xhuvani, &Hyseni (2015); Nadja Dreca (2014); Bahtiar, Henny Setyo, & Tiara (2019); and Le
Hoang, Nguyen Hoang, Le Thi Minh, & Cao Bich (2022) give the same results With the same arguments, therefore, in this research paper, the author hypothesizes the loans ratio as follows:
H 2 : The loans ratio has a negative effect on the capital adequacy ratio.
In this study, return on assets (ROA) is used to measure the profits of a bank In general, commercial banks raise capital through retained earnings A higher return allows a bank to increase capital through retained earnings A higher return also makes a bank more attractive in raising capital However, return on assets in Ahmet Büyükşalvarci (2011); Triyuwono, Aulia Rahman, Abusharba, Ismail, & Rahman (2013); Bateni, Vakilifard, & Asghari (2014); Nadja Dreca (2014); Kadek, Swandewi, & Purnawati (2021); Chi (2018); and Vu & Dang (2020) give mixed results With opposite arguments, therefore, in this research paper, the author expects a positive relationship between ROA and CAR, showing that the more return on assets increases, the more actual CAR, as the results of the majority of previous studies:
H3: Return on assets has a positive effect on the capital adequacy ratio.
Non-performing loans ratio (NPL)
Non-performing loans (NPL) are loans to customers that are facing high risks in recovering principal and interest due to customers facing difficulties Non- performing loans include debt groups from 3 to 5 If this ratio is higher than the industry average, it means that the bank is having difficulty in managing the quality of loans Hence, it will also lead to a restriction on the bank's lending activities.However, non-performing loans ratio in Triyuwono, Aulia Rahman, Abusharba,Ismail, & Rahman (2013); Shingjergji, Xhuvani, & Hyseni (2015); and Kadek,
Swandewi, & Purnawati (2021) give the same results With the same arguments, therefore, in this research paper, the author hypothesizes non-performing loans as follows:
H 4 : Non-performing loans ratio has a negative effect on the capital adequacy ratio.
The deposits ratio (DEP) is calculated as a ratio of a bank’s total deposits to its total assets In order to attract deposits, a bank would have to be adequately capitalized with sufficient liquidity so that depositors could feel safe and protected If a bank doesn’t have sufficient capital and liquidity, a run could occur when all depositors are rushing to withdraw their deposits, some depositors might not be paid However, the deposit ratio in Nadja Dreca (2014); Gabriel Ogere, Peter, & E.E (2013); Chi (2018); and Le Hoang, Nguyen Hoang, Le Thi Minh, & Cao Bich (2022) give the same results With the same arguments, therefore, in this research paper, the author hypothesizes the deposits ratio as follows:
H 5 : The deposits ratio has a negative effect on the capital adequacy ratio.
The inflation rate (INF) is measured by the growth of the consumer price index (CPI).Stable inflation is necessary for the economic development of a country When inflation increases, people tend to withdraw bank deposits to invest in different channels, such as gold, foreign currencies, etc., to avoid the risk of currency devaluation Therefore, the bank needs a large amount of cash to meet this demand.However, the inflation rate in Bhattarai (2020); Kalifa & Bektaş (2017); and GabrielOgere, Peter, & E.E (2013) give mixed results With opposite arguments, therefore, in this research paper, the author expects a negative relationship between INF and CAR according to the results of the majority of previous studies:
H 6 : Inflation rate has a negative effect on the capital adequacy ratio.
The selected variables and its hypothesis are presented in the table below:
Table 3.1.2 Summary of expected hypothesis
SIZE Bank size has a negative effect on CAR.
LOA The loans ratio has a negative effect on CAR.
ROA Return on assets has a positive effect on CAR.
NPL Non-performing loans ratio has a negative effect on CAR. DEP The deposits ratio has a negative effect on CAR.
INF Inflation rate has a negative effect on CAR.
RESEARCH RESULTS
Descriptive statistics
The results of the descriptive statistics of the measured variables in the regression model are presented in the table below:
Variable Obs Mean Std Dev Min Max
Source: Data processing results through Stata 16.0
Table 2 shows that all variables in the research model are balanced panel data, with 200 observations from 20 commercial banks over a 10-year period The results of descriptive statistics for each variable are as follows:
The capital adequacy ratio variable (CAR) has a mean value of 12.98% and a standard deviation of 3.59% The bank with the highest capital adequacy ratio of 33.4% belonged to Kien Long Commercial Joint Stock Bank in 2012 Inversely, the bank with the lowest capital adequacy ratio is An Binh Commercial Joint Stock
Bank, with 5.00% in 2012 Vietnamese commercial banks will have a capital adequacy ratio that changes depending on economic situations in each period.
The variable bank size (SIZE) is measured by taking the natural logarithm of total assets, and the analysis results from 20 commercial banks in Vietnam in the period 2012-2021 show that BIDV (with total assets of VND 1,761,695 billion in 2021) is the bank with the largest asset size In contrast, the bank with the smallest asset size is Tien Phong Commercial Joint Stock Bank, with total assets of VND 15,120 billion in
2012 In 2021, BIDV, VCB and CTG are the top 3 banks in terms of size and asset growth rate.
The variable loans ratio (LOA) is measured by taking the total loans divided by the total assets The variable has an average value of 58.59%, with a standard deviation of 11.3% Therefore, the lowest LOA was 22.53% belonging to MSB in
2014, and the highest was 80.06% belonging to BIDV in 2020.
The bank's Return on asset (ROA) shows a mean of 0.97% and a standard deviation of 0.7% In which, there is Techcombank with the highest ROA of 3.65% in
2021 and Eximbank with the smallest ROA value of 0.028% in 2015.
The variable non-performing loan ratio (NPL) has a mean value of 2.09% with a standard deviation of 1.31% over the period 2012-2021 In which, the non- performing loan ratio fluctuated from the lowest of 0.00% to the highest of 8.81%. The ratio of customer deposits to total assets (DEP) of commercial banks ranges from the lowest at 41.41% and the highest at 89.37% The average deposit ratio is 66.86%, and the standard deviation of the deposit ratio is 10.59%
The average inflation rate (INF) variable for the period 2012-2021 is 3.8%, with a standard deviation of 2.3% The highest volatile inflation rate was 9.09% in 2012, and the lowest was 0.63% in 2015.
Research results
The results of the correlation coefficient between the pairs of variables are presented in the table below:
CAR SIZE DEP ROA LOA NPL INF
Source: Data processing results through Stata 16.0
We can see from the results that the absolute value of the correlation coefficient between the independent variables is less than 0.8 According to Peter (2008), if there is no pair of variables in the model with the absolute value of correlation coefficient greater than 0.8, there will be no correlation phenomenon Therefore, the variables are linearly independent of each other Therefore, the variables are suitable for the study.
The results of the mean value of VIF of all independent variables are presented in the table below:
Source: Data processing results through Stata 16.0
The results from Table 4 show that the mean value of VIF (Variance InflationFactor) of all independent variables in the model is less than 10 Therefore, it concludes that the model does not have multicollinearity.
Regression results of the research model
The author conducted regression of panel data collected with three estimation methods namely Pooled OLS, FEM and REM to determine the influence of
Source: Data processing results through Stata 16.0 independent variables on the dependent variable through estimation coefficients The results are synthesized in the table below:
Table 4.4 Regression results of models
Models Pooled OLS FEM REM
Note: *** corresponds to the 1% significance level, ** corresponds to the 5% significance level, and * corresponds to the 10% significance level.
Source: Data processing results through Stata 16.0
4.3.1.1 Comparison of regression results between two models, Pooled OLS and FEM
The study compares two models, Pooled OLS and FEM, by F-test with hypothesis H0: Selecting the Pooled OLS model is appropriate.
Source: Data processing results through Stata 16.0
Based on the results of the F-test of the dependent variable CAR with the significance level α = 5%, Prob = 0.0000 < 5%, thus rejecting hypothesis H0 In other words, the author chooses the FEM model as the more suitable model.
4.3.1.2 Comparison of regression results between two models, FEM and REM
In order to find a more suitable model for the study, the author uses the Hausman test to choose between two models, FEM and REM, with hypothesis H0: Selecting the REM model is appropriate.
Table 4.6 Hausman Test Test: H 0 : Difference in coefficients not systematic chi2(6) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 25.25
With the significance level α = 5%, Prob>chi2 = 0.0003 < 5%, thus rejecting
Source: Data processing results through Stata 16.0 hypothesis H0 In other words, the author chooses the FEM model as the more suitable model.
Conclusion: After comparing three models, Pooled OLS, FEM, and REM, the author chooses the FEM model to determine the factors influencing the capital adequacy ratio of Vietnamese commercial banks.
Implement the Modified Wald test in the FEM model with the following hypothesis:
Hypothesis H0: There is no heteroskedasticity.
H 0 : There is no heteroskedasticity chi2 (20) = 3456.95 Prob>chi2 = 0.0000
Source: Data processing results through Stata 16.0
With the significance level α = 5%, Prob>Chi2 = 0.0000 < 5%, thus rejecting hypothesis H0 In other words, there is a heteroskedasticity phenomenon.
Implement the Wooldridge test in the FEM model with the following hypothesis:Hypothesis H0: There is no autocorrelation.
Source: Data processing results through Stata 16.0
Source: Data processing results through Stata 16.0
With the significance level α = 5%, Prob>F = 0.0000 < 5%, thus rejecting hypothesis H 0 In other words, there is an autocorrelation phenomenon.
Conclusion: Through the above tests, it can be seen that the research model both has heteroskedasticity and autocorrelation phenomenon.
Based on the test results of the above model, it can be seen that the model exists in both heteroskedasticity and autocorrelation Therefore, the author uses the FGLS model to overcome these two phenomena to complete the final model.
Table 4.9 Regression results of FGLS
Source: Data processing results through Stata 16.0
Note: *** corresponds to the 1% significance level, ** corresponds to the 5% significance level, and * corresponds to the 10% significance level.
Source: Data processing results through Stata 16.0
With the dependent variable CAR, after using the Feasible Generalized Least Squares (FGLS) method to overcome autocorrelation and heteroskedasticity phenomenon, with the significance level α = 5% (Prob>Chi2 = 0.0000), the regression model can be written as follows:
CAR it = 0.7397 – 0.019SIZE it + 0.2176NPL it – 0.029DEP it + ε it
CONCLUSION AND POLICY IMPLICATIONS
Conclusion
Through theoretical research, regression analysis of panel data and data estimation of 20 Vietnamese commercial banks in the period 2012-2021, the study has conducted research and made initial hypotheses about the factors affecting the capital adequacy ratio of Vietnamese commercial banks, including 6 factors as mentioned in Chapter 3 However, through the research results, the topic has analysed the level of three statistically significant factors affecting Vietnamese commercial banks' capital adequacy ratio, including SIZE, NPL and DEP According to the research results in Table 10 in chapter 4, it shows that SIZE has a negative impact on capital adequacy ratio with a statistical significance of 1%, and in other conditions unchanged; when SIZE increases by 1 unit, the capital adequacy ratio of banks decreases by 0.019 units Besides, NPL has a positive impact on capital adequacy ratio at the 5% level of significance, and in other conditions unchanged;when NPL increases by 1 unit, the bank's capital adequacy ratio also increases by0.2176 units Finally, DEP has a negative effect on capital adequacy ratio at the 10% significance level, and under constant conditions, when DEP increases by 1 unit, the capital adequacy ratio of banks decreases by 0.029 units.
Policy Implications
From the research results, the thesis has provided policy implications and solutions for commercial banks in Vietnam to manage capital adequacy ratio.
Firstly , the study results indicate that expanding the size of a bank or investing in more assets will reduce the bank's capital adequacy ratio This means that the larger banks hold more risky assets than the smaller ones, the smaller the CAR will be Therefore, the author recommends that bank managers should expand new points of transaction in various places where banking services are few such as densely populated rural areas In addition, restructuring existing branches and points of transaction to maximize operational efficiency is also essential Taken together, it is essential to maintain the bank size at an appropriate level, and the increase in size through increasing credit activities should be controlled.
Secondly , since DEP has a negative effect on CAR, the bank can increase its capital adequacy ratio by reducing its capital mobilization ratio Customers tend to choose reputable branded banks to deposit money, so interest rates are only a reference channel, instead, banks should strengthen internal controls, and strict risk control processes, contributing to creating trust for customers In addition, diversifying the portfolio of capital mobilization products suitable for each audience, each industry and each region should be the best action.
Finally , as NPL increases, CAR increases, proving that banks lend a lot, and bad debts increase the capital adequacy ratio Bad debts have negatively impacted the circulation of capital flows, the safety, and business efficiency of the bank.Therefore, in parallel with promoting profit-seeking lending, banks should also provide selective lending, limiting lending to areas which have high risk However,the application of the above implication depends on the actual market situation If the market is in a bull cycle, then a bank may accept risks to seek profits and a lower level of capital adequacy is accepted.
Limitation of the thesis
Besides the obtained results, the study still has some limitations, as follows: The research sample has only 20 Vietnamese commercial banks in the period
2012 - 2021, not covering all commercial banks in Vietnam to have a more comprehensive and accurate result for the entire Vietnamese commercial banking system Moreover, the study only focuses on domestic commercial banks, excluding foreign banks, policy banks and non-equitized banks (Agribank), so the research results are not sufficiently objective.
In addition, the study did not consider the impact on each type of bank's size: small, large and medium In addition, during the research process, the author found that there are several other factors affecting a bank’s capital adequacy ratio, such as real interest rates, exchange rates, money supply, and financial crisis; however, due to time constraints as well as some difficulties when collecting data from banks over the years, the author has not included the above factors in the topic.
Proposing directions for further research
Increasing the number of research samples and adding banks with characteristics different from commercial banks, such as Agribank and foreign banks in Vietnam. Therefore, the research results are expected to be more objective and representative of the entire banking industry.
In addition to the dependent variables affecting the bank’s capital adequacy ratio in this study, less studied variables such as exchange rate, money supply, real interest rates, and financial crisis will be added It is expected that the following studies will have a view from many different aspects of the factors affecting bank’s capital adequacy ratio.
Based on the conclusions in Chapter 4, Chapter 5 has suggested some policy implications for commercial banks' managers to manage and maintain the capital adequacy ratio of Vietnamese commercial banks This chapter has pointed out the limitations of the topic, thereby giving suggestions for future research directions related to research time and space as well as research content.
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9 Bhattarai, B (2020, 8) Determinants of Capital Adequacy Ratio Commercial Banks in Nepal Asian Journal of Finance & Accounting, 12(1), 194.
10 Chi, N (2018) NGHIÊN CỨU CÁC YẾU TỐ ẢNH HƯỞNG ĐẾN HỆ SỐ AN TOÀN VỐN Tạp chí Khoa học và Công nghệ, 36B.
11 Friedman, M., & Schwartz, A (1963) A Monetary History of the United States,
12 Gabriel Ogere, A., Peter, Z., & E.E, I (2013) Capital Adequacy Ratio and
Banking Risks in the Nigeria Money Deposit Banks Research Journal of Finance and Accounting, 4(17).
13 Hoggarth, G., Reis, R., & Saporta, V (2002) Costs of banking system instability: Some empirical evidence Journal of Banking & Finance, 26(5), 825- 855.
14 Kadek, N., Swandewi, M., & Purnawati, N (2021) Capital Adequacy Ratio
Mediates the Effect of Non-Performing Loan on Returns on Assets in Public Commercial Banks.
15 Kalifa, W., & Bektaş, E (2017, 4) The impacts of bank-specific and macroeconomic variables on the capital adequacy ratio: evidence from Islamic banks.
16 Le Hoang, V., Nguyen Hoang, A., Le Thi Minh, T., & Cao Bich, V (2022). Factors affecting capital adequacy ratio of joint-stock commercial banks in Vietnam.
Journal of International Economics and Management, 22(1), 42-60.
17 Nadja Dreca (2014) Determinants of capital adequacy ratio in selected Bosnian banks Dumlupınar ĩniversitesi Sosyal Bilimler Dergisi, 12(1), 149-162.
18 Olalekan, A., & Sokefun, A (2013) CAPITAL ADEQUACY AND BANKS' PROFITABILITY: AN EMPIRICAL EVIDENCE FROM NIGERIA American
International Journal of Contemporary Research, 3(10).
19 Oloo, O (2011) Banking Survey Report, The best banks this decade 2001-2010.
20 Parvesh Kumar, A., & Nazneen, A (2014) An Empirical Analysis of Capital Adequacy in the Indian Private Sector Banks American Journal of Research
21 Peter, K (2008) A guide to econometrics (6 ed.) Malden: Mass: Blackwell Publishing.
22 Shingjergji, A., Xhuvani, U., & Hyseni, M (2015) THE DETERMINANTS OF THE CAPITAL ADEQUACY RATIO IN THE ALBANIAN BANKING SYSTEM DURING 2007-2014 International Journal of Economics, Commerce and
23 The State Bank of Vietnam (2014, 11) CIRCULAR No 36/2014/TT-NHNN on stipulating minimum safety limits and ratios for transactions performed by credit institutions and branches of foreign banks.
24 The State Bank of Vietnam (2016, 12) CIRCULAR 41/2016/TT-NHNN on stipulating the capital adequacy ratio for foreign-owned banks and branches of foreign-owned banks.
25 Thoa, P., Anh, N., & Minh, N (2020) The determinant of capital adequacy ratio: empirical evidence from Vietnamese banks (a panel data analysis) Afro-Asian
Journal of Finance and Accounting, 10(1), 60-70.
26 Triyuwono, I., Aulia Rahman, F., Abusharba, M., Ismail, M., & Rahman, A.
(2013) Determinants of Capital Adequacy Ratio (CAR) in Indonesian Islamic
27 Vu, H., & Dang, N (2020) Determinants influencing capital adequacy ratio ofVietnamese commercial banks Accounting, 871-878.
APPENDIX 1: LIST OF COMMERCIAL BANKS IN VIETNAM IN THE
1 An Binh Commercial Joint Stock Bank ABB
2 Asia Commercial Joint Stock Bank ACB
3 Joint Stock Commercial Bank for Investment and Development of
4 Vietnam Joint Stock Commercial Bank for Industry and Trade CTG
5 Joint Stock Vietnam Export Import Commercial Joint Stock Bank EIB
6 Ho Chi Minh City Development Joint Stock Commercial Bank HDB
7 Kien Long Commercial Joint Stock Bank KLB
8 Military Commercial Joint Stock Bank MB
9 Maritime Commercial Joint Stock Bank MSB
0 Nam A Commercial Joint Stock Bank NAB
1 Ocean Commercial Joint Stock Bank OCB
2 Petrolimex Group Commercial Joint Stock Bank PGB
3 Saigon-Hanoi Commercial Joint Stock Bank SHB
4 Thuong Tin Commercial Joint Stock Bank STB
5 Vietnam Technology and Commercial Joint Stock Bank TCB 1
6 Tien Phong Commercial Joint Stock Bank TPB
7 Viet A Commercial Joint Stock Bank VAB
8 Joint Stock Commercial Bank For Foreign Trade Of Vietnam VCB
9 Vietnam International Commercial Joint Stock Bank VIB 2
0 Vietnam Prosperity Joint Stock Commercial Bank VPB
Ticker Yea r CAR SIZE DEP ROA LOA NPL INF
ABB 2012 0.0500 31.4600 62.45% 0.91% 40.76% 2.84% 9.0947ABB 2013 0.1345 31.6850 64.49% 0.27% 41.03% 7.63% 6.5927ABB 2014 0.1490 31.8426 66.85% 0.19% 38.49% 4.51% 4.0846ABB 2015 0.1750 31.7957 73.83% 0.14% 48.02% 2.42% 0.6312ABB 2016 0.1507 31.9374 69.47% 0.35% 53.65% 2.56% 2.6682ABB 2017 0.1340 32.0678 68.52% 0.62% 56.69% 2.77% 3.5203ABB 2018 0.1282 32.1308 69.18% 0.82% 57.98% 1.89% 3.5396ABB 2019 0.1107 32.2614 67.84% 1.04% 55.39% 2.31% 2.7958ABB 2020 0.0905 32.3878 62.31% 1.02% 54.39% 2.09% 3.2209ABB 2021 0.1200 32.4263 56.10% 1.31% 57.04% 2.34% 1.8347ACB 2012 0.1350 32.8033 71.03% 0.34% 58.32% 2.50% 9.0947ACB 2013 0.1470 32.7466 82.90% 0.48% 64.34% 3.03% 6.5927ACB 2014 0.1410 32.8218 86.08% 0.55% 64.76% 2.18% 4.0846ACB 2015 0.1280 32.9366 86.83% 0.54% 67.18% 1.31% 0.6312ACB 2016 0.1319 33.0850 88.60% 0.61% 69.92% 0.87% 2.6682ACB 2017 0.1149 33.2811 84.90% 0.82% 69.82% 0.70% 3.5203ACB 2018 0.1281 33.4281 81.98% 1.67% 70.00% 0.73% 3.5396ACB 2019 0.1091 33.5804 80.34% 1.69% 70.06% 0.54% 2.7958ACB 2020 0.1106 33.7280 79.45% 1.86% 70.07% 0.59% 3.2209ACB 2021 0.1120 33.8997 71.99% 1.98% 68.57% 0.77% 1.8347BID 2012 0.0965 33.8147 62.51% 0.58% 70.12% 2.70% 9.0947BID 2013 0.1023 33.9380 61.80% 0.78% 71.31% 2.26% 6.5927BID 2014 0.0927 34.1085 67.73% 0.83% 68.53% 2.03% 4.0846BID 2015 0.0981 34.3769 66.39% 0.85% 70.36% 1.68% 0.6312
BID 2016 0.0950 34.5452 72.14% 0.67% 71.91% 1.99% 2.6682BID 2017 0.1090 34.7230 71.53% 0.63% 72.10% 1.62% 3.5203BID 2018 0.1034 34.8111 75.37% 0.60% 75.30% 1.90% 3.5396BID 2019 0.0934 34.9375 74.78% 0.61% 74.97% 1.75% 2.7958BID 2020 0.0950 34.9553 80.88% 0.48% 80.06% 1.76% 3.2209BID 2021 0.0900 35.1051 78.36% 0.66% 76.89% 1.00% 1.8347CTG 2012 0.1033 33.8527 57.42% 1.28% 66.20% 1.47% 9.0947CTG 2013 0.1320 33.9878 63.24% 1.08% 65.29% 1.00% 6.5927CTG 2014 0.1040 34.1251 64.15% 0.93% 66.52% 1.12% 4.0846CTG 2015 0.1060 34.2897 63.24% 0.79% 69.03% 0.92% 0.6312CTG 2016 0.1040 34.4860 69.06% 0.79% 69.79% 1.02% 2.6682CTG 2017 0.0940 34.6296 68.76% 0.73% 72.20% 1.14% 3.5203CTG 2018 0.0930 34.6909 70.93% 0.47% 74.29% 1.59% 3.5396CTG 2019 0.0950 34.7545 71.96% 0.79% 75.38% 1.16% 2.7958CTG 2020 0.0950 34.8325 73.83% 1.07% 75.69% 0.94% 3.2209CTG 2021 0.0910 34.9651 75.86% 0.99% 73.82% 1.26% 1.8347EIB 2012 0.1294 32.7677 41.41% 1.21% 44.03% 1.32% 9.0947EIB 2013 0.1447 32.7659 46.79% 0.39% 49.08% 1.98% 6.5927EIB 2014 0.1362 32.7071 63.30% 0.21% 54.42% 2.46% 4.0846EIB 2015 0.1652 32.4581 78.84% 0.03% 67.89% 1.86% 0.6312EIB 2016 0.1712 32.4893 79.46% 0.24% 67.46% 2.95% 2.6682EIB 2017 0.1598 32.6374 78.69% 0.59% 67.83% 2.27% 3.5203EIB 2018 0.1505 32.6592 77.75% 0.44% 68.16% 1.85% 3.5396EIB 2019 0.1381 32.7522 83.13% 0.54% 67.60% 1.71% 2.7958EIB 2020 0.1181 32.7089 83.47% 0.65% 62.81% 2.52% 3.2209EIB 2021 0.1230 32.7420 82.84% 0.59% 69.15% 1.96% 1.8347
HDB 2012 0.1401 31.5972 64.91% 0.67% 40.07% 2.35% 9.0947HDB 2013 0.1220 32.0880 72.35% 0.31% 51.06% 3.67% 6.5927HDB 2014 0.1070 32.2314 65.72% 0.51% 42.06% 2.28% 4.0846HDB 2015 0.1340 32.2990 70.00% 0.61% 53.11% 1.59% 0.6312HDB 2016 0.1253 32.6436 68.73% 0.71% 54.71% 1.46% 2.6682HDB 2017 0.1350 32.8745 63.66% 1.15% 55.19% 1.52% 3.5203HDB 2018 0.1210 33.0066 59.27% 1.58% 56.99% 1.53% 3.5396HDB 2019 0.1120 33.0668 54.92% 1.80% 63.76% 1.36% 2.7958HDB 2020 0.1210 33.3966 54.72% 1.69% 55.88% 1.32% 3.2209HDB 2021 0.1440 33.5569 48.93% 1.86% 54.25% 1.65% 1.8347KLB 2012 0.3340 30.5532 57.27% 1.93% 52.11% 2.93% 9.0947KLB 2013 0.2074 30.6931 62.25% 1.57% 56.75% 2.47% 6.5927KLB 2014 0.1838 30.7710 71.72% 0.79% 58.55% 1.95% 4.0846KLB 2015 0.1980 30.8627 79.30% 0.68% 64.05% 1.13% 0.6312KLB 2016 0.1635 31.0471 75.17% 0.43% 64.91% 1.06% 2.6682KLB 2017 0.1578 31.2507 69.99% 0.60% 66.13% 0.84% 3.5203KLB 2018 0.1662 31.3760 69.03% 0.58% 69.66% 0.94% 3.5396KLB 2019 0.1342 31.5648 64.42% 0.14% 65.52% 1.02% 2.7958KLB 2020 0.1205 31.6790 73.35% 0.23% 60.61% 5.42% 3.2209KLB 2021 0.1000 32.0597 61.32% 1.09% 45.80% 1.89% 1.8347MBB 2012 0.1115 32.7993 67.05% 1.48% 42.41% 1.84% 9.0947MBB 2013 0.1291 32.8261 75.45% 1.28% 48.64% 2.45% 6.5927MBB 2014 0.1211 32.9318 83.60% 1.31% 50.16% 2.73% 4.0846MBB 2015 0.1285 33.0294 82.14% 1.19% 54.90% 1.61% 0.6312MBB 2016 0.1250 33.1772 76.02% 1.21% 58.82% 1.32% 2.6682MBB 2017 0.1200 33.3800 70.15% 1.22% 58.68% 1.20% 3.5203
MBB 2018 0.1090 33.5236 66.23% 1.83% 59.25% 1.33% 3.5396MBB 2019 0.1068 33.6508 66.27% 2.09% 60.84% 1.16% 2.7958MBB 2020 0.1042 33.8355 62.82% 1.90% 60.26% 1.09% 3.2209MBB 2021 0.1000 34.0398 63.36% 2.40% 59.88% 0.90% 1.8347MSB 2012 0.1193 32.3308 54.21% 0.20% 26.33% 0.00% 9.0947MSB 2013 0.1056 32.3049 61.14% 0.30% 25.59% 2.71% 6.5927MSB 2014 0.1570 32.2790 60.57% 0.14% 22.53% 5.16% 4.0846MSB 2015 0.2453 32.2784 60.03% 0.11% 26.93% 3.41% 0.6312MSB 2016 0.2359 32.1594 62.18% 0.14% 37.92% 2.36% 2.6682MSB 2017 0.1948 32.3517 50.65% 0.12% 32.26% 2.23% 3.5203MSB 2018 0.1217 32.5566 46.11% 0.69% 35.39% 3.01% 3.5396MSB 2019 0.1025 32.6871 51.52% 0.71% 40.51% 2.04% 2.7958MSB 2020 0.1060 32.8055 49.53% 1.21% 44.90% 1.96% 3.2209MSB 2021 0.1150 32.9475 46.46% 2.12% 49.87% 1.74% 1.8347NAB 2012 0.2144 30.4041 54.52% 1.03% 42.78% 2.48% 9.0947NAB 2013 0.1347 30.9908 47.53% 0.60% 40.20% 1.48% 6.5927NAB 2014 0.1066 31.2498 54.49% 0.57% 42.53% 1.47% 4.0846NAB 2015 0.1292 31.1997 68.70% 0.53% 58.83% 0.91% 0.6312NAB 2016 0.1118 31.3888 79.53% 0.08% 56.10% 2.94% 2.6682NAB 2017 0.1263 31.6281 73.22% 0.49% 66.76% 1.95% 3.5203NAB 2018 0.1115 31.9493 72.19% 0.91% 67.70% 1.54% 3.5396NAB 2019 0.0966 32.1816 74.71% 0.86% 71.34% 1.97% 2.7958NAB 2020 0.0957 32.5312 73.15% 0.70% 66.39% 0.83% 3.2209NAB 2021 0.0950 32.6630 75.26% 1.00% 66.99% 1.57% 1.8347OCB 2012 0.2350 30.9424 55.69% 0.87% 62.86% 0.00% 9.0947OCB 2013 0.2241 31.1213 58.29% 0.80% 61.53% 5.14% 6.5927
OCB 2014 0.1710 31.2970 61.13% 0.61% 54.90% 0.00% 4.0846OCB 2015 0.1290 31.5319 59.67% 0.47% 56.01% 0.00% 0.6312OCB 2016 0.1110 31.7870 67.48% 0.68% 60.34% 1.75% 2.6682OCB 2017 0.1160 32.0654 63.11% 1.10% 57.16% 1.79% 3.5203OCB 2018 0.1208 32.2358 60.38% 1.91% 56.34% 2.29% 3.5396OCB 2019 0.1119 32.4031 58.52% 2.37% 60.16% 1.84% 2.7958OCB 2020 0.1205 32.6584 57.15% 2.61% 58.51% 1.69% 3.2209OCB 2021 0.1230 32.8486 53.56% 2.61% 55.31% 1.32% 1.8347PGB 2012 0.2260 30.5886 64.06% 1.30% 71.62% 8.44% 9.0947PGB 2013 0.1910 30.8449 55.72% 0.17% 55.74% 2.98% 6.5927PGB 2014 0.1710 30.8806 69.84% 0.52% 56.27% 2.48% 4.0846PGB 2015 0.2140 30.8371 68.33% 0.16% 64.35% 2.75% 0.6312PGB 2016 0.1810 30.8429 73.71% 0.50% 70.63% 2.47% 2.6682PGB 2017 0.1491 31.0085 78.09% 0.24% 73.11% 3.34% 3.5203PGB 2018 0.1455 31.0289 78.08% 0.43% 73.75% 3.06% 3.5396PGB 2019 0.1389 31.0834 80.41% 0.24% 75.05% 3.16% 2.7958PGB 2020 0.1224 31.2188 79.49% 0.50% 71.02% 2.44% 3.2209PGB 2021 0.1240 31.3328 69.28% 0.67% 67.86% 2.52% 1.8347SHB 2012 0.1418 32.3892 66.59% 1.80% 48.86% 8.81% 9.0947SHB 2013 0.1238 32.5982 63.19% 0.65% 53.27% 4.06% 6.5927SHB 2014 0.1133 32.7611 72.90% 0.51% 61.58% 2.02% 4.0846SHB 2015 0.1140 32.9526 72.70% 0.43% 64.20% 1.72% 0.6312SHB 2016 0.1300 33.0861 71.20% 0.42% 69.41% 1.87% 2.6682SHB 2017 0.1130 33.2870 68.14% 0.59% 69.33% 2.33% 3.5203SHB 2018 0.1179 33.4095 69.67% 0.55% 67.12% 2.40% 3.5396SHB 2019 0.1174 33.5316 70.97% 0.70% 72.60% 1.91% 2.7958
SHB 2020 0.1008 33.6537 73.56% 0.67% 74.06% 1.83% 3.2209SHB 2021 0.1190 33.8588 64.59% 1.09% 71.54% 1.69% 1.8347STB 2012 0.0953 32.6557 70.64% 0.68% 63.33% 2.05% 9.0947STB 2013 0.1022 32.7148 81.58% 1.42% 68.51% 1.46% 6.5927STB 2014 0.0987 32.8770 85.91% 1.26% 67.45% 1.19% 4.0846STB 2015 0.0951 33.3079 89.37% 0.27% 63.66% 5.80% 0.6312STB 2016 0.0961 33.4362 87.84% 0.03% 59.89% 6.91% 2.6682STB 2017 0.1130 33.5404 86.81% 0.34% 60.51% 4.67% 3.5203STB 2018 0.1188 33.6375 86.05% 0.46% 63.20% 2.13% 3.5396STB 2019 0.1153 33.7482 88.37% 0.57% 65.27% 1.94% 2.7958STB 2020 0.0953 33.8305 86.90% 0.57% 69.09% 1.70% 3.2209STB 2021 0.0990 33.8870 82.01% 0.67% 74.44% 1.50% 1.8347TCB 2012 0.1260 32.8236 61.95% 0.42% 37.94% 2.70% 9.0947TCB 2013 0.1403 32.6993 75.51% 0.39% 44.23% 3.65% 6.5927TCB 2014 0.1565 32.8009 74.87% 0.65% 45.65% 2.38% 4.0846TCB 2015 0.1474 32.8885 74.09% 0.83% 58.43% 1.66% 0.6312TCB 2016 0.1312 33.0922 73.69% 1.47% 60.59% 1.58% 2.6682TCB 2017 0.0940 33.2272 63.47% 2.55% 59.71% 1.61% 3.5203TCB 2018 0.1460 33.4024 62.75% 2.87% 49.83% 1.75% 3.5396TCB 2019 0.1550 33.5809 60.28% 2.90% 60.15% 1.33% 2.7958TCB 2020 0.1610 33.7169 63.12% 3.06% 63.13% 0.47% 3.2209TCB 2021 0.1500 33.9744 55.34% 3.65% 61.07% 0.66% 1.8347TPB 2012 0.1800 30.3471 61.31% 0.58% 40.23% 3.66% 9.0947TPB 2013 0.1981 31.0995 44.66% 1.62% 37.17% 2.33% 6.5927TPB 2014 0.1504 31.5722 42.01% 1.28% 38.54% 1.22% 4.0846TPB 2015 0.1213 31.9647 51.83% 0.88% 37.05% 0.81% 0.6312
TPB 2016 0.0923 32.2974 51.81% 0.62% 43.87% 0.75% 2.6682TPB 2017 0.0931 32.4523 56.64% 0.84% 51.10% 1.10% 3.5203TPB 2018 0.1024 32.5450 55.91% 1.39% 56.68% 1.12% 3.5396TPB 2019 0.1069 32.7336 56.22% 2.06% 58.16% 1.29% 2.7958TPB 2020 0.1259 32.9604 56.18% 1.89% 58.16% 1.18% 3.2209TPB 2021 0.1340 33.3106 47.66% 1.93% 48.23% 0.82% 1.8347VAB 2012 0.1624 30.8341 60.95% 0.70% 52.38% 0.00% 9.0947VAB 2013 0.1520 30.9281 69.63% 0.23% 53.23% 2.88% 6.5927VAB 2014 0.1749 31.2031 55.58% 0.15% 44.46% 2.33% 4.0846VAB 2015 0.1910 31.3658 58.36% 0.21% 48.40% 2.26% 0.6312VAB 2016 0.1577 31.7495 52.37% 0.19% 49.48% 2.14% 2.6682VAB 2017 0.1024 31.7967 53.39% 0.16% 53.12% 2.68% 3.5203VAB 2018 0.1009 31.8978 58.03% 0.17% 53.18% 0.00% 3.5396VAB 2019 0.0931 31.9676 62.04% 0.28% 55.76% 0.00% 2.7958VAB 2020 0.0850 32.0915 68.50% 0.41% 55.91% 2.30% 3.2209VAB 2021 0.0840 32.2465 67.00% 0.70% 53.90% 1.89% 1.8347VCB 2012 0.1483 33.6581 68.85% 1.13% 58.18% 2.40% 9.0947VCB 2013 0.1313 33.7816 70.84% 0.99% 58.49% 2.73% 6.5927VCB 2014 0.1161 33.9889 73.17% 0.88% 56.04% 2.31% 4.0846VCB 2015 0.1104 34.1448 74.31% 0.85% 57.49% 1.84% 0.6312VCB 2016 0.1113 34.3004 74.94% 0.94% 58.48% 1.50% 2.6682VCB 2017 0.1163 34.5735 68.44% 1.00% 52.49% 1.14% 3.5203VCB 2018 0.1214 34.6102 74.67% 1.39% 58.83% 0.98% 3.5396VCB 2019 0.0934 34.7399 75.93% 1.61% 60.09% 0.79% 2.7958VCB 2020 0.0956 34.8211 77.82% 1.45% 63.32% 0.62% 3.2209VCB 2021 0.0930 34.8857 80.25% 1.60% 67.91% 0.64% 1.8347
VIB 2012 0.1943 31.8058 60.07% 0.64% 52.12% 2.62% 9.0947 VIB 2013 0.1770 31.9732 56.25% 0.07% 45.84% 2.82% 6.5927 VIB 2014 0.1804 32.0213 60.81% 0.66% 47.33% 2.51% 4.0846 VIB 2015 0.1780 32.0655 63.22% 0.63% 56.67% 2.07% 0.6312 VIB 2016 0.1330 32.2804 56.70% 0.59% 57.58% 2.58% 2.6682 VIB 2017 0.1307 32.4445 55.52% 0.99% 64.85% 2.64% 3.5203 VIB 2018 0.1000 32.5667 60.98% 1.67% 69.08% 2.52% 3.5396 VIB 2019 0.0970 32.8488 66.31% 2.02% 70.02% 1.96% 2.7958 VIB 2020 0.1012 33.1310 61.45% 2.16% 69.28% 1.74% 3.2209 VIB 2021 0.1170 33.3660 56.08% 2.31% 65.11% 2.32% 1.8347 VPB 2012 0.1251 32.2626 57.96% 0.69% 35.94% 2.72% 9.0947 VPB 2013 0.1250 32.4290 69.14% 0.91% 43.27% 2.81% 6.5927 VPB 2014 0.1140 32.7263 66.38% 0.88% 48.01% 2.54% 4.0846 VPB 2015 0.1220 32.8982 67.19% 1.34% 60.25% 2.69% 0.6312 VPB 2016 0.1320 33.0637 54.11% 1.86% 63.24% 2.91% 2.6682 VPB 2017 0.1260 33.2578 48.08% 2.54% 65.77% 3.39% 3.5203 VPB 2018 0.1120 33.4096 52.85% 2.45% 68.66% 3.50% 3.5396 VPB 2019 0.1110 33.5638 56.72% 2.36% 68.18% 3.42% 2.7958 VPB 2020 0.1170 33.6690 55.71% 2.62% 69.40% 3.41% 3.2209 VPB 2021 0.1430 33.9362 44.18% 2.38% 64.90% 4.57% 1.8347
APPENDIX 3: REGRESSION RESULTS WITH STATA 16.0
summariie CAR SIZE DEP ROA LOA NPL INF, separator(7)
Variable Obs Me an std Dev Min Max
corr CAR SIZE DEP RQA LOA NPL INF (obs&0)
Va ri ab le VIF
rsg CAR SIZE DEP ROA LOA MPL INF,beta
AR C DEÍ std Err t p>|t| Ee ta
FEM regression result xtreg CAR SIZE DEP ROA LOA NPL INF,fe
R - sq: within betwee n overall corr(u_ij xb)
Obs per group: min - avg ■ nax -
31E.“a_e 62666219 rhc 6319753 (fraction of variance duE to u_i)
xtreg CAR SIZE DEP ROA LOA NPL INP, re
Nuaber of obs - Number cf groups -
R - sq: obs per group: within - 0.2987 min - l between - 2.6236 avg - í 16.6 overall - 0.3477 max - l í
Wald chi2(6) - B1.23 corr(u_i, X) - 0 (assumed) Prob > chi2 - 6.2666
(íraction of variance duE to u_i)
(b) fem (B) rem (b-B) oifference sqrt(dĩag(v_b-v_B)) S.E.
F -.0225646 2221863 -.2227529 2225722 t = consistent under HO and Ha; obtained froffl xtreg
B = ỉnconsĩstent under Ha, efficient under Ho; obtaĩned frsm xtreg Test: HO : diííerence in coefficients not systematĩc chi2(6) = (b-B)'[(V_b-V_B) A (-I)](b-B)
Prob>chi2 = 2.3333 (V_b-V_B is not posĩtive deíĩnĩte)
Modĩíĩẽd Wald test for groupwĩ$e hetercskedastĩcĩty ĩn íixed eííect regression model H0: sigmaíi)^ = $ig"B’2 for all ĩ
xtserial CAR SIZE DEP ROA LOA NPL INF
Wooldridge test for autocorrelation in panel data