Determinants of credit risk in commercial banks of vietnam from 2016 2021

Meaning of the research: Learn about the macroeconomic and internal bank factors that influence credit risk in Vietnamese commercial banks, as well as how the degree of influence of thes

BANKING ACADEMY OF VIETNAM

ADVANCED PROGRAM

GRADUATION THESIS DETERMINANTS OF CREDIT RISK IN COMMERCIAL BANKS OF VIETNAM FROM

2016 - 2021

Student Name: Nguyen Hoang Long Student Code: 21A4010863

Course: 2018 - 2022 Class: K21CLCA Instructor: Ms Dao My Hang (MA)

Hanoi, May 2022

I would like to thanks Ms Dao My Hang for her enthusiastic help and orientation

in scientific thinking and working methods Those are very v aluable suggestions not only

in the process of writing this thesis but also as a stepping stone for me in the process of studying and setting up a career in the future

Thanks to my family, friends, and the K21CLCA class, who are always ready to share and help in study and life Hopefully, we will stick together forever

Finally, due to the limited professional and practical knowledge, the article may have shortcomings I look forward to receiving feedback from teachers to improve the article

Thank you and best regards

COMMITMENT

I hereby declare that the thesis "Determinants of Credit Risk in Commercial Banks of Vietnam from 2016 - 2021" is my own research work The sources cited, data and content used in this thesis are collected from facts with clear, honest, objective sources and no other people's work/research is used in this thesis without being properly cited I take full responsibility for the content and truthfulness of this thesis

Hanoi, 23th May 2022

Author Nguyen Hoang Long

TABLE OF CONTENTS

ACKNOWLEDGEMENTS i

COMMITMENT ii

LIST OF ABBREVIATIONS vi

LIST OF TABLES vii

LIST OF FIGURES vii

INTRODUCTION 1

CHAPTER 1: THEORETICAL BASIS OF CREDIT RISK AND FACTORS AFFECTING CREDIT RISK OF COMMERCIAL BANK 9

1.1 Overview of the credit risk of commercial banks: 9

1.1.1 Credit risk concept: 9

1.1.2 Causes of credit risk: 10

1.1.2.1 Cause from inside the bank: 10

1.1.2.2 Caused by external objective factors: 11

1.1.3 Consequences of credit risk: 13

1.1.3.1 Consequences for banks: 13

1.1.3.2 Consequences for the economy in general: 14

1.1.3.3 Consequences for customers: 14

1.1.4 Types of credit risk: 14

1.1.4.1 Based on the cause of the risk: 14

1.1.4.2 Based on the customer's ability to repay: 15

1.1.5 Indicators used to measure credit risk: 15

1.1.5.1 Non-Performing loan: 15

1.1.5.2 Allowance for Credit Losses: 16

1.2 Factors affecting the credit risk of commercial banks: 17

1.2.1 Macro factors: 17

1.2.1.1 Economic growth: 17

1.2.1.2 Inflation rate: 18

1.2.1.3 Unemployment rate: 18

1.2.2 Micro factor: 19

1.2.2.1 Bank’s size (SIZE): 19

1.2.2.2 Rate of return on equity (ROE): 19

1.2.2.3 Loan to Deposit (LDR): 20

1.2.2.4 Leverage ratio (LEV): 20

1.2.2.4 Net interest margin (NIM): 21

1.2.2.5 Operating expenses ratio (OER): 22

CONCLUSION OF CHAPTER 1 22

CHAPTER 2: MODEL BUILDING AND RESEARCH METHODS 23

2.1 Research models: 23

2.2 Building dependent and independent variables in the research model: 23

2.2.1 Dependent variable: 23

2.2.2 Independent variables: 24

2.2.2.1 Bank internal factors: 24

2.2.2.2 Bank external factors: 25

2.3 Research hypothesis: 26

2.4 Verification Model: 27

2.5 Research Methods: 27

2.5.1 Statistical analysis: 27

2.5.2 Correlation analysis: 28

2.5.3 Regression analysis: 28

2.5.2.1 Multicollinearity test: 29

2.5.2.2 Heteroscedasticity test: 29

2.5.2.3 Autocorrelation test: 29

2.6 Research Process: 30

2.7 Research data source: 30

2.7.1 Research data source: 30

2.7.2 Research sample: 31

2.8 Model research results: 32

2.8.1 Descriptive statistics: 32

2.8.2 Autocorrelation analysis: 34

2.8.3 Testing for violation of regression assumptions: 35

2.8.3.1 Checking for Multicollinearity: 35

2.8.3.2 Checking for Autocorrelation: 36

2.8.3.3 Checking for Heteroscedasticity: 36

2.8.3.4 Summary of test results: 37

2.8.4 Comparison between two models: Fixed effects model and Random effects model: 37

2.8.5 Fixed effects model defect testing: 39

2.8.5.1 Checking for Autocorrelation on FEM model: 39

2.8.5.2 Checking for Heteroscedasticity on FEM model: 39

2.8.5.3 Summary of test results: 40

2.8.6 Research results: 40

2.9 Discuss research results: 41

CONCLUSION OF CHAPTER 2 44

CHAPTER 3: CURRENT SITUATION OF CREDIT RISK AND SOLUTIONS TO LIMIT CREDIT RISK AT COMMERCIAL BANKS IN VIETNAM 46

3.1 Overview of activities of Vietnamese commercial bank: 46

3.2 Current status of credit risk of Vietnamese commercial banks: 49

3.2.1 Credit growth rate: 49

3.2.2 Non – Performing loan: 53

3.3.1 Solution from Commercial Bank: 57

3.3.1.1 Solution based on the results of the research model: 57

3.3.1.2 Solutions to improve lending activities and credit risk management: 58

3.3.2 Solutions from the Government and State Bank: 60

CONCLUSION OF CHAPTER 3 63

CONCLUSION 64

REFERENCES 65

OER Operating Expense Ratio

GLS Generalized Least Squares

OLS Ordinary Least Squares

LIST OF TABLES

Table 2.1: List of 21 Vietnamese commercial banks in research data 32

Table 2.2: Descriptive statistics for the variables 33

Table 2.3: Results of autocorrelation analysis of variables 34

Table 2.4: Test results for multicollinearity between independent variables 35

Table 2.5: Test results for correlation between independent variables 36

Table 2.6: Test results have no correlation between independent variables 36

Table 2.7: Fixed effects model 37

Table 2.8: Random effects model 38

Table 2.9: Hausman test results 38

Table 2.10: Test results for correlation between independent variables 39

Table 2.11: Test results for heteroscedasticity between independent variables 39

Table 2.12: Fix Autocorrelation, Heteroscedasticity by Generalized Least Square (GLS) method 40

Table 2.13: Regression results of factors affecting credit risk 41

Table 3.1: Assets and capital of commercial banks in Vietnam as of December 31, 2021 48

Table 3.2: Capital adequacy ratio of credit institutions 49

LIST OF FIGURES Figure 2.1: Research process 30

Figure 3.1: Credit growth rate in Vietnam from 2016 to 2021 49

Figure 3.2: Non-Performing ratio and Credit growth rate in Vietnam from 2016 to 2021 54

INTRODUCTION

1 Reasons for choosing the topic:

The banking industry plays a lifeline role in the economy, holding many extremely important functions for the economic development of Vietnam Commercial banks today not only operate for the purpose of making profits but also provide capital for the economy, act as a bridge between businesses and the mark et, shorten the circulation of goods and money, contributes to the increase of operational efficiency of the economy

Credit activities are clearly the major business activities and the primary source

of income for most Vietnamese commercial banks nowadays Through data from many reports of commercial banks from 2016 to 2021, we can see that this is a profitable activity that accounts for a significant amount of the income structur e (about more than 70%), and credit development is recognized by the whole in dustry, which Banks and the Government put priority However, the hotter the growth, the greater the associated risks Indeed, credit risk is always the primary issue and challenge that commercial banks face Credit risk can result in financial loss, a decrease in the market value of a bank's capital, and, in more catastrophic circumstances, a loss in the bank's operations or a loss of money Bankruptcy led to the collapse of the banking system of the whole economy

On the other hand, if risk reduction in credit activities is well managed and implemented, it will benefit the bank in a variety of ways, including cost reduction, improved income, capital preservation for commercial banks, creating trust information for depositors and investors, and creating a foundation to expand the market and increase the bank's reputation, position, image, and market share, all of which are signs of good financial health Therefore, minimizing risks in credit activities is necessary and must be prioritized to ensure long-term stability and safety for the entire banking system

Realizing the importance of credit risk in the banking business, the author has selected to conduct the topic "Factors affecting credit risk of Vietnamese commercial banks" to find out, understand and analyze more closely the influence of Factors

influencing bank’s credit risk, thereby proposing solutions to the Government, SBV, commercial banks to limit credit risk, contributing to stabilizing the banking system in Vietnam

2 Objectives of the study:

2.1 Overall objectives:

The overall objective of the study is to find and study the factors affecting credit risk at Vietnamese commercial banks in the period 201 6 - 2021, thereby proposing solutions to limit credit risks in order to improve Vietna m's banking system

2.2 Specific goals:

- Identify the factors that can affect credit risk in Commercial banks in Vietnam and measure the influence of these factors

- Analysis of the current situation of credit risk at Vietnamese commercial banks

- Proposing appropriate solutions to minimize credit risks at Vietnamese commercial

banks

3 Research question:

- What factors can affect Vietnamese commercial bank's credit risk?

- How influential are factors of banking characteristics on credit risk of Vietna mese commercial banks?

- What solutions to limit credit risk in the system of commercial banks in Vietnam?

4 Objects and scope of research:

5 Meaning of the research:

Learn about the macroeconomic and internal bank factors that influence credit risk in Vietnamese commercial banks, as well as how the degree of influence of these factors on credit risk varies over time The author will use the findings of the study to provide recommendations for limiting credit risk in Vietnamese commercial banks

6 Literature Reviews:

6.1 Overview of foreign studies:

Fofack (2005), research on credit risk and bad debt ratio of banks in sub -Saharan Africa in the period 1993 - 2002 Research results show that GDP per capita growth rate has an impact the negative impact on bad debt implies that credit risk tends to be especially high during a prolonged economic downturn Changes in real interest rates and exchange rates are positively correlated with an increase in the NPL ratio

Das and Ghosh (2007), have studied the factors affecting the bad debt of banks in India including state-owned banks, private banks and foreign banks in the period of time period 1993 - 2005 in both the group of macro variables and the group of micro variables (specific to banks) The results show that an increase in GDP has a simultaneous effect

on a decrease in problem debt, on the other hand real interest rates do not seem to have

a significant effect on problem debt At the micro level, credit growth with a 1 -year lag has a strong and positive impact on problem loans Finally, larger banks appear to have higher problem loans than smaller banks Most other variables (branch expansion strategy, operating expenses or profit margin) have no significant impact on the bank's bad debts

Ali and Daly (2010), use comparative analysis to investigate important macroeconomic variables for Australia and the US They also study the effects of macroeconomic shocks on default rates in both countries The results show that with the same macroeconomic factor, there will be different effects on the default rate of the two countries, although the US economy is more sensitive to the side effects of macroeconomic shocks

Foos et al (2010), conducted a study on whether or not credit growth affects the risk of private banks in 16 countries using data from more than 160,000 private banks in period 1997 - 2007 Research results show that credit growth leads to an increase in bank's credit risk with a lag of two to four years At the same time, the research discovered no connection between bank size and credit risk

Nkusu (2011), with a sample of data for the period 1998 - 2009 of 26 economies, the study found a negative correlation between bad debt and economic growth and housing prices, relationship between bad debt and unemployment rate

Zribi and Boujelbene (2011), examine factors affecting credit risk of Tunisian banks with panel data of 10 commercial banks in the period 1995 –2008 Research results show that the state ownership structure increases bank credit risk while prudent capital regulations reduce bank credit risk Besides, the characteristics of the bank are also important factors affecting the risk level of Tunisian banks such as the rate of return on assets has a positive relationship with credit risk while the rate of return o n assets has a positive relationship with credit risk, and capital safety has an inverse relationship Finally, the results show that the macro variables are the factors that affect the credit risk

of Tunisian banks, in which the variables of GDP growth, i nflation rate, exchange rate and interest rate have the opposite effect increase credit risk

Louzis et al (2012), using the dynamic panel data method to study macroeconomic variables and bank - specific variables affecting the bad debt of the Greek banking system for each type of loan (consumer loans, business loans and home loans) in the period 2003 - 2009 The study found that macroeconomic variables (GDP growth, unemployment rate, interest rates) has a strong impact on the level of bad debts

in which GDP growth has a negative impact on credit risk while unemployment rate and interest rate have a positive impact on credit risk In addition, bad debts are also affected

by a number of bank-specific variables such as efficiency and performance ratios

Park and Zhang (2012), study the influence of macro factors and specific factors

of banking activities on bad debt of three types of loans (real estate, commercial and industrial, consumption) in two periods (2002 - 2006 and 2007 - 2010) on the database

of 2670 banks in the US The research results show that the variables of GDP growth, unemployment rate, ROE ratio and the equity-to-total-assets ratio has a strong negative influence on bank’s bad debt

Castro (2013), analyzes the interface between macroeconomic advancement and credit hazard with a test of banks in 5 European nations (Greece, Ireland, Portugal, Spain, and Italy) in the period 1997 - 2011 According to research findings, bank credit risk has

a negative relationship with GDP growth, housing and stock price indexes, while credit risk has a relationship positively related to unemployment rate, interest rate, loan growth and exchange rate

Chaibi and Ftiti (2015), approached dynamic panel data to examine the factors affecting the non-performing loan ratio (NPLs) of commercial banks in France and Germany during the period from 2005 to 2011 Research results shows that except for the inflation rate variable, the set of macroeconomic variables used have a strong influence on the NPL ratio of the whole economy (including GDP growth, nominal interest rate, exchange rate, unemployment rate) The study also shows that for variables related to bank characteristics, the bad debt ratio of French banks is determined by th e credit risk provision variable and the inefficient variable while the bad debt ratio of German banks depends on the bank's leverage ratio

Alexandri and Santoso (2015) studied 26 Indonesian banks, and found that Return

on Assets (ROA) had a positive and significant impact on non-performing loans among the five variables investigated from 2009 to 2013 (bank size, capital adequacy, Return

on Assets, GDP, and Inflation) Non-performing loans were affected negatively by bank size and GDP, whereas capital adequacy and inflation had a minor beneficial effect

Despoina Arvanitaki & Konstantinos Balafas (2018), using panel data from 140 banks in 26 European Union countries over a 6 -year period from 2011 to 2016, the total level of issue loans was calculated using both bank-specific and macro-specific characteristics The European Union is struggling at the time of the study due to continued sovereign debt concerns in countries such as Greece, Spain, Italy, Cyprus, Ireland, and others In this sense, they are focusing on this particular period of crisis,

marked by excessive credit, rising interest rates, growing unemployment, small changes

in GDP growth, real estate prices production fell sharply and the international economic environment was very shaky

Donjeta Morina (2020) collected data for the study during a seven-year period, from 2012 to 2018 The data came from the Kosovo Agency of Statistics and the Central Bank of Kosovo's publications (Quarterly evaluation of macroeconomic developments, Financial System Quarterly Evaluation) (data for GDP and Inflation) The information was reviewed on a quarterly basis They investigated 6 factors in the study to conduct the empirical phase of the study that offers them the response to the relationship between credit risk and its determinants Non-performing loans are a dependent or explanatory variable impacted by independent variables such as bank size, bank profit (measured by ROA), loan interest rate, inflation, and economic growth (GDP)

6.2 Overview of domestic research:

Bui Ngoc Toan, Vo Thi Quy (2014), study the factors affecting credit risk on 26 Vietnamese commercial banks in the period of 2009 - 2012 To ensure that the generated estimates are reliable and efficient, panel data with the GMM approach are utilized to overcome the problem of automatic first-order correlation between errors and endogenous variable phenomena The findings reveal that the previous bank credit risk, the past credit growth rate, and the past GDP growth rate all have a negative and significant impact on the credit risk of Vietnamese commercial banks with a one-year lag

Nguyen Thi Ngoc Diep & Nguyen Minh Kieu (2015), using panel data and the regression method, the study attempts to identify the group of bank characteristics that affect credit risk at Vietnamese commercial banks (OLS) The study's data comes from

32 Vietnamese commercial banks' financial statements from 2010 to 2013 , and discovered that characteristics such as bank size and the ratio of operating expenses to lending operating revenue have the same positive impact on credit risk

Nguyen Van Thep & Nguyen Thi Bich Phuong (2016) research on 29 Vietnamese commercial banks in the period 2007 – 2014 Research results on factors affecting credit risk in Vietnamese banks through fixed effects model have shown factors such as: Return

on Equity (ROE), business growth economy (GDP), has a negative effect on the dependent variable In contrast, capital adequacy ratio (CAR), operating expense ratio and bank size have a positive (+) effect

Bui Huu Phuoc, Ngo Thanh Danh, Ngo Van Toan (2018) with the study "Factors affecting credit risk at foreign trade bank Kien Giang branch", Journal of KTDN No 98 Using data from 120 bank credit records, this study examines factors affecting credit risk

at the Joint Stock Commercial Bank for Foreign Trade of Vietnam (Kien Giang branch)

To assess the elements that affect credit risk, binary logic models and polynomial logic models are used Polynomial logit outperforms binary logit, according to the findings

At credit risk level 1, the following factors have an impact on risk signals: collateral, client financial capability, diverse business operations, bank staff and examiner experience, and loan monitoring Only four factors connected to signals affect commercial bank credit risk at credit risk level 2, compared to less than one such factor

at credit risk level 1, and collateral has no effect on credit risk exposure 2 It makes policy and risk management advice to assist minimize credit risk

Truong Quoc Cuong (2019), quantitatively examines the macro factors and specific factors of banking activities that affect credit risk at Vietnamese commercial banks with the research sample in the period 2008 - 2018 Quantitative research results show that the variables of credit risk in the past with 1 -year lag, current-year credit growth and variable rate of profit margin have a positive relationship with credit risk, while credit growth with 1-year lag and exchange rate have negative impact on credit risk of Vietnamese commercial banks

Pham Thai Son (2019)research on 27 banks representing Vietnam's commercial banking system in the period 2010-2018 Macroeconomic factors affecting credit risk include: Credit growth (LG), Ratio between operating expenses and operating income (CIR), Return on equity (ROE), the economic growth rate (GDP) has a negative

relationship with the bad debt ratio; The ratio between net income from business activities before credit provision expenses and total credit balance (EBP) has a positive relationship with the bad debt ratio of Vietnamese commercial banks

Le Phuong Dung (2019) research 32 Vietnamese commercial banks retrieved in

12 years from 2007 to 2018 The study has shown the following factors: Bank internal factors: credit risk provision (LLP), inefficiency (EFF), non -interest income (NII) and bank size (SIZE) Macro factors: GDP growth (GGDP), exchange rate (EXR) and unemployment rate (UNR)

CHAPTER 2: MODEL BUILDING AND RESEARCH METHODS

CHAPTER 3: CURRENT SITUATION OF CREDIT RISK AND SOLUTIONS

TO LIMIT CREDIT RISK AT COMMERCIAL BANKS IN VIETNAM

CONCLUSION

1 CHAPTER 1: THEORETICAL BASIS OF CREDIT RISK AND FACTORS AFFECTING CREDIT RISK OF COMMERCIAL

BANK 1.1 Overview of the credit risk of commercial banks:

1.1.1 Credit risk concept:

Before understanding the concept of credit risk, we need to understand what credit and bank credit are:

According to article 20, clause 10 of the law on credit institutions No: 02/1997/QH10, credit is the transfer of capital based on credit and according to the principle of repayment, whereby the lender transfers the right to use a certain amount of property to the borrower for a certain period

According to Investopedia, bank credit is a credit relationship between banks, credit institutions (CIs), and businesses or individuals (borrowers) In which, the bank

or credit institution will transfer the property to the borrower for use within a certain period, when due, the borrower must repay both principal and interest to the credit institution There are many types of bank credit, but, currently, bank credit is divided into the following main types:

- Personal credit: Serving the needs of using personal capital such as buying a house, buying a car, doing business, covering personal life,

- Corporate credit: Serving the capital needs of businesses such as purchasing assets, paying debts, supplementing working capital,

- Short-term credit: The term does not exceed 12 months

- Medium-term credit: The term is from 12 months to 60 months

- Long-term credit: The term is greater than 60 months

Bank credit has some basic characteristics as follows:

First, the basis for deciding on a credit is the bank's confidence in the customer's correct use of the loan and its ability to repay the loan on time

Second, credit is the transfer of the right to use an amount of money or property from one entity to another, without changing the ownership of them

Third, credit always has a term and must be repaid unconditionally The bank performs the function of "borrowing to lend", so all credits must have a term to ensure the bank repays the mobilized capital when the customer deposits money to withdraw or the bank uses the source again and loans it to other customers

Thus, according to the above definition, credit risk will occur when one of the above characteristics is violated There are many definitions of credit risk, but the most common are the following definitions:

According to Investopedia: credit risk is the occurrence of loss due to the borrower defaulting on a loan or failing to meet contractual obligations Credit risk can

be understood as the risk that lenders may not receive principal and interest, resulting in disrupted cash flows and increased collection costs

According to the Basel Committee on Banking Supervision: Credit risk is most simply defined as the potential that a bank borrower or counterparty will fail to meet its obligations in accordance with agreed terms

Thus, although the definition is slightly different, the concept of credit risk has one thing in common: the possibility that the bank's assets can be lost when the borrower does not fully fulfill the payment obligation , which has been established A violation of the repayment principle may be either the failure or inability of the borrower to perform part or all of the obligation committed

1.1.2 Causes of credit risk:

1.1.2.1 Cause from inside the bank:

The credit policy is incomplete, asynchronous, and inconsistent The risk appetite

of each bank reflects the attitude towards risk-taking to a certain extent/limit Depending

on business goals and risk appetite, each bank builds its own credit policy In t he face of business pressure and increasingly fierce competition, banks extend credit excessively, leading to poor customer selection, incorrect credit granting, and potential risks for

banks The quality of staff involved in credit appraisal is not good: Lack of skills in appraising customers, appraising production, business, and financial situations leads to

an unclear understanding of customers' activities Understand the customer's strengths and weaknesses Since then, it is not possible to determine th e credit relationship, and the professional measures to be taken The manifestation of these problems is that the appraisal results are sketchy and fuzzy In many cases, the appraisal results show that the customer has many potential problems, but evades a nalysis, intentionally grants credit, and does not give credit take preventive measures Operational departments at banks do not comply with the credit process, lack supervision and post -lending management: disbursed without grounds or to the wrong borrowers, resulting in loans being used for other purposes Lack of post-lending inspection, management and supervision or there is an inspection but heavy on document review, no actual control or inspection of the customer's production performance, debt securi ty check Theoretical loans, lack of grounds to determine the real asset value lead s to actual goods in inventory lower than the book value, leading to an imbalance of loans Some officials lack ethics, although not the main cause, the level of risks and co nsequences are often serious For example, an officer assisting a customer to fake a loan application, raising the value of a security asset too high compared to its actual value, or appraising this property file but assessing the current state of another property, the project is inefficient but still calculates effectively, fails to assess the risks for credit granting to customers In addition, the problem of moral hazard also occurs when the bank's leaders have an interesting relationship with customers, directing loans, not on the basis of a specific business plan, which clearly leads to loss of control for the purpose of using the loan, potentially causing risks for the bank

1.1.2.2 Caused by external objective factors:

❖ Cause from customers:

The customer's financial capacity is not high, so it has low tolerance to abnormal fluctuations of the economy as well as the market, affecting business activities In

addition, the weak executive and management capacity of customers leads to inefficient business activities, thereby affecting the bank's ability to repay loans Customers use capital for the wrong purpose compared to the loan demand provided to the bank The loan plan is an important basis for the bank to consider granting credit in addition to t he customer's financial situation and debt repayment ability The Bank only grants credit for the purpose of lawful borrowing, in accordance with the registered business lines, the needs of customers and the business plan appraised by the bank as effective and capable However, customers use the bank's loan capital for a purpose other than that of the disbursement request, using it for high-risk activities with the expectation of high profits but the results are not satisfactory want to make it difficult f or the bank to repay the loan In fact, the majority of cases where customers are no longer able to repay their loans to the bank stem from the improper use of loan capital for the purpose at which it was disbursed

Customer cheat, defraud the bank, intentionally provide inaccurate data, cause falsification in the bank's appraisal and credit decision, leading to difficulties in debt recovery This is also a fairly common cause of credit risk for banks Customers use many sophisticated tricks, bypassing the bank's eyes to create fake loan documents and appropriate the bank's money Once a customer has deliberately cheated, it is difficult to recognize in the context that the quality of the credit staff is still weak The customer himself lacks goodwill in repaying the debt, does not worry, does not care about the debt

to the bank Although their financial capacity is still enough to ensure the ability to repay the bank, they are still sluggish in repaying the debt

❖ Caused by external factors:

Natural causes such as natural disasters, fires, epidemics These are objective causes due to changes in the natural environment This is a risk that both the bank and the customer do not expect and cannot foresee, causing customers to face difficulties and obstacles in business activities, thereby affecting the ability to repay loans to the bank Even for customers with strong financial capacity, it takes time to stabilize the

production situation, and for customers with weak financial capacity, the possibility of default is very high The causes stem from the weakness of the macro -economy, the deterioration of economic conditions and the poor development of the market During the crisis, economic activity is slowed down, the volume of products and sales of businesses decrease, the demand for goods and services is lower, reducing income sources, increasing the possibility of non-productivity repay their debt On the contrary,

in the boom period of the economy, the number of products is created more, the demand for goods and services is higher, businesses earn more profits, borrowers will easily repay loans to banks and the risk of default is reduced Other causes such as changes in fiscal policy, money supply, import-export policies, trade restriction policies, or changes

in the financial market will also affect the production and business situation , and the financial situation of the customer leads to increased risk for the bank

1.1.3 Consequences of credit risk:

Credit risk is the oldest and most important financial risk, causing financial crisis for the economy and causing significant losses for banks

1.1.3.1 Consequences for banks:

Credit risk increases costs, reduce bank profits, and reduces bank profitability When the bank incurs bad debts, the bank has to pay a lot of problem debt settlement costs such as travel costs, staff costs, meeting costs to handle debt and in addition, th e bank also has to pay for non - performing loan Loss of opportunity cost of lending, slows down the credit cycle and thereby reduces the cost efficiency of the bank Credit risk affects profitability (ROA, ROE) of banks and reduces bank profitability acc ording to the study on the relationship between credit risk and profitability of Gizaw and associates (2015), Li and Zou (2014)

Credit risk also reduces the reputation of banks The banks when facing difficulties, doing business inefficiently, will lose cr edibility in the market This is an invisible loss that cannot be measured in value

1.1.3.2 Consequences for the economy in general:

Risks in the lending activities of this bank also indirectly affect other banks Banks play an important role in the market economy It is related to all of industries, all economic sectors are the key stage in providing capital for the economy Therefore, banks have a great influence on monetary policy, on the macro -regulatory tool of the state

If there is a big loss in credit activity even at one affiliated lending bank, if it is not remedied in time, it can cause a "chain reaction" that threatens the safety and stability

of the entire banking system, causing great consequences for the developmen t of the economy

1.1.3.3 Consequences for customers:

When there is a delay in payment of principal and interest to the bank, the borrower will have to pay penalty interest on the late payment, leading to an increase in the financial costs of the business

In case the borrower is unable to pay the due obligations as committed, the customer's reputation will be reduced, then there will be no opportunity to access bank loans and face many difficulties Difficulty in mobilizing other capital sources due to low credit rating, making the financial situation of enterprises increasingly difficult, possibly leading to bankruptcy and sale of assets to repay debts

1.1.4 Types of credit risk:

1.1.4.1 Based on the cause of the risk:

Transaction risk: is a form of credit risk that arises due to weaknesses in the process of transaction and loan approval and customer evaluation

Portfolio risk: The risk arising from weaknesses in the bank's loan portfolio management, including intrinsic risk and concentration risk

Intrinsic risk: Derived from the unique and unique internal factors and characteristics of each borrower or economic sector or field It comes from the characteristics of the borrower's operation or use of capital

Concentration risk: When banks concentrate too much lending capital for a few customers; lending to too many enterprises operating in the same industry, economic sector, or in a certain geographical area, the same type of high -risk lending

Operational risk: The risk of direct or indirect loss caus ed by the bank's staff, inadequate or ineffective processing, internal systems, or by external events affecting banking operations row

1.1.4.2 Based on the customer's ability to repay:

Risk of not repaying debt on time: When establishing a credit relationship, banks and customers must agree on a loan repayment period However, by the conventional time, the bank has not yet recovered the loan

Risk of insolvency: The risk that occurs in the event that a borrowing enterprise

is insolvent, the bank must liquidate the enterprise's special assets to collect the debt

Credit risk is not limited to lending activities: Including other activities of the bank's credit nature such as guarantees, commitments, trade finance approvals, interbank market lending, credit risk, using hire purchase, co-finance…

1.1.5 Indicators used to measure credit risk:

1.1.5.1 Non-Performing loan:

The bad debt ratio represents credit risk This high ratio reflects the high credit risk of the bank and vice versa Credit risk can be assessed through the bad debt ratio, which is calculated according to the ratio of bad debts to total outstanding loans according to the studies of Salas and Saurina (2002), Das and Ghosh (2007), Chaibi and Ftiti (2015) From the perspective of commercial banks, bad debt can be understood as unprofitable loans or non-performing loans (NPLs: Non-Performing Loans), loans that become unprofitable when the borrower stops making payments and the loan begins to

default NPL ratio that is high compared to the industry average and tends to increase may be a sign that the bank is having difficulty in managing the quality of loans Bad debt is determined according to common international standards and specific regulations

of each country In the view of the ECB, bad debt is defined by two factors: (i) the loan's inability to be recovered; (ii) although recovered, the recovered value is incomplete Thus, the ECB's view on bad debt is approached based on the results of the bank's debt collection According to the IMF's Theoretical Framework, a debt is considered a bad debt when the interest and/or principal payment is overdue by 90 days or more; or unpaid interests of 90 days or more have been principally refinanced, refinanced, or delayed as agreed; or payments that are less than 90 days past due but there are solid reasons to doubt the loan's ability to be paid in full With this point of view, bad debt is approached based on the overdue payment time and debt repayment ability of the customer (it may

be that the customer is completely unable to pay the debt or the customer's debt repayment is incomplete) According to the Basel Committee on Banking Supervision,

a debt is considered bad debt when it meets one or both of the following conditions: the bank finds the borrower unable to repay the loan in full or the borrower has passed a repayment period is more than 90 days Then, the essay will use this variable to represent credit risk

1.1.5.2 Allowance for Credit Losses:

According to the IMF's Theoretical Framework, the guideline has two types of provision accounting: specific provision (Specific Provision) and general provision (General Provision) Accordingly, specific standards of provision are made for each customer's loan that is significantly impaired and there is objective evidence of a loss that has been incurred, which means that the debt is past due specific reserve fund decisions arise While general provision is made on the total outstanding loans without objective evidence of loan deterioration for each customer, the total outstanding balance

is still assessed at a discount due to credit risk and the amount of money outstanding This provision is included in Tier 2 capital as a buffer to limit future losses Provisioning

for credit risk is the process of identifying loan losses from which to estimate the possibility of a bank's loss of assets When making a loan, the bank faces the risk of the borrower not paying the principal and interest in full When a customer's debt is determined to be partially or completely uncollectible, the bank creates reserves to cover credit losses On a bank's balance sheet, provision is an item of assets and makes the value of assets available to reflect the impairment of assets before possible losses On the income statement, provision is a non-cash expense recorded to reduce the bank's profit The larger the provision for credit losses, the more impaired assets are reflected

1.2 Factors affecting the credit risk of commercial banks:

1.2.1 Macro factors:

1.2.1.1 Economic growth:

Economic growth is an increase in the production of economic goods and services, compared from one period of time to another When the economy grows, consumers increase the consumption of goods, then revenue and profits of businesses increase Enterprises then have the need to invest in expanding production, thereby hiring more workers, and increasing wages for existing employees Increased profits of businesses and individuals' incomes after being increased lead to an incre ase in the ability to repay borrowed debts Conversely, when the economy worsens, economic stagnation and recession cause consumers to reduce the number of goods consumed, leading to a decline in revenue and profits of businesses, and inventory turnover decrease, directly affecting the business and business of individuals and enterprises, thereby affecting the borrower's willingness to pay Moreover, low-interest rates during the recession of the economy contribute negatively to bank profits, reduce profit s and directly affect the business activities of banks Most studies on the relationship between economic growth and bank credit risk show a negative relationship (Vítor Castro, 2012)

1.2.1.2 Inflation rate:

When inflation increases, the real income of borrowers decreases, while the amount of loan interest payable increases because during inflationary periods, increased inflation will lead to an increase in the floating interest rate According to the formula of nominal interest rate = real interest rate + inflation rate, it can lead to a decline in debt repayment capacity or the borrower's inability to repay, leading to higher bad debts Therefore, the hypothesis is that INF has a positive effect on credit risk Inflation has different effects on credit risk of commercial banks There are two empirical evidences

on the relationship between inflation and credit risk of commercial banks Inflation can increase a borrower's ability to repay because it reduces the real value of outstanding debts The negative relationship between inflation and credit risk is found in the study

of Zribi and Boujelbene (2011) Inflation can also negatively affect credit risk because

it will reduce a borrower's real income when wages are fixed, reducing the borrower's ability to repay The positive relationship between inflation and credit risk is found in the research of Olga Bohachova (2008), Funda Yurdakul (2013) However, these two different results are supported by the study of Castro (2013), who explains that the effect

of the inflation rate on credit risk can be either positive or negative depending on the operating state of the economy

1.2.1.3 Unemployment rate:

Unemployment rate is the percentage of the working force that stays unemployed Individuals who would like to work, but they are not able to do so, due to a disability or lack of some other characteristics, are not considered as unemployed In periods of financial crisis and recession, a high, or at least higher than the usual one, unemployment rate is almost inevitably The positive impact of unemployment to an increase of non-performing loans is relatively expected, as the growth of unemployment rate leads to a general decline of households' income, causing individuals to not be able to pay their loan obligations The expected positive relationship between unemployment rate and the NPLs’ level, is also confirmed by the papers of Jordan et al (2013), Jouini and Messai

(2013) and Bellas et al (2014) When unemployment increases, it will lead to consumers cutting back on spending (due to lack of work), leading to a decrease in the ability to generate income, and as a result, reducing the customer's ability to repay or insolvency even occurred in some customers Therefore, it is hypothesized that the impact of UNR

on credit risk is positive

1.2.2 Micro factor:

1.2.2.1 Bank’s size (SIZE):

Jin-Li Hu et al (2004) found a negative relationship between bank size and bank credit risk The authors argue that large banks have better risk management systems and

of course, these banks have more opportunities to hold the least risky loan portfolio so they can limit risks credit risk than small banks In addition, Somanadevi Thiagarajan

& ctg (2011) and Hess & ctg (2008) also found similar results In contrast, Daniel Foos

et al (2010) argue that no significant effect of bank size on bank credit risk was found Nabila Zribi & Younes Boujelbène (2011) also gave similar results This result is explained by the authors by the fact that the banks in Tunisia are almost simi lar in size and most of them are in line with the regulations and requirements of the banking system,

so the size of the banks is similar and does not affect bank credit risk Size can be the market value of the bank (Jimenez and Saurina, 2006), which is t he logarithm of the bank's total outstanding loans (Foos et al., 2010) In Vietnam, the stock market has only developed at an early stage, so only a few banks have shares listed on the stock exchange, which means that only a few banks have market value dat a For this reason, the study chose to measure bank size by the base 10 logarithms of total outstanding loans The author uses the logarithmic function to adjust the value of the variable, which has a very large value similar to other variables in the mode l

1.2.2.2 Rate of return on equity (ROE):

ROE - net return on equity, calculated by dividing Profit After Tax by Equity, Profit After Tax by Total Assets: ROE represent the bank's business performance When

profits are poor, it means that the quality of management is not good, which in turn will lead to a higher bad debt ratio This index is an accurate measure to evaluate a dollar of capital spent and accumulate how much profit This coefficient is often analyzed by investors to compare with stocks of the same industry in the market, thereby referencing when deciding which company to buy shares The higher the ROE ratio, the more effectively the company's management is using the shareholders' capital, so it is often an important criterion for considering a company's stock investment opportunities Thus, the hypothesis is that ROE has a negative effect on credit risk

it is hypothesized that LDR has a negative effect on credit risk

1.2.2.4 Leverage ratio (LEV):

The Total Liabilities to Total Assets Ratio, it is commonly used as Debt Ratio, or Leverage Ratio Debt is the part of the balance sheet that shows the obligation of the company that is monitored; thus, the debt ratio is interpreted as the leverage that a company has due to its obligations In the case of commercia l banks’ balance sheet though, liabilities, that is obligation to depositors or debt, is consisted mostly by the deposits of the bank clientele Generally, Total Debt to Total Assets ratio provides a comparison measure that shows the bank assets that are f inanced by deposits, or else

bank loans, rather than equity In our research, we want to examine if the level of leverage in terms of assets, for bank sector, is significant and able to determine the level

of NPLs of a bank’s loan portfolio, and in what extend Louzis et al (2012), in their study concerning the determinants of NPLs for the Greek banking sector, they did not manage

to find the expected signs neither if this specific variable was statistically significant, for any type of loans category of their study Calculated by dividing Total Debt by Total Assets, The Total Debt to Total Assets ratio provides a comparative measure that shows

a bank's assets are financed with deposits, or other bank loans, rather than equity The greater the leverage, the greater the bank's risk Therefore, it is hypothesized that LEV has a positive effect on credit risk

1.2.2.4 Net interest margin (NIM):

Net interest margin (NIM) is a measurement comparing the net interest income a financial firm generates from credit products like loans and mortgages, with the outgoing interest it pays holders of savings accounts and certificates of deposit (CDs) Expressed

as a percentage, the NIM is a profitability indicator that approximates the likelihood of

a bank or investment firm thriving over the long haul This metric helps prospective investors determine whether or not to invest in a given financial services firm by providing visibility into the profitability of their interest income versus their interest expenses NIM measures how much disparity between payment receipts and cost payments can be achieved by a bank through rigorous scrutiny of its productive resources and pursuit of low-cost capital; for maximum budget rows, maximize revenue from the budget and minimize expenses from the budget NIM is the effective size measure as well as the declaration NIM indicates the ability of the board of directors as well as the bank staff to maintain the growth of revenues relative to the increase of expenses Therefore, it is assumed that NIM has a negative relationship with credit risk Therefore,

it is hypothesized that OER has a negative effect on credit risk

1.2.2.5 Operating expenses ratio (OER):

The operating expense ratio (OER) is a measurement of the cost to operate a piece

of property, compared to the income brought in by the property It is calculated by dividing a property's operating expense (minus depreciation) by its gross operating income OER is used for comparing the expenses of similar properties An investor should look for red flags, such as higher maintenance expenses, operating income, or utilities that may deter him from purchasing a specific property Expense management

is an important job, demonstrating the talent of the bank's management team If banks manage costs well, they are likely to manage other activities well, including effective policies for credit activities Hess et al (2008) chose the ratio of operating expenses to operating income as one of the factors affecting credit risk to st udy The results of this study also show that inefficient banks have a higher level of credit risk than other banks Therefore, it is hypothesized that OER has a positive effect on credit risk

CONCLUSION OF CHAPTER 1

Chapter 1 presents an overview of the concept of credit risk as well as ways of measuring credit risk and factors affecting credit risk of commercial banks In addition, chapter 2 also presents an overview of previous studies on factors affecting credit risk of banks

CHAPTER 2: MODEL BUILDING AND RESEARCH

METHODS 2.1 Research models:

Through reference to previous studies, it can be seen that bank credit risk is affected by many factors, including macro and micro factors This study will select variables affecting credit risk in many economies to conduct research on data collected

in Vietnam

The thesis references the approach of Despoina Arvanitaki & Konstantinos Balafas (2018), Donjeta Morina (2020), Jehona Shkodra, Hysen Ismajli (2017) to build

a research model to identify factors that affect Vietnamese commercial banks' credit risk

NPLit = α + βjXi,t + vi + εi,t

In there:

- Dependent variable: NPLit: Credit risk is represented by non-performing loan variable

The independent variable includes:

- Bank internal variables: SIZE, ROE, NIM, LDR, LEV, OER

- Macroeconomic variables: GDP, INFL, UNP

- Coefficient of intercept: α

- Xi,t is a vector of independent variables, including both macro and internal variables in bank

- βj is the impact of the independent variable vector on the bad debt ratio

- vi are idiosyncrasies that are not observed across banks

- i,t is the residual of the model

2.2 Building dependent and independent variables in the research model:

2.2.1 Dependent variable:

In previous empirical studies when researching credit risk, the authors when assessing credit risk can use many evaluation criteria Credit risk can be assessed through the bad debt ratio, which is the ratio of the bad debt balance divided by the total deb t, Some other studies measure credit risk through the risk provision ratio Research by Vo Thi Quy and Bui Ngoc Toan (2014) has measured credit risk by taking the ratio of the value of provision for credit risk in year t divided by the total loan balance i n year t-1 Research by Donjeta Morina (2020) uses NPLs (NPLs) as a measure of credit risk because they reflect the state of credit risk in a country Research by Donjeta Morina (2020) also shows that using the non-performing loan ratio will accurately reflect the nature of credit risk of Vietnamese commercial banks compared to the bad debt ratio in the past period NPL ratio reflects a bank's credit quality and is considered an indicator

in credit risk management Specifically, bad debt shows how well a ba nk is managing credit risk because it determines the percentage of credit losses relative to total credit Therefore, the thesis approaches the research Donjeta Morina (2020) using the non-performing loan ratio as the dependent variable of the model

𝐿𝐸𝑉 = 𝑇𝑜𝑡𝑎𝑙 𝐷𝑒𝑏𝑡

𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠

❖ Loan to Deposit Ratio:

The thesis uses the variable loan to deposit ratio (denoted as LDR) to represent the ratio of credit balance to the bank's mobilized capital LDR is determined by the formula:

𝐿𝐷𝑅 = 𝑇𝑜𝑡𝑎𝑙 𝐿𝑜𝑎𝑛𝑠

𝑇𝑜𝑡𝑎𝑙 𝐷𝑒𝑝𝑜𝑠𝑖𝑡𝑠

❖ Net Interest Margin:

The thesis uses the variable cost to income ratio (denoted as CIR) to represent the bank's operational efficiency CIR is determined by the formula:

𝑁𝐼𝑀 = 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝐼𝑛𝑐𝑜𝑚𝑒 − 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝐸𝑥𝑝𝑒𝑛𝑠𝑒𝑠

𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐸𝑎𝑟𝑛𝑖𝑛𝑔 𝐴𝑠𝑠𝑒𝑡𝑠

❖ Size of The Bank:

The thesis uses the variable SIZE (denoted as SIZE) to represent the bank's dimensions SIZE is determined by the formula:

SIZE = Log(SIZE)

❖ Operating Expenses Ratio:

The thesis uses the variable Thesis uses variable operating expenses ratio (denoted as OER) to represent the bank's dimensions OER is determined by the formula:

𝑂𝐸𝑅 =𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝐸𝑥𝑝𝑒𝑛𝑠𝑒𝑠

𝑅𝑒𝑣𝑒𝑛𝑢𝑒𝑠

2.2.2.2 Bank external factors:

❖ Gross Domestic Product Growth Rate:

The thesis uses the variable gross domestic product growth rate (denoted as GDP)

to represent the economic growth factor GDP is determined by the formula:

❖ Hypothesis 1: Non-performing loan with one year lag has an effect on credit risk of Vietnamese commercial banks

❖ Hypothesis 2: Bank size has an influence on the credit risk ratio of Vietnamese commercial banks

❖ Hypothesis 3: Cost to income ratio has an effect on the credit risk ratio of Vietnamese commercial banks

❖ Hypothesis 4: Return on equity has an influence on credit risk ratio of Vietnamese commercial banks

❖ Hypothesis 5: Return on total assets has an influence on the credit risk ratio of Vietnamese commercial banks

❖ Hypothesis 6: Gross Domestic Product Growth Rate has an influence on the credit risk ratio of Vietnamese commercial banks

❖ Hypothesis 7: Economic growth rate has an influence on credit risk ratio of Vietnamese commercial banks

❖ Hypothesis 8: Inflation index has an influence on credit risk ratio of Vietnamese commercial banks

Based on the research hypotheses presented above, the research model to test the factors affecting credit risk at Vietnamese commercial banks is rewritten as follows:

2.4 Verification Model:

NPL = α + β1 ROE + β2 SIZE + β3 LDR + β4 LEV + β5 GDP + β6 INFL + β7 NIM

+ β8 OER + β9 UNP + ε

Where:

α is the intercept factor

NPL is the credit risk measurement variable SIZE represents the Log(SIZE) of the bank ROE is return on equity

LDR is loan-to-deposit ratio LEV is financial leverage ratio OER is operating expense ratio NIM is net interest margin GDP is economic growth rate INFL is inflation rate

UNP is unemployment ratio

2.5 Research Methods:

2.5.1 Statistical analysis:

The descriptive statistics method describes the basic characteristics of the collected data set to have an overview of the research sample Statistics of explanatory

and dependent variables of Vietnamese commercial banks in the period 201 6 to 2021 through Research problem Factors affecting applied credit risk of Vietnamese banks Research objectives to Determine the influence of the factors affecting credit risk applied

by commercial banks, and then proposing possible solutions Research method Qualitative method Statistics, description and inference Quantitative method Testing the fit of the model; Using the general least squares method to determine the factors affecting the research problem Research results Proposing solutions to limit credit risk applied by

21 Vietnamese banks mean, standard deviation, maximum and minimum value of each variable in the model as well as sample size

2.5.2 Correlation analysis:

Correlation matrix analysis is used to examine the relationship between the explanatory variables and the dependent variables, as well as the correlation between the explanatory variables with each other The results of the correlation matrix initially assess the relationship between the explanatory variable and the dependent variable In the case that the explanatory variables are highly correlated with each other, in particular, the correlation matrix coefficient between the explanatory variables is greater than 0.8 Then, the model has high multicollinearity

2.5.3 Regression analysis:

To assess the determinants affecting commercial bank credit risk, the thesis employs a multivariable regression model that includes Pooled OLS, Fixed Effect, and Random Effect models To begin, the study regresses panel data using the Pooled OLS pooled least squares regression model We use two FEM models and REM models to overcome the limits of the Pooled OLS approach, which is limited by the geographical and temporal properties of the data We use an F-test to choose between two models in order to find the best one When comparing pooled OLS and FEM, if the probability value Prob (Chi-square) is less than 5% significance level, the FEM model is more optimal When comparing FEM and REM models, if the probability value Prob (Random) is less than 5% significance level, the FEM model is more ideal (Chris brooks,

2008) Following the selection of the best model, we will re-test the OLS regression model's assumptions of multicollinearity, autocorrelation, and variance When the regression assumptions are violated, we use the general least squares (GLS) regression

to fix the problem

2.5.4 Testing for violation of regression assumptions:

2.5.2.2 Heteroscedasticity test:

The variance must be constant according to the OLS regression model's assumption If the model suffers from this defect that can make the regression coefficient test results unreliable (it makes the independent variables more significant in the model), the estimates obtained in the model Figures are inefficient estimators To detect the phenomenon of variable variance, the Breusch and Pagan Lagrangian test was used, with the hypothesis H0: no variable variance was detected, if Prob < 0.05, it means that there

is a variable variance phenomenon

If there is a phenomenon of variable variance or autocorrelation, to overcome the topic, use the generalized least squares method - GLS (Generalized Least Squares)

2.6 Research Process:

Figure 2.1: Research process

2.7 Research data source:

2.7.1 Research data source:

To ensure that the data collected is highly reliable, the data used for this study was gathered from financial websites such as Vietstock, Fialda, Fireant, and from the bank’s financial statements The data used in this study are mainly secondary data on ROE ratio, bank size (SIZE), extracted from financial statements collected from the website of the State Bank of Vietnam (SBV) www.sbv.gov.vn), of joint stock commercial banks in Vietnam Data is processed by software STATA 14.2

2.7.2 Research sample:

Research data is collected in the period from 2016 to 2021 The data is collected from audited financial statements and annual reports of Vietnamese commercial banks Due to the incomplete disclosure of financial indicators of banks, to ensure complete data during the research period, the author only collected data of 21 Vietnamese commercial banks, not including 4 commercial banks belonging to the government, and there are only 21 joint-stock commercial banks in the private sector Data is collected at the end of the year for each bank Data to build macro -factor variables including GDP index, inflation index, and unemployment index are collected each year at the end of each year from the database of the General Statistics Office

Symbol

Number of years of observation

1 Joint Stock Commercial Bank for Foreign Trade of

4 Asia Commercial Joint Stock Bank ACB 6

5 An Binh Commercial Joint Stock Bank ABB 6

6 BAC A Commercial Joint Stock Bank BAB 6

7 Lien Viet Commercial Joint Stock Bank LVB 6

9 Southeast Asia Commercial Joint Stock Bank SSB 6

10 The Maritime Commercial Joint Stock Bank MSB 6

11 Vietnam Technological and Commercial Joint

Stock Bank

12 Ocean Commercial One Member Limited Liability

Bank

13 Military Commercial Joint Stock Bank MBB 6

14 Vietnam International Commercial Joint Stock

Bank

15 Sai Gon Commercial Joint Stock Bank SCB 6

16 Saigon-Hanoi Commercial Joint Stock Bank SHB 6

17 Saigon Thuong Tin Commercial Joint Stock Bank STB 6

18 Tien Phong Commercial Joint Stock Bank TPB 6

19 Vietnam Commercial Joint Stock Bank for Private

Enterprise

20 Vietnam Export Import Commercial Joint Stock EIB 6

21 Ho Chi Minh city Development Joint Stock

Commercial Bank

Table 2.1: List of 21 Vietnamese commercial banks in research data

2.8 Model research results:

2.8.1 Descriptive statistics:

The study using sum function in Stata 14 software for descriptive statistical analysis was carried out for the purpose of summarizing the characteristics of the data Descriptive statistics analyze common criteria such as mean, standard deviation, minimum value, maximum value Statistical results describing the basic parameters of the research data are presented in Table 2.2