Bank specific and macro economic factors affect the liquidity risk of commercial banks in vietnam

MINISTRY OF EDUCATION AND TRADING THE STATE BANK OF VIETNAM BANKING UNIVERSITY OF HO CHI MINH CITY GRADUATE THESIS SPEACIALITY FINANCE – BANKING TOPIC BANK SPECIFIC AND MACRO ECONOMIC FACTORS AFFECT T[.]

MINISTRY OF EDUCATION AND TRADING THE STATE BANK OF VIETNAM

BANKING UNIVERSITY OF HO CHI MINH CITY

HO CHI MINH CITY, JULY 2022

MINISTRY OF EDUCATION AND TRADING THE STATE BANK OF VIETNAM

BANKING UNIVERSITY OF HO CHI MINH CITY

HO CHI MINH CITY, JULY 2022

ABSTRACT SUMMARY

The thesis "Bank-specific and macroeconomic factors affect the liquidity risks of

commercial banks in Vietnam" conducts a study on the group of micro factors and the

group of macro factors affecting the liquidity risk of Vietnamese commercial banks in

the period period 2010 – 2020, including Dependence on external financing source

ratio (EFD); Loan to total assets ratio (TLA); Earning quality (NIITA); Loan to total

deposit ratio (LDR); Size of the bank (SIZE); Return on equity (ROE); Money supply

growth (M2); Economic growth rate (GDP) and Inflation rate (INF)

The research topic applies Pooled-OLS, FEM, and REM models, but the obtained

results show that the research model encounters autocorrelation and variable variance,

so the author continues to apply the model FGLS model to overcome the research

model Then, the author applies the GMM model to test the endogenous phenomenon

occurring in the research model and receives high accuracy research results, so the

author receives the analysis results from the GMM model as the final result

The research results show that the research factors affect bank liquidity risk, in

which the variables NIITA, SIZE, and M2 have a negative impact on bank liquidity

risk, on the contrary, the variables EFD, TLA, LDR, ROE, GDP, and INF have the

same effect on liquidity risk, only variable M2 is not statistically significant

After receiving the research results from the GMM model, the author discusses

the research results affecting the liquidity risk of Vietnamese commercial banks in the

period 2010 - 2020 and makes recommendations for the bank Commercial banks and

risk managers can limit the weak liquidity position of Vietnamese banks

DISCLOSURE

I hereby declare that the thesis "Bank-specific and macro-economic factors

affect the liquidity risks of commercial banks in Vietnam" is the author's research

work, and the research results received are truthful The information, data, and content cited are collected by the author from many different sources, have high reliability, and are cited in the reference section

Ho Chi Minh City,…… 2022

Author

Vo Thi Hoang Thu

THANK YOU

First of all, with deep sincere gratitude, I would like to thank the teachers who are lecturers at the Banking University of Ho Chi Minh City, and the school's management board for creating favorable conditions for teaching and guiding me a lot of knowledge about the banking industry, teaching both soft skills and ethical training during my study here

I would like to express my deep gratitude to the person who guided me in the process of completing my thesis – Dr Le Ha Diem Chi, who oriented, guided, supported, and encouraged me throughout the process of completing my research thesis

Finally, I would like to express my gratitude to my family and friends around me for helping me, sharing my experiences, and encouraging me when I faced difficulties

CONTENTS

ABSTRACT SUMMARY i

DISCLOSURE ii

THANK YOU iii

CONTENTS iv

LIST OF ACRONYMS vii

LIST OF TABLES viii

LIST OF GRAPHICS ix

CHAPTER 1 INTRODUCTION TO THE RESEARCH TOPIC 1

1.1 Reasons for choosing the topic 1

1.2 Research objectives 1

1.3 Research question 2

1.4 Subject and scope of the study 2

Subject of the study 2

1.4.1 Scope of the study 2

1.4.2 1.5 Contribution of research 2

1.6 Structure of research 3

CONCLUSION CHAPTER 1 4

CHAPTER 2 THEORETICAL BASIS AND OVERVIEW OF PRIOR STUDIES 5

2.1 Basic concept overview 5

The concept of liquidity and liquidity risk 5

2.1.1 Measure liquidity risk 6

2.1.2 2.2 Factors impacting liquidity risk 7

Bank – specific factors 7

2.2.1 Macroeconomic factors 9

2.2.2 2.3 An overview of previous studies 10

Review of domestic research 10

2.3.1 Review of foreign research 12

2.3.2 CONCLUSION CHAPTER 2 14

CHAPTER 3 RESEARCH MODELS AND METHODS 15

3.1 Research model 15

3.2 Measurement of research variables 16

FGAP (Funding Gap) 16

3.2.1 EFD (Dependence on external financing source ratio) 16

3.2.2 TLA (Loan-to-total assets ratio) 16

3.2.3 NIITA (Earning quality) 16

3.2.4 LDR (Loan to total deposit ratio) 17

3.2.5 SIZE (Size of the bank) 17

3.2.6 ROE (Return on Equity) 17

3.2.7 M2 (Money Supply) 17

3.2.8 GDP (Economic Growth Rate) 18

3.2.9 INF (Inflation Rate) 18

3.2.10 3.3 Research hypothesis 18

3.4 Research data and research methods 22

3.4.1 Research data 22

3.4.1 Research Methods 23

3.4.2 CONCLUSION CHAPTER 3 26

CHAPTER 4 RESEARCH RESULTS AND DISCUSSION 27

4.1 Descriptive statistical analysis 27

4.2 Correlation analysis 29

4.3 Estimating the regression model and testing the regression hypotheses 30

Compare Pooled model – OLS and Fixed Effects Model (FEM) 30

4.3.1 Compare the FEM model and the REM model 32 4.3.2

Check for multicollinearity 32

4.3.3 Check for autocorrelation 33

4.3.4 Test for the phenomenon of autocovariance change 34

4.3.5 4.4 Overcoming the research model and GMM regression model method 35

The results of overcoming the research model 35

4.4.1 GMM Regression Model Method 36

4.4.2 4.5 Summarize and discuss research results 38

CONCLUSION CHAPTER 4 46

CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS 48

5.1 Conclusion of the results obtained from the research 48

5.2 Recommendations on bank liquidity risk 49

The bank should have a policy on the development and use of capital in each 5.2.1 period 49

The bank need to control lending activities and control the use of lending 5.2.2 sources 49

The bank needs to increase total existing assets 50

5.2.3 The bank need to develop a policy to use reasonable profits 50

5.2.4 Vietnamese commercial banks need to manage liquidity in the face of changes in 5.2.5 lending interest rates and money supply growth 50

In addition to the liquidity risk control policies, the bank has built a team to 5.2.6 monitor, forecast, and promptly handle signs of liquidity risk occurring 51

5.3 Limitations of the research and suggestions for new research directions 51

Limitations of the research topic 51

5.3.1 Proposing new research directions 51

5.3.2 CONCLUSION CHAPTER 5 52

REFERENCES 53

APPENDIX 56

LIST OF ACRONYMS

1 OLS Pooled OLS regression model

2 FEM Fixed Effects Model

3 REM Random Effect Model

4 FGLS Feasible generalized least squares model

5 GMM Generalized Method of Moments

LIST OF TABLES

Table 3.1 Statistics of expected signs of variables in the model 22

Table 4.1 Statistics of variables used in the research model 27

Table 4.2 Correlation coefficients between research variables 29

Table 4.3 Results of Pooled - OLS Model 30

Table 4.4 Results of Fixed Effects Model 31

Table 4.5 Results of the Hausman test 32

Table 4.6 Results of multicollinearity test 33

Table 4.7 Wooldridge test results 34

Table 4.8 Modified Wald test 34

Table 4.9 FGLS model troubleshooting results 35

Table 4.10 GMM endogenous test results 36

Table 4.11 Summary of results of model analysis methods 38

LIST OF GRAPHICS

Graph 4.1 Relationship between EFD and FGAP 39

Graph 4.2 Relationship between TLA and FGAP 40

Graph 4.3 Relationship between NIITA and FGAP 41

Graph 4.4 Relationship between LDR and FGAP 42

Graph 4.5 Relationship between SIZE and FGAP 43

Graph 4.6 Relationship between ROE and FGAP 44

Graph 4.7 Relationship between GDP and FGAP 45

Graph 4.8 Relationship between INF and FGAP 46

CHAPTER 1 INTRODUCTION TO THE RESEARCH TOPIC

1.1 Reasons for choosing the topic

Today's economy is growing and expanding, opening up many opportunities for integration and socio-economic development in the country, however, facing the impacts of the COVID-19 pandemic and changes in the rapid growth in the current economy have had a great impact on the socio-economic development of countries, especially negatively affecting the banking sector of countries around the world One

of the most concerning issues is the bank's liquidity at the moment Liquidity risk is the most dangerous risk because it can affect other risks such as credit risk, when one bank has a problem liquidity risk can spread to other banks, gradually affecting other banks and affecting the domestic banking system, even affecting the banking system of countries around the world

To manage liquidity risk, the bank must balance the allocation and use of liquidity reserves, if the bank holds too much liquidity, it will waste profitable investment opportunities for the bank, on the contrary, if the bank holds too few reserves, it will affect the bank's liquidity, more seriously, the liquidity risk will increase Therefore, the proper allocation and use of liquidity are very important in relation to the future growth of the bank

For these reasons, the author chooses the topic "Bank-specific and macroeconomic factors affect the liquidity risks of commercial banks in Vietnam" to study the factors affecting the liquidity risks of banks, thereby giving a recommendation number Recommendations to reduce liquidity risk and improve liquidity for Vietnamese commercial banks

1.2 Research objectives

The overall objective of this study is to identify and analyze the factors affecting the liquidity risk of Vietnamese commercial banks, thereby making recommendations

to identify and reduce the risks of Vietnamese commercial banks in particular and

Vietnam's banking system in general

1.3 Research question

To achieve the stated research objectives, the thesis focuses on answering the following research questions:

• What factors affect the liquidity risk of Vietnamese commercial banks?

• How have the above factors affected the liquidity risk of Vietnamese commercial banks?

• What is the direction of the impact of the above factors on the liquidity risk of Vietnamese commercial banks?

• Which research model and research method are applied to measure the liquidity risk of Vietnamese commercial banks?

• What solutions are there to improve liquidity and identify liquidity risks?

1.4 Subject and scope of the study

Subject of the study

1.4.1.

The object of the research in the essay is the factors affecting the liquidity risk

of Vietnamese commercial banks

Scope of the study

1.4.2.

• Scope of spatial research: The study was conducted based on data collected

from 28 commercial banks in Vietnam

• Scope of time research: The study was conducted based on data collected in

the period from 2010 to 2020

1.5 Contribution of research

The research results in this thesis can be used as a reference for scholars, administrators, policymakers, etc to contribute to improving the efficiency of banking operations, reducing risks, and improving liquidity for Vietnam's commercial banking

system, thus contributing to giving a different perspective on the problem to easily identify risks and take appropriate measures

1.6 Structure of research

Chapter 1: INTRODUCTION TO THE RESEARCH TOPIC

This chapter presents an overview of the research paper including the following contents: reasons for choosing the topic; research problem; objectives of the study; research question; object and scope of the study; research significance; research paper structure

Chapter 2: LITERATURE REVIEW

In this chapter, the study will first present the theoretical basis of liquidity and liquidity risk, then present the factors affecting liquidity risk, and finally summarize the models of liquidity risk in previous research related to this topic

Chapter 3: RESEARCH MODEL AND METHODOLOGY

Based on the content presented in chapter 2, chapter 3 will focus on presenting the content related to the research model, research variables, research data, research methods, and processes to achieve results for which the aim of the study is concerned

Chapter 4: RESEARCH RESULTS AND DISCUSSION

Chapter 4 focuses on two topics: descriptive statistics of the research variables and testing of the research model, thereby obtaining research results and analyzing the correlation relationship, direction, and level of influence of the impact of variables on the liquidity risk of Vietnamese commercial banks

Chapter 5: CONCLUSIONS AND RECOMMENDATIONS

After collecting the research results from chapter 4, chapter 5 will re-evaluate the research results, give comments on the limitations of the study (if any), and finally make recommendations to improve the efficiency of liquidity operations for Vietnamese commercial banks

CONCLUSION CHAPTER 1

In chapter 1, the author introduced the research topic and presented the basic issues surrounding the research topic, including research objectives, research questions, research object and scope, model and research methods, structure of the study and the contributions that the research topic brings

CHAPTER 2 THEORETICAL BASIS AND OVERVIEW OF

PRIOR STUDIES

2.1 Basic concept overview

The concept of liquidity and liquidity risk

2.1.1.

The topic of research on liquidity risk or liquidity risk situation has attracted a lot of attention in recent years, the concepts of liquidity and liquidity risk are popularized in many different ways of thinking, in various aspects Even so, the concepts of liquidity and liquidity risk have been formed for a long time, Duttweiler (2011) popularized the basic concept of liquidity in banking as the ability of a bank to fulfill its obligations payment obligation when due Along with that concept, the Basel Committee Supervision (2008) views liquidity from a different angle, the Basel committee believes that liquidity is when a bank has due obligations, and the bank must meet the requirements Such obligation is to avoid bad losses to the bank and at the same time, the bank must increase its total assets to meet liquidity needs According to Tiến (2009) liquidity is considered from a different perspective close to banking activities, the liquidity of a bank is understood as in the process of banking activities such as lending, receiving deposits, payments, financial transactions, etc will arise obligations and the bank needs to promptly settle those arising obligations

Contrasting with the concept of liquidity, Tiến (2009)states that liquidity risk is when the bank cannot face situations where unexpected financial obligations arise or the bank will have to pay large costs to be able to raise capital immediate response to liquidity.Regarding liquidity risk, according to Article c, Clause 2, Article 8, Section 2

of Circular No 08/2017/TT-NHNN, Cơ sở dữ liệu quốc gia về văn bản pháp luật Ngân hàng Nhà nước (2017), there are clear provisions on liquidity risk, and is understood as

a financial institution's inability to be liquid when it is due, or be liquid when it is due, but it takes a higher cost to be able to meet the bank's liquidity obligations

Through the assessment of liquidity and liquidity risk, it can be understood that liquidity risk is when the bank does not have enough resources and does not have enough capital, which leads to the inability of the bank to pay for its operations Financial movements arise unexpectedly and become due or the bank has to raise capital at a much higher cost to be able to meet the liquidity deficit immediately

Measure liquidity risk

2.1.2.

There are many different ways to assess a bank's liquidity position, one of the easiest to apply is to use liquidity ratios through the following four basic liquidity ratios:

 Liquidity to total assets

L1 = Liquid assets/Total assets

This ratio indicates the percentage of liquid assets as a percentage of total assets, the higher this ratio shows the better the bank's liquidity, and vice versa, the lower this ratio, the better the bank's liquidity the liquidity of the bank gradually decreased

 Ratio of loans to total assets

L2 = Loans/Total Assets

This ratio shows what percentage of total assets are loans, the higher this ratio proves to the bank that the bank's liquidity is weaker and vice versa, the liquidity of the bank is weak The higher the bank, the lower the ratio

 Ratio of total loans to total deposits and short-term deposits

L3 = Total loan/ (Deposit + short-term mobilized capital)

This ratio shows what percentage of total deposits and short-term deposits will total loans account for, the higher this ratio indicates the weaker the bank's liquidity and vice versa, the higher this ratio is The lower the liquidity, the higher the bank's liquidity

 Ratio of liquid assets to total deposits and short-term mobilized capital

L4 = Liquid assets/ (Deposit + short-term mobilized capital)

The higher the ratio of liquid assets to total deposits and short-term deposits, the stronger the bank's liquidity, and vice versa, the lower the ratio, the weaker the bank's liquidity

2.2 Factors impacting liquidity risk

Bank – specific factors

2.2.1.

 Dependence on external funding sources (EFD)

Dependence on external funding sources is one of the factors that show the bank's ability to use capital, thereby showing the bank's ability to meet liquidity based

on existing capital EFD is measured as the ratio of loans from other financial institutions to total equity EFD factor appears in the author's studies Hạnh and Vy (2019), Thông (2013), Thuận and Tuyết (2021), Yen, Thuy, Long, and Tu (2019), and Shen, Chen, Kao, and Yeh (2009), showing that dependence on external capital sources has a positive effect on bank liquidity risk

 Loan-to-total assets ratio (TLA)

Loan-to-total assets (TLA) ratio shows how much of a bank's lending activity is based on its total assets, it also shows the ability to generate profit from lending activities based on a bank's total assets and shows the bank's liquidity position According to previous studies by the author Hạnh and Vy (2019), Dân (2015), Thông (2013), Nga (2019), and Zaghdoudi and Hakimi (2017) research shows that TLA has a relationship has a positive relationship with bank liquidity risk, but the author's research (Moussa, 2015) gives the opposite research results

 Earnings Quality Ratio (NIITA)

The earning quality is one of the factors that demonstrate a bank's financial strength, and at the same time shows the bank's liquidity at different stages, as

measured by interest income and earnings same on total assets According to the author's research Tibebe (2020), Lestari and Khafid (2021), Abdulla, Ebrahim, Kumaraswamy, & Junaid (2020), and Anam and Afrohah (2020) show that there is a positive relationship between earnings quality ratio and liquidity, conversely, there is a negative relationship between earnings quality ratio and liquidity risk

 Loan to total deposit ratio (LDR)

Loan-to-deposit ratio is measured by total customer loans to total customer deposits, this is a profit-making indicator of the bank by lending to customers based on total deposits, that the customer has deposited at the bank This is one of the indicators affecting the bank's liquidity risk as research by Yen, Thuy, Long, and Tu (2019), and Mehmed (2014) shows that there is a positive relationship between LDR and liquidity risk, but the author's research Moussa (2015), and Thùy and Điệp (2018) gives the opposite research results

 Size of bank (SIZE)

The bank size variable is a popular research variable and is used a lot in research models, there are many ways to measure bank size, but the author applies the measurement by taking the logarithm of the total assets of the bank According to the research results Moussa (2015), Shen, Chen, Kao, and Yeh (2009), Mehmed (2014), Vodova (2013), and Mahmood, Khlaid , Waheed, and Arif (2019) shows that there is a positive relationship between SIZE and liquidity risk of the bank However, there are many other studies with opposite results, such as the author's research Hạnh and Vy (2019), Dân (2015), Thông (2013), Thuận and Tuyết (2021), Vodova (2011), and Zaghdoudi and Hakimi (2017)

 Return on Equity (ROE)

ROE is measured by the after-tax return on equity formula, which shows how much profit a dollar of owner's equity gives owners after deducting corporate income tax The research results by Hạnh and Vy (2019), Thùy and Điệp (2018) shows that

there is a positive relationship between ROE and liquidity risk, on the contrary, in some other studies Nga (2019), Moussa (2015), and Mehmed (2014) shows that there

is a negative relationship between ROE and liquidity risk

Macroeconomic factors

2.2.2.

 Growth rate of money supply (M2)

The money supply growth rate is one of the factors measuring liquidity in the economy, in addition, the money supply growth rate is also a tool to forecast inflation

in the economy The money supply includes cash, demand deposits, and savings deposits The higher the growth rate of the money supply, the greater the liquidity of the money supply, which means that the liquidity risk will be reduced, however, the excessive increase in the money supply will cause negative consequences for the economy economy, inflation will increase at this time Previous studies by the authors Nga & Hương (2018), Yen, Thuy, Long, and Tu (2019), Mahmood, Khlaid , Waheed, and Arif (2019), and Onyeiwu (2012) how that there is a negative relationship between money supply and payment risk On the contrary, the results Thông (2013) show that money supply has a positive impact on liquidity risk, but no statistically significant

 Economic Growth Rate (GDP)

The economic growth rate shows the development trend of the economy through each different period and is one of the commonly used indicators in research models The research results of the author Vodova (2011), Vodova (2013), Mehmed (2014), and Thông (2013) show that there is a positive relationship between GDP and liquidity,

on the contrary, according to the research results of Shen, Chen, Kao, and Yeh (2009), Zaghdoudi, and Hakimi (2017), Moussa (2015), Mahmood, Khlaid, Waheed, & Arif (2019), and Hạnh and Vy (2019) show a positive relationship between GDP and liquidity risk

 Inflation Rate (INF)

The inflation rate is a factor commonly used in research models and affects other factors in the economy, easily causing unpredictable consequences for economic development, so this is an indicator of inflation very important According to research

by Hạnh & Vy (2019), Vodova (2011), Moussa (2015), Shen, Chen, Kao, & Yeh (2009) shows that there is a relationship positive relationship between INF and liquidity risk

2.3 An overview of previous studies

Review of domestic research

2.3.1.

The author's research Dân (2015) on factors affecting liquidity risk of commercial banks in Vietnam, which research variables include: Bank size (SIZE); Equity-to-total capital ratio (ETA); Loan-to-total assets (TLA) ratio; Return on Equity (ROE); Economic growth rate (GDP) and inflation rate (INF) The research results show that the SIZE factor has a negative impact on liquidity risk, the TLA factor has a positive impact on liquidity risk, and the remaining factors have no statistical significance

Thông (2013) study the factors affecting liquidity risk of Vietnam's commercial banking system in the period 2002 - 2011 The research results show that the internal factors affecting liquidity risk include the following factors: Size of total assets (SIZE); Interbank loan (EFD); Loan to total assets (TLA) In addition, the group of macro factors such as economic growth, inflation, and policy lag, have impacted bank liquidity risk

Hạnh and Vy (2019) studying the factors affecting liquidity risk of Vietnam's commercial banking system in the period 2008-2017, thereby obtaining research results showing that: Loan-to-total assets (LTA); Return on Equity (ROE), and External Financing Dependence (EFD) have a positive effect on bank liquidity risk, however, bank size (SIZE) has negative effect on bank liquidity risk In addition, macro factors

such as economic growth rate (GDP) and inflation rate (INF) have a positive impact on the bank's liquidity risk

Nga and Hương (2018) research on factors affecting bank liquidity risk has been empirically studied in Vietnam in the period 2005 - 2015 Through the SGMM method that the author has applied, the research results show that: Liquid asset quality; Bank capital; Provision for credit risks; Net interest income; Inflation rate, and money supply both have an impact on the liquidity risk of Vietnamese commercial banks during this period

Another study by Nga (2019) showed that bank size (SIZE); Bank Capital Ratio (CAP); Return on Equity (ROE); Bank liquidity securities ratio (SER); Effective management of banking operating expenses (ME) have a negative impact on bank liquidity risk, only the loan-to-total assets (LOAN) ratio has a positive impact on bank liquidity risk

Thuận and Tuyết (2021) studying the factors affecting the liquidity risk of

Vietnamese commercial banks in the period 2013 - 2019, the research results show that the bank's size, the ratio of equity to total capital, the ratio of Loans to total mobilized capital, and liquidity reserve ratio have a negative impact on liquidity risk, but only the ratio of dependence on external sources has a positive impact on bank liquidity risk during this period

Yen, Thuy, Long, and Tu (2019) studies the factors affecting bank liquidity risk

in Vietnam in 5 years from 2010, the obtained results show the dependence on external financing source ratio (EFD), loan-to-deposit ratio (LDR), and economic growth rate (GDP) have a positive impact on bank liquidity risk, in addition, the research results also show that found that money supply M2 has a negative impact on bank liquidity risk in model 1

According to the author's research results Thùy and Điệp (2018) the bank's equity ratio (CAP); Return on Equity (ROE); Bank's bad debt ratio (NPL) have a

positive relationship with liquidity risk, on the contrary, loan-to-deposit ratio (LDR) has a negative relationship with liquidity risk In addition, the bank size factor and the credit risk provision ratio are not statistically significant in the research model

Review of foreign research

Anam & Afrohah (2020) study on how profit growth, firm size, and liquidity affect earnings quality Research results show that all three factors: Profit growth, company size, and liquidity have an impact on earnings quality The study shows that liquidity and earnings quality have a positive relationship with each other On the same research topic, Lestari & Khafid (2021) has studied the adjustment of profitability, profit growth, leverage, and liquidity to quality earnings, giving research results Similar to the above study, the relationship and earnings quality is a positive relationship

Mahmood, Khlaid, Waheed, and Arif (2019) studies macro factors and micro factors through the approach that FMOLS affects bank liquidity, receiving the research results that it is the monetary policy based on money supply M2 has a positive effect

on bank liquidity, conversely, economic growth rate (GDP) and bank size (SIZE) have

a negative effect on bank liquidity

Mehmed (2014) research on liquidity risk in Bosnia and Herzegovina shows the research results that return on equity (ROE), and economic growth rate (GDP) negatively affect liquidity risk, on the contrary, bank size positively affects liquidity risk In addition, the loan-to-deposit (LTD) ratio has both a negative impact on

liquidity risk in model 1 and a positive impact on liquidity risk in model 2 Together with the research topic, the authors (Moussa, 2015) studied Tunisia and got the results that ROE, LDR, TLA have a positive influence on liquidity, that is, ROE, TLA and LDR have a negative effect on liquidity risk In addition, factors such as SIZE, GDP, and INF negatively affect liquidity

In the author's research Onyeiwu (2012) on monetary policy and economic growth in Nigeria from 1981 to 2008, the results obtained show that there is a positive relationship between money supply and balance of payment, leading to a positive relationship between money supply and liquidity

Next is the study of the authors Shen, Chen, Kao, & Yeh (2009) studying liquidity risk in the period 1994 - 2006 and the results obtained from this study show that SIZE, EFD, GDP, and INF have a positive impact on liquidity risk

The author Tibebe (2020) studies the financial performance of private commercial banks in Ethiopia through the CAMEL method, the obtained research results show that income quality has a positive effect on total financial bank assets, increasing liquidity, from which it can be seen that earnings quality has a negative relationship with bank liquidity risk

Vodova (2011) studies the liquidity of banks in the Czech Republic including the following factors: Equity ratio to total assets (CAP); Ratio of bad debts to total loans (NPL); Return on Equity (ROE); Bank size (TOA); Financial crisis dummy variable (FIC); the Growth rate of gross domestic product growth (GDP); Inflation rate (INF); Interest rate on interbank transactions (IRB); Interest rate on loans (IRL); Deviation rate between the lending rate and deposit rate (IRM); The monetary policy interest rate (MIR) and the unemployment rate (UNE) The research results show that the inflation rate negatively affects liquidity in models 1 and 2, bank size positively affects liquidity in models 2 and 4, and GDP has a positive effect on liquidity, same direction to liquidity in model 3 With the same research topic, the author Vodova

(2013) used the research variables mentioned above to study liquidity in Hungary and

in the results of this study, TOA negatively affects liquidity in model 1 and GDP

positively affects liquidity in model 1, in the remaining research models, two variables

GDP and TOA are not statistically significant

Finally, the author Zaghdoudi & Hakimi (2017) studied liquidity risk in Tunisia

and got the research results that the ratio of giving to total assets and the speed of

economic development has a relationship positive relationship with liquidity risk, bank

size, and inflation rate have a negative relationship with liquidity risk, however, the

inflation rate is not statistically significant in this model

CONCLUSION CHAPTER 2

In chapter 2, the author gives the theoretical basis of liquidity and vice versa,

liquidity risk to have a clear view of liquidity risk, then the author presents the criteria

for measuring liquidity risk Next, the author describes the variables in the research

model that are divided into two groups: the group of factors inside the bank and the

group of factors outside the bank Finally, the author synthesizes results from previous

studies to build a research model to be implemented in the next chapter

CHAPTER 3 RESEARCH MODELS AND METHODS

3.1 Research model

In order to inherit the results of Dân (2015) and Thông (2013) research on the liquidity of commercial banks, as well as the factors affecting the liquidity risk of commercial banks in each period from which to synthesize and proposed research models, so the research model is presented below the author:

FGAPit = α+ β 1EFDi,t + β 2TLAi,t + β 3NIITAi,t + β 4LDRi,t + β 5SIZEi,t+

β 6ROEi,t + β 7M2t + β 8GDPt + 9INFt + εi,t

+ NIITAi,t: Earning quality ratio of commercial banks (i) at a time (t)

+ LDRi,t: The ratio of loans to total deposits of commercial banks (i) at a time (t)

+ SIZEi,t: Size of commercial bank (i) at time (t)

+ ROEi,t: Return on equity of commercial bank (i) at a time (t)

+ M2t: Growth rate of money supply in a year (t)

+ GDPt: The economic growth rate at a time (t)

+ INFt: Inflation rate at a time (t)

+ i,t: Random error

3.2 Measurement of research variables

FGAP (Funding Gap)

3.2.1.

FGAP (Funding Gap), also known as liquidity gap, is one of the indicators used

to identify signs of liquidity risk in a bank If the liquidity gap is positive and high, it indicates that the bank is in a situation of high liquidity risk and vice versa, the liquidity gap formula is presented by the following formula:

increase its level of safety liquidity for the bank, so the liquidity risk ratio in the bank will be significantly reduced thanks to the quality of earnings

SIZE = Log(Total assets)

ROE (Return on Equity)

GDP (Economic Growth Rate)

3.2.9.

The economic growth rate is one of the common factors, related to many problems occurring in the economy GDP shows the development of the economy over the years, and at the same time can see the development trend of the economy, from which it is possible to forecast new opportunities and challenges for economic development

INF (Inflation Rate)

3.2.10.

The inflation rate is one of the common and important factors, measured by the consumer price index The INF shows the rate of change in commodity prices over the years, thereby predicting the trend of the inflation rate so that the central bank can develop economic policy in accordance with the trends of the exchange rate inflation rate

3.3 Research hypothesis

 Dependence on external funding sources (EFD)

The ratio of dependence on capital sources outside the bank is a measure of the efficiency of the bank's capital use and is an indicator of the bank's liquidity status If the ratio of dependence on capital sources outside the bank is larger, it shows that the bank has used capital inefficiently, leading to a decrease in liquidity, so in previous studies by author Thông (2013), Hạnh and Vy (2019), Thuận and Tuyết (2021), Shen, Chen, Kao, and Yeh (2009), and Yen, Thuy, Long, and Tu (2019), it was found that there is a positive relationship between EFD and bank liquidity risk, therefore, the author expects that EFD will have a positive impact on bank liquidity risk in this research model, the research hypothesis is built as follows:

Hypothesis H1: Dependence on external funding sources has a positive impact

on bank liquidity risk

 Loan-to-total assets ratio (TLA)

The loan-to-total asset ratio (TLA) shows a bank's ability to generate profit from lending activities based on a bank's total assets, which is a measure of how efficiently it

is using its total assets The empirical study of Dân (2015), Thông (2013), Hạnh and

Vy (2019), Nga (2019), and Zaghdoudi and Hakimi (2017) shows a positive relationship between TLA and bank liquidity risk, however, according to the study of Moussa (2015), the research results are opposite to the above studies Therefore, in this research model, the author expects TLA to have a positive impact on liquidity risk, the research hypothesis is built as follows:

Hypothesis H2: The ratio of loans to total assets has a positive impact on bank liquidity risk

 Earnings Quality Ratio (NIITA)

The earnings quality ratio is one of the indicators used to measure the bank's financial ability, this ratio also shows the bank's liquidity level According to author Abdulla, Ebrahim, Kumaraswamy, and Junaid (2020), Anam and Afrohah (2020), Lestari and Khafid (2021), and Tibebe (2020) the relationship between liquidity risk and earnings quality is negative Therefore, in the research model, the author expects the sign of the relationship between NIITA and bank liquidity risk to be negative, the research hypothesis is built as follows:

Hypothesis H3: The earnings quality ratio has a negative impact on bank liquidity risk

 Loan to Deposit Ratio (LDR)

The loan-to-deposit ratio shows the trade-off between liquidity and profitability based on lending activity According to previous studies Thùy and Điệp (2018), and Moussa (2015) shows that LDR negatively affects liquidity risk, and Yen, Thuy, Long, and Tu (2019), Mehmed (2014), shows that LDR positively affects liquidity risk in two different models Therefore, in this study, the author expects the research variable LDR will have a positive impact on liquidity risk, and the hypothesis is built as follows:

Hypothesis H4: The ratio of loans to total deposits has a positive effect on bank liquidity risk

 Size of the bank (SIZE)

Bank size indicates the total assets that the bank is holding, and is one of the indicators to measure the liquidity level of the bank According to a previous study Mahmood, Khlaid, Waheed, and Arif (2019), Mehmed (2014), Moussa (2015), Shen, Chen, Kao, and Yeh (2009), and Vodova (2013) there is a positive relationship between SIZE and bank liquidity risk In contrast to those studies, the results of Dân (2015), Thông (2013), Hạnh and Vy (2019), Nga (2019), Thuận and Tuyết (2021), and Vodova (2011) showed the opposite results Therefore, the author expects a negative sign between these two research variables, the research hypothesis is presented as follows:

Hypothesis H5: Size of the bank has a negative effect on bank liquidity risk

 Return on Equity (ROE)

Return on equity (ROE) is one of the indicators showing the profitability of a bank based on capital use, in addition, it is also a measure of the trade-off level between liquidity safety and profitable investment The research of Nga (2019), Mehmed (2014), and Moussa (2015) shows a negative relationship between ROE and liquidity risk, on the contrary, the research results of Hạnh and Vy (2019), Thùy and Điệp (2018) give the opposite result Therefore, we expect ROE to have a negative impact on liquidity risk in this study, the hypothesis is presented as follows:

Hypothesis H6: Return on equity has a negative impact on bank liquidity risk

 Money supply growth rate (M2)

The money supply growth rate is a macro factor that banks cannot control, but banks can still forecast the influence of money supply on liquidity risk to promptly propose measures to reduce losses caused by changes in the money supply According

to previous studies Mehmed (2014), Onyeiwu (2012), Nga and Hương (2018), and

Yen, Thuy, Long, and Tu (2019) show that money supply has a negative impact on liquidity risk, but the research Thông (2013) shows the opposite result but is not statistically significant Therefore, the author expects money supply to negatively affect liquidity risk in this research model, the hypothesis is presented as follows:

Hypothesis H7: The growth rate of money supply has a negative impact on bank liquidity risk

 Economic growth rate (GDP)

The economic growth rate over the years shows the development trend of the economy, at the same time, shows the opportunities to expand investment, develop the network of fields, and at the same time forecast the negative or negative impacts on the economic circumstances that may cause damage to the economy According to previous studies of Hạnh and Vy (2019), Mahmood, Khlaid, Waheed, and Arif (2019), Moussa (2015), Shen, Chen, Kao, and Yeh (2009), Yen, Thuy, Long, and Tu (2019) and Zaghdoudi and Hakimi (2017) has a positive effect on liquidity risk, however, according to studies Thông (2013), Mehmed (2014), Vodova (2011), Vodova (2013) the results are opposite Therefore, the author expects GDP will have a negative impact

on bank liquidity risk in this research model, the research hypothesis is posed as:

Hypothesis H8: The economic growth rate has a negative impact on bank liquidity risk

 Inflation rate (INF)

The inflation rate represents the degree of currency loss through rapidly rising commodity prices, but this is an important factor that helps regulators and state banks

to make decisions appropriate financial policies According Thông (2013), Nga and Hương (2018), Zaghdoudi and Hakimi (2017) shows a negative relationship between INF and bank liquidity risk, on the contrary, according to the results of Hạnh and Vy (2019), Moussa (2015), Shen, Chen, Kao, and Yeh (2009), Vodova (2011) shows the

opposite result Therefore, the author expects that INF will have a positive impact on the bank's liquidity risk, the research hypothesis is set as:

Hypothesis H9: Inflation rate has a positive impact on bank liquidity risk Table 3.1 Statistics of expected signs of variables in the model

1 EFD

Dependence on external financing source ratio

Borrowing from other credit institutions/ Total

capital

+

2 TLA Loan to total assets

ratio Total loans/ Total assets +

9 INF Inflation rate Annual inflation rate +

3.4 Research data and research methods

3.4.1 Research data

3.4.1.

The research topic collects secondary data from balance sheets and business results in the period 2010-2020, which have been audited by Vietnamese commercial banks Surveying 31 joint-stock commercial banks in Vietnam excluding 100%

foreign-owned banks and venture banks, the author found that there are 28 stock commercial banks in Vietnam with full secondary data from the previous year from 2010 to 2020, banks are excluded because they have not updated secondary data for 2020, there are banks that started operating after 2010 so there is no data for a few years during this period,… Second data source levels of macro-research variables are collected by the author at The World Bank's website

joint-The research topic will apply the panel data method to fully represent the contents of the micro and macro research variables of each Vietnamese commercial bank in the period from 2010 to 2020, multicollinearity rarely occurs, more degrees of freedom, more efficient results After collecting enough data, the author will put it into STATA analysis software to analyze the above research model

as follows:

Pooled OLS regression model: The model is also known as the least-squares

regression model, which is widely applied in the research This model applies the basic regression model and removes spatial data and time data

Fixed Effects Model (FEM): is a regression model of factors that have a fixed

effect on the dependent variable but affect the time range

Random Effects Model (REM): The REM model is a model that analyzes

random fluctuations among variables but is not correlated with the explanatory variable

Feasible generalized least squares (FGLS): is a model used to overcome

defects that occur such as autocorrelation, variance, variable variance, that may occur

in the research model assist

Generalized Method of Moments (GMM): The GMM model is a model used to

eliminate endogenous variables in the research model, and at the same time, GMM also solves the phenomena occurring in the model such as methodological phenomena the difference, autocorrelation, etc To prove that the GMM model is appropriate, the research model needs to ensure the following 4 conditions are met:

i The number of tools in the research model does not exceed the number of research groups

ii The P-value in the Arellano-Bond test must be greater than 10%, in which the research hypothesis is set as:

H0: The research model does not occur sequence autocorrelation

H1: The research model occurs the phenomenon of series autocorrelation

iii The P-value in the Sargan test must be greater than 10%, and the hypothesis is: H0: Instrumental variables are appropriate and endogeneity does not occur H1: Instrumental variables are not suitable and endogenous phenomenon does not occur

iv The P-value in the Hansen test must be greater than 10%, the hypothesis posed

in this test is:

H0: Instrumental variables are appropriate

H1: Instrumental variables do not match

3.4.2.2 The order of execution

After entering secondary data collected from 28 Viet Nam joint-stock commercial banks from 2010 to 2020, the author will enter the data into STATA analysis software, then combine the panel data method and 4 independent estimation methods are presented in the above content to analyze the factors affecting liquidity

risk of Vietnamese commercial banks in the period 2010 - 2020 The order of implementation is presented as follows:

Step 1: Analyze descriptive statistics to get an overview of the independent and

dependent variables in the research model, followed by analyzing the correlation relationship between the dependent variable and the independent variable, between the independent variable with the independent variable Correlation analysis will determine the linear relationship between the research variables in the research model

Step 2: Apply separate formulas to the estimation methods in turn, then compare

the two methods in turn, then select the best results to apply to the research model

First, compare the linear regression model (Pooled OLS) and the fixed effects model (FEM) by F-test, then choose one of the two models with the most suitable results Next, compare the fixed effects model (FEM) and random effects model (REM) by the Hausman test, then choose a model with higher efficiency to apply

Step 3: After obtaining the results from step 2, the author will test the regression

hypothesis of the research model and record the results received

Check the variance of the error constant or not? Because when the variance of the error is constant, it shows that the regression is not efficient, and no longer meaningful Next, analyze whether the research model has autocorrelation or not? Because when the model suffers from this phenomenon, the model will be ineffective and no longer meaningful Finally, the author will check that the variables in the model have multicollinearity through the VIF criterion

Step 4: The results obtained from step 3 will be processed by the author using

the feasible generalized least squares (FGLS) method to overcome the defects in the research model

Step 5: Finally, apply the GMM regression method to control endogeneity in the

research model, then record research results from the GMM regression method

CONCLUSION CHAPTER 3

In chapter 3, the author introduces the research model and research hypotheses

for each independent variable that the author sets out, through which the author

summarizes how to measure the independent variable and the research sign that the

author expects achieved in this study In addition, the author also introduces the data

that the author has collected, the research method used in this thesis is the Pooled-OLS,

FEM, REM, FGLS, and GMM regression model method Finally, the author presents

the order to carry out the research steps that the author has applied in this thesis

CHAPTER 4 RESEARCH RESULTS AND DISCUSSION

4.1 Descriptive statistical analysis

The thesis applies a descriptive statistical method of research variables through SUM command in STATA software to get an overview of research variables such as the total number of observations, mean value, standard deviation, minimum value, and maximum value Secondary data collected from 28 Vietnamese commercial banks and The World Bank in the period from 2010 to 2020 is shown through the research variables in the following statistical table:

Table 4.1 Statistics of variables used in the research model

(Source: Analysis results from STATA software)

Statistical results show that there are a total of 308 observations from 28 Vietnamese commercial banks in the period 2010-2020, the average value of liquidity risk (FGAP) of 28 Vietnamese commercial banks at -9.31%, and the lowest FGAP is -38.57% and the highest is 18.4%, which shows that Vietnamese commercial banks in

particular and the banking system in general always maintain a good liquidity position and control the situation in which the liquidity risk is not too high

Dependence on external financing source ratio (EFD) has an average value of 64.21%, the highest is about 424.9% and the lowest is 0%, which shows that during this period Vietnamese commercial banks have a dependency heavily on external sources of capital

Loan to total assets ratio (TLA) averaged 54.9%, highest at 78.81% and lowest

at 14.48%, indicating a sizable asset-based lending activity

The ratio of earning quality (NIITA) has the average statistical result at 7.47%, the highest value at 16.03%, and the lowest at 0%

Loan to total deposit ratio (LDR) has an average value of 86.42%, the highest at 157.16% and the lowest at 36.33%, this proves that 28 Vietnamese commercial banks

in this period are concentrating on lending activities based on customer deposits is very high

The size of the bank (SIZE) has an average statistical value of 805.9%, with the highest value at 919.54% and the lowest at 691.52%, proving the total asset size of each bank holding is growing

The ratio of return on equity (ROE) has the average statistical result at 8.68%, with banks having the highest ratio of 26.82% and also banks with the lowest ROE ratio of -82%

Money supply growth (M2) has the average statistical result at 17.65%, the highest at 29.7%, and the lowest at 11.9% The economic growth rate (GDP) has an average value of 5.99%, the highest value is 7.1% and the lowest value is 2.9% Inflation rate (INF) statistics show that the average value is 5.81%, the highest value is 18.7% and the lowest is 0.6%

4.2 Correlation analysis

The thesis analyzes the correlation relationship between the dependent variable

FGAP with the research variables in turn: Dependence on external financing source

ratio; Loan-to-total assets ratio; Earnings Quality; Loan to total deposit ratio; Size of

the bank; Return on equity; Money supply growth rate; Economic growth rate;

Inflation rate The results of the correlation analysis are presented in the following

table:

Table 4.2 Correlation coefficients between research variables

(Source: Analysis results from STATA software)

The results of the correlation analysis among the research variables show that

the independent variables have a correlation relationship with the dependent variable

FGAP, except for the variables EFD, SIZE, M2, and GDP The variables TLA, NIITA,

LDR, ROE, and INF have a positive relationship with FGAP The variable TLA has a

positive correlation with liquidity risk of 0.4259, showing that the relationship between

TLA and FGAP is positive to get a raise

The variable NIITA has a positive correlation with liquidity risk of 0.1299,

showing that the relationship between NIITA and FGAP is positive, meaning that the

higher the quality income, the higher the bank's liquidity risk