The impact of credit and liquidity risk on financial stability at vietnamese commercial banks

TRUONG THANH NGUYEN THE IMPACT OF CREDIT AND LIQUIDITY RISK ON FINANCIAL STABILITY AT VIETNAMESE COMMERCIAL BANKS GRADUATION THESIS THE MAJOR: FINANCE – BANKING CODE: 7 34 02 01 HO CH

TRUONG THANH NGUYEN THE IMPACT OF CREDIT AND LIQUIDITY RISK ON FINANCIAL

STABILITY AT VIETNAMESE COMMERCIAL BANKS

GRADUATION THESIS THE MAJOR: FINANCE – BANKING

CODE: 7 34 02 01

HO CHI MINH CITY, YEAR 2022

STATE BANK OF VIETNAM MINISTRY OF EDUCATION & TRAINING

HO CHI MINH UNIVERSITY OF BANKING

STUDENT: TRUONG THANH NGUYEN STUDENT ID: 050606180265

CLASS: HQ6-GE10 THE IMPACT OF CREDIT AND LIQUIDITY RISK ON FINANCIAL

STABILITY AT VIETNAMESE COMMERCIAL BANKS

GRADUATION THESIS THE MAJOR: FINANCE – BANKING

CODE: 7 34 02 01 SUPERVISOR

Dr LE HA DIEM CHI

HO CHI MINH CITY, YEAR 2022

ABSTRACT

The thesis " the impact of the credit risk and liquidity risk on financial bank stability

of Vietnamese commercial banks in 2011 – 2021" including non-performing loans ratio (NPLR ); Loan to total assets ratio (TLA); Loan loss provision ( LLP ); Leverage (LEV); Loan to total deposit ratio (LDR); Size of the bank (SIZE); Return

on equity (ROE); Capital adequacy ratio (CAP); Growth Revenue (IGR); Economic growth rate (GDP) and Inflation rate (INF)

The research topic applies Pooled-OLS, FEM, and REM models However, the results show that the research model encounters autocorrelation and variable variance, so the author continues applying 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 Hence, the author receives the analysis results from the GMM model as the final result

Research on the impact of credit and liquidity risk on financial stability at Vietnamese commercial banks for 2011-2021, in which the group of internal factors that harm the bank's stability is NPLR, ROE, CAP, and LEV The factors LDR, TLA, and LLP, have a positive impact on the financial stability of the bank The remaining variables are not statistically significant in the model not statistically significant in the model

COMMITMENT

I hereby declare that the thesis " the impact of credit and liquidity risk on financial stability at vietnamese commercial banks" 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

Truong Thanh Nguyen

ACKNOWLEDGMENT

First of all, I would like to thank the teachers who are lecturers at Banking University of Ho Chi Minh City, the school's Board of Directors for creating favorable conditions for teaching and guiding me with a lot of knowledge about banking and teaching taught both soft skills and moral training during my time here

I would like to thank and send kind words to the person who guided me in the process of completing the thesis - Dr Le Ha Diem Chi, who oriented, guided, helped and encouraged me throughout the process of completing the thesis into a research thesis

Finally, I would like to express my gratitude to my family and friends around for helping and encouraging me when I faced difficulties in the process of completing

my thesis, and at the same time, I would like to sincerely thank my colleagues in the organization my authority They gave me a lot of practical knowledge while working here

TABLE OF CONTENTS

ABSTRACT i

COMMITMENT ii

ACKNOWLEDGMENT iii

LIST OF ACRONYMS vii

LIST OF TABLES viii

LIST OF GRAPHICS ix

CHAPTER 1 INTRODUCTION TO THE RESEARCH TOPIC 1

1.1 Reason for Research 1

1.2 Objective of study 1

1.2.1 General objective 1

1.2.2 Specific objective 1

1.3 Research question 2

1.4 Subject and scope of the study 2

1.4.1 Subject of the study 2

1.4.2 Scope of the study 2

1.5 Contributions 3

1.6 Structure of reasearch 3

SUMMARY OF THE CHAPTER 1 5

CHAPTER 2 REVIEW OF THE LITERATURE 6

2.1 Overview of basic concept 6

2.1.1 The concept of credit risk 6

2.1.2 The concept of liquidity risk 6

2.2 The relationship of credit risk and liquidity risk 7

2.3 An overview of previous studies 8

SUMMARY OF THE CHAPTER 2 10

CHAPTER 3 RESEARCH MODEL AND METHODOLOGY 11

3.1 Reasearch model 11

3.2 Mesurement of research variables 12

3.2.1 Z- Score ( Bank Stability Ratio) 12

3.2.2 LDR (Loan to deposit ratio) 12

3.2.3 NPLR (Non-Performing Loans Ratio) 12

3.2.4 IGR ( Revenue growth rate) 12

3.2.5 SIZE (Bank size) 13

3.2.6 ROE (Return on equity) 13

3.2.7 TLA ( Total Loan to Asset ) 13

3.2.8 CAP ( Capital adequacy ratio) 13

3.2.9 LLP ( Loan loss provisions) 13

3.2.10 LEV (Leverage ) 14

3.2.11 INF (Inflation) 14

3.2.12 GDP (Economic growth) 14

3.3 Research hypothesis 14

3.4 Research data and research method 18

3.4.1 Research data 18

3.4.2 Research Methods 18

SUMMARY OF THE CHAPTER 3 21

CHAPTER 4: RESEARCH RESULTS AND DISSCUSSION 22

4.1 Descriptive statistical analysis 22

4.2 Correlation Matrix 24

4.3 Estimating the regression model and testing the regression hypotheses 26 4.3.2 Compare the FEM model and the REM model 29

4.3.3 Check for multicollinearity 29

4.3.4 Check for autocorrelation 30

4.4 Overcoming the research model and GMM regression model method 32

4.5 Summarize and discuss research result 34

SUMMARY OF THE CHAPTER 4 42

CHAPTER 5 CONCLUSION AND RECOMMENDATION 43

5.1 Conclusion of the result obtain from the research 43

5.2 Recommendation on financial stability of bank 43

5.2.1 Solutions to increase business efficiency 43

5.2.2.Credit growth solutions 44

5.2.3 Solutions to ensure capital safety 45

5.3 Limitations of the topic 45

5.4 Further research directions 46

SUMMARY OF THE CHAPTER 5 46

REFERENCES 47

APPENDIX 51

LIST OF ACRONYMS

LIST OF TABLES

Table 3 1 Statistics of expected signs of variables in the model 17

Table 4 1 Statistics of variables used in the research model 22

Table 4 2 Correlation Matrix 25

Table 4 3 Result of Pooldes – OLS Model 27

Table 4 4 Result of Fixed Effects Model 28

Table 4 5 Result of the Hausman test 29

Table 4 6 Results of multicollinearity test 30

Table 4 7 Wooldridge test results 31

Table 4 8 Breusch and Pagan Lagrangian test result 31

Table 4 9 GMM test result 32

Table 4 10 Synthesize the results from the model analysis using STATA software 34

LIST OF GRAPHICS

Graphic 4 1 Relationship between Z-Score & LDR 36

Graphic 4 2 Relationship between Z-Score & NPLR 37

Graphic 4 3 Relationship between Z-Score & TLA 38

Graphic 4 4 Relationship between Z-Score & ROE 39

Graphic 4 5 Relationship between Z-Score & CAP 40

Graphic 4 6 Relationship between Z-Score & LLP 41

Graphic 4 7 Relationship between Z-Score & LEV 42

CHAPTER 1 INTRODUCTION TO THE RESEARCH TOPIC

1.1 Reason for Research

The commercial banking system in Vietnam has a core role in developing the country's economy In the past, our country and the world were heavily influenced

by the 2008 economic-financial system This crisis has almost engulfed the entire world economy From the situation in the world, we can see the importance of the banking system to a country

For financial stability in a banking system, risk management is an issue that needs to be focused on in a country Credit risk and liquidity risk are one of the risks that have the most impact on the stability of the banking system In Vietnam, research papers on the influence of credit risk and liquidity risk on the banking system's strength have been quite long and not suitable for the current economic situation of the country Therefore, this article will show the impact of liquidity and credit risk on the stability of the current banking system

The specific objectives of this study are:

● Determining the factors of liquidity risk and credit risk affecting the stability of Vietnam's commercial banking system

● Identify model from previous studies

● Determining the level of impact of factors on the financial stability of the Vietnamese banking system

● Propose solutions and recommendations to commercial banks in Vietnam,

to improve financial stability in commercial banking systems and limit risks

affecting the financial stability of banks commercial goods from credit and liquidity risks

● What solutions to balance financial stability in Vietnam's commercial banking system?

1.4 Subject and scope of the study

1.4.1 Subject of the study

The object of the research in the essay is the impact of credit and liquidity risk

on financial stability at vietnamese commercial banks

1.4.2 Scope of the study

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

collected from 27 commercial banks in Vietnam

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

in the period from 2011 to 2021

1.5 Contributions

The research results in this thesis can be used as a reference for scholars, administrators, policy makers, 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 reasearch

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 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 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

5.2 Thematic structure detail

The detailed layout of the research paper is expected to be designed for each specific chapter, section, and subsection as follows:

CHAPTER 1: INTRODUCTION TO THE RESEARCH TOPIC

CHAPTER 2: REVIEW OF THE LITERATURE

2.1 Overview of basic concept

2.1.1 The concept of the risk of liquidity

2.1.2 The concept of the risk of credit

2.2 The relationship of credit risk and liquidity risk

2.3 An overview of previous studies

CHAPTER 3: RESEARCH MODELS AND METHODS

3.1 The research model and the research hypothesis

3.2 Measurement of research variables

4.8 Discussing research results

CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS

5.1 Conclusion of the result obtain from the research

5.2 Recommendation on financial stability of bank

5.3 Limitations of the topic

5.4 Further research directions

SUMMARY OF THE CHAPTER 1

In chapter 1, the author introduces an overview of the selected topic and presents issues surrounding the research topic, including the reasons for choosing the subject, research objectives, research questions for the case, The object and scope of the research, the structure of the topic and the contributions of the research topic

CHAPTER 2 REVIEW OF THE LITERATURE 2.1 Overview of basic concept

2.1.1 The concept of credit risk

According to the State Bank of Vietnam Credit risk in banking activities is the possibility of loss to the bank due to customers' failure to perform or inability to perform their obligations as committed (2005)

Credit risk management is critical to measuring and optimizing the profitability of banks The long-term success of any banking institution depends on an effective system of ensuring the repayment of the borrower's loans, which is critical in dealing with asymmetric information problems, while at the same time reducing the loan loss (Basel, 1999)

To minimize loan loss as well as credit risk, banks need to have an effective credit risk management system (Santomero, 1997); (Basel, 1999) Because of the asymmetric information between banks and borrowers, banks must have a system in place to ensure that they can analyze and assess default risk without their knowledge

The bank's business may face difficulties when the quality of loans deteriorates slightly Poor loan quality originates from the information processing mechanism (Liukisila, 1996) and then increases at the loan approval, monitoring and control stages This problem is exacerbated when guidelines for credit risk management in terms of policies, strategies and procedures related to credit processing do not exist

or are weak or incomplete

2.1.2 The concept of liquidity risk

The concept of liquidity in the economic literature refers to the ability of a financial agent to exchange its existing assets for goods and services or other assets

Liquidity and solvency are natural twin banks, often indistinguishable An illiquid bank can quickly become insolvent and an insolvent bank When the Basel

Committee on Banking Supervision was first established was founded in 1975, its President, George Blunden, at its first meeting, vowed to try to strengthen the capital and liquidity operations of the central international commercial banks The downward trend in banks' capital ratios was halted and then reversed by Basel I The benefit of doing so is evident in most banks' more vital capital positions In the current context

(GOODHART, 2008)

Some researchers have found a significant relationship between the two variables They argue that reducing liquidity risk positively impacts the bank performance, (Bourke, 1989); (Lartey, 2013); (Étienne Bordeleau and Christopher Graham , 2010) However, some people find the opposite true (Konadu, 2009) Furthermore, some studies found no meaningful relationship between the two variables (Lamberg, S., & Vålming, S (2009)); (Li, 2007)) The difference in results in all of these studies is due to different effects across regions and over different periods

2.2 The relationship of credit risk and liquidity risk

According to (Dermine, 1986) liquidity risk is seen as a profit-lowering cost A loan default augments liquidity risk because of the lowered cash inflow and depreciations it triggers Based on the theory financial intermediation (Bryant, 1980); (Douglas W Diamond and Philip H Dybvig, 1983) and the industrial organization approach to banking, which features in the Monti-Klein model of banking (Eliezer Z., Prisman, Myron B.Slovin, Marie E.Sushka, 1986), there is a relationship between liquidity and credit risk According to (Samartı́n, 2003)) and (Rajkamal Iyer, Manju Puri, 2012), based on these models, show that risky bank assets trigger bank shocks Based on these models, liquidity and credit risk should

be positively related and jointly contribute to bank instability

(ITAY GOLDSTEIN and ADY PAUZNER, 2005) researchers evaluate from a theoretical point of view, the results show that liquidity at the level that affects credit risk

(Diamond, Douglas W and Raghuram G Rajan, 2005) show a positive relationship between liquidity and credit risk They clarified that if loans financed too many economic projects, the bank would not be able to meet depositors' demands So these depositors will claim their money back if these assets depreciate This implies that liquidity risk and credit risk increase at the same time

The relationship between the two risks is positive and contributes to bank instability The idea of a positive relationship focuses on times of financial crisis (Cevik et al., 2012)

The theoretical literature of intermediate finance modeled essentially by (Bryant, 1980), (Diamond, Douglas W and Raghuram G Rajan, 2005) Based on these models, liquidity and credit risks are positively related They simultaneously contribute to bank instability

2.3 An overview of previous studies

The recent financial crisis that led to banks' collapse has negatively impacted the real economy Therefore, a paying particular attention to the consequences of economic instability on established economies (Agnello, L., & Sousa, R.M., 2012) The impact of credit and liquidity risk on bank stability has been the subject of several academic works (Acharya and Mora, 2013); (Adusei, 2015); (Tijani Amara, Mohamed Mabrouki, 2019); (Imbierowicz and Rauch, 2014); (Wassim RAJHI, Slim A HASSAIRI, 2013), etc.) Their results were inconsistent Some studies have shown that these two risks have destabilized banks On the other hand, the second group of authors emphasizes the positive impact of these two risks on the stability and sustainability of the bank The third research group shows the neutral effect of credit and liquidity risk on bank stability, which depends on other factors

(Imbierowicz and Rauch, 2014) analyzed the relationship between liquidity risk and credit risk and their overall impact on the probability of bank default for a sample of 4,300 US commercial banks over the period 1998–2010 period In addition,

their interaction's effect depends on the bank's overall risk profile and can both increase or decrease the risk of bank failure

For (Acharya and Mora, 2013), the role of banks as liquidity providers is a powerful guarantor during the 2008 financial crisis They have demonstrated that the banks that failed in the recent financial crisis had liquidity problems Banks fail to attract deposits by offering high-interest rates, which is one of their main results Indirectly, the results indicate that the presence of both liquidity risk and credit risk can push banks to bankruptcy

(Fu X., Lin Y., Philip M., 2014) focus on the competitiveness and financial stability of Asian-Pacific banks The authors use data from 14 Asia-Pacific economies from 2003 to 2009 to investigate the impact of banking competition, national concentration, regulation, and institutions on the fragility of banks Individual banks by calculating the bank's probability of bankruptcy and z-score [11, P 64–77] Research shows that greater concentration contributes to Higher financial volatility, and lower market power also causes banking risks as macroeconomic conditions, different banking characteristics, regulations, and institutions are governed

In the study of (Anh L.N.Q, Quoc N.Q, Thanh L.T.P, 2020), they showed that the stability of banks positively impacts equity on assets, bank size, and loan-to-money ratio Meanwhile, the most apparent finding emerging from this study is that net profit margin is the most appropriate criterion to assess the stability of banking operations lower bank stability

(David (Dongheon) Shin and Baeho Kim, 2015), the authors studied the impact of credit and liquidity risk on borrower default verification for Nigerian banks Research using Pearson correlation also shows that the effects of liquidity risk and signal risk lead to bank debt verification

(Vo Xuan Vinh, Mai Xuan Đuc, 2020) showed that the bank's size, operating cost management efficiency and revenue growth have an impact on the bank's stability Banks with high total assets will operate stably and efficiently Therefore, banks need to manage asset quality well, gradually increase the proportion of non-credit operating income to minimize the risk of loss of total assets

SUMMARY OF THE CHAPTER 2

In chapter 2, the author provides a theoretical basis for liquidity risk and credit risk and the impact of the above risks on the stability of commercial banks to have a better view of the risk and how liquidity and credit risk affect the stability of the Bank At the same time, it can be seen how liquidity risk and credit risk interact Finally, the author summarizes the results from previous studies to build the research model that will be implemented in the next chapter

CHAPTER 3 RESEARCH MODEL AND METHODOLOGY

3.1 Reasearch model

Based on previous studies of many authors on credit risk and liquidity risk, along with the impact of risks on the stability of the domestic and foreign commercial banking system in each period Different periods from which to propose a separate research model for the financial stability of Vietnam's commercial banking system in the period from 2010 to 2021, the following is the author's research model:

Z-Score

= β

+ β

LDR

+ β

NPLR

+ β

IGRit + β

TLA

+β

SIZE

+

β

ROE

+ β

CAP

+ β

LLP

+ β

LEV

+ β

GDP

+ β

INF

+ ɛ

o LDRit: Loan to deposit ratio of the commercial bank (i) at the time (t)

o NPLRit: Non-Performing Loan Ratio of the commericial bank(i) at the time (t)

o IGRit: Revenue growth of the commercial bank (i) at the time (t)

o TLAit: Total Loan Assets of the commercial bank (i) at the time (t)

o SIZEit: Bank size of the commercial bank (i) at the time (t)

o ROEit: Return on equity of the commercial bank (i) at the time (t)

o CAPit: Capital adequacy ratio of the commercial bank (i) at the time (t)

o LLPit: The ratio of loan loss provisions of the commercial bank (i) at the time (t)

o LEVit: Leverage of the commercial bank (i) at the time (t)

o INFt: Inflation rate at the time (t)

o GDPt : Economic growth at the time (t)

3.2

of research variables

3.2.1 Z- Score ( Bank Stability Ratio)

A bank's financial stability is usually measured by the score The calculation of score was proposed by (Laeven, L & Levine, R., 2009) and (Martin Čihák, Heiko Hesse, 2007) The higher the z-Score, the more stable the bank because it is related

z-to the inverse of the bank's insolvency ratio

𝑍 − 𝑆𝑐𝑜𝑟𝑒 = 𝑅𝑒𝑡𝑢𝑟𝑛 𝑜𝑛 𝑎𝑠𝑠𝑒𝑡 𝑟𝑎𝑡𝑖𝑜 − 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝑡𝑜 𝑎𝑠𝑠𝑒𝑡 𝑟𝑎𝑡𝑖𝑜

𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑡𝑎𝑡𝑖𝑜𝑛 𝑅𝑒𝑡𝑢𝑟𝑛 𝑜𝑛 𝑎𝑠𝑠𝑒𝑡 𝑟𝑎𝑡𝑖𝑜

3.2.2 LDR (Loan to deposit ratio)

The ratio of total outstanding loans divided by total deposits assesses the extent to which customer loans are financed by customer deposits This variable can reflect the liquidity position of the bank

𝑇𝑜𝑎𝑙 𝑠ℎ𝑜𝑟𝑡 − 𝑡𝑒𝑟𝑚 𝑑𝑒𝑝𝑜𝑠𝑖𝑡𝑠

3.2.3 NPLR (Non-Performing Loans Ratio)

This ratio high indicates that debt is likely to lose capital high bank So this variable has the value The higher the bank, the more stable decrease and vice versa

𝑁𝑃𝐿𝑅 =𝑁𝑜𝑛 − 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝐿𝑜𝑎𝑛𝑠

𝑇𝑜𝑡𝑎𝑙 𝐿𝑜𝑎𝑛𝑠

3.2.4 IGR ( Revenue growth rate)

Revenue growth is the rate of increase growth of the bank's total revenue for the year current compared to the previous year

IGR = 𝑅𝐺𝑛 –𝑅𝐺(𝑛−1)

𝑅𝐺(𝑛−1)

3.2.5 SIZE (Bank size)

Bank size is measured by taking the logarithm of the bank's total assets

SIZE = Logarithm(Total assets)

3.2.6 ROE (Return on equity)

This ratio reflects the efficiency of the bank's management in using equity, is calculated as after-tax return on equity, a measure of profitability per dollar of equity

ROE = 𝑃𝑟𝑜𝑓𝑖𝑡 𝑎𝑓𝑡𝑒𝑟 𝑡𝑎𝑥𝑇𝑜𝑡𝑎𝑙 𝑒𝑞𝑢𝑖𝑡𝑦

3.2.7 TLA ( Total Loan to Asset )

Banks often focus on using capital sources in traditional activities of lending Ordinary loans have low liquidity; Therefore, large and unpredictable withdrawals are possible leading to bank insolvency

𝑇𝐿𝐴 = 𝑇𝑜𝑡𝑎𝑙 𝐿𝑜𝑎𝑛𝑠

𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠

3.2.8 CAP ( Capital adequacy ratio)

Capital adequacy ratio is one of the most important issues in the banking sector today efficiency and stability of the bank The capital adequacy ratio is calculated using the formula of total equity to total assets

𝐶𝐴𝑃 = 𝑇𝑜𝑡𝑎𝑙 𝑒𝑞𝑢𝑖𝑡𝑦

𝑇𝑜𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡𝑠

3.2.9 LLP ( Loan loss provisions)

LLP (Loan Loss Provision) is the ratio of loan loss provision and net interest income, used to measure the quality and size of investment in risky assets by managers

.𝐿𝐿𝑃 = 𝐿𝑜𝑎𝑛 𝑙𝑜𝑠𝑠 𝑃𝑟𝑜𝑣𝑖𝑠𝑖𝑜𝑛𝑠+𝑅𝑒𝑣𝑒𝑛𝑢𝑒

𝑇𝑜𝑡𝑎𝑙 𝑙𝑜𝑎𝑛𝑠

3.2.10 LEV (Leverage )

Leverage is a combination of liabilities and equity in operating the fianancial policies of a bank It will thrive in bussiness with a higher proportion of liabilites than equity Conversely, it will be low when the proportion of liabilities is smaller than the proportion of equity

3.2.12 GDP (Economic growth)

The economic growth index is one of the macro factors affecting all business activities across all economic sectors, calculated by the annual economic growth index

3.3 Research hypothesis

LDR (Loans-to-Deposits Ratio): The ratio of total outstanding loans divided by

total deposits assesses how customer deposits finance customer loans This variable can reflect a bank's liquidity position (Dong et al 2014) The higher the value of the LDR, the higher the bank's liquidity risk, so the lower the bank's stability

H1: LDR is inversely related to the financial stability of the Vietnamese banking system

Non-Performing Loans Ratio (NPL): negatively affects the stability of the bank,

NPL can increase the risk of bankruptcy Indeed, higher credit risk increases bankruptcy In other words, higher credit risk is associated with a greater probability of bank failure.( 2017)

H2: Non-Performing Loans Ratio has has negative impact on Bank Stability

Revenue Growth Rate (IGR): According to Research by (Laeven, L & Levine,

R., 2009), the revenue growth rate of commercial banks has a positive impact on the bank's financial stability The author finds that the revenue growth of the current year compared to the previous year contributes to the cash flow of commercial banks, which is one of the factors that significantly impact the bank's stability

H3: Revenue Growth Rate has positive impact on Bank Stability

TLA: Total loans to assets positively impact banks' financial stability (Pham Thuy

Tu, Dao Le Kieu Oanh, 2021) When the bank's credit scale grows, there are also many potential risks if it does not control its credit safety policy well Credit activities are considered an essential source of income for the bank The higher the credit balance, the more outstanding the contribution to revenue So this factor reflects good governance capacity of bank managers goods (Sadikoglu & Zehir, 2010)

H4: Total loan assets has a positive impact on banks stability

BANKSIZE: (Fernández de Guevara, J., Maudos, J., Perez, F., 2005) (Tabak B.,

Fazio D., Cajueiro D., 2012) found a negative relationship between credit risk and bank size row Therefore, the size of total assets and financial stability of banks are expected to have positive relationship

H5: Banksize ratio is positively related to the financial stability of commercial banks of Vietnam

ROE (Return on equity): According to (Nguyen Huu Tai, Nguyen Thu Nga,

2017), credit risk can reduce the operational efficiency of banks This demonstrates

an inverse relationship between ROE and credit risk Therefore, the impact of ROE

on financial stability is expected to have a positive effect

H6: Return on equity has a positive impact on Bank stability

Capital adequacy ratio (CAP) has a positive and significant impact on a bank's

stability In fact, capital plays a role the role of a safety net for banks in times of crisis, so this reduces risk of bank insolvency (Ameni Ghenimi, Hasna Chaibi, Mohamed Ali Brahim Omri, 2017) This confirms the results of (Imbierowicz and Rauch, 2014)where capital to assets is negatively related to the probability of bank failure

H7: Capital adequacy ratio has positive impact on Bank Stability

The ratio of risk provision to interest income loan (LLP) represents quality and

scale bank's risky investments When this ratio increases, it shows that the quality of investments is decreasing Therefore, the variable LLP has a positive relationship with credit risk (Vo Xuan Vinh, Mai Xuan Đuc, 2020) According to the authors' research () credit risk has a negative effect on the bank's stability, so it is predicted that the variable LLP has a negative impact on the stability of the bank

H8: Loan loss provision has negative impact on Bank Stability

Leverage (LEV): According to research by Chalermchatvichien et al (2014), we

add a leverage factor to the research model of bank stability, but the study results are not statistically significant However, the author of this thesis wants to understand better the impact of leverage on the bank's stability The author's expected effect is positive on the stability of the bank

H9: Leverage has positive impact on Bank Stability

Inflation rate (INF) has a positive impact on bank stability and has similar results

with the study of (Pham Thuy Tu, Dao Le Kieu Oanh, 2021) indicated that the inflation rate positively affects the profitability of banks, increasing the level of financial stability for banks

H10: Inflation rate has positive impact on Bank Stability

GDP: has a positive impact on the financial stability of banks and has a statistical

significance of 1% This shows that when the economy grows well, banks will be positively affected by that development This result is consistent with previous

H11: GDP has positive impact on Bank Stability

Bank stability in this research model, the research hypothesis is set as:

Table 3 Statistics of expected signs of variables in the model

LDR

Loans to deposit ratio

(Total loans)/(Toal short-term deposits) +

NPLR

Non-Performing Loans Ratio

(Total equity)/(Total

LLP

Loan Loss Provision

(Loan loss Provisions+Revenue )/(Total loans)

INF Inflation Inflation rate at the

foreign-by the author at the World Bank website

The study uses panel data method to fully present the variables included in the micro and macro research models of commercial banks in Vietnam from 2011 to

2021 After collecting enough data , the author put panel data into STATA software

to analyze the proposed research model

3.4.2 Research Methods

3.4.2.1 Estimation methods

The author applies four independent estimation methods to find out the factors in the research that affect the stability of joint stock commercial banks in Vietnam, thereby finding the relationship between the variables as well as the influence direction of the independent and dependent variables by three estimation methods can be displayed

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

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 collecting enough secondary data from 27 commercial banks in Vietnam from

2011 to 2021, the author put panel data into STATA data analysis software, then used four methods as mentioned above access The above is an analysis of the impact of the factors in the research model on the stability of the Vietnamese commercial banking system from 2011 to 2021 The specific steps in data collection analysis are 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 dependent variable and the dependent variable independent of the independent variable Correlation analysis will determine the linear relationship between the research variables in the research model

Step 2: Apply separate formulas for estimation methods, 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) using the F-test, then choose one of the two models with the best fit Next, compare the fixed effect model (FEM) and random effects model (REM) using Hausman test, then choose the 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 conflict 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

SUMMARY OF THE CHAPTER 3

In chapter 3, the author introduces the research model and points out the theoretical foundations of the variables in the model Besides, in chapter 3, the methods and procedures for data analysis of the model from 2011 to 2021 are clearly outlined

CHAPTER 4: RESEARCH RESULTS AND DISSCUSSION

4.1 Descriptive statistical analysis

The topic applies descriptive statistical method of research variables through SUM command in STATA software to get an overview of research variables such as total number of observations, mean, standard deviation, minimum value, and greatest value Secondary data was collected from 27 Vietnamese commercial banks and The World Bank period 2011 - 2021 is shown through research variables in the following table:

Table 4 1 Statistics of variables used in the research model

Statistical results show that there are a total of 271 observations from 27 Vietnamese commercial banks in the period 2011-2021, the Bank's stability coefficient (Z-Score) of 27 Vietnamese commercial banks is at 71.64 , and the lowest Z-Score is 3.18 and the highest is 536, showing that Vietnamese commercial banks have a large divergence when the distance between the two extremes is relatively long and the standard deviation of this factor is very large

The average loan-to-deposit ratio (LDR) of the whole sample reached 86.2%, the maximum value was 139%, the smallest value was 30% and standard deviation 16.9% The description of the LDR variable shows that the Vietnamese banking system is performing well role in providing capital to the economy Lending is still the core activity of banks

Non – performing loans ratio (NPLR) based on Table 4.1, it can be seen that the mean of variable is 2.6%, with the standard deviation of the bad debt ratio of 5.6% The largest value for the period from 2011 to 2021 is 67% and the smallest is 0.35%

Revenue Growth Rate (IGR): Based on 271 observations during the period from

2011 to 2021, it can be seen that the average value of IGR during this period is 15%, the standard deviation is 27.3% The minimum value of the sales growth rate

is -45% and the maximum value is 137%

The mean of total loan assets is 55%, with a standard deviation of 11.7%/ in which the minimum value is 21.6% (Asia Commercial Joint Stock Bank (ACB)) and the maximum value is 74.7% (Vietnam Joint Stock Commercial Bank for Industry and Trade (CTG))

The mean of Bank size ( SIZE) is 8.06, with a standard deviation of 0.48 In which the minimum value is 7.17 (Saigon Bank For Industry And Trade (SGB)) and the maximum value is 9.18 (Vietnam Joint Stock Commercial Bank for Industry and Trade (CTG))

The mean of Return on Equity is 9.4%, with a standard deviation of 6.5% in which the minimum value is 0.2% (National Citizen Commercial Joint Stock Bank (NVB)) and the maximum value is 26.8% (Asia Commercial Joint Stock Bank (ACB))

The mean of Capital adequacy ratio (CAP) is 9%, with a standard deviation of 3.6% in which the minimum value is 4.06% (Joint Stock Commercial Bank for Investment and Development of Vietnam (BID) and the maximum value is 23.8% Saigon Bank For Industry And Trade (SGB)

The mean of loan loss provision ratio (LLP) is 6.1%, with a standard deviation of 2.64% in which the minimum value is 2.32% (Sai Gon Thuong Tin Commercial Joint Stock Bank (HOSE: STB)) and the maximum value is 17.5% (Vietnam Prosperity Joint Stock Commercial Bank (HOSE: VPB)

The mean of leverage ratio (LEV) is 7.07, with a standard deviation of 3.02 In which the minimum value is 1.8 (Tien Phong Commercial Joint Stock Bank (HOSE: TPB)) and the maximum value is 17.89 (Joint Stock Commercial Bank for Investment and Development of Vietnam (BID)

The mean value of the economic growth rate (GDP) is 5.94% with a standard deviation of 1.23% In which, the smallest value is 2.6% belonging to 26 commercial banks in the observational scope of the study in 2021 and the largest value is 7.08% in 2018

The mean of the inflation rate (INF) is 5% with a standard deviation of 2.30% In which, the smallest value is 0.63% belonging to 27 joint stock commercial banks in the observation range of the study in 2015 and the largest value is 18.7% in 2011

4.2 Correlation Matrix

A correlation matrix is a statistic that measures the correlation relationship between two variables The correlation coefficient can range from -1 to +1, with -1 indicating a perfect negative correlation, +1 indicating a perfect positive correlation

A variable that is correlated with itself will always have a correlation coefficient of

1 A correlation coefficient of 0 (or close to 0) means that the two variables have nothing to do with each other If the value of the correlation coefficient is negative,

it means that as x increases, y decreases (and conversely, when x decreases, y increases)

Table 4 2 Correlation Matrix

-GDP -0.0148 -0.0478 -0.1815 0.1841 -0.0284 -0.0568

-0.1085

0.0539 0.152 0.0567 1.0000

-INF -0.2038 0.0424 0.0031 0.4138 -0.3194 -0.2067 0.1081 0.2607 0.3623

-0.3226 0.0356 1.0000

(Source: Analysis results from STATA software)

The results of the correlation matrix of Table 4.2 show that the relationship between the variables is at an acceptable level because the absolute values of the correlation coefficients of the variables are all less than 0.8 Variables NPLR, LDR, ROE, CAP, LLP, LEV, GDP and INF are negatively correlated with the dependent variable Z-Score ; while the remaining variables are positively correlated with Z-Score

The variable LDR has a negative correlation with bank stability of 0.0490 indicating

a negative relationship between LDR and Z-Score, so the higher the loan-to-deposit ratio, the lower the bank's stability

The variable NPLR has a negative correlation with bank stability of 0.0619 indicating a negative relationship between NPLR and Z-Score, so the higher the Non – performing loans ratio, the lower the bank's stability

The variable ROE has a negative correlation with bank stability of 0.2404 indicating

a negative relationship between ROE and Z-Score, so the higher the Return on Equity ratio, the lower the bank's stability

The variable CAP has a negative correlation with bank stability of 0.1343 indicating

a negative relationship between ROE and Z-Score, so the higher the Capital adequacy ratio, the lower the bank's stability

The variable LLP has a negative correlation with bank stability of 0.1698 indicating

a negative relationship between LLP and Z-Score, so the higher the loan loss provision ratio , the lower the bank's stability

The variable GDP has a negative correlation with bank stability of 0.0148 indicating a negative relationship between LLP and Z-Score, so the higher the economic growth rate r, the lower the bank's stability

The variable INF has a negative correlation with liquidity risk of 0.2038 indicating

a negative relationship between INF and Z-Score, so the higher the inflation rate, the lower the bank's stability

4.3 Estimating the regression model and testing the regression hypotheses 4.3.1 Compare Pooled model – OLS and Fixed Effects Model (FEM)

To choose a suitable model, the author tests the Pooled-OLS and FEM models to find a more suitable model The test hypothesis is:

H0: The Pooled-OLS model is more suitable for the research variables

H1: The FEM model is more suitable for the research variables

After processing the research data and putting it into the STATA analysis software, the analysis results of the two models are respectively:

INF -430.5898 122.2856 -3.52 0.001 -671.3905 -189.7892

(Source: Analysis results from STATA software)

● Fixed effect model (FEM)

Table 4 4 Result of Fixed Effects Model

Fixed-effects (within) regression

The test results show that Prob > F = 0.000 is less than 0.05, showing that reject hypothesis H0, which means that the FEM model is more suitable for research variables compared with the Pooled-OLS model

4.3.2 Compare the FEM model and the REM model

After choosing the FEM model between the two Pooled models - OLS and FEM will continue to choose two models to choose the appropriate Research model for the next steps, the thesis applies Hausman test to compare with the Effective Model Fixed response (FEM) and Random Effects Model (REM)

With Hausman test, the research hypothesis is posed as:

H0: There is no correlation between the independent variables and the residuals, which means that the REM model is more suitable

H1: There is a correlation between the independent variables and the residuals, which means that the FEM model is more suitable

Table 4 5 Result of the Hausman test

Test: Ho: difference in coefficients not systematic

(Source: Analysis results from STATA software)

Based on the analysis results from STATA software, it shows that P-value = 0.3893 > 0.05, so the hypothesis H0 is accepted, which means that the REM model is more suitable

4.3.3 Check for multicollinearity

To check whether the research model has independent variables or not by testing the multicollinearity in the research model through the VIF command in STATA software The results obtained VIF < 10 means that the research model does not