Factors Affecting Liquidity Risk Of Commercial Banks In Vietnam Khoa Luận Tốt Nghiệp Đại Học Tu Le Lan Huong. - Tp. Hồ Chí Minh.pdf

MINISTRY OF EDUCATION AND TRAINING THE STATE BANK OF VIET NAM

HO CHI MINH UNIVERSITY OF BANKING

MINISTRY OF EDUCATION AND TRAINING THE STATE BANK OF VIET NAM

HO CHI MINH UNIVERSITY OF BANKING

PH.D NGUYEN THI MY HANH

HO CHI MINH CITY, 2024

ABSTRACT

Summary in English: 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 receives high accuracy research results, so the author receives the analysis results from the FGLS model as the final result The research results show that the research factors affect bank liquidity risk, in which the variables with research significance are ROA, CAP, LDR, NIM, SIZE and INF In which the group of internal factors that have a negative impact on the bank's liquidity are NIM, SIZE The factors ROA, CAP, LDR and INF have a positive impact on the liquidity risk of the bank The remaining variables are not statistically significant in the model

Summary in Vietnamese: Nghiên cứu áp dụng các mô hình Pooled-OLS, FEM và REM, nhưng kết quả thu được cho thấy mô hình nghiên cứu gặp phải hiện tượng tương quan tự hồi quy và biến phương sai, do đó tác giả tiếp tục áp dụng mô hình FGLS để khắc phục Sau đó, tác giả nhận được kết quả nghiên cứu chính xác cao, vì vậy tác giả chấp nhận kết quả phân tích từ mô hình FGLS là kết quả cuối cùng Kết quả nghiên cứu cho thấy các yếu tố nghiên cứu ảnh hưởng đến rủi ro thanh khoản của ngân hàng, trong đó các biến có ý nghĩa nghiên cứu là ROA, CAP, LDR, NIM, SIZE và INF Trong đó, nhóm yếu tố nội tại có tác động tiêu cực đến thanh khoản của ngân hàng là NIM, SIZE Các yếu tố ROA, CAP, LDR và INF có tác động tích cực đối với rủi ro thanh khoản của ngân hàng Các biến còn lại không có ý nghĩa thống kê trong mô hình

DECLARATION OF AUTHORSHIP

I hereby declare that this thesis was carried out by myself under the guidance and supervision of Dr Nguyen Thi My Hanh I affirm that the contents and findings are original and have been conducted in accordance with ethical research standards All data and figures derived from various sources have been properly credited in the references section

Furthermore, any external commentary, critiques, and data from other authors or organizations have been appropriately recognized and cited within the text

I will take completely responsibility for the integrity of my study , and I confirm that the Banking of Ho Chi Minh University holds no liability for any potential

copyright violations that may arise from my thesis Ho Chi Minh City, 2024

Author

Tu Le Lan Huong

ACKNOWLEDGMENTS

First of all, with the deepest and most sincere feelings, I extend my heartfelt thanks to the faculty of Ho Chi Minh City Banking University for their unwavering support throughout my academic journey and thesis research During the time since I started studying at school until now, I have received a lot of attention and help from teachers and friends

With the deepest gratitude, I would like to send to Dr Nguyen Thi My Hanh for imparting valuable knowledge to me while I am studying at school Thanks to the dedication and teachings of my teachers, my graduation thesis was able to be completed successfully

Once again, I would like to sincerely thank Dr Nguyen Thi My Hanh - who has directly helped, cared for, and guided me to complete successfully my research during the past time

The graduation thesis is completed over a period of 10 weeks However, my graduation thesis is still limited because knowledge is limitless, my understanding is finite, so shortcomings cannot be avoided I look forward to receiving your valuable comments teachers so that my knowledge in this field can be more complete and at the same time have the opportunity to supplement and raise my awareness

TABLE OF CONTENTS

ABSTRACT I DECLARATION OF AUTHORSHIP II ACKNOWLEDGMENTS III TABLE OF CONTENTS IV LIST OF ACRONYMS VII LIST OF TABLES VIII

1.4.3 Scope of time research: 4

1.5 Contributions of the study 4

4.4.2 Comparison between FEM model and REM model 40

4.4.3 Test for the phenomenon of heteroskedasticity 41

4.4.4 Check for autocorrelation 42

4.5 FEASIBLE GENERALIZED LEAST SQUARED ESTIMATION 42

5.2.1 Declining in bank size and liquidity balance 54

5.2.2 Handling bad loans and improving credit quality 55

5.2.3 Decreasing in equity size (CAP) and using efficiently capital 56

5.2.4 Complying with regulations and ensuring safety in liquidity 56

5.3 LIMITATIONS OF THE TOPIC 57

5.4 FURTHER RESEARCH DIRECTIONS 57

LIST OF ACRONYMS

1 OLS Pooled OLS regression model

2 FEM FEM Fixed Effects Model

3 REM Random Effect Model

4 FGLS Feasible generalized least squares model

5 NIM Net interest margin

6 ROA Return on asset

7 CAP Equity ratio

8 LLD Provision ratio for credit risk

9 LDR The ratio of loan outstanding to total deposits

10 GDPG Gross domestic product growth rate

11 INF Inflation rate

12 COVID_19 an infectious disease caused by the SARS-CoV-2 virus 13 HOSE Hochiminh Stock Exchange

14 HNX Hanoi Stock Exchange

15 UPCoM a stock trading market of unlisted limited liability

companies or joint stock companies

16 SBV The State Bank of Vietnam

17 STATA a suite of programs used in Quantitative Economics and

statistics

LIST OF TABLES

Table 3.1 Statistics of expected signs of variables in the model 27

Table 4.1 Statistics of variables used in the research model 33

Table 4.2 Correlation coefficients between research variables 35

Table 4.6: Results of multicollinearity test 37

Table 4.3: Results of Pooldes - OLS Model 38

Table 4.4: Results of Fixed Effects Model 39

Table 4.5: Hausman test 41

Table 4.7: Breusch and Pagan Lagrangian multiplier test for random effects 41

Table 4.8: Wooldridge test 42

Table 4.9: FGLS model troubleshooting results 43

Table 4.10: Summary of results of model analysis methods 44

Table A 1 Descriptive statistic 65

Table A 2 Correlation between variables 65

Table B 1 Results of Pool-OLS model 66

Table B 2 Results of FEM model 66

Table B 3 Results of REM model 68

Table B 4 Results of FGLS model 68

Table B 5 Comparison between models 70

Table C 1 VIF test 74

Table C 2 Hausman test 74

Table C 3 Breusch and Pagan Lagrangian multiplier test for random effects 76

Table C 4 Wooldridge test 76

Table D 1 Data 77

LIST OF GRAPHS

Graph 4.1: Relationship between LIQ and ROA 47

Graph 4.3: Relationship between LIQ and NIM 48

Graph 4.4: Relationship between LIQ and SIZE 49

Graph 4.5: Relationship between LIQ and CAP 50

Graph 4.6: Relationship between LIQ and LDR 51

Graph 4.7: Relationship between LIQ and INF 52

CHAPTER 1 INTRODUCTION 1.1 RESEARCH PROBLEM

Liquidity risk is one of the most common risks in banking activities A bank facing payment risk will entail risks related to other activities In addition, liquidity risk can originate from one bank and be transmitted to another bank or from one business organization to banks due to economic activities of the market or the current cross-ownership situation Today, with the current banking restructuring situation, the content of liquidity or liquidity risk is one of the urgent and important content for the banking system Therefore, joint stock commercial banks today focus on building policies and operating activities to ensure the bank's liquidity as well as manage liquidity-related risks

The operation of the banking system is a decisive factor in helping to develop the economy In particular, liquidity is a key factor determining the existence and development of a bank Liquidity risk is the most dangerous risk among banking risks, it not only threatens the safety of each commercial bank, but is also related to the safety of the entire system (Eichberger & Summer, 2005) The global financial crisis of 2007 - 2008 and the mass collapse of financial institutions around the world have shown shortcomings in liquidity management of financial institutions, leading to alarming about liquidity shortages at banks Since the crisis, liquidity risks at banks have gradually received great attention from policymakers and researchers around the world In particular, the beginning of 2023 saw the collapse of four banks and one bank on the brink of bankruptcy Although the incident only occurred in 11 days, including Silicon Valley Bank - the second largest bank in the US, the cause of the collapse was loss of liquidity Specifically, SVB's failure stemmed from risk management problems, especially liquidity risk management, and overconfidence in interest rate predictions During the period of low market interest rates, SVB had abundant capital and actively invested in US government bonds along with long-term deposits at other banks When interest rates increased, SVB's bond investments fell into a loss situation, causing depositors to worry and spread word of mouth to

withdraw money massively, causing the bank to have to sell a lot of assets (bonds) at a loss to pay debt, leading to pressure on bank liquidity In Vietnam, a similar incident recently occurred - the SCB bank incident where people competed to withdraw money However, the issue worth mentioning here is that this incident is completely different from 15 years ago since the 2008 crisis because now there are many financial risk management tools in place and many control barriers resulting in the liquidity of the entire system being much stronger than in history Therefore, the potential risk of liquidity risk of Vietnamese Commercial Banks is still quite high as well as the liquidity risk monitoring problem of the State Bank of Vietnam is still not as expected To ensure the safety of a bank's operations and the stability of the entire system, analyzing factors affecting bank liquidity risks is an issue of constant concern

The author has chosen the topic: "Factors affecting the liquidity risk of Vietnam Commercial Banks in Vietnam" as a research topic, in order to find out the main

factors affecting the liquidity risk of commercial banks in Vietnam and give a more general overview of this issue

Relating to the research topic on the impact of liquidity risk on commercial banks, it has received the attention of many domestic and foreign scholars, as researched by (Ha, D T., Hang, H T T., Huy, T T., & Phung, N T K ,2022) using Machine learning research method in Python language with 28 commercial banks in Vietnam during the period 2009 to 2020, (NGUYEN, H C., 2022) using GMM regression model method with 26 Vietnamese commercial banks from 2008 to 2018, (Bhati, S S., De Zoysa, A., & Jitaree, W., 2019) foreign research sample is commercial banks in India between 1996 and 2016 research uses random effects panel data regression model and (Alim, W., Ali, A., & Metla, M R., 2021) validation study in commercial banks in Pakistan from 2006 to 2019 using panel data regression model

Based on previous research, the author analyzes factors affecting banks' liquidity risks in Vietnam and other countries around the world Most previous studies

have not updated the data to date The results of this author's research contribute to the updated scientific literature with additional data up to the most recent year and conducted on 32 Vietnamese commercial banks, thereby discovering new results that create new contributions with many different aspects Firstly: The research will make certain contributions to perfecting the theoretical framework of banking liquidity risk in Vietnam Second: Research to identify factors affecting bank liquidity risks in Vietnam Third: On the basis of inheriting the previous research model and adjusting the research variables to suit the Vietnamese situation, the project selected variables with strong positive correlations among the variables inherited from previous studies to evaluate the impact on liquidity risk at Vietnamese commercial banks In practical terms, the results of the study help bank managers have a method to approach and measure the basic factors affecting the bank's liquidity risk At the same time, researching and selecting variables with a strong positive correlation according to the latest data is the basis for bank managers to update and complete the policy framework for managing and operating the banking system in both aspects of banking management and administration agencies with the goal of well controlling liquidity risks for the current banking system

1.2 RESEARCH OBJECTIVES 1.2.1 General objectives

The general research objective of thine study is to estimate the level of impact of factors affecting liquidity risk of Vietnamese commercial banks

The research object of the study is factors that affects liquidity risk of Vietnamese commercial banks

1.4.2 Research scope

Scope of spatial research: The study focuses on data of 32 Vietnamese commercial banks

1.4.3 Scope of time research:

The study was conducted in the period 2011 - 2022 This study was chosen for a number of reasons: first, a period of more than ten years, although not too long, is enough for the business to stabilize its operations, and at the same time This is the period when businesses gradually recovered after the global financial crisis in 2009; as well as the period when Vietnamese commercial banks encountered the COVID-19 pandemic At the same time, this is also the stage where the author collects enough data for the necessary research

1.5 Contributions of the study

The research has both academic and practical significance as follows:

Firstly, the study provides an overall analysis of the level and direction of factors that affect the liquidity risk of commercial banks and helps Banks pay attention to preventing liquidity crises

Secondly, the research results contribute to helping state banks and especially commercial banks have more data to consider and take measures effective and appropriate solutions in adjusting policies to minimize liquidity risks in Vietnamese commercial banks

1.6 RESEARCH STRUCTURE Chapter 1: INTRODUCTION

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 from 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 limit liquidity risks and improve the efficiency of liquidity operations for Vietnamese commercial bank

CONCLUSIONS OF 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 LITERATURE REVIEW 2.1 THEORETICAL FRAMEWORK

2.1.1 Liquidity Risk

The concept of liquidity followed by Duttweiler (2011) refers to ability to fulfill financial obligations on time, the ability to turn assets into cash quickly and the market's acceptance of those assets Liquidity plays an important part in commercial banks, because they must meet the capital needs of customers and business activities such as withdrawals, loans, payments and capital transactions (BIS, 2009 ; Praet & Herzberg, 2008)

The concept of liquidity was introduced by Keynes (1930) and Fisher (1930) in monetary theory According to them, money is the most liquid asset, but if you keep too much money, you will lose profitable investment opportunities Therefore, commercial banks that want to increase profits will tend to invest in high-risk assets, reducing the ratio of liquid assets and weakening liquidity (Nguyen, 2016) In contrast, highly profitable banks are often concerned with safety and credit control, as well as enhancing liquid assets to avoid default risk (Bunda & Desquilbet, 2008; Chatterjee & Eyigungor, 2009; Rychtarsik, 2009) Wilson, Casu, Girardone & Molyneux (2010) said credit is the main business activity of banks For banks, using short-term capital to provide commercial loans and finance businesses' current assets is an effective solution to maintain liquidity

However, according to Smith (1776), credit activities can encounter great difficulties during periods of financial crisis when banks face sudden withdrawals of customer deposits due to the influence of psychology crowd management Moulton (1918) states that liquidity is the ability of a commercial bank to minimize liquidity risk by allocating assets to investments that are highly liquid or can be converted into cash at a reasonable price certain rate (Toby, 2006) as well as the ability to generate profits and retain profits for reinvestment On the contrary, in theory, credit activities will lose liquidity when a large number of depositors decide to withdraw money

Prochnow (1949) believes that income from assets is formed from one-time and regular debt repayments throughout the life of the asset, this source of income will increase the liquidity of the asset This theory has made important contributions to the study of term structure, expected return or profitability of assets as a factor in assessing liquidity Profitability is the most suitable index to evaluate a bank's performance and health (Lopez & Saurina, 2007)

According to BIS (2009), liquidity risk is one of the main causes of bank weakness, when banks cannot meet customers' withdrawal needs This can lead to a liquidity trap (Jeanne & Svensson, 2007), when banks have to look for other sources of capital at high costs or rely on the intervention of the central bank or the interbank market (Diamond & Rajan, 2005)

Liquidity risk can be divided into two types: financial liquidity risk and market liquidity risk (Decker, 2000; Pham, 2019; Gomes & Khan, 2011) Market liquidity risk is the risk that banks cannot sell assets on the market quickly and at low cost Financial liquidity risk is the risk that a bank will not be able to pay debt obligations when due due to being unable to liquidate assets or lacking capital These two types of risks affect each other through contagion effects in the financial system (Diamond & Rajan, 2005)

Another cause of liquidity risk is macroeconomic factors and the bank's financial, operating and management policies (Ali, 2004) From the perspective of bank liquidity management, both surplus and deficit reflect bank imbalances Liquidity surplus occurs when the economy lacks effective investment projects, capital is not used due to credit appraisal capacity or overdeveloped capital On the contrary, liquidity deficit is when banks do not have enough capital to operate, leading to loss of business opportunities, loss of customers, loss of market and reduced reputation from the public (Truong, 2012; Brunnermeier & Yogo, 2009; Falconer, 2001; Plochan, 2007; Ahmed & Duellman, 2013; Goodhart, 2008; Goddard & Wilson, 2009)

2.1.2 Liquidity supply, demand, and net liquidity position

Liquidity becomes an issue when banks face withdrawal demands from customers At that point, banks need to balance not only the demand for withdrawals with the available funds but also with the ability to mobilize further capital Therefore, assessing a bank's liquidity position needs to consider its dynamic state, meaning it must be examined in the context of the supply-demand relationship of available capital in each specific phase

• Loan repayments by customers

This is considered the second most important source of liquidity supply Lending activities are the primary function of banks, providing the largest source of revenue for banks but also carrying high inherent risks, affecting a bank's ultimate repayment ability If all loans are repaid on time, not only is business efficiency ensured, but it also becomes a significant source of liquidity supply for banks

• Borrowing in the money market

Banks can increase their liquidity supply by borrowing in the money market, including new loans, extensions, and revolving loan repayments Transactions occur between banks themselves or with the central bank."

• Income from asset sales

To meet liquidity needs, banks can convert a portion of liquid assets into cash Revenue from service provision

Income for banks in providing services to customers such as guarantee fees, letter of credit opening fees

• Issuance of shares in the market

The issuance of shares by banks into the market is also a significant source of liquidity supply for banks

2.1.2.2 Liquidity demand

Liquidity demand reflects the need to withdraw funds from banks at various times This demand depends on the following factors:

• Customer deposit withdrawal demand

This is a frequent and immediate liquidity demand, including demand deposits, current deposits, term deposits at maturity, and premature withdrawals Among these, demand deposits and current deposits require banks to maintain a reserve to meet payment demands from these accounts Factors contributing to this liquidity demand may include fluctuations in inflation in the economy, significant differences in deposit interest rates among banks, and differential returns on investment opportunities (stocks, real estate, gold, foreign currencies) compared to depositing funds in banks

• Borrowing demand from customers

This also strongly influences liquidity demand for banks This demand is affected by factors such as business investment demand, competitive lending rates of banks compared to other banks, and other sources of capital becoming less accessible "

• Repayment of borrowings

This refers to the funds that banks must repay for borrowings from economic organizations, individuals, other financial institutions, or the central bank

• Service provision costs and interest expenses

These are expenses related to interest payments on mobilized funds, interest payments on issued securities that have matured, which banks must pay to customers

• Dividend payments to shareholders

This is the money that banks must pay to their shareholders

In terms of time, the liquidity demand of a bank includes both short-term and long-term aspects

Short-term liquidity demand is immediate or nearly so Transactional deposits, term deposits at maturity, and instruments mobilized in the money market fall within the scope of short-term liquidity demand Meeting this type of liquidity demand

requires banks to maintain a relatively large amount of highly liquid assets (cash in hand, deposits at the central bank and other financial institutions, government securities )

Long-term liquidity demand is driven by seasonal, cyclical, and trend-based factors For example, individual withdrawal or borrowing demands tend to increase significantly during festive occasions throughout the year for spending and shopping To meet this type of liquidity demand, banks need to proactively reserve liquidity from various sources and at a higher level than short-term liquidity demand Examples include planning to attract new deposits, negotiating long-term loans from the public or from reserve funds of other banks

2.1.2.3 Net Position Liquidity (NPL)

The Net Position Liquidity (NPL) is calculated by the following formula:

“NPL = Liquidity Supply - Liquidity Demand”

Therefore, the net position liquidity represents the difference between total supply and total demand for liquidity at a given point in time If liquidity demand exceeds liquidity supply, the bank will face a liquidity shortfall, meaning it lacks funds to meet obligations To continue operating, the bank must identify how to supplement liquidity and at what cost to restore liquidity balance Conversely, excess liquidity supply over demand can also occur Having excess liquidity also poses risks to the bank as idle reserves do not generate income Therefore, banks need to make decisions to efficiently utilize available excess capital In the case where NLP=0, the bank achieves liquidity balance, which is an ideal but challenging state to attain in practical banking operations

2.2 EMPIRICAL RESEARCH

The financial crisis that lead to bank’s collapse from 2008 has negatively impacted on the real economy Therefore, a paying particular attention to the consequences of Vietnam economy through the study of Ha, D T., Hang, H T T.,

Huy, T T., & Phung, N T K., (2022) assessing the impact of internal and external factors on the liquidity of Vietnamese commercial banks in the period from 2009 to 2020 collected from 28 commercial banks with using Machine learning on the python platform for observational data Model results and regression coefficients show that profitability on liquidity of commercial, equity ratio, credit risk provision ratio, inflation rate have a negative impact and bank size, the ratio of loan outstanding, growth rate have a positive impact on the liquidity of Vietnamese commercial banks The study NGUYEN, H C (2022) uses the audited financial statements of 26 Vietnamese commercial banks listed on the Ho Chi Minh City Stock Exchange (HOSE) and Hanoi Stock Exchange (HOSE) during the 2008–2018 period to estimate the system GMM model, which provides empirical evidence on the effect of the variables of customer deposit to total assets (DEPO) ratio, loan to assets (LTA) ratio, liquidity of commercial banks (LIQ), credit development (CRD) ratio, external funding (EFD) ratio, and credit loss provision (LLP) ratio on liquidity risk The study confirms that commercial banks’ internal factors play the most important role, and there is no empirical evidence on macro variables that affect liquidity risk Finally, in accordance with the theoretical framework, the study uses an estimation method with the R language and the bootstrap methodology to give empirical proof of the nonlinear correlation and U-shaped graph between commercial bank size and liquidity risk The importance of commercial bank size in absorbing and moderating the effects of liquidity shocks is demonstrated, however, excessive growth in commercial bank size would increase liquidity risk in commercial bank operations

The recent economic crisis caused by COVID – 19 epidemic Hao, N T N., & Wong, W K (2021) focused on bank's internal and macroeconomic variables on affecting liquidity risk on the performance of commercial banks in Vietnam in the period 2010 - 2020 They assumed that rising income from interest increases liquidity risk There is not enough evidence to assess the risk of dummy variables - shocks during the study period COVID-19 in influencing liquidity risk Total loans on total

assets ROA, ROE, and NIM have positively impact on the bank performance In addition, we find that liquidity risk could have the opposite effect

Tran, T T., Nguyen, Y T., & Long, T (2019) determined the factors affecting the liquidity of commercial banks in the Vietnamese banking system in the period 2010 – 2015 and used the measurement method by four ratios to evaluate bank's liquidity, which is current assets to total assets, asset liquidity to short-term deposits and mobilized capital, total outstanding loans to total assets and total outstanding short-term loans, mobilized capital and deposits The independent variables are divided into two groups: internal factor and external factor, including: size of the bank, ratio of liquid reserves to total assets of a bank, dependence on capital of the bank, equity ratio, loan to capital ratio, credit risk reversal ratio to total loans, and economic growth rate, Money supply

Research article Nguyen, H T V., & Vo, D V (2021) examined the factors that determine the liquidity of 17 commercial banks listed on the Vietnam Stock Exchange, HOSE, HNX and UPCoM The study used quarterly audited financial statements from the first quarter of 2006 to the first quarter of 2020; it includes 496 observations The author collected GDP and inflation data compiled from the International Monetary Fund and the General Statistics Office of Vietnam Then use the unbalanced panel data method The results show that the size of total assets, return on total assets and credit growth have a positive correlation with the liquidity of listed banks; Meanwhile, the interaction between bank size and return on total assets has a negative impact on the liquidity of commercial banks listed on HNX, HOSE, UPCoM

The research by Alim, W., Ali, A., & Metla, M R (2021), the author used panel data for Ordinary least squares analysis to determine liquidity risk on financial performance of commercial banks in Pakistan conducted from 2006 to 2019 using archived data State Reserve on Bank of Pakistan website The results of the study show that higher liquidity increases bank performance in commercial banks of

Pakistan This conclusion is consistent with several studies and available literature This study can become a good reference for future policy decisions regarding the minimum liquidity requirements of banks in the region

The study of Golubeva, O., Duljic, M., & Keminen, R (2019) investigated the impact of liquidity on bank profits after implementation of Basel III regulations and assume that liquidity ratios have different effects on bank profits, depending on specific indicators and macroeconomics of banking The study makes 180 observations for the period 2014-2017 and 37 observations for 2018 analyzed in a population of 45 European banks The author uses multiple proxies for bank liquidity, including the Liquidity Coverage Ratio, a new measure inspired by the Basel III framework, and the loan-to-deposit and financing spreads

2.3 FACTORS AFFECTING LIQUIDITY RISK

LIQR Liquidity risk financing gap ratio NGUYEN, H C (2022) LIQ Liquidity Short term assets/ Short

term liabilities

NGUYEN, H C (2022) LIQ Liquidity ratio Current assets/ Current

liabilities

NGUYEN, H C (2022) ETA/

loan balance during the year/ Total loan balance at the beginning of the

year

NGUYEN, H C (2022)

DEPO Term deposit The ratio of customer deposits to total assets -

the size of deposits

NGUYEN, H C (2022) LLP The loan loss

provisions to loan ratio

The loan loss provisions to loan ratio

NGUYEN, H C (2022) LTA Loans to total assets NGUYEN, H C

(2022), Tran, T T., Nguyen, Y T., &

Long, T (2019) EFD (The ratio of external

funding to total liabilities Interbank loan + Loan

from other credit institutions)/ Total

capital

NGUYEN, H C (2022)

ROA Return on asset Net income/ Total assets NGUYEN, H C (2022) , Tran, T T.,

Nguyen, Y T., & Long, T (2019), Ha,

D T., Hang, H T T., Huy, T T., & Phung, N T K.,

(2022) ROE Return on equity Net income/ shareholder

equity

NGUYEN, H C (2022), Tran, T T.,

Nguyen, Y T., & Long, T (2019)

NNII Net Interest Margins

(Total interest income - total interest expense)/

Total earning assets

NGUYEN, H C (2022) Cash Cash status index

variable

(Cash + Deposits in State Bank + Deposits in

Financial institution)/ Total assets

Hao, N T N., & Wong, W K (2021)

LDR Credit to capital mobilization ratio

Loan Capital/ Mobilized Capital

Hao, N T N., & Wong, W K (2021) FGAP Funding gap (Credit balance -

mobilized capital) / Total assets

Hao, N T N., & Wong, W K (2021) ETA Equity to Total

assets ratio

Equity/ Total assets Hao, N T N., & Wong, W K (2021), Tran, T T., Nguyen,

Y T., & Long, T (2019), Ha, D T., Hang, H T T., Huy,

T T., & Phung, N T K., (2022) DEP Customer deposit

Mobilized Capital

Hao, N T N., & Wong, W K (2021)

LA Liquid Assets

Cash in hand + State Bank Vietnam balances

+ T-bills and bonds -

Alim, W., Ali, A., & Metla, M R (2021)

Balances due to other banks

Vo, D V (2021) CGR Loan growth rate (Loan year t - loan year

t-1)/ Loan year t-1

Nguyen, H T V., & Vo, D V (2021) FCRER Fund for credit risk Provisions/ Loans Nguyen, H T V., &

Vo, D V (2021), Ha, D T., Hang, H

T T., Huy, T T., & Phung, N T K.,

(2022) SIZE*R

OA

Interaction between the size of total assets and the rate

of return on total assets

SIZE*ROA Nguyen, H T V., & Vo, D V (2021)

NPM Net profit margin Net income/ Revenue Golubeva, O., Duljic, M., & Keminen, R (2019) EBTDA Earnings before

taxes, depreciation and amortization

(EBITDA + interest income - interest expense)/ Revenve

Golubeva, O., Duljic, M., & Keminen, R (2019) LCR (Loan cover ratio),

a measure inspired by Basel III rules to

High quality liquid assets/ total net cash

Golubeva, O., Duljic, M., & Keminen, R (2019)

estimate risks from potiential liquidity

shortages

outflows expected within 30 days

LTD (Loan to deposit) an alternative liquidity ratio

Net loans/ total deposits Golubeva, O., Duljic, M., & Keminen, R (2019) ,

Tran, T T., Nguyen, Y T., & Long, T (2019), Ha, D T., Hang, H T T., Huy,

T T., & Phung, N T K., (2022) FGR (Financing gap

ratio) an alternative liquidity ratio

(Net loans - total deposits)/ total assets

Golubeva, O., Duljic, M., & Keminen, R (2019) NPLL Provisions

established for possible defaults by

customers on loans from banks (a proxy for a credit

risk of a bank)

Loan loss provision/net loans

Golubeva, O., Duljic, M., & Keminen, R (2019) ,

Tran, T T., Nguyen, Y T., & Long, T

(2019) SS Securities gains and

losses, a realised net (as a measure of

a bank's credit risk)

Securities gains - securities losses/Total

bank revenue

Golubeva, O., Duljic, M., & Keminen, R (2019) EFD Dependence on

external financing source

the proportion of to tal interbank loans to

total capital

Tran, T T., Nguyen, Y T., & Long, T

(2019)

LTR Long-term lending interest rate

Tran, T T., Nguyen, Y T., & Long, T

(2019) SIZE Size of total assets

of the banks (The large scale increases the power

in the market and improves technology efficiency at low

cost)

Logarithm of bank's total assets to proxy size

Tran, T T., Nguyen, Y T., & Long, T (2019), Ha, D T., Hang, H T T., Huy,

T T., & Phung, N T K., (2022)

M2 Money supply World Bank datasets Tran, T T., Nguyen, Y T., & Long, T

(2019)

INF Inflation rate World Bank datasets Tran, T T., Nguyen, Y T., & Long, T (2019), Ha, D T., Hang, H T T., Huy,

T T., & Phung, N T K., (2022) GDPG Economic growth World Bank datasets Tran, T T., Nguyen,

Y T., & Long, T (2019), Ha, D T., Hang, H T T., Huy,

T T., & Phung, N T K., (2022)

CONCLUSIONS OF CHAPTER 2

In this chapter, the study will first present the theoretical basis of liquidity and liquidity risk, then present the factors affecting liquidity risk, which collected from previous study Finally, order and choose factors from previous analysis studies to make analytical models for future chapter

CHAPTER 3 RESEARCH METHOD 3.1 RESEARCH MODEL

The model of this paper is mainly based on the inheritance of previous authors (Ha, D T., Hang, H T T., Huy, T T., & Phung, N T K., 2022) Similar to the previous research, in this study, the authors have selected the dependent variable representing the liquidity of commercial banks which is the variable "The ratio of liquid assets to total assets" And the independent variables that the authors use in the model are bank size (SIZE) and equity ratio (CAP) as well as macro-independent variables including economic growth rate (GDPG) and inflation rate (INF) as factors affecting the liquidity of commercial banks and credit risk provision ratio (LLD) and profitability (ROA) and ratio of loan oustanding balances to total deposits (LDR) At the same time, many studies show that the loan loss provisions to loans ratio affects the liquidity risk of commercial banks such as (NGUYEN, H C., 2022), ect Therefore, in order to increase the accuracy and stability of the research model, the author has selected 1 more variable including the loan loss provisions to loans ratio (LLP) is independent variable in the model to analyze their impact on liquidity of commercial banks in Vietnam In this study, data was collected from 32 commercial banks in Vietnam from 2011 to 2022 The experimental research model is as follows:

LIQRit = β0 + β1ROAit + β2NIMit + β3SIZEit + β4CAPit + β5LLDt + β6LDRt + β7GDPGit + β8INFit + ɛi

Where: β0: Intercept

β1, … β8: The individual regression coefficents of the independent variables i: Represents for banks

t: Represents for years

ɛ: Represents the error of the model

CAP: Represents equity ratio (calculated as a percentage)

LLD: Represents the provision for credit losses (as a percentage)

LDR: Ratio of outstanding loans to total deposits (calculated as a percentage) GDPG: Represents GDP growth rate (as a percentage)

INF: Represents Vietnam's inflation rate (as a percentage)

3.2 MESUREMENT OF RESEARCH VARIABLES • LIQ

The liquidity risk of banks is usually measured by the LIQ The calculation of LIQ was proposed by (Tran, T T., Nguyen, Y T., & Long, T., 2019) and (NGUYEN, H C., 2022) The lower the LIQ, the more stable the bank because it is related to the bank's insolvency ratio

LIQ = Liquid assets/ Total assets

• ROA

ROA is used to measure the profitability of the banks and measured as the net income to total assets High ROA shows that the financial position of the banks is

stable, and they are not interested in investing in risky loans because of less pressure to generate income ROA’s formula is presented by the following formula:

ROA = Net income/ Average Total Assets

• NIM

The net interest income ratio is a percentage difference between interest income and the bank's interest expenses payable, indicating how much the banks benefit from the interest rate difference between mobilization and credit investment

NIM = (Interest income – interest expense)/ Total assets

• SIZE

Bank size is measured by taking the logarithm of the bank's total assets, the SIZE factor shows the total assets that the bank currently has, and also represents the bank's liquidity, through the following formula:

SIZE = log(Total Assets)

• CAP

CAP: Equity ratio is measured by equity divided by total assets, this ratio shows the capital adequacy and financial strength of a bank A low ratio of this index indicates that the bank uses a lot of financial leverage leading to high risk, which can reduce the bank's profitability when the cost of capital decreases Research on this factor has high significance for liquidity This ratio has the formula:

CAP = (Equity)/(Total Assets) • LLD

Provision ratio for credit risk is measured by provision for credit losses on total value of loans Provision is calculated on bad debts in group 3, group 4 and group 5 according to the regulations of the State Bank, so the higher this ratio, the higher the credit risk

LLD = (Provision for credit losses)/(Total outstanding loans)

• LDR

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

LDR = ( Total outstanding loans) / (Total deposits)

• GDPG

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

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

Return on assets (ROA): Return on assets is measured by net income after tax

to total assets Muharam and Kurnia (2013); Rahman and Banna (2015); Alzoubi (2017); İncekara and Çetinkaya (2019) showed that ROA has a positive impact on liquidity risk

H1: ROA has a positive impact on liquidity risk

Net interest margin (NIM): Net interest margin measures the gap between

what the bank pays savers and what the bank receives from borrowers Due to

Delécha, Henao, Muthoora and Vtyurina (2012), NIM positively affects liquidity assets

H2: NIM has positively associated with liquidity assets

Bank’s size (SIZE): Bank size is measured as the natural logarithm of the total

assets Moussa (2015); Cucinelli (2013); Alzoubi (2017); Mahmood, Waheed and Arif (2019) revealed Bank’s size positive with liquidity risk

H3: Banksize ratio is positively related to commercial banks of Vietnam

Equity ratio (CAP) According to (Vodová, 2013), profitability of many banks

declined quite substantially, liquidity remains almost at the same level or slightly decreased This demonstrates the positive link between profitability and liquidity

H4: CAP has positive impact on commercial banks in Vietnam

LLD (Provision ratio for credit risk): has significantly impact on liquidity

When the provision for credit risks of banks tends to increase, the bank's liquidity decreases This is also consistent with the previous experimental studies of Sufian Chong (2008), Vong Chan (2009), Inoca Minaryeanu (2012)

H5: Provision ratio for credit risk has negative impact on commercial banks in Vietnam

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

by total deposits assesses how customer deposits finance customer loans The total capital mobilized is mainly short-term and the bank uses a lot for credit activities, less liquid assets will be financed and liquidated (Vu Thi Hong, 2015) Therefore, the loans to deposits ratio and liquidity are expected to have negative relationship

H6: Loans to deposits ratio has has negative impact on commercial banks in Vietnam

Economic growth (GDPG): The economic growth index is one of the macro

factors affecting all business activities across all economic sectors, calculated by the

annual 4 economic growth index The Paper of Moussa (2015); Vodova (2011); İncekara, and Çetinkaya(2019) indicated that GDP negatively with liquidity risk

H7: GDP has negative impact on commercial banks of Vietnam

Inflation (INF): The variable inflation rate is calculated by the inflation rate of

the year of observation, showing the trend of the economy and serving as an indicator for the State Bank to adjust economic policy in line with the economic trend in that period Vodova (2011); Moussa (2015); İncekara and Çetinkaya (2019) revealed INF has a negative effect on liquidity risk Hua Shen et al (2009); Cucinelli, D (2013)

H8: INF is inversely related to commercial banks of Vietnam

Table 3.1 Statistics of expected signs of variables in the model

No Symbol Variable Name

Measurement Method

Expectation Reference

Bank-specific factors 1 ROA Return on

Assets

“Net income / Total Assets”

+

Muharam and Kurnia (2013); Rahman and Banna (2015); Alzoubi, T (2017); İncekara and Çetinkaya (2019)

interest margin

“(Interest income-interest expense)/ Total assets”

+

Delécha, Henao, Muthoora and Vtyurina (2012)

3 SIZE Size of Banks

“The natural logarithm of

Total Assets” +

Moussa (2015); Cucinelli (2013); Alzoubi, T (2017); Mahmood, Waheed and Arif (2019)

4 CAP Equity ratio

“Equity / Total

(Vodová, 2013)

5 LLD Provision ratio for credit risk

“Provisions for credit losses / Total

outstanding loans”

+

Sufian Chong (2008), Vong Chan (2009), Inoca Minaryeanu (2012)

6 LDR Loan to Deposits ratio

“Total loans / Total short-term deposits”

-

(Vu Thi Hong, 2015)

Macroeconomic factors 7 GDPG Gross

Domestic Product growth rate

The annual growth in real GDP in year t (World bank database)

-

Moussa (2015); Vodova (2011); İncekara, and Çetinkaya(2019)

8 INF Inflation rate

The annual inflation rate in year t (World bank database)

-

Vodova (2011); Moussa (2015); İncekara and Çetinkaya (2019) revealed INF has a

negative effect on liquidity risk Chung Hua Shen et al (2009); Cucinelli, D (2013)

3.4 Research data

The data source of the study was taken from the audited financial statements of 32 Vietnamese commercial banks listed on Ho Chi Minh City Stock Exchange (HOSE) and Hanoi Stock Exchange (HNX) during 2011–2022 period including the

following joint stock commercial banks: “An Binh Commercial Joint Stock Bank (ABB), Asia Commercial Joint Stock Bank (ACB), The Vietnam Bank for Agriculture and Rural Development (AGR), Bac A Commercial Joint Stock Bank (BAB), Bao Viet Commercial Joint Stock Bank (BaoVietBank), Bank for Investment and Development of Vietnam (BIDV), Viet Capital Commercial Joint Stock Bank (BVB), Vietnam Joint Stock Commercial Bank for Industry and Trade (CTG), Vietnam Export Import Commercial Joint-Stock Bank (EIB), Ho Chi Minh City Development Joint Stock Commercial Bank (HDB), Kien Long Bank (KLB), LienViet Post Joint Stock Commercial Bank (LPB), Military Commercial Joint Stock Bank (MBB), Vietnam Maritime Commercial Joint Stock Bank (MSB), Nam A Commercial Joint Stock Bank (NAB), National Citizen Commercial Joint Stock Bank (NVB), Orient Commercial Joint Stock Bank (OCB), Petrolimex Group Commercial Joint Stock Bank (PGB), Saigon Commercial Joint Stock Bank (SCB), Saigon Bank For Industry And Trade (SGB), Saigon-Hanoi Commercial Joint Stock Bank (SHB), Southeast Asia Commercial Joint Stock Bank (SSB), Sai Gon Thuong Tin Commercial Joint Stock Bank (STB), Vietnam Technological, Commercial Joint Stock Bank (TCB), Tien Phong Commercial Joint Stock Bank (TPB), Joint Stock Commercial Bank for Foreign Trade of Vietnam (VCB), Vietnam International CJS Bank (VIB), Vietnam –