Factors Affecting Liquidity Risk Of Commercial Banks In Vietnam.pdf

THE GRADUATION THESIS OUTLINE

FACTORS AFFECTING LIQUIDITY RISK OF COMMERCIAL BANKS IN VIETNAM

SPECIALIZED: FINANCE AND BANKING CODE: 7340201

TRAN NGUYEN LOAN CHAU

HO CHI MINH CITY, 2024

THE GRADUATION THESIS OUTLINE

FACTORS AFFECTING LIQUIDITY RISK OF COMMERCIAL BANKS IN VIETNAM

SPECIALIZED: FINANCE AND BANKING CODE: 7340201

SCIENCE INSTRUCTOR DR NGUYEN MINH NHAT

HO CHI MINH CITY, 2024

ABSTRACT

Title: Factors Affecting Liquidity Risk of Commercial Banks in Vietnam

This thesis is based on theoretical foundations and previous empirical studies on liquidity and liquidity risk of commercial banks in Vietnam and globally The analysis and assessment of liquidity risk of commercial banks in Vietnam during the period 2012 – 2022 were conducted using secondary data collected from audited consolidated financial reports of 29 commercial banks and reputable electronic portals such as the World Bank and the General Statistics Office

The research used Pooled OLS, FEM, and REM regression methods on two models with dependent variables being Loan-to-deposit ratio (LDR) and Liquidity Financing gap (FINGAP) The independent variables included Bank Size (SIZE), Net Interest Margin (NIM), Return on Equity ratio (ROE), Loans to Total Asset ratio (LTA), Equity to Total Asset (ETA), Loan Loss Provision (LLP), Gross Domestic Product (GDP), and Inflation rate (INF)

The study then tested the model's shortcomings and utilized FGLS and GMM regression methods to determine the most effective models The GMM regression results showed that in Model 1, with LDR as the dependent variable, SIZE, ROE, and LTA had positive impact on liquidity risk, while NIM had an inverse correlation with liquidity risk For Model 2, with FINGAP as the dependent variable, NIM, ETA, LLP, GDP, and INF showed positive correlations with liquidity risk, whereas ROE had a negative correlation with liquidity risk

Based on these findings, the research provides managerial recommendations for commercial banks and the central bank to prevent and minimize liquidity risk during their business operations Additionally, the study identifies research gaps and suggests future directions for further development of this topic

Keywords: Liquidity risk, Generalized Method of Moments model, Commercial banks, Vietnam

DECLARATION OF AUTHENTICITY

My name is Tran Nguyen Loan Chau, currently a student in the High Quality Bachelor's program, majoring in Finance - Banking, belonging to the HQ8-GE08 class at the Ho Chi Minh University of Banking

I hereby declare that all the content presented in this graduation thesis is the result of my independent research The content, data, and research results in the report are clearly cited and transparent about the data collection sources in the reference section and have not been published in any other form before

I take full responsibility for any fraud or misconduct in the content of the report I sincerely thank you!

Ho Chi Minh City, March 20th 2024 Author

Trần Nguyễn Loan Châu

ACKNOWLEDGEMENT

I sincerely thank the Board of Directors of the Ho Chi Minh University of Banking, as well as all the lectures who dedicatedly conveyed and guided me through the essential practical knowledge during the period studying at the university, enabling me to have the necessary foundation to complete this thesis

I would also like to express my deep gratitude to Dr Nguyen Minh Nhat for his guidance and support throughout the process of completing this graduation thesis, allowing me to successfully fulfill my research

I am truly thankful!

2.1 Overview of liquidity risk 8

2.1.1 The concept of risk 8

2.1.2 The concept of liquidity and liquidity suppliers – demands 8

2.1.3 Liquidity risk 10

2.2 Overview of previous studies 15

2.3 Overview of research variables 24

2.3.1 Dependent Variables 24

2.3.2 Independent Variables 25

CHAPTER 3 RESEARCH METHODS AND MODELS 29

3.1 Research Methods 29

CHAPTER 4 RESEARCH RESULTS AND DISCUSSION 41

4.1 Descriptive statistical analysis 41

4.2 Correlation analysis 46

4.3 Estimating the regression models and testing the regression hypotheses 48 4.3.1 Comparison of the fit between the Fixed Effects Model (FEM) and the Random Effects Model (REM) 50

4.3.2 Comparison of the fit between the Fixed Effects Model (FEM) and the Pooled OLS Model 51

4.4 Checking the models’ defects 52

4.4.1 Multicollinearity 52

4.4.2 Autocorrelation 53

4.4.3 Heteroskedasticity 54

4.5 Remedying the research models 55

4.5.1 Generalized Least Squares (FGLS) method 55

4.5.2 GMM Method 57

4.6 Research results discussion 61

CHAPTER 5 CONCLUSION AND POLICY IMPLICATIONS 68

LIST OF TABLES

Table 2 1 The previous studies 20

Table 3 1 Statistics of expected affect and previous studies of variables in the models 38

Table 4 1 Descriptive Statistics of Variables in Model 1 42

Table 4 2 Descriptive Statistics of Variables in Model 2 45

Table 4 3 Correlation matrix among the explanatory variables in Model 1 46

Table 4 4 47

Table 4 5 Summarize research model included OLS-FEM-REM for model 1 49

Table 4 6 Summarize research model included OLS-FEM-REM for model 2 49

Table 4 7 Hausman test for model 1 50

Table 4 8 Hausman test for model 2 51

Table 4 9 F-test for model 1 52

Table 4 10 F-test for model 2 52

Table 4.11 Research of multicollinearity test 52

Table 4 12 Wooldridge test result for model 1 53

Table 4 13 Wooldridge test result for model 2 53

Table 4 14 Modified Wald test result for model 1 54

Table 4 15 Modified Wald test result for model 2 54

Table 4.16 Summarize research model included OLS-FEM-REM-FGLS for model 1 55

Table 4.17 Summarize research model included OLS-FEM-REM-FGLS for model 2 56

Table 4 18 Results of determining endogenous variables in model 1 57

Table 4.19 Summarize model included OLS-FEM-REM-FGLS-GMM for model 1

58

Table 4 20 GMM regression results 58

Table 4 21 Results of determining endogenous variables in model 2 59

Table 4 22 Summarize model included OLS-FEM-REM-FGLS-GMM for model 2 60

Table 4 23 The GMM regression results 60

Table 4 24 Summary of influence levels of independent variables in model 1 61

Table 4 25 Summary of influence levels of independent variables in model 2 64

LIST OF GRAPHICS

Figure 3 1 Research Process 30

FEM Fixed Effects Model

FGLS Feasible Generalized Least Square FINGAP Financing Gap

GDP Gross Domestic Product

GMM Generalized Method of Moments GSOV General Statistics Office of Vietnam IMF International Monetary Fund

INF Inflation rate

LDR Loan-to-deposit ratio LLP Loan Loss Provision LTA Loans to Total Asset NIM Net Interest Margin OLS Ordinary Least Square REM Random Effects Model

SBV The State Bank of Vietnam VIF Variance Inflation Factor

CHAPTER 1 INTRODUCTION

In Chapter 1, the study provides an overview of the liquidity situation and liquidity risk in the banking systems globally and domestically Additionally, Chapter 1 outlines the fundamental issues of the thesis through the following sections:

1.1 Introduction

The banking industry plays a key role in national economies, strongly influencing the global financial system (Weisbrod and Rojas-Suárez, 1995) Therefore, risks arising during banks business operations are the primary concern of countries, with liquidity risks are considered to be prioritized to observe, prevent, and overcome promptly Because liquidity represents a bank's ability to immediately meet cash demands, a bank with good liquidity will be able to use available capital at a reasonable cost at the right time At the same time, they can avoid the risks of sudden increases in capital mobilization costs and even default when we cannot meet the cash requirement, threatening the stability of the entire banking system

In world financial history, we experienced the big financial crisis in the United Statesin the period 2007 - 2008, also known as the subprime mortgage crisis,which is regarded one of the historic crises causing economic recession and serious impact on the entire world economy The main cause of the crisis is alleged to be investment banks in the US providing mortgage loans to people individuals who couldn't afford them When loans mature, bad debts accumulate, causing financial and real estate bubbles bursting At that time, real estate prices plummeted, a series of banks and financial institutions collapsed continuously, and the unemployment rate skyrocketed to 10% The peak was on September 15th, 2008, when Lehman Brothers Holdings filed for bankruptcy after 158 years of operation Starting in the United States, the crisis rapidly extended to other nations, resulting in a worldwide crisis Global trade nearly collapsed, falling 15% from 2008 to 2009 (Rodini, 2023) By 2010 the total number of jobs lost was 30 million Vietnam is also a country affected by the financial crisis Although the financial system has not yet been affected, import-export

production and business, investment capital attraction, remittances., etc have been significantly affected (Nguyen Van Tao, 2012) These severe losses are the consequences of banks' lack of liquidity Therefore, liquidity plays an extremely important role, demonstrating the prestige and position of the Bank in particular as well as the safety of the whole banking system in general (Dang Van Dan, 2015); and liquidity risk is considered as a top management priority

In Vietnam, since The Great Crisis, the State Bank has paid more attention to liquidity issues, issued many innovative policies and achieved certain achievements However, some cases of liquidity risks causing serious impacts on the banking system still occur (Dang Van Dan, 2015) In December 2009, the liquidity of commercial banks showed signs of stress when the ratio of liquid capital/deposits mobilized from the economy decreased in all groups of commercial banks compared to the end of 2008 while the capital mobilization balance of commercial banks from the interbank market increased by 65.8% compared to the end of 2008 In the period from October 2010 to January 2011, the credit/capital mobilization ratio of the entire credit institution system increased significantly from 98.6% in October 2010 to 100.07% in October November 2010 Because credit growth was faster than capital mobilization growth continuously within 6 months since October 2010, it caused this liquidity tension (Do Hoai Linh and Lai Thi Thanh Loan, 2018) Banking is a chain system, so maintaining stability in the banking system is a vital task to ensure the safety of the entire economy

Therefore, domestic and international research projects have addressed this topic Mugenyah (2015) in the research article "Determinants of liquidity risk of commercial banks in Kenya" relied on statistical results from data from Central Banks in Kenya to conclude that capital adequacy ratio, liquid asset ratio, ownership type, size and leverage were significant determinants of liquidity risk From there, the study recommended that bank managers can effectively manage liquidity risk by focusing on those factors to make reasonable proposals to minimize liquidity risk In Vietnam, Truong Quang Thong (2014) in the study "Factors Affecting Liquidity Risk in the

System of Vietnamese Commercial Banks" based on the annual reports of 27 commercial banks concluded that the internal factors, such as total asset size, liquidity reserve, inter-bank loan, and ratio of equity to capital, and the external ones, such as growth rate, inflation, and effects of policy lags had impact on liquidity risk Through that, the study also proposed some recommendations to enhance the efficacy of liquidity risk management at commercial banks in Vietnam Risk management is a core function of banks (Waemustafa and Sukri, 2016) while the economy is still changing in complexity every day

Based on the inheritance of previous research and the current situation of business activities of commercial banks in Vietnam, the author chose the topic "Factors affecting liquidity risks of commercial banks in Vietnam" as a research topic for thesis The study evaluates the factors and their influence on the liquidity risk of commercial banks in the period 2012 - 2022 Based on updated and current results, the article provides recommendations on improving the efficiency of liquidity risk management at banks in Vietnam

1.2 Research objectives

1.2.1 Overall Objectives:

The purpose of the study is to determine the factors and measure, evaluate their level and direction of impact on liquidity risk of commercial banks in Vietnam, thereby proposing recommendations to minimize the commercial banks' liquidity risk and effectively enhance the liquidity management

1.2.2 Specific Objectives:

− Determine factors affecting liquidity risk of commercial banks in Vietnam − Measure and evaluate the level and direction of impact of factors on liquidity

risk of commercial banks in Vietnam

− Propose recommendations to minimize liquidity risk and enhance liquidity management of commercial banks in Vietnam

1.4 Research subjects and scope

− Research subjects: The factors affecting liquidity risk of commercial banks in Vietnam

− Research scope:

About research space: The study is based on data from the financial statements of 29 commercial banks in Vietnam with the criteria that banks operate throughout the research process, along with data published transparently on each bank's financial statements

About research time: Data was collected from 2012 to 2022 This is a period of high research value because the domestic economy has gone through many processes of crisis and self-recovery as the result of (1) the complex fluctuations of the real estate crisis in the period 2012 - 2013 and 2018 – 2020; (2) the stock market plummeted to the bottom in 2018 after just reaching a historic peak in 2017; (3) and the global economic crisis due to the outbreak of the Covid-19 pandemic from 2022 to present These economic events have caused a significant impact on the whole Vietnamese economy, particularly the banking industry

1.5 Research Methodology

The study uses both quantitative and qualitative research methods to identify the influencing factors and the extent of their impact on liquidity risk using secondary data collected from 29 commercial banks in Vietnam during the period 2012 – 2022 Qualitative research method: This approach is used to explore and analyze the theoretical foundations related to liquidity and liquidity risk, reviewing previous studies both domestically and internationally This helps establish a basis for building the research model, discussing the results, and proposing solutions

Quantitative research method: including descriptive statistical methods, correlation analysis, and panel data regression techniques, using OLS, FEM, REM models with Stata 17.0 software to determine trends and the level of influence of factors on the liquidity risk of Vietnamese commercial banks Subsequently, model selection tests and model deficiencies testing are performed To overcome model deficiencies, the study utilizes the feasible generalized least squares (FGLS) regression method and checks for endogeneity within the model, addressing them using the Generalized Method of Moments (GMM) approach

1.6 Contribution of the study

Theoretical contribution: The study inherits and adjusts the research model based on previous empirical studies; Thereby contributing to building more solidity in the research foundation and presenting limitations as well as research directions for future research

Practical contribution: The research results provide empirical evidence to help commercial banks in Vietnam identify the current situation of liquidity risk; thereby being able to implement measures to limit risks and ensure safety and efficiency during business operations

1.7 Research Content

To identify, analyze and evaluate the factors and their level of impact on the liquidity risk of 29 commercial banks in Vietnam in the period of 2012 - 2022

Thereby, propose recommendations to improve the operational efficiency of banks The report is divided into five parts:

Chapter 1: Introduction

This chapter introduces and highlights the urgency of the topic; thereby determining research objectives, research questions, research methods, research scope, previous studies, and the significance of the topic's contribution

Chapter 2: Overview of theoretical framework and experimental research Theories related to the liquidity risk of commercial banks are mentioned in this chapter; simultaneously, the author mentioned the dependent variables of the research model based on previous studies

Chapter 3: Research methods and models

This chapter presents the research method of the topic as well as the data collection process, data processing and the research model chosen by the author

Chapter 4:Research results and discussion

In this chapter, the author presents the results obtained by analyzing the data and running the models using STATA 17.0 software; simultaneously inspects the defects of model Draw conclusions and address the topic from there

Chapter 5: Conclusion and Policy Implications

This chapter synthesizes all research issues and results From there, the author will present limitations as well as directions for future research; at the same time, propose some recommendations to improve the operational efficiency of commercial banks in Vietnam

SUMMARY CHAPTER 1

Chapter 1 provides an overview of the liquidity situation and liquidity risk at commercial banks globally, as well as in Vietnam specifically, laying the groundwork for the fundamental issues addressed in the thesis The key points covered in this

chapter include the research objectives, research subjects, research methods, and the structure of the thesis Through this, Chapter 2 will delve deeper into providing an overview of the theoretical framework and empirical research of the topic

CHAPTER 2 OVERVIEW OF THEORETICAL FRAMEWORK AND EXPERIMENTAL RESEARCH

Chapter 2 provides the theoretical foundations of liquidity, as well as the characteristics, measurement methods, causes of liquidity risk in commercial banks, and the overview of previous studies

2.1 Overview of liquidity risk 2.1.1 The concept of risk

Commercial banks management needs to always be focused and continuously improved to prevent risks during operations including interst rate risk, credit risk, liquidity risk, and capital risk (Peter & Sylvia, 2008) Puspitasari et al (2021) also announced in their research that there are four main risks that can threaten the sustainable and stable operation of the banking system, namely market risk, credit risk, liquidity risk, and operational risk According to Business dictionary (2012), risk is defined as "a probability or threat of a damage, injury, liability, loss, or other" In general, risks must be understood as potential unforeseen consequences that bring positive or negative impacts to the recipient (Cao, 2013) This report refers to bank liquidity risk, through which commercial banks fall into a state of insolvency, bankruptcy or are declared bankrupt by competent authorities (Altman, 1968; Nguyen

alternative asset (Nikolaou, 2009)

The definition of funding liquidity by the Basel Committee on Banking Supervision emphasizes a bank's capacity to fulfill its obligations, unwind, or settle positions as they mature (BIS, 2008) Likewise, the International Monetary Fund

(IMF) offers a parallel definition for funding liquidity, focusing on the ability of financially sound institutions to make timely payments as agreed upon

According to Peter (2001), "Bank liquidity is the ability of a bank to obtain available capital at low cost at the time the bank needs it." Duttweiler (2011) defines liquidity as the capability to fulfill all impending payment obligations, the simplicity of converting an asset into cash, and the market's willingness to accept it In the context of commercial banks, liquidity referred to the capacity to effectively deploy available funds for various business activities, including deposit payments, lending, transactions, and capital transactions (BIS, 2009)

Peter and Sylvia (2008) determined that liquidity is the availability of cash at a time of need at a reasonable cost The size and volatility of cash demand affects a bank's liquidity position

In general, based on previous research, liquidity is the ability to convert assets into cash to promptly meet cash demands at a reasonable cost, serving the bank's different needs

2.1.2.2 Liquidity suppliers – demands

According to Peter and Sylvia (2008), Liquidity supplies are the bank's sources to

meet liquidity demand including:

• Incoming Customer Deposits

• Revenues from the Sale of Nondeposit Services • Customer Loan Repayments

• Sales of Bank Assets

• Borrowings from the Money Market

Liquidity demands are the need to pay for the bank's committed financial obligations, and created by the following main factors:

• Withdrawals Customer’s Deposit

• Credit Requests from Quality Loan Customers • Repayment of Nondeposit Borrowings

• Operating Expenses and Taxes • Payment of Stockholder Dividends

2.1.2.3 Net Liquidity Position (L)

According to Peter (2001), the difference between liquidity supply and demand at a given point in time is expressed through the Net Liquidity Position, calculated by the formula:

• L < 0 indicates that total supply is less than total liquidity demand (liquidity deficit) In this case, banks need to consider increasing supplementary liquidity supply, such as: selling secondary reserves, overnight interbank borrowing, discounting refinancing from the SBV, etc

2.1.3 Liquidity risk

2.1.3.1 The concept of liquidity risk

According to the "Principles for the Management and Supervision of Liquidity Risk" by Basel (2008), liquidity risk is the risk that a financial institution may not be able to obtain sufficient funding to meet its maturing obligations without adversely

affecting its day-to-day operations and without causing a negative impact on its

financial condition

Duttweiler (2009) contends that liquidity risk is the risk that arises when a financial institution is not capable to make payments at a certain point in time, or has to mobilize funds at a high cost to meet payment obligations, or due to other reasons that compromise the institution's payment capability This may lead to adverse consequences for the financial institution

Liquidity risk can be categorized into two forms, namely market liquidity risk and financial liquidity risk (Decker, 2000; Gomes & Khan, 2011; Pham, 2019) Market liquidity risk pertains to the potential failure of a bank to swiftly and cost-effectively sell assets in the market On the other hand, financing liquidity risk involves the risk that a bank may be unable to fulfill its debt obligations when they mature due to the inability to liquidate assets or a lack of funding These two risk types frequently interact with each other, exhibiting a contagion effect within financial markets and institutions (Diamond & Rajan, 2005)

According to Vodová (2013), liquidity risk encompasses two types of risks: capital liquidity risk and market liquidity risk Capital liquidity risk is the risk that a bank may not efficiently meet the present and future cash flow needs and disbursement requirements without affecting the financial conditions of the company Market liquidity risk is the risk that a bank may find it challenging to offset or eliminate at market prices

Liquidity risk is a situation where a bank is unable to meet all the demands of depositors, either entirely or partially, within a specified period (Jenkinson, 2008) Liquidity risk can also be interpreted as the bank's incapacity to fulfill short-term financial needs It not only impacts the operational efficiency of the bank but also affects the bank's reputation

2.1.3.2 Causes of liquidity risk

Many studies have relatively consistently pointed out that liquidity risk can arise from both asset and liability sides or from off-balance sheet activities of the asset balance sheet of commercial banks (Valla et al., 2006) Furthermore, Nguyen Van Tien (2010) identified three underlying reasons that banks must confront liquidity risk:

The first reason: Banks mobilize and borrow with short-term maturity while continuing to lend with longer maturities As a result, many banks face the risk of mismatched maturities between their assets and liabilities In practice, banks often have a substantial amount of loans that need to be repaid immediately if depositors demand, such as on-demand deposits and prematurely withdrawable fixed-term deposits Therefore, banks must always be prepared for liquidity

The second reason: Sensitivity of financial assets to interest rate changes Depositors tend to deposit where the interest rate is higher when interest rates rise, and borrowers may repay or fully withdraw credit lines with lower contracted interest rates Thus, changes in interest rates affect both deposit and lending cash flows and ultimately impact the liquidity of the bank Additionally, interest rate changes will affect the borrowing cost in the bank's money market

The third reason: Banks must always meet liquidity demands perfectly Liquidity disruptions can erode public trust in the bank Imagine what would happen to a bank if its cash desks or ATMs closed temporarily due to a lack of cash, inability to settle incoming checks, or failure to meet maturing deposits One crucial task for bank managers is to maintain close contact with customers holding large cash balances and those with large unused credit lines to understand their plans for when and how much they intend to withdraw, ensuring a reasonable liquidity plan

2.1.3.3 Impact of liquidity risk on socio-economic activities and on the operations of commercial banks

For the national financial system and the system of commercial banks

The commercial banking system plays an extremely vital role in the financial market Throughout its operations, commercial banks are constantly exposed to various inherent risks, with the most significant ones being credit risk and liquidity risk The occurrence of any type of risk results in certain losses for the banks, causing an increase in operational costs and a reduction in bank profitability In severe cases, banks may face losses leading to bankruptcy This would result in shareholders losing their investments, and depositors losing their savings The trust of depositors, stability, and payment capability of the entire banking system are diminished The bank's reputation in the market is eroded, leading to increased withdrawal of deposits by the public Simultaneously, the bank cannot attract new deposits as people lose confidence in it The escalating liquidity shortage will eventually lead the bank to lose liquidity, potentially resulting in bankruptcy, adversely affecting the financial situation of other banks, triggering a chain reaction, and disrupting the overall stability of the national financial market

For the economic and social system

The banking activities are closely related to the entire economy, affecting all small, medium, and large businesses, as well as all segments of the population When banks face liquidity risks, manifested as an inability to meet payments, the banking system loses its ability to serve as a financial intermediary, leading to a lack of capital for business operations This, in turn, results in economic decline, increased unemployment, and social instability

2.1.3.4 Measurement method

To measure bank liquidity risk, different methods can be used to quantify bank liquidity risk (Truong, 2013) Within the scope of research, the topic presents a number of methods to measure liquidity risk at banks as follows: Financing Gap (Saunders & Cornett, 2006; Lucchetta, 2007; Bunda & Desquilbet, 2008; Dang, 2015; Chen et al., 2018; Phan & Tong, 2021) and Liquidity ratios were calculated from balance sheets’ data of commercial banks (Aspach et al., 2005; Bunda & Desquilbet,

2008; Giannotti 2010; Angora & Roulet, 2011; Vodová, 2011; Delechat et al., 2012; Bonfim & Kim, 2014; Ferrouhi & Lahadiri, 2014)

Liquidity ratios:

This method uses liquidity indicators calculated from items on the balance sheet to assess the liquidity level of the bank Some studies focus on liquidity ratios, specifically the four following liquidity ratios:

L1 = Liquid AssetsTotal Assets

The L1 ratio will provide information on the overall liquidity capability of a bank, including cash, balances with central banks and other banks, and government-issued debt securities The higher this ratio, the higher the liquidity of the bank, but it also implies that the bank is holding too many assets in non-yield-bearing reserve form

Deposits + Short − term mobilized capital

The L2 ratio also uses liquid assets like L1 Nevertheless, this ratio focuses on the bank's sensitivity to various funding sources, including deposits from households, businesses, banks, and other financial institutions Therefore, L2 ratio will reflect the bank's problems related to these sources The higher this ratio, the better the bank's liquidity

L3 = Loans Total Assets

While L1 provides information about the proportion of liquid assets in the total assets, L3 provides information about the proportion of loans in the total assets of the banks Therefore, the higher this ratio, the higher the liquidity risk, as the bank concentrates the funds raised in lending activities If the funds lent are not recovered, the bank faces the risk of being unable to settle maturing deposits for customers due to a lack of a source for redemption

L4 = Loans

Deposits + Short − term Financing

L4 is the ratio used to measure the proportion between illiquid assets such as loans and highly liquid capital sources such as deposits and short-term capital sources This ratio reflects how many times the loan amount is greater than the mobilized amount Therefore, same as L3 ratio, the higher this ratio, the higher the liquidity risk ratio will be Because deposits and loans are the main and traditional business products of commercial banks, the study chooses L4 ratio as the dependent variable for model 1 The method of measuring liquidity risk using liquidity ratios can be easily calculated based on the data from the balance sheets of commercial banks and is commonly used in research studies However, Poorman & Blake (2005) argue that relying solely on liquidity ratios to measure bank liquidity may not be sensitive enough and may not be a comprehensive solution Therefore, the study proposed adding the financing gap ratio and using this ratio as the dependent variable for the model 2

Financing Gap:

Liquidity gap is the difference between the average total loan portfolio and the average total raised capital It serves as a warning sign for potential liquidity risk in the future for a bank This method measures the disparity between capital and assets at both current and future time points (Vodová, 2011)

Financing Gap = Credit balance − Total deposit Total Assets

If this gap is positive and the bank has a significant financing gap, banks will be forced to reduce cash reserves and decrease liquid assets or borrow additional funds from the money market If the gap is negative, indicating that the bank has excess raised capital, it may increase liquidity reserves by purchasing highly liquid assets or use them for lending purposes (Dang Van Dan, 2015)

2.2 Overview of previous studies

Foreign research

Bunda and Desquilbet (2008) in the research article "The bank liquidity smile across exchange rate regimes" also used data from 36 countries in the period from 1995 to 2004 to evaluate the liquidity risk of banks The results show that bank size (SIZE), the ratio of equity to asset (ETA), lending rates (LR), the government spending to GDP and the inflation rate (INF) are positively correlated with liquidity risk The results also show that the financial crisis had a positive impact on liquidity risk in case of fixed exchange rates mechanisms and negative impact in case of floating exchange rate mechanism

Vodová (2011, 2013) used data from Czech, Hungary and Finland commercial banks in the period from 2011 to 2009 to determine the determinants of liquidity of Czech, Hungary and Finland banks Results from regression analysis showed that there was a positive correlation between bank liquidity and equity ratio (ETA), the interest rate on loans (IRL) in three nations; share of non-performing loans (NPL) and interest rates on interbank transaction (IRI) in Czech and Finland, whereas inflation rate (INF), business cycle, economic growth rate (GDP) and financial crisis were negatively correlated with liquidity in Czech Republic but positively in Finland

Ganic (2014) in the report "An Empirical Study on Liquidity Risk and its Determinants in Bosnia and Herzegovina" examined the liquidity risk of 17 commercial banks in Bosnia and Herzegovina (B&H) based on data from reports financial in the period 2002 - 2012 The study analyzed data by using multiple regression model to test for statistical significance and explanatory power, as well as data analysis techniques including correlation, R-squared, ANOVA, and the F-test The results concluded that Return on Equity ratio (ROE), Reserve Ratio (RR) had significant negative correlation with liquidity risk, whereas Loan Loss Reserves ratio, Growth rate of gross domestic product growth (GDP) were positively correlated with liquidity risk

Mugenyah (2015) in the research article "Determinants of liquidity risk of commercial banks in Kenya" used secondary data obtained from the website of the Central Bank of Kenya, the websites of the respective banks and the multiple regression model to evaluate the determinants of liquidity risk The regression results indicated that capital adequacy (CAR) had a positive impact on liquidity risk while the ratio of liquid asset, ownership type, size and leverage had negative impact The study concluded that capital adequacy ratio, size, ownership type, liquid assets ratio and leverage were significant determinants of liquidity risk From there, the study recommends that bank managers can effectively manage liquidity risk by focusing generally capital adequacy ratio, size, ownership type, liquid assets ratio and leverage to make reasonable strategies to minimize liquidity risk

Chowdhury et al (2016) in the research article "Relationship between Liquidity Risk and Net Interest Margin of Conventional Banks in Bangladesh" used data from annual reports of conventional banks in Bangladesh over the period 2011 - 2015 to determine the impact of liquidity risk on the Net Interest Margin (NIM) variable of conventional banks The study used descriptive statistics, correlation, and regression model to present results Thereby, the research concluded that liquidity risk had a considerable impact on the NIM of the selected banks NIM correlated positively with liquidity risk ratios

Alzoubi (2017) in the study "Determinants of liquidity risk in Islamic banks" identified factors affecting liquidity risk in Islamic banks based on data of 42 Islamic banks from 15 countries in the period 2007 - 2014 The results show that cash ratio, securities held by bank, bank size (SIZE), total equity to total assets ratio (ETA) have negative correlation with liquidity risk Because equity is a more reliable source of funding for banks, and a larger equity ratio reduces liquidity risk On the other hand, high profit assets and bad financial decisions are positively correlated with liquidity risk

Abdul-Rahman et al (2018) in the research work “Does financing structure affect bank liquidity risk?” used data of 27 conventional banks and 17 Islamic banks in Malaysia from individual bank financial statement data from the Bureau Van Dijk Bankscope database, publicly available audited reports and the websites of Global Market Data Index (GMDI) for the period from 1994 to 2014 to evaluate how determinants of the financing structure affected liquidity risk The study used the unbalanced panel regression method with two models and tested the models using Hausman test, Likihood ratio Test, and F-Statistics to select the model that better reflects the results Research results showed that bank profitability (ROA) had a positive correlation with the entire banking system, Capital Adequacy Ratio (CAR) had a negative correlation with liquidity risk for conventional banks, and Inflation Rate (INF) had a negative correlation for Islamic banks

Domestic Research

Truong Quang Thong (2013) in the research article "Factors Affecting Liquidity Risk in the System of Vietnamese Commercial Banks" identified factors affecting the liquidity risk of commercial banks in Vietnam in the period 2002 - 2011 Based on data from audited and published financial reports of 27 commercial banks; and data on macro variables from the IMF The study used Descriptive Statistics, Regression Analysis including fixed effects model (FEM) and random effects model (REM), and Durbin-Watson statistics, Hausman test to conclude that the ratio of equity to total assets (ETA), ratio of bank loans and other loans to total asset (LTA) is positively correlated with liquidity risk, whereas ratio of liquidity reserve to total asset (LRA) is negatively correlated with liquidity risk Higher GDP growth in the present year reduces liquidity risk, but increases it in the following year Inflation rate fluctuations do not impact liquidity risk in the present year, but do lessen it in subsequent years

Dang Van Dan (2015) in the research article " Các nhân tố ảnh hưởng đến rủi ro thanh khoản của các Ngân hàng thương mại tại Việt Nam" was based on data collected from the annual financial reports of 15 large commercial banks in Vietnam over the

period 2007 - 2014 combined with macroeconomic data collected from the General Statistics Office of Vietnam (GSOV) to evaluate factors affecting the liquidity risk of commercial banks The study used panel data regression analysis with three models such as Pooled OLS, Fixed Effects Model (FEM), Random Effects Model (REM) and Haussman test to conclude that banks size (SIZE) had negative correlation and Total loans to total assets ratio (TLA) had a positive correlation with liquidity risk

The research article "Các yếu tố ảnh hưởng đến thanh khoản của các ngân hàng thương mại Việt Nam" by Vu Thi Hong (2015) used data from 37 commercial banks in Vietnam during the period 2006 - 2011 Through Statistical analysis, correlation and regression of panel data were not proportional to the Fixed Effect effect, the study found the impact of a number of factors on the liquidity of commercial banks in Vietnam The results showed that “equity to total capital” (CAP), “Non-performing loan ratio” (NPL) and “Profit ratio – Return on Equity” (ROE) had a positive correlation; On the contrary, "Loan-to-deposit ratio" (LDR) had a negative correlation with the liquidity of Vietnamese commercial banks

Nguyen Thi Bich Thuan and Pham Anh Tuyet (2021) in the research article "Nhân

tố ảnh hưởng đến rủi ro thanh khoản tại các ngân hàng thương mại Việt Nam" used

panel data collected from legitimate financial reports Audited results of 25 Vietnamese commercial banks in the period 2013 - 2019 and Hausman and Breusch and Pagan Lagrangian multiplier tests to select the model that best explains the factors affecting liquidity risk The study used regression with panel data with Pooled Ordinary Least Square (Pooled OLS), Fixed Effects Model (FEM), Random Effects Model (REM) Results showed that Dependence on external funding (EFD) was positively correlated with liquidity risk The remaining factors include: (1) Banks size (SIZE); (2) Ratio of equity capital to total asset (ETA); and (3) Loan to total deposit ratio (LTD) and liquidity reserve ratio (LRA) had negative correlation with liquidity risk

Phan Thi My Hanh and Tong Lam Vy (2021) in the article "Các yếu tố ảnh hưởng đến rủi ro thanh khoản của hệ thống ngân hàng thương mại Việt Nam" analyzed and evaluated the impact of factors on liquidity risk of 21 commercial banks in Vietnam in the period 2008 - 2017 by using financing gap (FGAP) to measure liquidity risk The study used Pooled, FEM and REM models to analyze the data Research results showed that the larger the bank's size (SIZE), the lower its liquidity risk, whereas the higher the ratio of equity to total capital (CAP), loan to total assets ratio (LTA), and return on equity ratio (ROE); the more the liquidity risk will increase The study also showed that dependence on external funding sources increases banks' liquidity risk Besides, macroeconomic factors such as economic growth rate (GDPG) and financial crisis have a positive impact on liquidity risk

Nguyen Hoang Chung (2022) in the research article "Factors Affecting Liquidity Risks of Joint Stock Commercial Banks in Vietnam" used data of 26 commercial banks listed on the Ho Chi Minh City and Hanoi Stock Exchanges during the period from 2008 to 2018 to evaluate factors affecting banks' liquidity risks The research applied the Pool OLS, FEM, REM, FGLS, D&K, GMM – SGMM and LM models by R programming language and the Bootstrap technique to estimate the impact of micro and macro factors on liquidity risk The results showed that customer deposit to total assets ratio (DTA) was negatively correlated with liquidity risk, whereas the loan to asset ratio (LTA), commercial bank liquidity, credit development ratio, the ratio of external funding ratio and the ratio of loan loss provision (LLP) all had positive impact on liquidity risk

Table 2 1 The previous studies

Studies Authors Objective Data Methods Conclusions Foreign research

The bank liquidity smile across

Bunda and Desquilbet

(2008)

Evaluate the liquidity risk of banks

From 36 countries in the period

Panel data regression analysis

SIZE, ETA, GDP, INF variables have positive

exchange

rate regimes from 1995 to 2004 correlation with liquidity risk

Liquidity of Czech Commercial

Banks and its Determinants

Vodová (2011,

2013)

Determine the

determinants of liquidity of Czech, Hungary and Finland banks

From Czech, Hungary and Finland commercial banks in the period from 2001 to 2009

Panel data regression analysis, Fixed effects regression

ETA, IRL NPL have positive

correlation with liquidity risk, whereas INF, GDP and financial crisis were negatively correlated with liquidity risk

An Empirical

Study on Liquidity Risk and its Determinants

in Bosnia and Herzegovina

Ganic (2014)

Examined the liquidity risk of commercial banks in Bosnia and Herzegovina

From reports financial of 17 banks in the period 2002 - 2012

Multiple linear regression, Correlation,

R-squared, ANOVA and F-test

have negative correlation with liquidity risk, whereas LLR, GDP have positive correlation with liquidity risk

Determinants of liquidity

risk of commercial

banks in Kenya

Mugenyah (2015)

Evaluate the determinants of liquidity risk

From the website of the Central Bank of Kenya

Multiple linear regression, Cook-Weisberg test, Jarque-Bera test, F-test

CAR has a positive correlation with liquidity risk, whereas SIZE,

ownership type, liquid assets ratio and leverage have negative correlation with liquidity risk

Relationship between Liquidity Risk and Net

Interest Margin of Conventional

Banks in Bangladesh

Chowdhury et al (2016)

Determine the impact of liquidity risk on the Net Interest Margin (NIM) variable of conventional banks

From annual reports of conventional banks in Bangladesh over the period 2011 - 2015

Descriptive statistics, correlation, and

regression model

NIM correlated positively with liquidity risk ratios

Determinants of liquidity

risk in Islamic

banks

Alzoubi (2017)

Identified factors affecting liquidity risk in Islamic banks

From 42 Islamic banks of 15 countries in the period 2007 - 2014

Multiple linear regression

SIZE, ETA have negative correlation with liquidity risk, whereas high profit assets, bad financial decisions have positive correlation with liquidity risk

Domestic research

Factors Affecting Liquidity Risk in the

System of Vietnamese Commercial

Banks

Truong Quang Thong (2013)

Identified factors affecting the liquidity risk of

commercial banks in Vietnam

data from audited and published financial reports of 27 commercial banks in the period 2002 - 2011

Descriptive Statistics, Regression Analysis including fixed effects model (FEM) and random effects model (REM), and Durbin-Watson statistics,

ETA, LTA have positive correlation with liquidity risk, whereas LRA has a negative correlation with liquidity risk Higher GDP growth in the present year reduces liquidity risk, but increases it in the following

Hausman

test year Inflation rate fluctuations do not impact liquidity risk in the present year, but do lessen it in subsequent years

Các nhân tố ảnh hưởng

đến rủi ro thanh khoản của các Ngân

hàng thương mại tại Việt

Nam

Dang Van Dan (2015)

Evaluate factors affecting the liquidity risk of

commercial banks

From the annual

financial reports of 15 large

commercial banks in Vietnam over the period 2007 - 2014

Panel data regression analysis

SIZE had negative correlation with liquidity risk, whereas TLA had a positive correlation with liquidity risk

Các yếu tố ảnh hưởng đến thanh khoản của các ngân hàng thương

mại Việt Nam

Vu Thi Hong (2015)

Evaluate the impact of a number of factors on the liquidity of

commercial banks in Vietnam

From 37 commercial banks in Vietnam during the period 2006 - 2011

Statistical analysis, correlation and

regression of panel data

ETA, NPL,

positive correlation with liquidity risk, whereas LDR had a negative correlation with the liquidity risk Nhân tố ảnh

hưởng đến rủi ro thanh khoản tại các

ngân hàng thương mại

Việt Nam

Nguyen Thi Bich Thuan and Pham Anh

Tuyet (2021)

Identified factors affecting the liquidity risk of

commercial banks in Vietnam

From audited results of 25 Vietnamese commercial banks in the period 2013 - 2019

Regression with panel data and Hausman and Breusch and Pagan Lagrangian

positively correlated with liquidity risk, whereas SIZE, ETA, LTD, LRA ) had negative correlation

multiplier

tests with liquidity risk

Các yếu tố ảnh hưởng đến rủi ro thanh khoản của hệ thống ngân hàng thương mại

Việt Nam

Phan Thi My Hanh and Tong Lam Vy

(2021)

Analyzed and evaluated the impact of factors on liquidity risk

commercial banks in Vietnam

data from audited and published financial reports of 21 commercial banks in the period 2008 - 2017

Panel data regression analysis with three models such as Pooled OLS, FEM, REM

SIZE has a negative correlation with liquidity risk, whereas ETA, LTA, ROE, GDP have positive correlation with liquidity risk

Factors Affecting Liquidity Risks of Joint Stock Commercial

Banks in Vietnam

Nguyen Hoang Chung (2022)

Evaluate factors affecting banks' liquidity risks

used data of 26

commercial banks listed on the Ho Chi Minh City and Hanoi Stock Exchanges during the period from 2008 to 2018

the Pool OLS, FEM, REM, FGLS, D&K,

SGMM and LM models by R

programming

language and the Bootstrap technique

Customer deposit to total assets ratio was negatively correlated with liquidity risk, whereas LTA, credit development ratio, the ratio of external funding ratio, and LLP all had positive impact on liquidity risk

(Source: Compiled by the author)

2.3 Overview of research variables

Based on the underlying theoretical framework and previous studies, the variables in the model are defined and measured as follows:

2.3.1 Dependent Variables

FINGAP (Financing Gap)

The Liquidity Risk of bank (i) at time (t) can be measured by two methods: the financing gap (or liquidity gap) or liquidity ratios According to the study by Vodova (2013), the liquidity gap is the difference between assets and capital at the current and future points in time, while liquidity ratios are various coefficients calculated from the balance sheet Saunders and Cornett (2006) proposed using the term “financing gap” to measure liquidity risk Dang Van Dan (2015) stated that the financing gap method is the most suitable approach in quantitative analysis The financing gap index fundamentally reflects the liquidity capability of the bank, where the funding gap is the difference between the average balance of loans and the average balance of funding sources In the research by Truong Quang Thong (2013), liquidity risk is measured by the difference between credit and deposit divided by total assets This forms the basis for the author to use the financing gap as the dependent variable in the proposed research model

𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑛𝑔 𝐺𝑎𝑝 = 𝐶𝑟𝑒𝑑𝑖𝑡 𝑏𝑎𝑙𝑎𝑛𝑐𝑒 − 𝑇𝑜𝑡𝑎𝑙 𝑑𝑒𝑝𝑜𝑠𝑖𝑡 𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠

LDR (The loans to deposit ratio)

The liquid assets ratio (LDR), the L4 index assessing the liquidity of commercial banks, was introduced in Chapter 2 LDR is the ratio used to measure the proportion between illiquid assets such as loans and highly liquid capital sources such as deposits and short-term capital sources This ratio reflects how many times the loan amount is greater than the mobilized amount Therefore, the higher this ratio, the higher the liquidity ratio will be

𝐷𝑒𝑝𝑜𝑠𝑖𝑡𝑠 + 𝑆ℎ𝑜𝑟𝑡 − 𝑡𝑒𝑟𝑚 𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑛𝑔

2.3.2 Independent Variables

SIZE (Bank Size)

The bank's scale is calculated by taking the natural logarithm of the total assets of the bank to measure its scale This helps identify the relationship between the scale

of total assets and liquidity risk According to economic theory, larger banks, or those with significant total assets, are less likely to face liquidity risk because they can rely on interbank markets or receive liquidity support from ultimate lenders (Aspachs et al., 2005) However, in reality, with the support of the state, large-scale commercial banks may focus on expanding credit activities or investing in portfolios with higher risks to increase profitability, leading to an increase in liquidity risk for these banks

𝑆𝐼𝑍𝐸 = 𝐿𝑜𝑔𝑎𝑟𝑖𝑡ℎ𝑚 (𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠)

NIM (Net Interest Margin)

Net Interest Margin is the percentage difference between interest income and interest expenses of a bank It is one of the indicators used to measure the efficiency and profitability of a bank in generating net interest income from loans and investments The higher the NIM ratio, the better the bank’s profitability

𝑁𝑒𝑡 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑀𝑎𝑟𝑔𝑖𝑛 = 𝑁𝑒𝑡 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝐼𝑛𝑐𝑜𝑚𝑒𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑇𝑜𝑡𝑎𝑙 𝐸𝑎𝑟𝑛𝑖𝑛𝑔 𝐴𝑠𝑠𝑒𝑡𝑠

ROE (The return on equity ratio)

The Return On Equity ratio (ROE) is measured by dividing the after-tax profit of the bank by the average equity The return on equity ratio indicates the efficiency of each bank in utilizing its equity capital ROE indicates the level of profit generated by a company's equity A positive ROE signifies the bank's profitability, while a negative value indicates that the bank is operating at a loss

𝑅𝑒𝑡𝑢𝑟𝑛 𝑂𝑛 𝐸𝑞𝑢𝑖𝑡𝑦 =𝑃𝑟𝑜𝑓𝑖𝑡 𝑎𝑓𝑡𝑒𝑟 𝑡𝑎𝑥𝑇𝑜𝑡𝑎𝑙 𝐸𝑞𝑢𝑖𝑡𝑦

LTA (The loans to total assets ratio)

Lending is a common activity among banks in Vietnam, with a focus on utilizing capital for lending operations Since loans typically have low liquidity, increasing the volume of loans will raise the proportion of less liquid assets The higher this ratio

signifies that a bank is heavily reliant on loans, resulting in lower liquidity Conversely, a low ratio suggests a high default rate for the bank

𝐿𝑜𝑎𝑛𝑠 𝑡𝑜 𝑡𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠 =𝐿𝑜𝑎𝑛𝑠 𝑡𝑜 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠

ETA (The equity to total assets)

The equity to total assets (ETA) is measured by dividing equity by total assets, indicating the capital structure of banks and their ability to self-finance with their capital A greater ETA indicates a higher proportion of the bank's assets being self-owned, or conversely, it establishes the bank's degree of leverage A lower ratio suggests that the bank heavily relies on financial leverage in its business activities, posing potential liquidity risks that could negatively impact profitability and liquidity However, if the cost of borrowing is reasonable, efficient business operations can increase profitability and liquidity, offsetting these potential negative effects on business activities

𝐸𝑞𝑢𝑖𝑡𝑦 𝑡𝑜 𝑡𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠 = 𝐸𝑞𝑢𝑖𝑡𝑦𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠

LLP (The loan loss provisions ratio)

The loan loss provisions ratio (LLP) is calculated as the percentage ratio between the value of loan loss provisions and the total loans outstanding A bank with a high loan loss provisions ratio is holding assets with high liquidity, meaning it is prepared to address potential losses that may occur when customers fail to meet their borrowing obligations This reduces liquidity risk for the bank The higher the ratio, the more extensive the loan provisions, resulting in a reduction in net income and earnings per share

𝐿𝑜𝑎𝑛 𝐿𝑜𝑠𝑠 𝑃𝑟𝑜𝑣𝑖𝑠𝑖𝑜𝑛 𝑟𝑎𝑡𝑖𝑜 = 𝐿𝑜𝑎𝑛 𝐿𝑜𝑠𝑠 𝑃𝑟𝑜𝑣𝑖𝑠𝑖𝑜𝑛𝑇𝑜𝑡𝑎𝑙 𝐿𝑜𝑎𝑛𝑠

GDP (The gross domestic product)

The rapid economic growth is a crucial macroeconomic factor that significantly influences almost all industries in society, particularly in the financial and banking sector A robust economic growth will lead to an increase in household income, making it easier for banks to mobilize capital Therefore, the economic growth factor will have a positive impact on the liquidity of banks, reducing potential liquidity risks that banks may encounter

INF (The inflation rate)

Inflation is the increase in the general price level of goods and services over time and the erosion of the value of currency, and it is measured by the Consumer Price Index (CPI) A rise in inflation can impact activities across the economy, including those within commercial banks Inflation will lead to a reduction in the reserve levels of liquidity for banks, affecting customers' business activities, resulting in difficulties in meeting timely debt payments to the bank, ultimately leading to a loss of liquidity for the bank

CHAPTER 2 SUMMARY

In Chapter 2, liquidity risk in banks can be measured by two methods: (i) the group of 4 liquidity ratios and the liquidity gap Additionally, the study lists factors influencing liquidity risk, including factors from commercial banks and macroeconomic factors based on previous empirical studies reviewed to establish the research framework for the subsequent chapters Based on Chapter 2, the research methodology and research model of the thesis will be developed in Chapter 3