Factors affecting the liquidity risk of joint stock commercial banks on stock exchanges in viet nam

The result reveals that from 2010 to 2018, size of the bank and the ratio of equity to total assets have positive effects on liquidity risk and this can be explained by the famous “too b

MINISTRY OF EDUCATION AND TRAINING STATE BANK OF VIETNAM

BANKING UNIVERSITY OF HO CHI MINH CITY

CODE: 52340201

HO CHI MINH CITY, JANUARY 2020

MINISTRY OF EDUCATION AND TRAINING STATE BANK OF VIETNAM

BANKING UNIVERSITY OF HO CHI MINH CITY

ABSTRACT

Liquidity problems are re-emphasised as Vietnamese commercial banks are making an effort in deploying Basel II for hoping a greater stability and decrease the likelihood of repeating the financial crisis events in 2007 Therefore, the aim of this research is to identify factors that affect liquidity risk of 17 Joint-stock commercial banks listed on stock exchanges in Vietnam and the data covers the period from 2010 to 2018 Multivariate regression models (Pooled-OLS, FEM, REM) were used to test the effects and levels of determinants; and after being selected by F-test and Hausman test, REM was the most appropriate However, REM had heteroscedasticity in variance of error and plus, autocorrelation in the dataset Therefore, FGLS regression model is used to fix autocorrelation and unconstant variance of error to ensure a consistent and effective estimation

The result reveals that from 2010 to 2018, size of the bank and the ratio of equity to total assets have positive effects on liquidity risk and this can be explained

by the famous “too big to fail” theory that big banks are seemed to secure against liquidity risk exposure not by holding high liquidity, but by assistance from interbank market or Lender of Last Resort (Vodova, 2013); plus, equity is considered as one of the last defense, a shield that against many kinds of risk If banks see themselves as “big banks”, their motivation to hold liquidity is limited Besides, the relation between liquidity risk and return on equity, non-performing loan ratio and provision credit losses ratio is ambiguous

From the result obtained, the study proposes conclusions and a number of recommendations to banks themselves to increase the efficiency and improve the liquidity of Vietnamese commercial banks, as well as to the Governement on the management of the banking system in the coming period

SUMMARY

In recent year, along with the emergence of globalization and free trade, economic individuals have created an environment of growth and competition Financial markets are no exception, particularly the commercial banks – intermediaries that connect individuals, companies and other institutions together, keep the economy going In addition to competition from domestic financial institutions, banks also face foreign financial ones which enter Vietnam gradually Banking industry is obviously one of the most sentimental activities not just in Vietnam but worldwide and plays an extremely important role in economic development Banks not only influence but also promote the integration of economic activities such as resource mobilization, development activities, allocation of public finance and even social welfare distribution The administration

of banking is therefore always a matter of particular concern for the government to carry out its management and supervisory activities Banks need to adapt, thrive and evolve effectively to survive in harsh environments, if they do not, they will be eliminated With a default of one bank, it could lead to the collapse of the entire financial and economic system due to its interconnectability Global financial crisis that happened in 2007 could be a typical example of the banks’ strong influence on the economy that led to a series of bankruptcy, pushing the economic stagnation to its peak

Besides, the stock market in Vietnam is still quite young, the financial system is not really healthy and open, creating difficulties and barriers for banking activities Thus, as liquidity problems are re-emphasised as Vietnamese commercial banks are making an effort in deploying Basel II for hoping a greater stability and decrease the likelihood of repeating the financial crisis events in 2007 Moreover, after joining the ASEAN Economic Community in 2015, Vietnam has committed itself

to alleviating restrictions in the banking sector, giving this sector many oppoturnities; but also many challenges such as competitive pressure from regional

banks and international banks, in particular with regard to the limited financial potential of Vietnam compared to other banks in other countries

Therefore, the aim of this research is to identify factors that affect liquidity risk

of Joint-stock commercial banks listed on stock exchanges in Vietnam If the banks have strong liquidity, this not only helps to stabilize the financial market but also helps to grow the economy in Vietnam Thus, to determine and evaluate the level of impact of these determinants and give conclusions and recommendation from the obtained results

This research systematized the theoretical framework including theory definitions and liquidity risk impacts to the customers, the bank itself and the economy; and then evaluated the factors affecting liquidity risk in Vietnamese commercial banks and give empirical evidence based on previous studies There are two basic types of determinants of liquidity risk which are objective factors and subjective factors However, due to limited time, the author only focused on subjective factors without considering the affect of factors on “market” level and government policies on bank liquidity Model of this research is based on Vodova (2011) and Trương Quang Thông (2013) panel data regression models as follows:

In which, LR is liquidity risk as a dependent variable; ETA, NPL, ROE, LnSIZE, PCL is ratio of equity to assets, non-performing loan ratio, return on equity, size of the bank, provision for credit losses respectively as independent variables; is error term; is the 17 joint-stock commercial banks according to the list on the Government’s website; is the year from 2010 to 2018 The data was collected from financial statements of 17 Join-stock commercial banks that listed on stock exchanges in Vietnam The estimated effects have also been presented with a positive correlation between LR and ROE, LnSIZE, PCL and a negative correlation between LR and ETA, NPL

Stata software was then used to describe statistically the dataset and test the correlation matrix between variables and the result was that ETA has a negative correlation with LR, whereas ROE and LnSIZE has a positive correlation with LR Multivariate regression models (Pooled-OLS, FEM, REM) were used to test the effects and levels of determinants; and after being selected by F-test and Hausman test, REM was the most appropriate Although REM did not have multi-collinearity phenomenon, it still had heteroscedasticity in variance of error and plus, autocorrelation in the dataset Therefore, FGLS was used to fix autocorrelation and unconstant variance of error to ensure a consistent and effective estimation The result is as follows:

Due to the characteristics of FGLS, the R2 value does not count as meaningful when it comes to measure the suitability of the model, however, it can be used to calculate statistical values as above Whereby, both ETA and LnSIZE has positive effects on LR Firstly, the higher bank’s size, the higher liquidity risk exposure which is consistent with hypothesis H4 This result can be explained by the “Too big

to fail” theory as big banks are seemed to secure against liquidity risk exposure not

by holding high liquidity, but by assistance from interbank market or Lender of Last Resort (Vodova, 2013) Secondly, there is a strong positive effect of the ratio of equity-to-assets to liquidity risk meaning when the ratio of equity-to-assets decreases, liquidity risk will decrease as well This result is inconsistent with hypothesis H1, but suprisingly consistent with the result on the influence of the bank’s size on liquidity risk Equity is considered as one of the last defense, a shield that against many kinds of risk If banks see themselves as “big banks”, their motivation to hold liquidity is limited This result is in line with the result of Trương Quang Thông (2013) However, the relation between liquidity risk and return on equity, non-performing loan ratio and provision credit losses ratio is ambiguous

From the result obtained, the study proposes a number of conclusions and recommendations to increase the efficiency and improve the liquidity of Vietnamese commercial banks in the coming period

Particularly, due to banks’ reliance too much on the Gorvernment, the Government has enacted the Law Amendments to some articles of the Law on Credit Institutions (Law No 17/2017/QH14), is effective from January 15, 2018 that banks can be able to go bankrupt if they are poorly operating and are put under special control by the Government, which has changed entire situation Therefore, banks need to rely more on themselves than on passive strategies as they used to, which is why the author then gave some recommendations to banks themselves to improve their liquidity and operational management, as well as some recommendations to the Governement on the management of the banking system In particular, banks need to strengthen internal control system, ensure capital mobilization, prepare specific plans for upcoming risk cases from the best to the worst The Government needs to their leadership role for banks, inspect and control banking activities effectively, improve the organizational structure and apply effectively the Basel’s principles on managing liquidity

However, there still exists some limits of this research such as: this research is only conducted on join-stock commercial banks, not the whole banking system in Vietnam; the author only used one measurement to measure liquidity of the bank; the result of FGLS model can not be given out R-squared value to measure the suitability of the model; this study only conducted internal determinants Therefore, the author hopes to study further to provide a more general measurement of liquidity risk, plus to build a better model to make it a more useful reference for students’ extensive researches

ASSURANCE LETTER

I assure that the “factors affecting liquidity risk of joint-stock commercial banks

on stock exchanges in Vietnam” dissertation is my own report The figures and sources of information in this research are derived clearly and honestly from the banks' consolidated financial statements In addition, the tests were conducted publicly and transparently with no intervention to correct the results of regression models, in which there are no previously published content or content made by others except for full citations in the report

Author

Nguy n Thu Ng n

THANK YOU LETTER

I would like to thank the teachers and friends in the Banking University in Ho Chi Minh city; and with the deepest gratitude, I would like to send to the personnel

in the Department of Finance and Department of Banking the most sincere thanks for the knowledge and dedication, who has devoted to us during our school time Especially in the program of implementing the graduation dissertation with the guidance of ssociation Professor and Doctor of Philosophy ng V n D n, I have been helped a lot in choosing the topic, writing the research, as well as in-depth guidance in how to work properly

Finally, I would like to thank my family, friends and relatives who have always been there to support and encourage me to complete my graduation dissertation

I sincerely thank!

INDEX

ABSTRACT i

SUMMARY ii

ASSURANCE LETTER vi

THANK YOU LETTER vii

INDEX viii

LIST OF ACRONYMS xii

LIST OF TABLES xiii

LIST OF GRAPHS xiv

CHAPTER 1 INTRODUCTION 1

1.1 Introduction 1

1.2 Previous studies 2

1.3 Research objectives 4

1.4 Research questions 4

1.5 Research subjects and scope 5

1.5.1 Research subjects 5

1.5.2 Research scope 5

1.6 Methodology 5

1.7 Contribution of the study 6

1.8 Dissertation structure 6

CHAPTER 2 THEORETICAL FRAMEWORK 8

2.1 Theory of liquidity risk of join-stock commercial banks 8

2.1.1 Joint stock Commercial banks 8

2.1.2 Bank liquidity risk 8

2.1.3 Liquidity risk impacts 10

2.2 Previous studies on factors affecting liquidity risk of joint-stock commercial banks 11

2.2.1 External factors 11

2.2.2 Internal factors 12

SUMMARY OF CHAPTER 2 16

CHAPTER 3 RESEARCH MODEL 17

3.1 Dataset 17

3.2 Analysis process 17

3.3 Research model and hypothesis 19

3.3.1 Dependent variable 20

3.3.2 Independent variables 21

SUMMARY OF CHAPTER 3 24

CHAPTER 4 RESEARCH RESULTS 25

4.1 Descriptive statistics 25

4.2 Correlation analysis of variables 26

4.3 Regression analysis 28

4.4 Defect tests 30

4.4.1 Multi-collinearity test 30

4.4.2 Homoskedasticity test 31

4.4.3 Autocorrelation test 32

4.5 Final model 33

4.6 Summary 35

SUMMARY OF CHAPTER 4 36

CHAPTER 5 CONCLUSION AND RECOMMENDATION 37

5.1 Conclusion 37

5.2 Recommendation 38

5.2.1 For commercial banks 39

5.2.2 For the Government 42

5.3 Limits and extensive researches 43

5.3.1 Limits of the research 43

5.3.2 Direction for extensive research 44

SUMMARY OF CHAPTER 5 45

SUMMARY 47

REFERENCE i

APPENDIX iv

A Joint-stock Commercial Banks list iv

B Calculated dataset vi

C Regression results with Stata 13 x

C.1 Panel data description x

C.2 Variables statistics x

C.3 Variables correlation x

C.4 Pooled-OLS regression xi

C.5 FEM regression xi

C.6 REM regression xii

C.7 Pooled-OLS, FEM, REM regression xii

C.8 Hausman test xiii

C.9 Multi-collinearity test xiv

C.10 Homoskedasticity test xiv

C.11 Autocorrelation test xv

C.12 FGLS regression xv

C.13 FGLS regression after excluding ineffective variables xvi

LIST OF ACRONYMS

ASEAN Association of Southeast Asian Nations

BLUE Best Linear Unbiased Estimation

FGLS Feasible Generalised Least Squares

Pooled-OLS Pooled Ordinary Least Square

TBTF Too big to fail / Too big to fall

LIST OF TABLES

Table 2.1 Summary of previous research results 12

Table 3.1 Variables description 19

Table 3.2 Estimated effects 21

Table 4.1 Variable statistics 25

Table 4.2 Variables correlation 27

Table 4.3 Regression results of Pooled-OLS, FEM, REM 28

Table 4.4 F-test 30

Table 4.5 Hausman test 30

Table 4.6 Multi-collinearity test 31

Table 4.7 Homoskedasticity test 32

Table 4.8 Autocorrelation test 32

Table 4.9 Regression result of FGLS 33

Table 4.10 Regression result of FGLS after excluding ineffective variables 34

LIST OF GRAPHS

Graph 3.1 Analysis process 17Graph 3.2 Data collection process 18

CHAPTER 1 INTRODUCTION

1.1 Introduction

Banking is one of the most sentitive industries not only in Vietnam but also throughout the world and it plays an extremely important role in economic development Banks do not only affect but also facilitate the integration of economic activities such as mobilizing resources, production activities, public finance distribution and even distribution of social welfare Therefore, banking management is always a matter of special concern by government carrying out management and supervision activities

typical example of the banks’ heavy influence on economy is the global financial crisis that happened in 2007 which led to a series of bankruptcies, bringing the economic stagnation to its peak According to Bank for International Settlements, during global financial crisis, many banks struggled to sustain adequate liquidity, a number of banks still failed, being forced into mergers even when receiving extraordinary support from the central banks Several years before the crisis, liquidity and its management was not really a priority, funding was available

at low cost However, this crisis has totally changed market conditions that captured the importance of related liquidity issues measurement thus its management

It is evident that liquidity risk measurement is up-to-date and is playing an important role, which is why Basel III has been officially introduced since 2013, putting a considerable effort into the design of banking regulation as a way of reducing the damage to the economy by banks Many financial market participants including Vietnam are still struggling to deploy Basel II for hoping a greater stability and decrease the likelihood of a repeat of the events in 2007 In addition to this, after joining the ASEAN Economic Community in 2015, Vietnam has committed to ease restrictions in the banking industry, giving this sector many opportunities such as increasing the level of economic integration, increasing the opportunities to access and attract capital, etc but also many challenges such as

competitive pressure from regional banks and international banks, especially in the context of our country's limited financial potential compared to other banks in other countries

Therefore, the study of liquidity issues in the banking system is extremely necessary, if the banks have good liquidity, it does not only help stabilizing financial market but also developing the country's economy Especially, in the current conditions of Vietnam, liquidity issues are of one of the most concern and are often discussed from the beginning of every year Those are the reasons for the author to chose to study on the topic "Factors affecting the liquidity risk of joint stock commercial banks listed on stock exchanges in Vietnam"

1.2 Previous studies

Aspachs et al (2005) provided a comprehensive analysis of factors that affect

liquidity policy of banks in United Kingdom In particular, they investigated how central bank’s policy affected liquidity buffers and how the economic cycle changed the liquidity buffers The result was that monetary policy rates affected negatively

on UK banks’ amount of liquid assets which meant when central banks attempted to reduce the interest rate and increase the monetary base, banks seemed to keep the additional liquidity on their balance sheets Secondly, banks appeared to increase their liquidity buffers while economic downturn and drop them down in economic upturn This study used unconsolidated financial reports on a quarterly basis from

1985 to 2003

Praet and Herzberg (2008) indicated the complex relationship between banks

and financial markets, in which banks are dependent on and exposed to financial markets as regards liquidity The authors investigated the mechanics of liquidity crisis in the market and its impact on bank’s liquidity, as well as spillovers to other banks The result was that asset liquidity considerably rely on functioning of financial markets, especially for secured lending transactions and securitization market They also found that low interest rates have accelerated liquidity in the

market beyond sustainable level Together with liquidity management, they also realized that a greater transparency could reduce asymmetric information which reduces market vulnerability However, information appears to be limited that a comprehensive and comparable information gaps were large in 2007 when the financial crisis took place

Vodova (2011) focused on the causes of liquidity risk that she identified

determinants of liquidity in Czech commerical banks from 2001 to 2009 with liquidity measured by different balance sheet indices The result revealed that there

is a positive connection between liquidity and capital adequacy as well as ratio of non-performing loans and interest rate on loans However, the connection between size of banks and liquidity is unclear Vodova also found that larger banks present lower liquidity according to the “too big to fall” theory, that larger banks are less motivated to hold liquidity as they rely more on government supports when in shortages

Vodova (2013) used 3 formulas to evaluate liquidity positions of Hungarian

commerical banks from 2001 to 2010: (1) liquid assets-to-total assets ratio gives us

a general look on liquidity shock absorption capacity; (2) liquid assets-to-deposits and short-term borrowing ratio focuses on sensitivity to selected funding types; (3) liquid assets-to-deposits ratio captures bank’s liquidity when bank cannot borrow in interbank market when they need to The result was liquidity of banks was related positively to capital adequacy, interest rate of loans, profitability whereas it was related negatively to the bank’s size, interest margin, interest rate of monetary policy, interest rate on interbank market transactions The impact of the growth rate

of GDP on liquidity was unclear

rương Quang hông (2013) used Financing Gap formula to evaluate liquidity

risk in 27 Vietnamese commercial banks from 2002 to 2011 Plus, he divided two determinants into two groups: internal and external variables He found out that increasing the size of banks would eventually increase liquidity risk, the increase of

the ratio of liquid reserve to total assets will reduce liquidity risk, whereas the decrease of the ratio of bank loans and other loans to total capital also helps banks reduce liquidity risk Thus, he found a negative correlation between liquidity risk and the ratio of equity to total assets, as higher the bank’s equity, the higher liquidity risk exposure In addition, the result also showed the impact of the growth rate of GDP and inflation rate on liquidity risk

h ng (2015) researched on determinants of liquidity of 37 commercial

banks in Vietnam from 2006 to 2011 The study was based on Vodova (2011)’s liquidity measurements as having two liquid indices and two illiquid indices; and was also based on Basel’s principles on liquidity management to build a set of factors as independent variables The result highlights that the liquidity of banks is higher when equity ratio, non-performing loan ratio and net income is higher Meanwhile, liquidity is negatively liked with loans-to-deposits ratio Furthermore, the relationship between liquidity and size of the bank, provision credit losses ratio

- What are the factors affecting liquidity risk of JSCBs?

- What is the level of impact of factors on liquidity risk of banks?

- What conclusions can be drawn from the evaluation results?

1.5 Research subjects and scope

1.5.1 Research subjects

Research object is the liquidity risk of JSCBs listed on Vietnam stock exchanges (HOSE, HNX, UPCoM)

1.5.2 Research scope

- Scope of space: 17 JSCBs that are listed on HOSE, HNX and UPCoM

- Scope of time: Data were surveyed during the period 2010-2018 because in this period: (i) The economy recovered after being affected by the Great Depression in 2007; (ii) Vietnam joined the ASEAN Economic Community (2015), that in which Vietnam has committed to ease banking policies; (iii) Banks need to mobilize medium and long-term capital to meet Circular No 06/2016/TT-NHNN dated May 27th, 2016; (iii) The influence of objective factors such as the US-China war, Fed rate changes or Basel II piloted on 10

banks from Q4/2017, etc

1.6 Methodology

The study is based on researches and panel data regression models of Vodova (2011) and Trương Quang Thông (2013) to build a model of liquidity risk and its impact factors as follow:

In which, LR : Liquidity Risk

ETA : The ratio of equity to total assets NPL : Non-performing loan ratio ROE : Return on Equity

LnSIZE : Size of the bank PCL : Provision credit losses ratio

From empirical result and the collected data, author will perform statistical analysis to describe the basic characteristics of sample data on the average of the measurement variables; perform correlation analysis to determine the relationship between the independent variables to evaluate the predictable and forecast level of the model; perform panel data regression analysis to measure the level of influence and indicate the direction of the impact of each independent variable on the dependent variable to answer the dissertation's question about factors affecting liquidity risk of JSCBs via Pooled Ordinary Least Square (Pooled-OLS), Fixed Effect Model (FEM); Random Effect Model (REM) with Stata 15 software

1.7 Contribution of the study

This paper is based on reliable quantitative methods of processing reliable data from the audited financial statements to provide empirical evidence on the impact of determinants on the liquidity risk of JSCBs in the period 2010 - 2018

Proposing solutions to complete the policy framework in the management and administration of Vietnam's commercial banking system in order to improve ability

to face liquidity shocks and improve the competitiveness of the current commercial banking system in Vietnam

Chapter 2: Theoretical framework

State some concepts used in the study, present the theoretical basis related to liquidity risk at the bank as well as introduce an overview of determinant variables

based on reference models of previous studies for the purpose of establishing an impact model

Chapter 3: Research model

Presenting the research model, research methods, methods of data sampling and processing, building and testing scales to measure the impact factors on liquidity risk

Chapter 4: Research results

Presenting the result from the estimation model and discuss the obtained result

Chapter 5: Conclusion

Summarize the main findings of the study, the significance and contribution of the research to the banking sector in particular and to the economy in general At the same time, giving some suggestions to improve the liquidity of the bank This research should create a basis for others to continue to explore and develop, while showing some limitations of research and proposing further research directions

CHAPTER 2 THEORETICAL FRAMEWORK

2.1 Theory of liquidity risk of join-stock commercial banks

2.1.1 Joint stock Commercial banks

According to Article 4 of the Law on Credit Institutions (Law No 47/2010/QH12), commerical bank is a type of bank where it is allowed to conduct all banking activities and other business activities in accordance with this Law for profit purposes In which, Joint stock commerical bank (JSCB) is a commercial bank organized in the form of a joint stock company Under this Law, banking activity is the business of monetary and banking services with the main activities are receiving deposits, using this money to provide credit and payment services via accounts

Commercial banks are one of the financial intermediaries that play an important role in establishing the financial environment With core activity is transferring money from capital surplus to capital shortage, banks make the idle money to be fully utilized and make money available to consumers and businesses that they might not be able to earn, or at least not for a very long time Besides, banks also create creditworthiness of customers by safeguarding money so that good money is only for good loans and not lost on bad loans In other words, banks connect individuals, businesses and other institutions together that helps keeping the economy going Therefore, if banks fall, it will cause a collapse for a whole system

of the economy, and because banks and money are that essential to maintain not only economies but entire societies, they are extremely regulated and must operate

by strict procedures and principles

2.1.2 Bank liquidity risk

2.1.2.1 Bank liquidity risk theory definition

Bank for International Settlement defines liquidity as the ability of bank to finance increased assets and meet obligations when due, without incurring unacceptable losses Therefore, liquidity risk arises when the bank is unable to meet

capital needs at some point of time; or must raise capital from other sources with high costs to meet its obligation; or due to other subjective reasons that affects the solvency of the bank, accordingly it will lead to undesireable consequences In other words, this is the type of risk that occurs in cases when the bank is insolvent due to falling to promptly liquidate assets in a short period of time and at less than market prices

According to Vodova (2011), there are two types of liquidity risk: market liquidity risk and funding liquidity risk In which, market liquidity risk arises when

a bank can not sell their assets in the market in the shortest possible time and at the lowest cost, whereas, funding liquidity risk arises when a bank can not meet efficiently current and future cash flow needs without affecting their operations or their financial conditions These two types of liquidity risk often interact with each other among financial markets which will affect many financial institutions, including commercial banks

2.1.2.2 Liquidity risk measurement

Liquidity risk can be measured in two ways: liquidity gap and liquidity ratios

Liquidity gap is first mentioned in Risk Management in Banking by J Bessis

(2009) which is estimated by the difference between assets and liabilities at both present and future dates However, the drawback of this method is that it is very hard to collect data since a minority banks publish their annual reports with liquidity gaps which leading to a strongly unbalanced dataset that is unable to estimate Another measurement is liquidity ratios - various balance sheet ratios that can be used to identify the main liquidity trends A number of studies has indicated various liquidity ratios as follows:

2.1.3 Liquidity risk impacts

As mentioned above, low liquidity will lead to undesirable consequences:

- To customers and the bank itself: regard the main function of the banks is intermediaries, banks act as a bridge between borrowers and lenders; and the interest difference is the largest source of revenue When bank liquidity decreases, the bank is forced to race a capital raise, thus, leading to high deposit rates; high deposit rates force lending rates to increase and making it difficult to lend; the bank is forced to pay high deposit interest but hard to make a loan, then clearly, the bank will suffer losses Furthermore, the failure to meet demands of withdrawals will lead to the loss of trusts of depositors (including interbank transactions) and fail to meet disbursement needs for credit extensions

- To the economies: low liquidity will affect investment activities as capital mobilization reduction because money are concentrated in banks due to high deposit rates Besides, it also affects business activities of many companies

due to high credit interest rates, then product and service prices will escalate which as a result, inflation rate was higher than expected

In summary, if the bank does not have enough money to meet the market’s demands, the solvency will be lost as well as creditworthiness, finally is the breakdown of the whole system In contrast, banks will have good liquidity or they

do not face liquidity risk when they have available captal at reasonable costs at the very right time Therefore, it is very necessary to study liquidity issues and its determinants in the banking system for the sake of stable markets and economies

2.2 Previous studies on factors affecting liquidity risk of joint-stock commercial banks

The famous studies have mainly focused on two main groups of factors that can affect the liquidity of banks

2.2.1 External factors

Group of external factors are exogenous factors in macroeconomic level such as Gross Domestic Product rates (GDP), inflation rates, unemployment rates or even interest lending rates, interbank rates, etc Bunda (2003) found a positive correlation between bank liquidity and lending rates, GDP, inflation rate Meanwhile, Vodova (2011) found a positive correlation between liquidity in Czech Commercial Banks and interest rate on loans, whereas, he also found a negative correlation between illiquidity and interest rate on interbank transactions, inflation rate and a positive correlation between illiquidity and GDP However, in 2013, Vodova continued his work on Hungarian banks and found that there was a positive correlation between liquidity and GDP which was in contrast to his result in 2011 Besides, Vodova found no relationship of unemployment rate with liquidity Furthermore, liquidity risk might be also affected by policies of government such as monetary policies, as well as market trend on technologies or competition, etc

However, they are external factors that their characteristics are to be uncontrollable or unexpected from the banks’ views While change is inevitable,

having the flexibility to deal with unexpected market variation is different from banks to banks; and one of the most effective way to be flexible and adaptive is to develop a framework or an environmental scan which is PESTLE analysis: Political, Economic, Social, Technological, Legal and Environmental Listing, selecting and analyzing each element can take quite a long time and effort to complete a full evaluation of external factors on liquidity risk Therefore, in this paper, the author only focuses on subjective factors without considering the affect

of factors of “market” level and government policies on bank liquidity As a result, these objective factors will not be elaborated and analyzed any further

2.2.2 Internal factors

According to Aspachs (2005) and Nikolaou (2009), liquidity does not simply depend on exogenous factors but more importantly, it is influenced by endogenous factors such as net income, equity, size of the bank, non-performing loan, etc

Table 2.1 Summary of previous research results

Correlation between

Liquidity Risk and…

total assets ratio

Equity-to- performing loans

Non-Return on equity

Size of the bank

Provision for credit losses

Equity-to-total assets ratio shows how much of the assets are funded by equity

shares Equity is the resource that the bank own itself, eventhough it accounts for only a small proportion in the total capital of the banks, equity is one of the basic factor determining the existence and development of a bank Besides, equity is also considered as a collateral to build trusts towards customers, maintains solvency and liquidity to the bank Many studies have found a positive correlation between this ratio and bank liquidity meaning the higher this ratio is, the higher liquidity of the bank is such as Bunda (2003), Vodova (2011), Tr n o ng Ng n (201 ); whereas Trương Quang Thông (2013) found it was a negative correlation between the ratio

of equity to assets and bank liquidity

Non-performing loans are those of group 3, 4 and 5 that are more than 90 days

overdue, according to Article 3 of Circular No 02/2013/TT-NHNN on classification of assets, levels and method of setting up of risk provisions, and use

of provisions against credit risks in the banking activity of credit institutions, foreign banks’ branches Moreover, banks also need to base on the repayment capacity of customers to account loans into the appropriate groups Non-performing loan ratio (NPL) is the ratio of non-performing loan to total loans which are from group 1 to 5 Thus, NPL has a significant influence on creditors as well as the banks, leaving both at risk of capital losses Therefore, many previous studies from Luchetta (2007), Vong et al (2009) showed a negative correlation between NPL and bank liquidity owever, studies of Vodova (2011), V Th ng (201 ) and Mai

Th Phương Th y (201 ) showed a positive correlation between these two variables and their explanation is that when NPL arises, the bank will have more motivation

to neutralize it with liquid assets leading to the bank liquidity increase

Return on equity is calculated by dividing net income by shareholder’s equity,

therefore, it reflects the level of effectiveness in the use of equity as a measure of financial performance Vodova (2011) expected to see a negative correlation between this ratio and bank liquidity but then witnessed no correlation in the regression result Meanwhile, in the research of V Th ng (201 ), there is a positive correlation between this two variables; which is similar to the result of Tr n

o ng Ng n (201 ) and Mai Th Phương Th y (201 )

Size of the bank in many previous studies will be calculated by taking the

natural logarithm of total assets Vodova (2011) found a positive correlation between size of banks and liquidity in Czech Commercial Banks, but then in 2013, Vodova continued her research in Hungarian Commercial Banks and received a negative correlation between these two variables Doriana Cucinelli (2013) also gave different results for the dependent variables calculated in two different ways that represented bank liquidity There are two points of view can be explained to these different results: (1) liquidity is increasing with the size of the bank; whereas (2) the hypothesis “too big to fail” meaning banks who have large assets would have less motivation to hold liquid assets because big banks can rely on other abundant resouces such as interbank market, or the Lender of Last Resort (Vodova, 2013), this hypothesis explains the negative correlation between size of banks and their liquidity

Provision for credit losses is an estimation of potential losses due to customers

not fulfilling their committed obligations The level of provision for credit losses in banking activities is prescribed in Article 12, Circular No 02/2013/TT-NHNN providing on classification of assets, levels and methods of setting up risk provisions and use of provisions against credit risks in the banking activity of credit institutions, foreign banks’ branches Whereby, specific provisioning rates are as follows:

- Group 1: 0%

Th Phương Th y (201 )

SUMMARY OF CHAPTER 2

In this chapter, the author states some concepts used in the study: theory of liquidity and liquidity risk of commercial bank, as well as its determinants The author also presents an overview of determinant variables based on reference models of previous studies

Starting from the basic theories and previous empirical research models on liquidity risk of commercial banks, also based on the practical situation of Vietnam banking market, the author has a basis to apply and establish a panel regression model of factors affecting the liquidity risk in Vietnam join-stock commercial banks that listed on HOSE, HNX and UPCoM, which will be presented in the next chapter

CHAPTER 3 RESEARCH MODEL

3.1 Dataset

This paper use panel data to analyse determinants of liquidity risk of JSCBs in Vietnam Data is obtained and calculated from consolidated reports, because most banks are now developing in the direction of multi-industry and multi-sector corporations, their unconsolidated reports cannot reflect the actual financial perfomance as well as the actual business activities of the whole system The author choose only JSCBs that are listed on stock exchanges (HOSE, HNX, UPCoM) to ensure the transparency and correctness of the reports Besides, data is also reconciled with management reports to ensure the consistency (Appendix B)

The dataset includes 152 observations of 17 JSCBs in Vietnam (cross-sectional units) over the period of 9 years from 2010 to 2018 (time series), and it is an unbalanced dataset due to the lack of B c Bank’s report in 2010 (Appendix A)

3.2 Analysis process

Graph 3.1 Analysis process

Choosing the main article

Setting research questions

Building theoretical case

Setting research strategy

Data collection

Data analysis

Write up

Graph 3.2 Data collection process

The author collected data of 17 Vietnam join-stock commercial banks from financial statements in the period of 2010-2018 and then calculated the independent and dependent variables After that, the author used Stata software to describe statistically the dataset and test the correlation matrix between variables, whether the obtained results are consistent with conditions for authors to compose economic models and are satisfied the conditions to use regression models including

Reject H0: FEM/REM

regression OLS, FEM, REM Next, the author used F-test and Hausman test to choose which model is more appropriate Afterwards, testing the defects of that model will be deployed and fixed incomplete model with FGLS method From the above results, the author gave her own conclusion and recommendations to banks to reduce liquidity risk

3.3 Research model and hypothesis

The study is based on researches and panel data regression models of Vodova (2011) and Trương Quang Thông (2013) to build a model of liquidity risk and its impact factors as follow:

ETA Ratio of equity

to assets

Vodova (2011), Trương Quang Thông (2013)

NPL

performing loan ratio

as deficit However, for this method, there is an uncertainty on the volume of deposits and also the volume of new requests for loans in the future Meanwhile, liquidity ratios are the balance sheet ratios that identify main liquidity trends Vodova (2011) used four formulas to indicate this trends as follow:

In which, L3 and L4 described illiquidity and the rest were used to describe liquidity For the purpose of this research, the author will use formulas that can demonstrate illiquidity which are L3 and L4 However, within the collected data sources, it is difficult to separate short-term loans and long-term loans in the total capital of JSCBs Therefore, the author will use only L3 to demonstrate illquidity in

order to indicate how much percentage of the assets is tied up in illiquid loans In other words, the higher this ratio is, the less liquid the bank is meaning the higher liquidity risk the bank has to face

3.3.2 Independent variables

Table 3.2 Estimated effects

3.3.2.1 The ratio of equity to assets (ETA)

Equity-to-total assets ratio is able to used as an replacement for Basel’s capital

adequacy ratio The lower this ratio is, the more debt that the bank has used to pay for its assets Furthermore, equity is also considered as a shield, a defense to combat different risks of a bank Therefore, the author indicates that the higher ratio of equity to assets is, the less liquidity risk that the bank has to face meaning lower LR ratio in this research is

Hypothesis H1: The ratio of equity to assets (ETA) has a negative effect on liquidity risk (LR)

3.3.2.2 Non-performing loan ratio (NPL)

Many previous studies from Luchetta (2007), Vong et al (200 ) showed a negative correlation between NPL and bank liquidity owever, studies of Vodova (2011), V Th ng (201 ) and Mai Th Phương Th y (201 ) showed a positive correlation between these two variables and their explanation is that when NPL arises, the bank will tend to strictly control on providing loans later on, then, liquidity risk will tend to decrease Therefore, in this paper, the author expects the higher NPL is, the lower loans of banks, meaning lower liquidity risk

Hypothesis H2: Non-performing loan ratio (NPL) has a negative effect on liquidity risk (LR)

3.3.2.3 Return on Equity (ROE)

ROE is calculated by dividing net income by shareholder’s equity, therefore, it reflects the level of effectiveness in the use of equity as a measure of financial performance Banks profit by earning more money than what they pay in expenses which mostly comes from the interest paid on its liabilities such as its deposits and the money they borrows The major portion of profitability of a bank comes from the fees that it charges on its services and the interest that it earns on its assets which are loans for individuals, businesses and other organizations Therefore, higher profitability means higher revenue from mostly loans or lower interest on liabilities, either way, it both means banks providing loans greater than the previous period, meaning bank liquidity risk increases Consequently, the author expects to witness a positive correlation between ROE and liquidity risk

Hypothesis H3: Return on equity (ROE) has a positive effect on liquidity risk (LR)

3.3.2.4 Size of the bank (Natural logarithm of total assets)

According to “too big to fail” theory, banks who have large assets would be less likely to have motivation to hold liquid assets because big banks can rely on interbank market, or the assistance from the Lender of Last Resort whereas medium and small banks would instead hold a buffer of liquid assets in their hands (Vodova, 2013) In this case, the author indicates that the bigger the bank, the bigger the loans due to their reliance on interbank market or on the Lender of Last Resort, and as the result, the higher LR ratio is

Hypothesis H4: Size of the bank has a positive effect on liquidity risk (LR)

3.3.2.5 Provision credit losses ratio (PCL)

Provision credit losses represents the level of credit risk in the bank (Chung-Hua Shen et al, 2009) and is therefore also used to measure the impact on liquidity The author indicates that the higher bank’s expenses for provision credit losses, the greater the likelihood of banks becoming subjective and careless in loans approval, meaning the higher LR ratio is

Hypothesis H5: Provision credit losses ratio (PCL) has a positive effect on liquidity risk (LR)

SUMMARY OF CHAPTER 3

From theories of liquidity risk and empirical evidence that are mentioned in chapter 2, the author has built a dataset as well as a analysis process to establish a panel regression model Particularly, the dependent variable is liquidity ratio (LR)

as illiquidity and independent variables are ratio of equity to assets (ETA), performing loan ratio (NPL), return on equity (ROE), natural logarithm of total assets as size of the bank (LnSIZE), and provision for credit losses ratio (PCL) Meanwhile, estimated effects have also been presented with a positive correlation between LR and ROE, LnSIZE, PCL and a negative correlation between LR and ETA, NPL

non-The next chapter will describe the dataset and analysis correlation between selected variables, and the final is the regression result after testing some defects