MINISTRY OF EDUCATION AND TRAINING HOCHIMINH UNIVERSITYOF BANKING HOANG DANG KHAI STUDENT CODE 050606180154 HQ6 GE04 FACTORS INFLUENCING THE LIQUIDITY OF COMMERCIAL BANKS IN VIETNAM GRADUATION THESIS[.]
INTRODUCTION
I NTRODUCTION
In theory, the liquidity of commercial banks is considered as the ability to meet the immediate needs of commercial banks for money, such as withdrawing deposits and disbursing committed credits, paying the costs of operations, or other needs to be paid by cash.
Liquidity is a crucial factor determining the safety of commercial banks A bank default due to the lack of liquidity will cause a domino effect on other commercial banks A typical example of this is the global financial crisis in 2008 due to the weakness of the financial system, specifically the lack of liquidity at commercial banks. During the global financial crisis, many banks had to find ways to maintain liquidity. Despite the financial help of the central bank, many banks still had to declare bankruptcy, and some were forced to merge with other banks Many commercial banks, although profitable, can still face difficulties in asset and capital management, which has led to a liquidity crisis After the banking system crisis in 2008, Basel III was officially introduced in 2013, with regulations relating to liquidity factors in order to minimize the economic damage caused by banks may cause.
In Vietnam, the state bank also imposed some regulations to ensure more liquidity for commercial banks According to Circular No 22/2019/TT-NHNN, dated November
15, 2019, of the State Bank of Vietnam stipulating the limits and ratios to ensure safety in operations of banks and foreign bank branches, from on January 1, 2020 (the effective date of the circular), the maximum loan-to-deposit ratio (LDR) is at 85% The maximum rate of short-term capital used for medium and long-term loans of banks and foreign bank branches currently applied by the State Bank is 34% from October 1,
2021, to the end of May 30, September 2022, then reduced to 30% from October 1,
2022, according to the latest revised Circular 22/2019/TT-NHNN.
In the face of the constantly changing and volatile situation of Vietnam's financial
2 sector in general and the banking industry in particular, the study was conducted to determine the factors affecting the liquidity of Vietnam's commercial banking system in the current context.
Banks are one of the vital components of each country's economy; they play a crucial role in regulating the macroeconomy through credit financing, facilitating the process of integration of economic and production activities Along with its essential role, banking governance is also an extremely important issue for governments in monitoring and management, in which liquidity is the top priority in banking governance.
Liquidity has long played an essential role in deciding the existence and development of each commercial bank in particular and the commercial banking system in general In recent times, the world economy and global financial markets have been continuously negatively affected by the Russia-Ukraine conflict, which has pushed up the prices of oil, gold, and other essential commodities due to sanctions, embargos from the West, and retaliatory measures from Russia Inflation has been running high for over a decade, prompting major central banks to race to raise interest rates to cool it down In Vietnam, after the pandemic passed and the economy reopened, commercial banks disbursed at full capacity to boost production and accelerate economic recovery. Therefore, the depletion of the credit room occurred from the beginning of 2022 and caused commercial banks to apply and request the State Bank to lose the credit room to be able to disburse credit Continuous hot credit growth will create many difficulties and obstacles in meeting liquidity needs in the future as well as in banking governance in a volatile world economy like today.
Liquidity issues of commercial banks in Vietnam are always a hot topic of interest at the beginning of each year The goal of liquidity is to ensure the bank's continuous and stable operation If banks have good liquidity, it will help stabilize the financial market as well as promote the smooth operation of the economy Therefore, the author has chosen to study the topic " Factors affecting liquidity at commercial banks in Vietnam " for the graduation thesis.
T HE NOVELTY OF THE TOPIC
The topic was written in the post-covid context when the SBV was stimulating the economy to recover after years of a stagnant pandemic Through monetary policies and interest rate support packages to encourage industries affected by covid-19,commercial banks quickly disbursed at full capacity, reaching the limit of credit room that the State Bank had applied at the beginning of 2022 Since the outbreak of the pandemic, the economy has been shut down, but banks have made even better profits than in previous years while keeping liquidity at a good level Therefore, this topic focuses on analyzing how the factors affecting the bank's liquidity have changed before and during the covid pandemic while still maintaining outstanding profitability From there, creating a premise for bank administrators to apply the recommendations and suggestions given by the author to help banks increasingly improve their safety under the Basel framework along with the ability to profitability, creating prestige and position for Vietnamese commercial banks.
T HE AIM OF THE THESIS
The overall objective of the study is to identify the factors affecting the liquidity of commercial banks in Vietnam, thereby providing solutions and recommendations to effectively manage the bank's liquidity.
+ Determining the factors impacting the liquidity of commercial banks in Vietnam. + Measuring and assessing the impact direction of each factor influencing the liquidity of commercial banks in Vietnam.
+ Proposing recommendations to manage the liquidity of Vietnamese commercial banks to ensure both liquidity and profitability.
T HE RESEARCH QUESTIONS
Based on the particular objectives mentioned above, the thesis will, in turn, answer the following questions:
+ What factors affect the liquidity of commercial banks in Vietnam?
+ What is the impact level and direction of these factors on the liquidity of Vietnam's commercial banks?
+ What are suggestions that can manage the liquidity of Vietnamese commercial banks to meet both liquidity and profitability?
S UBJECT AND SCOPE OF THE RESEARCH
+ Research subject: Factors influencing the liquidity of commercial banks.
+ Research scope: samples were collected from financial statements of 25 Vietnamese commercial banks listed on the Stock Exchange in the period 2012 -
2021 The criteria needed for the study are shown in the financial statements of the banks.
M ETHODOLOGY
+ Qualitative method: analyze and evaluate the situation of domestic and foreign research related to the research thesis and synthesize the necessary theoretical bases, test the research's hypothesis
+ Quantitative method: perform based on data collected from annual financial statements of banks The thesis uses commonly used estimation models for panel data: fixed effects regression model (FEM), random effects regression model(REM), and compound least squares model (POLS) In addition, there are tests and remedies for defects to establish the optimal regression model.
R ESEARCH CONTENT
The study analyzes and evaluates factors influencing the liquidity of Vietnamese commercial banks in the period 2012 - 2021 From that, it examines the impact level of these factors on the liquidity of Vietnamese commercial banks through the use of econometric models to test the effect and significance level of this effect The assessment and explanation of the factors influencing the liquidity of Vietnamese commercial banks based on the actual operation of the banking industry in the research
5 period will be carried out, and finally, propose solutions for improving the efficiency and safety of Vietnamese commercial banks.
C ONTRIBUTION OF THE THESIS
Theoretically, the thesis contributes to building an empirical research model in Vietnam to understand the financial and macro factors affecting the liquidity of commercial banks From the research results, the study will open up many new and more in-depth research directions.
Determining the factors affecting liquidity at Vietnamese commercial banks will show the relationship between these factors as well as the degree of negative or positive influence on the liquidity of Vietnamese commercial banks Thereby proposing solutions and appropriate management policies to maintain good liquidity atVietnamese commercial banks.
T HE PROPOSED LAYOUT OF THE THESIS
This chapter introduces an overview of the urgency of the thesis, the purpose, and scope of the research, presents domestic and international studies, as well as a quick view outline of the research methods used.
Presents basic concepts and underlying theories.
Focus on presenting the research method used in the thesis, including the research process, research hypothesis, conditions, and sampling procedure, how to collect input data sources, and how to determine the value of the data, variables, and test methods used for the study.
- Chapter 4: Data analysis, Findings, and Discussion
Mainly focus on discussing experimental research results in chapter 3.
Briefly present the results obtained from the study From that, gives conclusions
Chapter 1 points out the importance and urgency of analyzing the factors affecting the liquidity of Vietnamese commercial banks The thesis gives 3 particular research objectives from the general research objective and will be solved through 3 corresponding research questions Next, the topic presents the research's object and scope, which are 25 Vietnamese commercial banks from 2012 to 2021 The thesis uses qualitative research methods and quantitative research methods based on inheritance and expands previous studies to update factors affecting liquidity that can change over time Finally, this chapter will present the structure of the thesis consisting of 5 chapters and a summary of the main content of each chapter.
THEORETICAL FRAMEWORK
L IQUIDITY OF COMMERCIAL BANKS
From the perspective of assets, liquidity is the ability of assets to be converted into cash and vice versa An asset is considered liquid when it meets the following criteria: Availability of quantity to buy or sell, availability of market to trade, availability of time to trade, and reasonable price.
According to the Basel Committee on Banking Supervision, liquidity is a technical term that refers to the ability to meet the needs of using available capital, and serving business activities at all times, such as deposit payment, lending, payment, and capital transactions.
At different times, Basel had different concepts of liquidity, but in general, liquidity was defined as the ability to increase asset funds and meet due obligations at an acceptable cost.
In fact, assets with high liquidity include valuable papers such as Treasury bills, certificates of deposit, promissory notes, bills of exchange, etc., and low-liquid assets such as real estate, machinery, equipment, or production lines.
From a banking perspective, liquidity is the ability to fully and promptly meet financial obligations arising in the process of operations and transactions such as deposit payment, lending, payment, and other activities of financial transactions.
According to Basel (2008), the definition of bank liquidity is as follows: "Bank liquidity is the ability of a bank to both increase its assets and meet its debt obligations when they come due without incurring excessive losses." When a bank is unable to fulfill its obligations, such as disbursing loans, paying deposits at maturity, or making payments and financial transactions entirely and on time, it means that the bank is insolvent in liquidity” (Bessis, 2012).
With the definitions gathered above, the author can summarize the concept of bank liquidity, which is represented by the bank fully and timely meeting its committed obligations, such as paying deposits at maturity, disbursing loans, making payments and financial transactions at a reasonable and low cost to avoid causing significant losses leading to insolvency of the bank.
2.1.3 Supply and demand of liquidity and net liquidity position
2.1.3.1 Supply and demand of liquidity
Liquidity supply represents the bank's ability to supply cash to meet the payment needs of customers on time When a bank has an abundant liquidity supply, it will increase the bank's liquidity Liquidity supply is formed from many different sources, from available cash or can be mobilized in a short time.
Liquidity demand reflects the disbursement and payment needs of customers that the bank is obliged to meet immediately or in a short period of time In other words,liquidity demand is the amount that will reduce the bank's cash budget.
Table 2.1: Banking activities forming the supply and demand for liquidity
Accepting deposits Customers withdraw deposits
Loans are repaid Payment of due loans
Proceeds from providing products and services Payment of operating expenses
Money market loans Cash dividend payment
Proceeds from property sales Disbursing new loans or investment
Paying costs incurred in the process of providing products and services
The combination of a bank's liquidity supply and demand will make up its Net Liquidity Position (NLP) which is calculated according to Nguyễn Văn Tiến (2015) as follows:
NLP = Total liquidity supply - Total liquidity demand
If NLP > 0 or liquidity supply is more significant than liquidity demand, in this state, the bank is in a surplus of liquidity It can be seen that the bank's current liquidity is positive, but on the other hand, it shows that the bank's profitability has not been fully exploited Possible causes of a liquidity surplus include banks actively increasing liquidity reserves, unreasonable investment or a stagnant economy that does not have many investment opportunities, capital growing too fast, etc.
If NLP < 0, or liquidity supply is smaller than liquidity demand, in this state, the bank is in a liquidity deficit Liquidity deficit is one of the risk factors for commercial banks' operations A slight deficit makes it difficult for banks to operate In contrast, a 10
1 1 large deficiency leads to more severe problems such as losing customers, losing business opportunities, losing markets, reducing public confidence, etc (Trương Quang Thông, 2010) The necessary actions to offset liquidity can be listed as selling illiquid assets, using required reserves, borrowing interbank, borrowing from Lenders of last resort, etc.
If NLP = 0, or liquidity supply equals liquidity demand, the bank is in liquidity equilibrium This is the goal of the bank administrators, but in practice, the liquidity equilibrium is difficult to achieve.
L IQUIDITY MEASUREMENT METHOD
Vodova (2011) offered two methods to measure liquidity: measurement by liquidity gap and measurement by liquidity ratios.
The liquidity gap measurement method is a method of measuring the difference between capital and assets at present and in the future.
Liquidity ratios are a method of calculating various ratios collected from balance sheet data, thereby predicting liquidity movements.
The liquidity gap is the difference between the average total loan balance and the average total mobilized capital The liquidity gap represents a warning sign of the bank's future liquidity risk.
Suppose the liquidity gap is positive and has an enormous value In that case, the bank is forced to reduce cash reserves, reduce liquid assets, or borrow additional money in the money market, leading to an increase in the bank's liquidity risk (Đặng Văn Dân,
2015) The method of measuring liquidity by calculating the liquidity gap is the most appropriate method in quantitative analysis, and the liquidity gap index reflects the most basic of the bank's liquidity.
There are many ways to measure a bank's liquidity Rose (2004) measured the liquidity of a bank using the liquidity index method This method has been applied by many typical researchers, such as Vodova (2011), Bunda & Desquilbet (2008) This method includes four liquidity indicators as follows:
Liquid assets in L1 include cash, trading securities, and deposits with central banks or other credit organizations This ratio shows how much of a commercial bank's total assets are liquid assets Usually, a high index indicates an excellent liquidity position and low liquidity risk, but it also shows that the bank is operating inefficiently due to holding too many assets with low profitability or unprofitable assets.
( D e p o s i ts + s h o rt — te r m b o r r o wi n g) Liquidity ratio L2 shows how liquid assets are compared to deposits and mobilized capital This ratio is the same as L1; the higher the L2 ratio (greater than or equal to 100%), the better the bank's liquidity.
L 3 = =7——í————— X 1 0 0 %Total assets This ratio shows how much of a bank's total assets are outstanding loans The higher this ratio, the lower the bank's liquidity, which leads to a higher risk of the bank's liquidity risk.
( D e p o s its + s h o rt — te rm b o rr o wi n g)This ratio is similar to the L3 index The higher the ratio, the lower the bank's liquidity.
L1 and L2 indexes measure absolute liquidity, while L3 and L4 indexes measure relative liquidity.
Most authors have used all four liquidity ratios to assess the liquidity of banks,such as Vodova (2011), Aspachs et al (2005), Vũ Thị Hồng (2015) However, the author only chooses the L1 index to assess the liquidity of the bank because this index gives us the most general view of the bank's liquidity in both the short and medium- long term The L1 index shows how much of a bank's total assets are liquid assets that the bank can reserve to hedge against liquidity risk In the short term, if a bank inVietnam is facing a sudden liquidity shortage, it can still borrow money on the interbank market or with support from the Lender of Last resort to compensate liquidity shortage without leading to bankruptcy However, the cost to make up for this lack of liquidity is not cheap; if the situation lasts for a while, it will significantly affect the bank's profit and operational efficiency, forcing it to be on the verge of bankruptcy or the State Bank will repurchase it at 0 dongs in Vietnam Therefore, the L1 index will be superior to other indexes when assessing liquidity in both the short-term and long term.
F ACTORS INFLUENCING BANK LIQUIDITY
2.3.1 Group of factors inside the bank
Bank size is measured by taking the natural logarithm of total assets In terms of the Signaling theory (Spence, 1973), bank size has a positive effect on liquidity; that is, when a bank expands, it will bring a positive signal to the bank As a result, it creates an incentive for banks to expand capital mobilization from different sources of capital, increasing the bank's liquidity However, the opposite can happen That is, when the size of the bank is more extensive, it will reduce the bank's liquidity This can happen to banks with a "too big to fail" judgment (Grey, 2009) (large-scale banks, especially receiving strong support from the Government in difficult situations) when with backing, banks boldly invest in riskier assets in order to increase profits This can cause losses to banking operations and reduce liquidity From the above arguments, the author
1 4 hypothesizes that there is a positive relationship between bank size and bank liquidity.
Equity-to-total assets ratio (CAP)
This ratio is measured by taking the equity divided by the total assets of the bank. This ratio represents the capital adequacy and safety, and financial soundness of a bank. This low ratio shows that the bank uses high financial leverage, which contains a lot of risks and can make the bank's profit decrease when the cost of borrowing is high This can be considered as a substitute for Basel's capital adequacy ratio (CAR) within the framework of capital adequacy regulations (Vodova, 2013) Core capital is the buffer, the last line of defense against various risks of the bank.
Research by Vodova (2011) suggests that banks with low equity will pay more attention to liquidity risk management and hold large volumes of liquid assets In contrast, Vũ Thị Hồng (2015) found a positive result between equity and liquidity of the bank The result is explained that when banks have substantial capital, it means low default while increasing the bank's reputation and attracting large deposits from customers, helping to increase liquidity for banks.
Loan-to-deposit ratio (LDR)
This ratio is measured based on total loans divided by total mobilized capital In which, the source of capital mobilization is calculated according to Circular 13/2010/NHNN, and the prudential ratio for banks is 80% This ratio measures the outstanding balance of loans with stable capital, usually deposits from customers and non-financial institutions When loans exceed their mobilized capital, banks face a funding gap, so banks must quickly offset liquidity by borrowing money in the financial market Therefore, a large funding gap means too much dependence on market capital at a more expensive cost.
The higher the LDR ratio, the higher the bank is facing liquidity risk when the bank lends more than the mobilized capital Besides, when banks face liquidity difficulties, it will be challenging to mobilize cheap capital if they lend too much,reducing liquidity significantly Research by Aspachs et al (2005) has shown a negative correlation between the LDR ratio and bank liquidity.
This ratio is measured by dividing earnings after tax by equity Therefore, this index reflects the efficiency of the bank's management in using equity Many studies find the positive impact of ROE on banks' liquidity, typically the analysis of Bunda and Desquilbet (2008) and Vũ Thị Hồng (2015) However, some studies find the negative impact of ROE on liquidity, such as the study of Aspachs et al (2005).
Non-performing loan ratio (NPL)
This ratio is measured by dividing non-performing loan by total loans In fact, when non-performing loan increases, banks will have to conduct lending more cautiously, not to mention the control of the central bank to limit lending to that bank. Banks are forced to increase profits from other activities such as mobilizing capital, promoting services, etc This makes liquidity better The studies of Vodova (2011), Vũ Thị Hồng (2015) all showed the same result that the non-performing loan ratio has a positive impact on liquidity.
2.3.2 Group of factors outside the bank
Economic growth is expressed by the growth rate of GDP over the years The data is compiled from the website of the General Statistics Office of Vietnam (gso.gov.vn). Theoretically, during a recession, a bank will hold more liquid assets when it thinks lending will be riskier In contrast, during periods of strong economic growth, banks tend to reduce their liquid asset reserves to be able to lend more, while capital mobilization may decrease As a result, the funding gap increases, leading to a rise in liquidity risk (Shen et al., 2009) However, some other arguments have shown that in the period of economic growth, enterprises operate effectively, the financial resources of enterprises are more abundant, and the ability to pay debt obligations is better. Thereby increasing the liquidity supply for banks.
The rate of inflation is measured through the growth rate of the consumer price index (CPI) Low inflation results in less volatility, creating a less risky economic
1 6 environment On the contrary, high inflation will make the macroeconomic environment worse, thereby reducing the liquidity of banks (Vodova, 2011).
E MPIRICAL RESEARCH OVERVIEW
In the study of Aspachs et al (2005), the author analyzed the factors affecting the liquidity of 57 commercial banks in the UK from 1985-2003 The results showed that the factors affecting the liquidity of British commercial banks are divided into two groups: internal and external factors Internal factors such as ROE measure bank profitability The author has found a negative correlation between ROE and bank liquidity because it is assumed that the more assets a bank holds to meet liquidity needs, the lower the ability to generate profits and vice versa In addition, bank liquidity is also affected by external factors such as the ability to receive support from the central bank as the lender of last resort If the assessment of the possibility of support from the central bank is more significant, commercial banks have less incentive to hold liquid assets and use them for profitable investment.
In the study of Bunda & Desquilbet (2008), the author analyzed the relationship between liquidity risk and specific characteristics of 1308 banks in 36 developing countries from 1995-2004 The study's objective is to discover how the influence of the country's exchange rate affects the liquidity of commercial banks in that country.
Research results showed that: bank size, lending interest rate, and perception of financial crisis can cause poor liquidity for banks Meanwhile, the following variables are positively influenced such as equity ratio, inflation, and economic growth.
Chung-Hua Shen et al (2009), with the topic "Bank Liquidity Risk and Performance," studied the liquidity risk of commercial banks in 12 leading economies in the world, including Australia, Canada, France, Germany, Italy, Japan, Luxemburg,Netherlands, Switzerland, Taiwan, UK, and the USA for the period 1994-2006 using the funding gap method as the dependent variable and the independent variables including the size of total assets, the ratio of loans to total assets, the ratio of liquidity reserve to
1 7 total assets, the ratio of equity to total capital, the ratio of provisions for credit risks, economic growth, and inflation.
In the study of Vodova (2011), the author studied the determinants of liquidity of commercial banks in the Czech Republic from 2001-2009 The study has the advantage that it has assessed liquidity more objectively and comprehensively when using many different coefficients to determine the bank's liquidity On the other hand, the study has considered the prominent factor of the economy in the research period, which is the impact of the global financial crisis on bank liquidity In which, the author has pointed out that macro factors such as financial crisis, inflation rate, and economic growth rate (GDP) have a negative impact on bank liquidity In addition, the author also shows a positive relationship between liquidity and capital adequacy ratio and interbank lending rates.
Bonfim & Kim (2012), with the topic "Liquidity risk in banking: Is there herding?" studied the impact of internal and external factors on the liquidity risk of the
500 largest banks in 43 countries in Europe and North America, mainly banks in Canada and France, USA, UK, Netherlands, Germany, Italy, and Russia in the period 2002-2009 The author has divided the research time into two periods: before and during the economic crisis The research has shown that, in addition to the internal factors of the bank, the external macro factors also have a significant impact on liquidity risk, which banks often ignore Therefore, to manage liquidity risk well, banks need to pay attention to both internal and external factors.
Moussa (2015) used a sample of 18 banks in Tunisia for the period 2000-2020 to study the determinants of commercial bank liquidity Through the research, the author has shown that return on equity and GDP growth positively affect banks' liquidity In contrast, bank size, the ratio of equity to total assets, and the inflation rate have negative effects on bank liquidity.
Ahmad, F., & Rasool, N (2017) empirically researched the determinants of liquidity of commercial banks The author took a sample size of 31 listed commercial banks and State-owned banks of Pakistan from a total of 37 commercial banks from
2005 to 2014 The results of the regression analysis showed that independent variables such as the ratio of equity to total assets (CAP) and economic growth rate (GDP) have a positive impact on bank liquidity, while total asset size (SIZE) has a negative effect In addition, the return on equity or inflation rate showed no impact on liquidity.
In Vietnam, Trương Quang Thông (2013), with the topic "Factors affecting liquidity risk of Vietnam's commercial banking system," collected data from 27 Vietnamese commercial banks from 2002-2011, in that the funding gap is a dependent variable representing liquidity risk, independent variables affecting liquidity risk are divided into two groups, inside and outside the bank Research results show that liquidity risk depends not only on internal factors such as the ratio of equity to total assets, bank size, and liquidity reserve but also on external macro factors such as inflation, economic growth (GDP). Đặng Văn Dân (2015), with the topic "Factors affecting liquidity risk of Vietnamese commercial banks," analyzed the factors affecting liquidity risk through data collected from 15 commercial banks in Vietnam from 2007-2014 Like Trương Quang Thông (2013), the author has chosen the funding gap as a dependent variable to measure liquidity risk Independent variables affecting liquidity risk include bank size, the ratio of loans to assets, equity to total assets, ROE, GDP, or inflation The variables of equity-to-total assets, ROE, GDP, and inflation are not statistically significant, bank size has a negative relationship with the liquidity gap, and loan-to-total assets ratio has a positive relationship with the liquidity gap.
Vũ Thị Hồng (2015) experimented on 37 Vietnamese commercial banks from
2006 to 2011 The study shows that equity to assets ratio, non-performing loan ratio,and return on equity ratio have a positive effect, while the loan-to-deposit ratio (LDR) has a negative impact on liquidity However, no correlation was found between the credit risk provision ratio and bank size on liquidity The liquidity index used by the author in the study is Liquidity assets to total short-term mobilized capital (L2).
There are many studies in the world, but at different times, in other countries, the factors affecting the liquidity of commercial banks are various, leading to policy implications that cannot be applied to Vietnamese commercial banks.
When considering the impact of factors on liquidity, the studies in Vietnam mainly research the factors inside the bank, ignoring the effects from the outside Most of the studies were done by the author until 2019; the data from 2020-2021 has not been updated In the context of the post-covid-19 opening up, along with the volatility of the world economy, which has led to record high inflation in many countries, the liquidity issue of commercial banks should be given more attention.
This chapter has presented the entire theoretical basis used in research analysis on bank liquidity to help the thesis better generalize the impact of factors on the bank's liquidity This chapter is the basis for the author to build a research model on the effect of factors on the liquidity of Vietnamese commercial banks made in Chapter 3.
RESEARCH MODEL
A NALYSIS PROCESS
To find out the impact direction and level of impact of the factors affecting the liquidity of 25 Vietnamese commercial banks in the period 2012-2021, the study was carried out according to the process presented in the figure below:
Step 1 : Review the theoretical basis and related previous studies in Vietnam and other countries, then discuss previous studies to identify research gaps and design directions for the research model.
Step 2 : Based on the theoretical basis and empirical evidence, the thesis designs a research model, predicts regression equations, explains variables, and builds research hypotheses.
Step 3 : Determine the research sample suitable for the research objectives as well as the object and scope of the research, then collect and process data according to the research model in step 2.
Step 4: Identify methodology with specific analysis and estimation techniques: descriptive statistics, correlation analysis, and regression analysis of panel data according to OLS, FEM, and REM.
Step 5 : Test the research hypotheses, can use F-test or t-test with a significance level of 1%, 5%, or 10% to identify statistically significant independent variables to explain the dependent variable; at the same time, compare the two models Pooled OLS and REM by
F test with hypothesis H0: Selection of Pooled OLS model; use Hausman test to compare between 2 models FEM and REM with hypothesis H0: Select REM model, then choose the most suitable model.
Step 6 : Carry out testing of model defects, including multicollinearity, autocorrelation, and variable variance; if these defects are not present, then combine with step 5 to perform step 7; if there is one of these defects, it will be solved by GLS method as well as overcome the phenomenon of endogenous variables occurring in the study, and at the same time test the research hypotheses in section 5 and move to step.
Step 7 : This is the final step of the process based on the regression results; the topic conducts discussions, draws conclusions, and makes relevant suggestions and policy implications for answering the research questions as well as solving the research objectives set out.
S AMPLES AND RESEARCH DATA
The research is conducted based on secondary data collected from audited financial statements and related documents from 2012 to 2021 of 25 commercial banks in Vietnam.
The topic uses secondary data to measure the dependent and independent variables belonging to the group of micro-factors belonging to commercial banks, collected from audited financial statements from 2012 to 2021 of 25 commercial banks in Vietnam, which are listed on the stock market in Vietnam Secondary data to measure the independent variables of the group of macro factors collected from relevant official organizations from 2012 to 2021.
The data source for the dependent variable and the independent variables in the group of micro factors belongs to commercial banks: VietstockFinance.
Data sources for the independent variables in the group of macro factors: General Statistics Office (GSO) and World Bank.
The results of measuring the impact of factors affecting the liquidity of commercial banks in Vietnam are based on panel data with the support of Excel software and Stata16.0 software.
M ETHODOLOGY
Qualitative research methods are used to (i) approach and analyze the fundamental theories of liquidity, the theoretical basis of factors affecting liquidity, (ii) brief and discuss previous studies in Vietnam and other countries on the factors affecting the liquidity of commercial banks, (iii) design research models and interpret research hypotheses for each independent variable with the dependent variable, (iv) discuss research results, draw conclusions and give relevant suggestions and recommendations.
Quantitative research methods are used to determine the research results of influencing trends and influence levels of factors affecting the liquidity of Vietnamese commercial banks, including specific technical methods as follows:
Descriptive statistics are used to provide general information about the variables in the research model The indicators to be collected in descriptive statistics include Mean,Minimum, Maximum, Standard Deviation, and Observations
Step 2: Analyzing the correlation coefficient matrix
Correlation matrix analysis is used to examine the relationship between the independent variables and the dependent variable, as well as the correlation between the independent variables The results of the correlation matrix initially assess the relationship between the independent and dependent variables In case the independent variables are highly correlated, in particular, the correlation matrix coefficient between the explanatory variables is greater than 0.8 Then, the model has high multicollinearity. Therefore, multicollinearity should be handled by: (i) removing the variable with a high correlation with other variables, (ii) using principal components analysis, or (iii) doing nothing.
Step 3: Regression analysis of panel data
Using multivariable regression estimation to determine the relationship and level of impact of the independent variables on the dependent variable The estimation methods used are:
+ Pooled Ordinary Least Square (Pooled OLS)
At the same time, the tests of Breusch-Pagan (1980), Hausman (1978), and Likelihood Ratio are used to select a suitable model between the pair of estimator models.
Step 4: Test the heteroskedasticity and autocorrelation of the selected model. Step 5 : Select the model and analyze and comment
R ESEARCH MODEL AND HYPOTHESIS
Based on the authors' research presented in detail in Chapter 2, the research uses the method of measuring banks' liquidity by using the technique of liquidity ratios In addition, these studies also use micro factors in banks and macro factors in the author's research model to assess the impact on the liquidity of commercial banks From that, the thesis proposes a research model with the following equation:
L1 it = β 0 + β 1 SIZE it + β 2 CAP it + β 3 LDR it + β 4 ROE it + β 5 NPL it + β 6 GDP t + β 7 INF t + ε it
L1 it : Liquidity ratios of banks i year t
SIZE it : Size of commercial bank i in year t
CAP it : The ratio of equity in total assets of commercial bank i in year t
LDR it : Loan-to-deposit ratio of commercial banks in year t
ROEit : Return on equity of commercial bank i in year t
NPL it : Non-performing loan ratio of commercial bank i in year t
GDP t : Economic growth rate in year t
INFt : Inflation rate in year t
With i, t corresponds to the bank and the survey year; β0 is the intercept factor;β1 - β7 are the slopes of the independent variables, and εit is the statistical residual.
3.4.2 Description of variables and hypotheses
Table 3.1: Description of variables in the research model
1 The liquidity ratio L1 Liquid assets/Total assets
2 Bank size SIZE Logarithm (Total assets)
3 Equity ratio CAP Total equity/ Total assets
4 Loan-to-deposit ratio LDR Total loans/Total mobilized capital
5 Return on Equity ROE Earning after tax/ Total equity
6 Non-performing loan ratio NPL Non-performing loan/Total loans
7 Economic growth GDP General Statistics Office
8 Inflation rate INF General Statistics Office
Bank size in Vodova's study (2011); Bunda & Desquilbet (2008) give mixed results. However, given the fact that the bank's size is getting bigger, the study is expected to have a positive effect between bank size and liquidity This expectation corresponds to the Signaling Theory, at the same time, creates a positive belief for the development and expansion of the banking industry in Vietnam in the future.
H1: The size of the bank has a positive effect on liquidity.
This ratio is a formula to measure the bank's capital adequacy This ratio shows how the bank is, whether it has enough capital for liquidity or whether its financial position is healthy Most of the previous empirical studies have shown positive results, such as Vodova (2011), Vũ Thị Hồng (2015), and Aspachs et al (2005), which means that this high ratio will ensure the bank's security and avoid liquidity risk Therefore, this study expects the equity ratio to have a positive relationship with the liquidity of commercial banks.
H2: Equity ratio has a positive effect on liquidity.
Loan-to-deposit ratio (LDR)
This high ratio is equivalent to the possibility of insolvency as well as illiquidity because the level of lending exceeds the bank's mobilized capital, leading to liquidity risk at maturity Credit growth will increase illiquid assets, which reduces liquidity In the study of Aspachs et al (2005), Vũ Thị Hồng (2015) showed negative results between this ratio and liquidity Therefore, this study is also expected to find the same negative relationship as previous studies.
H3: The loan-to-deposit ratio has a negative effect on liquidity.
The ratio reflects the efficiency of the bank's management in using equity Bank profits are mainly generated from traditional business activities, such as the interest rate differential between lending and raising capital Therefore, the more assets a bank holds to meet liquidity needs, the lower its ability to generate profits and vice versa (Aspachs et al., 2005) For Vietnamese commercial banks, profit is mainly generated from credit activities, such as profit generated from the difference in interest rates between lending and mobilizing capital Therefore, the higher the profit, the lower the bank's reserve of liquid assets From the theory, arguments, and practical research results presented above, the author hypothesizes the negative relationship between return on equity and liquidity of banks.
H4: Return on equity has a negative effect on liquidity.
Non-performing loan ratio (NPL)
Non-performing loans are loans to customers that are facing high risks in recovering principal and interest due to customers facing difficulties Non-performing loans include debt groups from 3 to 5 If this ratio is higher than the industry average, it means that the bank is having difficulty in managing the quality of loans Hence, it will also lead to a restriction on the bank's lending activities, thereby increasing the bank's liquidity. Therefore, the author expects a positive relationship between LDR and liquidity.
H5: Non-performing loan ratio has a positive impact on liquidity.
Banks will keep more liquidity during an economic recession, when lending is riskier, whereas during economic growth, banks tend to reduce liquidity reserves to lend more Vodova (2013), Moussa (2015) found a positive correlation, while Bunda &
Desquilbet (2008), Aspachs et al (2005) found a negative correlation In the study, the author expects a negative relationship between GDP growth and liquidity.
H6: Economic growth has a negative effect on liquidity.
The inflation rate is measured by the growth of the consumer price index (CPI). Stable inflation is necessary for the economic development of a country When inflation increases, people tend to withdraw bank deposits to invest in different channels, such as gold, foreign currencies, etc., to avoid the risk of currency devaluation Therefore, the bank needs a large amount of cash to meet this demand Thus, the author hypothesizes the negative relationship between inflation rate and liquidity.
H7: The inflation rate has a negative effect on liquidity
Chapter 3 has shown data and research methods to build research models At the same time, it shows the expected impact of independent variables on the liquidity ofVietnamese commercial banks based on theory, theoretical basis, and previous empirical research results of domestic and foreign topics In addition, chapter 3 has also outlined the tests that will be used in the research model The content presented in chapter 4 will be the results obtained from the application of this research method.
RESEARCH RESULTS
D ESCRIPTIVE STATISTICS
The results of the descriptive statistics of the measured variables in the regression model are presented in the table below:
Table 4.1: Variable Statistics Variable Observations Mean Standard
Source: Data processing results through Stata 16.0
Table 4 shows that all variables in the research model are balanced panel data, with
250 observations from 25 commercial banks over a 10-year period The results of descriptive statistics for each variable are as follows:
The liquidity variable (L1) is measured by Total Liquidity Assets/Total Assets,with a mean value of 17.30% and a standard deviation of 7.68% The bank with the highest liquidity ratio of 52.11% belonged to SSB in 2012 Inversely, the bank with the lowest liquidity ratio is STB, with 4.52% in 2017 Vietnamese commercial banks will have the liquidity that changes depending on economic situations in each period.
The variable bank size (SIZE) is measured by taking the natural logarithm of total bank assets, and the analysis results from 25 commercial banks in Vietnam in the period 2012-2021 show that BID (with total assets of 1,761,696 billion dong in 2021) is the bank with the largest asset size In contrast, the bank with the smallest asset size is SGB, with total assets of VND 14,685 billion in 2013 In 2021, BID, VCB and CTG are the top 3 banks in terms of size and asset growth rate.
The CAP variable is measured as Total Equity/Total Assets, with the mean and standard deviation being 9% and 3.61%, respectively The bank with the lowest equity ratio is BID, with 4.06% in 2017 The highest equity ratio was 24.19% of SGB bank in 2012.
The LDR variable has an average value of 66.63% (meeting the maximum rate prescribed by the State Bank of 85% according to the latest circular) However, there are banks with the highest LDR, SGB with 98.45% in 2012, while there are banks with very low LDR, like SSB with 24.73% in 2012 This ratio is high or low depending on the management policies of banks based on assets and capital.
The bank's Return on equity (ROE) shows a mean of 9.63% and a standard deviation of 6.57% In which, there is VIB bank with the highest ROE of 26.39% in
2021 and NVB bank with the smallest ROE value of 0.03% in 2020 The bank's profit is still mainly generated from the traditional business of interest rate differential between lending and mobilizing capital.
The variable non-performing loan ratio (NPL) has a mean value of 2.13% with a standard deviation of 1.47% over the period 2012-2021 In which, the lowest non- performing loan ratio belongs to BID bank, with 0.001% in 2021, and NAB bank had the highest non-performing loan ratio at 9.19% in 2021.
The economic growth variable (GDP) has an average value of 5.59%, showing that the economic growth rate in Vietnam in the period 2012-2019 is relatively stable.However, from the end of 2019-2021, the economic growth rate has slowed down due to the shutdown of the economy because of the Covid pandemic Therefore, the lowest
GDP was 2.58% in 2021, and the highest in 2018, with 7.08% before the covid pandemic.
The average inflation rate (INF) variable for the period 2012-2021 is 3.8%, with a standard deviation of 2.3% The highest volatile inflation rate was 9.095% in 2012, and the lowest was 0.63% in 2015.
R ESEARCH RESULTS
L1 SIZE CAP LDR ROE NPL GDP INF
Source: Data processing results through Stata 16.0
Based on Table 4.2 analysis of the correlation matrix between L1 and variables, it can be seen that independent variables, including equity ratio (CAP) and inflation rate (INF), have a positive impact on bank liquidity (L1) In contrast, bank size (SIZE), loan- to-deposit rate (LDR), Return on equity (ROE), non-performing loan (NPL), and economic growth rate (GDP) have negative effects on bank liquidity.
The independent variable SIZE has a negative correlation with the dependent variable L1 of -0.1708, showing that bank size has a negative effect on bank liquidity.
Therefore, when this ratio increases, the bank's liquidity decreases accordingly.
The independent variable CAP has a positive correlation with the dependent variable L1 of 0.0010, showing that the equity ratio and the bank's liquidity have a positive relationship That is, when the equity ratio increases, the bank's liquidity also increases.
The independent variable LDR has a negative correlation with the dependent variable L1, which is -0.5572, showing that the bank's ratio of loan to deposit and liquidity has a negative relationship Therefore, the higher the loan-to-deposit ratio, the lower the bank's liquidity will be.
The independent variable ROE has a negative correlation with the dependent variable L1 of -0.0782, showing that Return on equity has a negative impact on the bank’s liquidity Therefore, when this ratio increases, the bank's liquidity decreases accordingly.
The independent variable NPL has a negative correlation with the dependent variable L1 of -0.0183, showing that the non-performing loan ratio has a negative impact on the bank’s liquidity Therefore, the bank's liquidity decreases when the non- performing loan ratio increases.
The independent variable GDP has a negative correlation with the dependent variable L1 of -0.1268, showing that the economic growth rate and the bank’s liquidity have a negative relationship When the economic growth rate increases, the liquidity ability of the bank will decrease accordingly.
The independent variable INF has a positive correlation with the dependent variable L1 of 0.2987, showing that the inflation rate and the bank's liquidity have a positive relationship Therefore, an increase in the inflation rate will lead to a rise in the bank's liquidity.
Source: Data processing results through Stata 16.0
The results from Table 4.3 show that the mean value of VIF (Variance InflationFactor) of all independent variables in the model is less than 10 Therefore, it concludes that the model does not have multicollinearity.
R EGRESSION RESULTS OF THE RESEARCH MODEL
Table 4.4: Regression results of models
Note: *** corresponds to the 1% significance level, ** corresponds to the 5% significance level, and * corresponds to the 10% significance level.
Source: Data processing results through Stata 16.0
Comparison of regressionresults between two models, FEM and REM
In order to find a more suitable model for the study, the author uses the Hausman test to choose between two models, FEM and REM, with hypothesis H 0 : Selecting the REM model is appropriate.
Table 4.6: Hausman test Test: H 0: difference in coefficients not systematic chi2(7) = (b-B)'[(V_b-V_B) ^ (-1)] (b-B) = 33.77
Source: Data processing results through Stata 16.0
With the significance level α = 5%, Prob>chi2 = 0.0000 < 5%, thus rejecting hypothesis H0 In other words, the author chooses the FEM model as the more suitable model.
Conclusion: After comparing three models, Pooled OLS, FEM, and REM, the author chooses the FEM model to determine the factors influencing the liquidity ofVietnamese commercial banks.
Defect tests
Implement the Modified Wald test in the FEM model with the following hypothesis:
Hypothesis H0: there is no heteroskedasticity.
Source: Data processing results through Stata 16.0
With the significance level α = 5%, Prob>Chi2 = 0.0000 < 5%, thus rejecting hypothesis H0 In other words, there is heteroskedasticity phenomenon.
Implement the Wooldridge test in the FEM model with the following hypothesis: Hypothesis H 0 : there is no autocorrelation.
Table 4.8: Wooldridge test Hypothesis H 0 : there is no autocorrelation
Source: Data processing results through Stata 16.0
With the significance level α = 5%, Prob>F = 0.0000 < 5%, thus rejecting hypothesis H 0 In other words, there is autocorrelation phenomenon.
Conclusion: Through the above tests, it can be seen that the research model both has heteroskedasticity and autocorrelation phenomenon.
Final model
Based on the test results of the above model, it can be seen that the model exists in both heteroskedasticity and autocorrelation Therefore, the author uses the FGLS model to overcome these two phenomena to complete the final model.
Table 4.9: Regression result of FGLS
Note: *** corresponds to the 1% significance level, ** corresponds to the 5% significance level, and * corresponds to the 10% significance level.
Source: Data processing results through Stata 16.0
With the dependent variable L1, after using the Feasible Generalized Least Squares (FGLS) method to overcome autocorrelation and heteroskedasticity phenomenon, with the significance level α = 5% (Prob>Chi2 = 0.0000), the regression model can be written as follows:
L1 it = 0.3273 – 0.3558LDR it + 0.1558ROE it + 0.3753INF t + ε it
S UMMARY
Table 4.10: Summary of expected hypothesis and experimental results
SIZE Positive Positive, no statistical significance
CAP Positive Positive, no statistical significance
LDR Negative Negative, 1% significance level
ROE Negative Positive, 5% significance level
NPL Positive Negative, no statistical significance
GDP Negative Negative, no statistical significance
INF Negative Positive, 1% significance level
Source: Data processing results through Stata 16.0
Bank size is calculated by taking the natural logarithm of total assets; when using this calculation, the difference in actual data about banks' total assets is narrowed; there is no significant difference Based on the research results, bank size and bank liquidity have a positive relationship with each other However, this relationship is not statistically significant.
In Vietnam, large commercial banks such as BIDV, Vietinbank, and Vietcombank are the largest banks in terms of total assets but not the group with the highest liquidity reserve ratio A high liquidity reserve ratio usually belongs to the group of banks with medium asset size Especially large commercial banks with State share capital will create their position, prestige and ability to easily access capital in the interbank market As a result, these commercial banks often have little incentive to increase their holdings of highly liquid assets In contrast, medium-sized banks with little support from the SBV will have to face a lot of pressure on liquidity management, so their liquidity reserve ratio is usually higher.
The equity ratio is measured as total equity divided by total assets The research results show that the equity and liquidity ratios have a positive relationship with each other However, this relationship is not statistically significant.
The studies of Vodova (2011), Aspachs et al (2005) all show similar results in terms of the equity ratio and liquidity of banks The higher the equity ratio the bank maintains, the lower the debt burden in its capital structure, hence higher liquidity due to less leverage During the period of the world financial crisis in 2008, Vietnamese commercial banks with small equity were shown to be weak to shocks of the economy, as demonstrated through weak liquidity The State Bank has gradually raised the minimum equity limit, which is currently 3,000 billion, to ensure the safety of commercial banks. Thereby, equity can play an essential role for commercial banks in Vietnam to withstand difficult situations.
4.4.3 Loan-to-deposit ratio (LDR)
The research results show that the loan-to-deposit ratio and the bank's liquidity have a negative relationship and are statistically significant—the higher the loan-to- deposit ratio, the lower the bank's liquidity.
According to the latest circular, the State Bank of Vietnam has regulated the loan- to-deposit ratio for commercial banks in Vietnam at 85% The higher this ratio, the more banks lend compared to mobilized capital When there is an economic crisis or unpredictable fluctuations, it will be challenging to mobilize cheap capital and decrease liquidity significantly Besides, short-term capital accounts for a high proportion of total mobilized capital; thus, the more banks lend, the fewer funding sources for liquid assets, and the bank's liquidity will also decrease significantly.
Return on equity has a positive relationship with liquidity and is statistically significant.
The study of Moussa (2015), Bunda & Desquilbet (2008), Vũ Thị Hồng (2015) also gave similar results on the rate of Return on equity with liquidity This means that when a bank does good business and generates a lot of profit, it will be able to offset liquidity better, improve its reputation in the financial market, and easily raise capital at a low cost from outside.
4.4.5 Non-performing loan ratio (NPL)
Based on the research results, the non-performing loan ratio has a negative relationship with liquidity However, this relationship is not statistically significant. This is explained that when a credit risk occurs, if a customer fails to repay a loan, the bank will lose capital and have to make provisions, reducing the bank's cash volume and leading to a decrease in total assets Besides, when a bank is announced a high non- performing loan ratio, it will make the general psychology of depositors feel scared, and they will come to withdraw their money to deposit in another bank, which is safer This tendency will cause the bank's liquidity to decrease significantly.
According to the research results, economic growth negatively affects liquidity. However, this relationship is not statistically significant.
This can be explained by the fact that in each period of economic growth or recession, the State Bank always has adjustment policies to control risks to the economy Therefore, the economic growth rate may have a negligible impact on the liquidity of commercial banks in Vietnam.
According to the research results, the inflation rate has a positive relationship with liquidity and is statistically significant.
In the study of Vodova (2011), Trương Quang Thông (2013) also gave similar results on the inflation rate and liquidity When the economy's inflation rate increases,banks will tighten credit, lending less to limit the amount of money circulating in the market to control inflation This trend will significantly increase the bank's liquidity.
In chapter 4, the author has implemented a regression model with factors influencing the liquidity of commercial banks in Vietnam; the data source is taken from
25 Vietnamese commercial banks from 2012 to 2021 Besides, the author used Stata 16.0 software to perform regression with models Pooled OLS, FEM, REM, FGLS and several other tests to find the most suitable estimation model.
The results show that factors including bank size, equity ratio, return on equity, and the inflation rate positively impact liquidity, loan deposit ratio, non-performing loan ratio, and economic growth rate negatively affect liquidity However, only the loan-to-deposit ratio, return on equity, and inflation rate variables were statistically significant.
The results of chapter 4 are the basis for the author to draw conclusions and management implications in chapter 5.
CONCLUSION AND POLICY IMPLICATIONS
C ONCLUSION
Through theoretical research, regression analysis of panel data and data estimation of 25 Vietnamese commercial banks in the period 2012-2021, to determine the factors affecting the liquidity of Vietnamese commercial banks Since then, the study has answered the following research questions:
+ Question 1: What factors affect the liquidity of commercial banks in Vietnam?
The study has conducted research and made initial hypothesis about the factors affecting the liquidity of Vietnamese commercial banks, including 7 factors as mentioned in Chapter 3 However, through the research results, the topic has identified three statistically significant factors affecting Vietnamese commercial banks' liquidity, including LDR, ROE and INF.
+ Question 2: What is the impact level and direction of these factors on the liquidity of Vietnam's commercial banks?
According to the research results in Table 4.9 in chapter 4, it shows that LDR has a negative impact on liquidity with a statistical significance of 1%, and in other conditions unchanged; when LDR increases by 1 unit, the liquidity of banks decreases by 0.3558 units Besides, ROE has a positive impact on liquidity at the 5% level of significance, and in other conditions unchanged; when ROE increases by 1 unit, the bank's liquidity increases by 0.1558 units Finally, the inflation rate has a positive effect on liquidity at the 1% significance level, and under constant conditions, when INF increases by 1 unit, the liquidity of banks increases by 0.3753 units.
+ Question 3: What are suggestions that can manage the liquidity of Vietnamese commercial banks to meet both liquidity and profitability?
To answer this question, the topic will give suggestions and propose related policy implications presented in section 5.2.
P OLICY I MPLICATIONS
From the research results, the thesis has provided policy implications and solutions for the SBV and commercial banks in Vietnam to meet both liquidity management and effective profitability.
+ Improving the ability to access capital
Banks need to periodically re-evaluate their efforts to establish and maintain relationships with owners, maintaining the diversification of sources of capital Building solid relationships with key suppliers will provide a liquidity buffer when banks face liquidity difficulties and form a crucial part of the liquidity management policy.
Banks need to develop policies to increase charter capital by issuing shares and using capital appropriately to ensure sustainable and effective development In addition, long-term convertible bonds can be issued, helping to obtain long-term capital without changing shareholders’ ownership before the conversion time and reducing tax payable when paying dividends to shareholders.
Considering the policy of accumulating capital from retained earnings contributes to helping commercial banks increase their solid financial capacity, become more financially autonomous, and improve liquidity.
+ Improving credit quality and controlling loans well
Strengthening the efficiency and effectiveness of banking inspection and supervision to ensure compliance with regulations on banking activities, especially regulations on credit financing, debt classification, provisioning credit risk and restrictions on the safety of credit operations.
In addition, banks need to avoid racing in sales, leading to lower lending standards and increasing credit risk Establishing credit relationships with customers with good basic business backgrounds and stable and healthy financial situations; building an effective loan portfolio; diversifying lending fields and industries to ensure loan recovery; distributing risks; diversifying investment portfolios in credit activities.
+ Improving operational and cost efficiency
Offering many credit products with flexible interest rates suitable for each specific customer segment and per each economic period to ensure profitability and risk control.
In addition, banks need to diversify their revenue from products and services to avoid relying too much on credit activities containing many risk factors.
Improve operational efficiency by improving the executive board's management level, assessing and developing employee professional capacity, regularly reviewing costs to determine appropriate spending levels, or restructuring and rearranging the operating apparatus to save costs.
Commercial banks should strengthen the expansion of non-credit activities and non-interest income sources, especially income from service segments, through promoting fee collection from activities such as insurance services, financial consulting, asset management, and financial investment.
5.2.2 For the State Bank of Vietnam
The SBV needs to coordinate with the authorities to control inflation at a number suitable to the economy Actively and flexibly operating monetary policy tools closely and synchronously with fiscal policy Focus on removing difficulties for production and business, developing the market, increasing purchasing power, and promoting the consumption of goods.
+ Strengthening inspection and supervision of the banking system
The SBV needs to implement financial analysis and an early warning system for systemic risks Commercial banks will do business in a safe environment under the management of the State Bank with high control and binding policies to prevent the risk of failure from spreading throughout the system In addition, the SBV needs to have more detailed and appropriate regulations for commercial banks in managing liquidity and organizing a comprehensive inspection of the banking system.
L IMITATIONS OF THE THESIS
Besides the obtained results, the study still has some limitations, as follows:
The research sample has only 25 Vietnamese commercial banks in the period 2012-2021, not covering all commercial banks in Vietnam to have a more comprehensive and accurate result for the entire Vietnamese commercial banking system Moreover, the study only focuses on domestic joint-stock commercial banks, excluding foreign banks, policy banks and non-equitized banks (Agribank), so the research results are not sufficiently objective In addition, the study did not consider the impact on each type of bank's size: small, large and medium.
In addition, during the research process, the author found that there are several other factors affecting bank liquidity, such as interest rates, exchange rates, money supply, and financial crisis; however, due to time constraints as well as some difficulties when collecting data from banks over the years, the author has not included the above factors in the topic.
The final limitation is that in the article, the author has only selected the L1 index
(Total liquid assets/Total Assets) to assess the bank's liquidity, while there are 4 fundamental liquidity indicators to evaluate the bank's liquidity.
P ROPOSING DIRECTIONS FOR FURTHER RESEARCH
Increasing the number of research samples and adding banks with characteristics different from JSCBs, such as Agribank and foreign banks in Vietnam In addition, the author will also classify by bank size for a more complete and detailed study The research results are expected to be more objective and representative of the entire banking industry.
In addition to the dependent variables affecting bank liquidity in this study, less- studied variables such as exchange rate, money supply, interest rates, and financial crisis will be added It is expected that the following studies will have a view from many different aspects of the factors affecting bank liquidity.
Based on the conclusions in Chapter 4, Chapter 5 has suggested some policy implications for commercial banks' managers to increase liquidity and meet the profitability of Vietnamese commercial banks This chapter has pointed out the limitations of the topic, thereby giving suggestions for future research directions related to research time and space as well as research content.
List of Vietnamese documents Đặng Thị Quỳnh Anh & Trần Lê Mai Anh (2022) Các yếu tố ảnh hưởng đến thanh khoản của ngân hàng thương mại cổ phần tại Việt Nam Tạp chí Khoa học & Đào tạo Ngân hàng, Số 241, 1-13. Đăng Văn Dân (2015) Các nhân tố tác động đến rủi ro thanh khoản của ngân hàng thương mại tại Việt Nam Tạp chí Tài chính, số tháng 11-2015, trang 60-64. Nguyễn Văn Tiến (2015) Nguyên lý và nghiệp vụ ngân hàng thương mại NXB Thống
Phan Thị Mỹ Hạnh & Tống Lâm Vy (2019) Các yếu tố ảnh hưởng đến rủi ro thanh khoản của hệ thống ngân hàng thương mại Việt Nam Tạp chí Nghiên cứu Tài chính – Marketing, số 51, 26-38.
Trương Quang Thông (2013) Các nhân tố tác động đến rủi ro thanh khoản của hệ thống
NHTM Việt Nam Tạp chí Phát triển Kinh tế, số 276, trang 50-62.
Vũ Thị Hồng, (2015) Các yếu tố ảnh hưởng đến thanh khoản của các ngân hàng thương mại Việt Nam Tạp Chí Phát Triển & Hội Nhập, Số 23 (33).
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Appendix A List of commercial banks in Vietnam in the research sample.
1 ABB An Binh Commercial Joint Stock Bank
2 ACB Asia Commercial Joint Stock Bank
3 BAB Bac A Commercial Joint Stock Bank
4 BID Joint Stock Commercial Bank For Investment And Development
5 BVB Viet Capital Commercial Joint Stock Bank
6 CTG Vietnam Joint Stock Commercial Bank For Industry And Trade
7 EIB Joint Stock Vietnam Export Import Commercial Joint Stock
8 HDB Ho Chi Minh City Development Joint Stock Commercial Bank
9 KLB Kien Long Commercial Joint –Stock Bank
10 LPB Lienviet Post Joint Stock Commercial Bank
11 MBB Military Commercial Joint Stock Bank
12 MSB Maritime Commercial Joint Stock Bank
13 NAB Nam A Comercial Join Stock Bank
14 NVB National Citizen Commercial Joint Stock Bank
15 OCB Ocean Commercial Joint Stock Bank
16 PGB Petrolimex Group Commercial Joint Stock Bank
17 SGB Saigon Bank For Industry And Trade
18 SHB Saigon –Hanoi Commercial Joint Stock Bank
19 SSB Southeast Asia Commercial Joint Stock Bank
20 STB Thuong Tin Commercial Joint Stock Bank
21 TCB Vietnam Technology And Commercial Joint Stock
22 TPB Tien Phongcommercial Joint Stock Bank.
23 VCB Joint Stock Commercial Bank For Foreign Trade Of Vietnam
24 VIB Vietnam International Commercial Joint Stock Bank
25 VPB Vietnam Prosperity Joint Stock Commercial Bank
TICKER YEAR L1 SIZE CAP LDR ROE NPL GDP INF
Appendix C Regression results with Stata 16.0
Variables statistics riable Va ob s
Mean std Dev Min Max
Sourc e ss f d MS Number ũf ũbs = 250
I Coef std Err t p> t| [95% Conf Interval]
71978586 (fracti on of variance due to u_i)
5 (fracti on of variance due to u_i)
Pooled-OLS, FEM, REM regression
0561476 b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test: Ho: diííerence in coefficients not systematic chi2(7) = (b-B)'[(V_b-V_B)-(-l)](b-B) = 33.77
Modified Wald test for gnoupbdise heteroskedasticity in íixed eííect regression model
H0: sigma(i)“2 = sigma A 2 for all i chi2 (25) = 82.16
xtserial LI SIZE CAP LDR ROE NPL GDP INF