HO CHI MINH, SEPTEMBER 2021 AND TRAINING HO CHI MINH CITY LÊ NGUYỄN QUỐC TOÀN MAJOR BANKING AND FINANCE DETERMINANTS AFFECTING THE LIQUIDITY OF COMMERCIAL BANKS THE STATE BANK OF VIETNAM BANKING UNIVE[.]
Objectives
The general objective is to determine the factors affecting the liquidity of commercial banks in Vietnam.
From there, propose some governmance implications to improve the liquidity and increase the competitiveness of Vietnamese commercial banks.
Firstly, the paper will research on theoretical basis of the liquidity of commercial banks from previous studies in Vietnam as well as all over the world.
Secondly, the paper will create an analysis model of factors affecting the liquidity of commercial banks for 2011- 2020.
Thirdly, the paper will determine specific impact of factors and the results of the empirical evaluation model of liquidity for the commercial banking in Viet Nam over the period 2011-2020.
Finally, the paper will provide the board of directors of commercial banks with specific factors affecting on financial performance It also proposes several solutions and management indications for the board of directors to improve the liquidity and competitiveness of commercial banks, and contributes to the development of banking system in the coming years.
Research questions
What factors affect the liquidity of commercial banks in Vietnam?
How are the factors affecting the liquidity of joint-stock commercial banks in Vietnam?
Proposing necessary policies or solutions to manage the liquidity of commercial banks in Vietnam in the most effective way?
Significant of the study
The financial system is largely dominated by commercial banks, which always plays an essential role in a national economy If finance is the lifeblood of trade, commerce and industry; the banking sector would be the backbone of modern business Commercial banks act as a financial intermediary and connect economic sectors together, which is an indispensable part in the organization of operations of all departments, all sectors in the economy In most countries, the banking industry is considered as an important area to monitor a smooth national economy Therefore, this sector is spent a great deal of attention and received the most close economic scrutiny by the government, especially in developing country such as Vietnam.
Currently, the Vietnamese banking system is facing many difficulties and challenges such as the world crisis, the inadequacies of the national economy as well as the weak and negative aspects of banking activities When the banking industry exposes many inadequacies, to stand firm in the volatile money market,Vietnamese commercial banks must promote efficiency in all business activities while still meeting liquidity requirements instant Therefore, the problem of all Vietnamese commercial banks is how to maintain liquidity at a stable level, develop sustainably, and endure in the context of international integration In addition, the evolution of the Covid-19 epidemic is increasingly complicated and unpredictable, but this is also an opportunity for banks to reassess their liquidity problems to find a solution.
However, the reality shows that in recent years, the banking industry has attracted special attention from many economic sectors and developed quite strongly with a rapid increase in the capital value of banks Therefore, banks will have more opportunities to lend large loans However, this also contains many potential risks It can be said that banking risks, especially liquidity, not only profoundly affect the safe operation of the bank itself, but also affect the operation of the entire financial system of that country For countries where the capital market has not developed as in Vietnam, the commercial banking system is the main source of capital for the economy The bank has good liquidity, or in other words, the bank does not face liquidity risk when it always has available capital at a reasonable cost at the right time when it needs it This means that if the bank does not have the necessary capital to meet all the needs of the market, it may become insolvent, lose credibility and lead to the breakdown of the whole system import of foreign banks, increasing competition among banks, affecting capital mobilization of previous banks, especially small banks, thereby affecting bank liquidity.
In terms of scientific significance, the study contributes to consolidating and expanding previous studies in determining the factors affecting the liquidity of commercial banks in Vietnam Influential factors include internal and external factors, helping to identify specific and overarching factors affecting liquidity. Some previous studies show that many factors are affecting the bank's liquidity, including internal factors such as capital efficiency, equity ratio, profitability, bank size, and external factors such as GDP, inflation, etc.
In addition to the factors mentioned in most of the previous studies, this study also addresses the impact of bad debt and loan-to-deposit ratio in the study At the same time, the study period of this study was 10 years (from 2011 to 2020), which is longer than previous studies, indicating a higher level of accuracy of the study.
In terms of practical significance, the research results will show the group of factors affecting the liquidity of Vietnamese commercial banks and the degree of influence of those factors This is a reference basis for banks in improving liquidity in line with Vietnam's economic conditions Recommendations to improve the liquidity of Vietnamese commercial banks.
Objects and Scope of study
The writer collects and does the statistics tabular data obtained from Vietnam Stock Exchanges and from the source of annual consolidated financial statements of Vietnam Joint Stock Commercial Banks The analysis is based on the statistical information and data from the financial result of 27 joint stock commercial banks with all types including: State owned commercial joint stock banks(Vietcombank, Vietinbank, ), non-state joint stock commercial banks(Sacombank, TPbank, ) for 10 years from 2011 to 2020 Because the source from Vietnam Stock Exchanges did not provide enough data as expected, the research sample obtained 270 observations.
Structure of paper
Beside table of contents, appendices and bibliography, research structure consists of 5 chapters:
Chapter 2: Theoretical framework and literature review:
Chapter 4: Empirical result and discussion
Chapter 5: Conclusion and management interpretation
In this chapter, the research report is organized under 5 chapters The first chapter provides a general overview of the study General information included in this chapter: research background, history of banking system in Vietnam,problem statement, research objectives, research questions, research methods,research scope, significant of the study and its structure The second chapter defines liquidity, how it is measured, and reviews the relevant literature on the determinants of bank liquidity The third chapter focuses on the research methodology and the chapter provides results and discussion The final chapter includes conclusions, recommendations and room for further studies and at the end references and appendices are attached.
THEORETICAL FRAMEWORK AND LITTERATURE REVIEW
Theoretical framework
As defined by the Bank for International Settlements (2008), “A bank's solvency is the ability of a bank to quickly raise capital and meet maturing needs without incurring losses”.
The Basel Committee on Banking Supervision said: “Liquidity is a specialized term for the ability to meet the needs of using available capital for business activities at all times such as payment deposits, loans, payments, capital transactions, etc.”
As such, liquidity is representative of the ability to perform all payment transactions when due, to the maximum extent, and in a specified currency For a bank, liquidity is considered from three angles, including the liquidity of assets, liquidity of capital, and liquidity of the bank.
An asset's liquidity is understood as its ability to convert to cash, measured in terms of time and cost An asset is highly liquid if it takes a short time and it costs low to convert it to cash.
Capital liquidity is a bank's ability to raise and expand the capital, as measured by the time and cost of expanding mobilized capital as needed The lower the time and cost of raising capital, the higher the liquidity.
A bank's liquidity is the ability of a bank to meet its financial obligations when they come due at a reasonable cost For commercial banks, liquidity is the ability to meet the payment, withdraw and apply for new loans according to the bank's valid credit request Thus, a bank is considered to have good liquidity if it can fully meet arising payment needs at reasonable costs, at the right time when customers or partners have needs.
According to Duttweiler (2009), two different aspects of liquidity need special attention, namely natural liquidity, and artificial liquidity Where natural liquidity means flows coming from an asset or liability but with a statutory repayment period In the banking sector, when a transaction with a customer is often repeatable, be it for the same amount or a smaller/larger amount, generally, this group of customers acts predictably This is true not only for assets but also for liabilities And artificial liquidity is created through the ability to convert assets to cash ahead of time Here it can be easy to convert particular security into cash, especially if there is still a company that wants to convert the securities into cash, the market can still accept the transactions.
From the perspective of bank managers: “Liquidity is the ability of a bank to fully and promptly meet financial obligations arising in the course of transaction activities such as payment of deposits, loans, accounting payments, and other financial transactions” (Nguyen Van Tien, 2012)
In addition, liquidity is the ability to access assets and capital that can be used to pay reasonable expenses as soon as capital needs arise Funds are said to be highly liquid when the funding costs are low and the deposit time is fast An asset is said to be highly liquid when its conversion cost is low and can be converted into money quickly Highly liquid assets include valuable papers such as treasury bonds, commercial papers, bills of exchange, etc., and other illiquid assets such as real estate, chains production, machinery, etc.
From the above concepts, we can come up with the basic concept of liquidity as follows: “Liquidity is the ability to convert an asset into cash quickly, with the lowest possible cost In other words, liquidity is the ability to access assets and capital at a reasonable cost to serve the different operational needs of the bank.
An asset is highly liquid when the cash cost is low and the time to cash is fast, while the capital is highly liquid when the cost of raising is low and the lead time is fast "
It can be seen that liquidity is not a specific amount or a certain rate, but liquidity is the expression of a bank's ability to fulfill its payment obligations The opposite of liquidity is “lack of liquidity”, meaning the bank cannot fulfill its payment obligations Thus, in this sense, liquidity represents a qualitative element of a bank's financial strength.
Liquidity supply is the amount of money that may or may not be available for a short time for a bank to use The components that make up the liquidity supply include additional customer deposits, service revenue, credit paid, asset sales, and money market borrowing In which, the most important source is an additional source from the customer's deposit, followed by refundable credit source and service revenue.
Liquidity demand is the amount a bank needs to pay immediately or in a short time The components that make up the liquidity need include customers withdrawing deposits, granting credit to customers, repaying loans, operating expenses and taxes, and paying cash dividends In which, the liquidity demand mainly comes from customers withdrawing deposits and providing credit to customers.
The Net Liquidity Position (NLP) is the difference between the total supply and the total demand for liquidity at any given time:
NLP > 0: The bank has to deal with a liquidity surplus, ie an interest-free cash surplus, the bank needs to determine whether it should be productively invested in this surplus.
NLP < 0: The bank is facing a liquidity deficit, i.e short of cash for payment, the bank needs to identify additional liquidity sources.
NLP = 0: When liquidity demand equals liquidity supply and demand, the bank reaches liquidity equilibrium But in reality, this is a rare case.
Assessing liquidity is an important and meaningful job for any commercial bank.
It is a frequent fact that the supply and demand for liquidity are rarely the same at any given time, which means that banks are often in a state of excess or lack of liquidity This requires managers to always capture and evaluate the bank's liquidity at any time in order to quickly make the right decisions to bring the best profit for the bank from the capital source surplus (in case the bank has excess liquidity), or find a timely source of capital for illiquid capital at a reasonable cost (in case of lack of liquidity) A good assessment of the liquidity position along with the timely decisions of the management helps the bank make good use of idle capital to seek profit On the other hand, it helps the bank to solve the liquidity problem well, improve the bank's liquidity and reputation and avoid the bank falling into liquidity risk.
From time to time, there are many different conceptions of liquidity But liquidity risk can be understood as the risk when a commercial bank becomes insolvent at a certain time or has to mobilize capital sources at a high cost to meet payment needs; or due to other subjective reasons make commercial banks insolvent, leading to slow operation, loss, loss of credit reputation and possible bankruptcy (Duttweiler, 2009).
Based on nature and demand, liquidity risk is divided into the following four groups:
Measurement of liquidity
Liquidity is the ability of a bank to finance an increase in assets and meet obligations as they come due without incurring unacceptable losses (BIS, 2008). Liquidity risk arises from the fundamental role of banks in the term conversion of short-term deposits into long-term loans It includes two types of risk: funding liquidity risk and market liquidity risk Funding liquidity risk is the risk that a bank will not be able to effectively meet both expected and unexpected current and future cash flow and collateral needs without impacting operations the day- to-day or financial status of the company Market liquidity risk is the risk that a bank cannot easily cover or remove a position at market prices due to insufficient market depth or market disruption.
According to The Basel Committee on Banking Supervision, liquidity is the ability to meet the demand of using usable capital for business activities at all times such as payment of deposits, loans, payments, and capital transactions.According to Duttweiler (2009), liquidity is a qualitative representation of a bank’s financial strength Liquidity consists of two aspects: natural liquidity and artificial liquidity Natural liquidity is a cash flows derived from assets and liabilities for a specified period of time, such as cash inflows from customers,customers repay bank loans, and revenue from providing service level Artificial liquidity is created from converting assets into cash before the maturity date.According to Rose (2002), liquidity is met by cash flows such as customers depositing money, issuing short-term valuable papers, and borrowing in the interbank money market (this cash flows are called liquidity supply from the capital source); selling securities, paying from customers, etc (these flows are called liquidity supply from assets) Hence, liquidity represents the ability to perform all maturity payment tasks.
According to Aspachs, Nier, and Tiesset (2005), there are three mechanisms that banks can use to insure against liquidity crises: (i) Banks hold a buffer of liquid assets on the asset side of the balance sheet A sufficiently large buffer of assets such as cash, balances with central and other banks, government-issued debt securities, and similar securities or counter-trades reduces liquidity needs can threaten the viability of the bank (ii) The second strategy is connected to the liabilities section of the balance sheet Banks can rely on the interbank market to borrow from other banks in case of liquidity needs However, this strategy is closely related to the liquidity risk of the market (iii) The final strategy also concerns the liabilities side of the balance sheet Central banks often act as Lenders of Last to provide emergency liquidity assistance to specific illiquid institutions and provide aggregate liquidity in the event of system-wide shortages.
Moulton (1918) also proposed the Possibility Theory, which suggests that commercial banks can hedge against liquidity risk by holding more liquid assets in their asset structure According to this theory, loans are the fundamental contradiction that is explained for liquidity problems This theory states that commercial lending will not guarantee the liquidity of commercial banks when there is a problem, the factors that can ensure liquidity are the ability to generate profits and accumulate capital, the ability to convert the property.
The Basel Committee on Banking Supervision (1997) states that liquidity risk is when banks cannot raise capital with assets or liabilities at the lowest cost.
Brunnermeier (2009) emphasizes that if banks do not manage liquidity risk appropriately, they will inevitably face liquidity shocks, often selling off illiquid assets and reducing lending For economy It can be seen that the liquidity issue of each commercial bank as well as of the whole banking system did not receive the attention of policymakers and bank administrators until the crisis global financial 2007-2009 occurred Therefore, it is essential to measure and give warnings about the possibility of systemic liquidity risk for the whole commercial banking system.
Liquidity risk can be measured using two main methods: liquidity gap and liquidity ratios The liquidity gap is the difference between assets and liabilities both now and in the future On any given day, a positive difference between assets and liabilities equates to a deficit (Bessis, 2009) The major limitation of this approach is the fact that only a few banks disclose their liquidity holes in their annual reports As a result, comparisons between a large number of banks are often not possible Liquidity ratios are different balance sheet ratios that should identify key liquidity trends These ratios reflect the fact that a bank must ensure that it has adequate capital at a low cost in the short term This may involve holding a portfolio of easily sellable assets (cash reserves, minimum required reserves, or government securities), holding a substantial amount of stable debt (especially deposits from retail depositors), or maintain lines of credit with other financial institutions.
Various authors such as Andries (2009); Aspachs et al (2005); Bunda &Desquilbet (2008); Ghosh (2010); Jiménez, Ongena, Peydró and Saurina (2010);Maechler, Mitra and Worrell (2007); Moore (2010); Praet & Herzberg (2008);Rychtárik (2009) or Tamarisa and Igan (2008) provide various liquidity ratios.For the purpose of this research we will use for evaluation of liquidity positions of Viet Nam commercial banks following four different liquidity ratios:
The liquidity ratio L1 provides information about a bank's overall liquidity shock absorption capacity Cash, balances with central and other banks, government- issued debt securities, and similar securities or trades against are liquid assets As a general rule, the higher the ratio of liquid assets to total assets, the higher the ability to absorb liquidity shocks since market liquidity is the same for all banks in the sample However, a high value of this ratio can also be interpreted as ineffective Since liquid assets provide low income, liquidity leads to high opportunity costs for banks Therefore, it is necessary to optimize the relationship between liquidity and profitability.
L2 = Liquid assets / (Deposits + Short-term borrowing)
The liquidity ratio L2 also uses the concept of liquid assets However, this ratio focuses more on a bank's sensitivity to selected funding types (we included deposits from households, businesses and banks, and financial institutions). capital and capital from debt securities issued by banks) Therefore, the L2 ratio should capture the vulnerability of the bank with these sources of funding The higher the value of this ratio, the higher the liquidity shock absorption capacity.
The liquidity ratio L3 is very similar to the liquidity ratio L2 However, it only includes household and business deposits In contrast to the L2 ratio, the L3 ratio measures the liquidity of a bank assuming that the bank cannot borrow from other banks in the event of a need for liquidity This is a relatively strict measure of liquidity, but it allows us to capture at least a portion of the market's liquidity risk The bank can meet its capital obligations (current assets high enough to cover volatile capital) if the value of this ratio is 100% or more A lower value indicates an increased sensitivity of the bank regarding deposit withdrawals.
L4 = Loans / ( deposits + short term capital)
The last liquidity ratio L4 relates illiquid assets with liquid liabilities the higher this ratio the less liquid the bank is.
The disadvantage of these liquidity ratios lies in the fact that they do not always capture all, or any of liquidity risk However, there are still in common use It is possible to calculate them only on the basis of publicly available data from bank balance sheets and it is easy to interpret their values.
Specific factors affecting commercial banks’ liquidity
Economic growth (GDP): Gross domestic product (GDP) is an indicator of the health of an economy Banks tend to hold more liquidity during recessions due to lending risk In contrast, during periods of economic growth with higher interest rates, banks reduce their liquidity to increase lending more, resulting in less liquid assets Fola (2015), Bunda and Desquilbet (2008) report that economic growth is positively related to liquidity, while the studies by Valla et al(2006), and Vodova (2011, 2012) find a negative relationship between these two variables.
Inflation (INF): Perry (1992) found that the effect of inflation on a bank's liquidity depends on the bank's expectations for inflation soon If inflation is expected to rise, banks will adjust interest rates to increase interest income faster than interest expenses Banks do not always have accurate forecasts about information This failure not only increases costs and reduces the bank's net profit, but also creates challenges in raising capital Bunda and Desquilbet
(2008), Vodova (2011, 2012), and Fola (2015) report a positive relationship between inflation and liquidity risk.
ROE reflects the efficiency of the bank's use of equity Previous research has shown a positive relationship between this index and liquidity position (Bunda,
2008) Some studies such as Lucchetta (2007), Valla et al (2006), and Vodova
(2011, 2012) also find a positive correlation between net profit margin and liquidity position However, many other studies have discovered a negative effect of ROE on bank liquidity (Aspachs et al., 2005; Lucchetta, 2007; Vodová, 2011). When banks increase lending to customers or invest in assets that are considered highly profitable, ROE will increase, but these are considered low-liquid assets. Research results show that the higher the ROE, the lower the bank's liquidity.
The main reason banks hold capital is to absorb risks - including liquidity risk, protection against bank operations, and many other risks Another risk, the most important being credit risk Although the reason why banks hold capital is motivated by their risk-converting role, recent theories suggest that bank capital can also affect a bank's ability to create liquidity These theories make conflicting predictions about the link between capital and liquidity creation.
Gorton and Winton (2000) indicate that higher capital ratios can reduce liquidity through another effect: overcrowding of deposits They argue that deposits are more effective liquidity protection for dealers than investments in bank equity. Indeed, deposits are fully or partially insured and withdrawable at face value In contrast, bank capital is ineligible and has a random value that depends on the underlying state of the bank and the liquidity of the stock market Thus, a higher capital ratio moves investors' funds from relatively liquid deposits to relatively illiquid bank capital Thus, the higher the bank's capital ratio, the lower the bank's liquidity.
According to the alternative “risk absorption” hypothesis, which is directly related to the role of banks in risk transformation, higher capital will enhance the liquidity creation of banks This insight is based on two parts of the document. One group includes articles that argue that creating liquidity puts banks at risk (e.g., Diamond and Dybvig 1983, Allen and Gale 2004) The more liquidity is created, the greater the likelihood and severity of losses associated with having to dispose of illiquid assets to meet the liquidity needs of clients The second section includes papers claiming that bank capital absorbs risk and expands a bank's risk tolerance (e.g Repullo 2004 and Thaised 2004) The combination of these two factors gives rise to the prediction that higher capital ratios could allow banks to generate more liquidity.
Bad debts (NPLs) are loans where a bank's customers fail to meet their contractual obligations for principal or interest payments over 90 days (Ghafoor,
2009) Bad debts are loans that have a negative impact on banks in developing the economy The growth of a bad debt portfolio contributed significantly to financial distress in the banking sector In particular, a high bad debt ratio can affect credit supply, reduce customer confidence, lead to customers withdrawing money massively due to customers' concerns about the bank's liquidity, leading to liquidity needs on a large scale Therefore, previous studies by Barr et al. (1994); Bloem and Gorter (2001); Lucchetta (2007); Vong and Chan (2009); Sabri et al (2020) show a negative relationship between bad debt ratio and bank liquidity.
Profitability explains the impact of better financial soundness on banks' risk tolerance and their ability to perform liquidity conversions (Rauch et al 2008 and Shen et al 2010) It has been suggested that the longer the loan volume lasts, the higher the interest income and thus the profitability potential for commercial banks At this point, it should be noted that banks with large loan volumes will also face higher liquidity risks Therefore, commercial banks need to balance between liquidity and profitability In contrast, when banks hold high liquidity, they do so at the opportunity cost of an investment, which can generate high returns (Kamau 2009) The trade-off that often exists between return and liquidity risk is evidenced by the observation that switching from short-term securities to long-term securities or loans increases a bank's profitability but also increases its profitability liquidity risk and vice versa are true Therefore, a high liquidity ratio indicates a less risky and less profitable bank (Hempel et al 1994).
Bank size is considered by many researchers to be an important factor and has a negative impact on bank liquidity (Kiyotaki & Moore, 1997; Holmstrom & Tirole, 1998; Kashyap et al., 1997); Vodová, 2011; Anamika & Anil, 2016). Their results show that small commercial banks often have a disadvantage in accessing capital markets, so they tend to hold more liquid assets In contrast, Valla et al (2006), Lucchetta (2007), and Vodova (2011, 2012) argue that the larger the total assets, the lower the liquidity risk Big banks can have many advantages In other words, it will be easier for these banks to raise capital from customers, lend on the interbank market, or get support from the central bank as a last resort In particular, these authors report that large commercial banks often enjoy hidden advantages and low capital costs Therefore, these banks often invest heavily in many risky projects, especially providing loans with high risk but also high expected returns In general, the liquidity risk of large banks is also higher than that of smaller banks Practically applied in Vietnam, it can be seen that there is a positive correlation between size and liquidity.
2.3.2.6 Lending on the deposit interest rate
Lending is a key asset of a bank, accounting for the largest proportion and generating the largest revenue on the financial statements of any bank, but it is an illiquid asset Vu's research (2012) suggests that banks that lend most of their short-term funds will finance less liquid assets In addition, the loans of commercial banks, on the balance sheet, are mainly loans to customers. Therefore, the higher this ratio, the worse the liquidity of the bank.
In Vietnam, a negative relationship was also found between LDR and bank liquidity from April 2012 to October 2015 The liquidity position of each bank was measured by determining the difference between money deposit and credit operations The LDR rate was high in April 2012, up to 95% Poor liquidity throughout the system From April 2012 to April 2013, the LDR dropped sharply to 86% It is easy to see that the liquidity position has improved and moves in the opposite direction to the LDR coefficient.
Figure 1: Specific factors affecting commercial banks’ liquidity overview of the liquidity policy determinants of UK banks In addition, it also explores the relationship between macroeconomic policies, especially Central Bank policy, and the business cycle, and how it affects the level of support (Liquidity Buffer) It is certain that the Central Bank will play an extremely important role in maintaining liquidity, they can provide capital in the event of a liquidity crisis of a commercial bank as a central lender final judgment This study uses data from balance sheets and quarterly income statements, from 1985 to 2003.
Later, Valla and Escorbiac (2006) also presented the results of their study However, this study also basically focuses on some internal and macro factors affecting the liquidity of UK banks as studied by Aspachs et al (2005) This study examines the determinants of bank-specific liquidity and macroeconomic factors on the liquidity of
UK banks They believe that liquidity depends on the following factors: Probability of final loan support, loan growth, gross domestic product growth, short-term interest rates; and bank profitability is negatively correlated with liquidity Conversely, bank size can be negatively or positively correlated with liquidity.
Luchetta (2007) argues that the risk-free rate of monetary policy has a negative impact on the bank's liquidity decision and lending decision in the interbank market, while the interbank rate has positively affected these decisions made by the bank The study uses unbalanced data of 5,066 European banks from the BankScope database from 1998 to
Literature review
3.1 Data collection methods and techniques
Firstly, in terms of analytical methods, to analyze the liquidity of the banking system and identify the factors affecting the liquidity as well as propose a model of the factors affecting the liquidity of banks in Vietnam, the author uses a quantitative research method According to this approach, based on the theoretical model, the research paper collects actual survey data of 27 Vietnam Joint Stock Commercial Banks Since the source from the Stock Exchange of Vietnam did not provide enough data as expected, the research sample collected 270 observations.
Secondly, in terms of data sources, with secondary data sources, the paper collects data from financial reports, websites of banks, reports, books, journals, theses, and related documents to the bank's performance, the website of the State Bank, the Ministry of Finance, the Tax Department, and the main data source For quantitative research, the paper uses primary data collected by direct survey questionnaires with bank employees and through consultation with bank managers.
About data collection method: most of the data used in this study is taken from Vietnam Stock Exchanges, annual consolidated financial statements, and annual reports of banks Vietnam Joint Stock Commercial Bank for the period 2011 to 2020 The final dataset includes observations for 27 banks This process is repeated for each year, 2011-2020.
Regarding variables, internal data to study the model are collected from the consolidated financial statements and annual reports (secondary data is used to calculate the indicators representing the internal factors) ) of 27 commercial banks in the period 2011-2020, including equity capital, total assets, total loans,deposits, bad debts (groups 3, 4, 5), liquid assets (cash, deposits at the State Bank and
DATA AND METHODOLOGIES
Data collection methods and techniques
Firstly, in terms of analytical methods, to analyze the liquidity of the banking system and identify the factors affecting the liquidity as well as propose a model of the factors affecting the liquidity of banks in Vietnam, the author uses a quantitative research method According to this approach, based on the theoretical model, the research paper collects actual survey data of 27 Vietnam Joint Stock Commercial Banks Since the source from the Stock Exchange of Vietnam did not provide enough data as expected, the research sample collected 270 observations.
Secondly, in terms of data sources, with secondary data sources, the paper collects data from financial reports, websites of banks, reports, books, journals, theses, and related documents to the bank's performance, the website of the State Bank, the Ministry of Finance, the Tax Department, and the main data source For quantitative research, the paper uses primary data collected by direct survey questionnaires with bank employees and through consultation with bank managers.
About data collection method: most of the data used in this study is taken from Vietnam Stock Exchanges, annual consolidated financial statements, and annual reports of banks Vietnam Joint Stock Commercial Bank for the period 2011 to 2020 The final dataset includes observations for 27 banks This process is repeated for each year, 2011-2020.
Regarding variables, internal data to study the model are collected from the consolidated financial statements and annual reports (secondary data is used to calculate the indicators representing the internal factors) ) of 27 commercial banks in the period 2011-2020, including equity capital, total assets, total loans,deposits, bad debts (groups 3, 4, 5), liquid assets (cash, deposits at the State Bank and bad debt, profitability, bank size, loan-to-total deposit, economic growth index,inflation index All variables have 270 observations.
Research Models
Based on the above theory and analysis, the basic model that is used to consider the components of the payment risk problem of commercial banks is proposed by the author as follows:
LIQ i,t = a o + β 1 ROE i,t + β 2 CAP i,t + β 3 NPL i,t + β 4 EA i,t + β 5 BS i,t + β 6 LDR i,t + β 7 GDP i,t + β 8 INF i,t +ε i,t Dependent variable
The author uses LIQ as the dependent variable to measure the liquidity of commercial banks This variable is measured using total liquid assets divided by total deposit balance When interest rates rise, some depositors withdraw their capital from banks to invest with higher rates of return This leads to a decrease in the deposit balance in the bank and the bank does not have enough capital to meet the loan credits Over time, the lack of liquidity will lead to loss of reputation as well as reduced profitability of commercial banks.
ROE variable: Efficiency in using equity To evaluate the impact of the efficiency of equity use or return on total equity on the bank's liquidity, the author uses the ratio of profit after tax to total equity Bank profits are mainly generated from traditional business, i.e 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; Vodová, 2011; in this study: The higher the efficiency of using equity, the lower the liquidity of the bank.
CAP variable: Equity ratio To examine the effect of the equity ratio on the liquidity of the bank, the author uses the equity to total assets ratio to measure the equity ratio The equity ratio represents the financial capacity of commercial banks Banks with this ratio higher than the industry average must have a better financial capacity in mobilizing, lending, and ensuring solvency The banking business today faces many risks These risks when occurring can cause great damage, in the worst case can cause the bank to go bankrupt At that time, a bank with strong equity will help cover the losses incurred and ensure the bank avoids the above risk In some cases where the bank is insolvent, its own capital will be used to repay customers The equity ratio has an inverse and significant relationship with liquidity risk (Ahmed et al., 2011; Vodová, 2011), meaning that increasing this ratio will reduce the liquidity risk of banks Commerce.
Hypothesis H2 is: Equity ratio has a positive effect on bank liquidity.
NPL variable: Bad debt To assess the impact of bad debt on the bank's liquidity, the author uses the ratio between the total bad debt (groups 3, 4, 5) on total outstanding loans The United Nations Statistics Office considers that debt is essentially considered bad debt when it is 90 days past due in interest and/or principal; or unpaid interests of
90 days or more that have been paid principal, refinanced, deferred as agreed, or are overdue for less than 90 days but there are good reasons to doubt the ability to repay the loan Full payment Thus, bad debt significantly affects creditors as well as banks, putting both at risk of capital loss Therefore,previous studies by Lucchetta (2007); Iqbal (2012); Vong and Chan (2009) both show a negative correlation between the bad debt ratio and the bank's liquidity However, liquidity.
EA variable : Profitability The author uses the ratio of net interest income to average interest-bearing assets to evaluate the impact of profitability on a bank's liquidity. According to the Pecking Order theory, commercial banks with high profitability will prioritize retaining profits to increase capital, limiting the increase in external funding sources, and accordingly, their liquidity will be improved benevolent This is similar to the results in Mahmood et al (2019), Cuinelli (2013), Vodová (2013) If the bank generates high profits along with maintaining good liquidity, it is the basis for achieving the goal of increasing asset value for owners The author hypothesizes H4: Profitability has a positive relationship with bank liquidity.
BS Variable: Bank Size Since the size of banks is often non-linear, we use the base 10 logarithms of total assets to measure the BS variable In terms of economies of scale, the larger a bank's total assets, the less liquidity a bank has Large banks can rely on the interbank market or liquidity support from the SBV (Vodová, 2013) In fact, commercial banks with abundant capital will have a development strategy to maximize profits and have the opportunity to lend large loans, which leads to risks, including liquidity risk However, if commercial banks have a large capital scale, they will be better able to support and overcome liquidity risks for small-scale commercial banks Therefore, the capital size of commercial banks affects their liquidity The study of Akhtar et al (2011) and Ahmed et al (2011) found a positive and strong relationship between a bank's size and its liquidity, i.e the larger the total assets, the lower the liquidity risk In contrast,Abdullah and Khan (2010) find an inverse relationship between this variable and liquidity, meaning that the higher the bank's total assets, the greater the liquidity risk InVietnam, usually, when banks have large total assets, it will have a positive impact on
LDR variable: Lending on the deposit interest rate The author uses the ratio of total loans to total deposits to measure the LDR variable The higher this ratio, the higher the bank's loan capital compared to the mobilized capital Therefore, when facing difficulties, banks will not have enough liquidity to meet lending needs and it will also be difficult to mobilize small capital sources if they lend too much On the contrary, if this ratio is low, it means that banks lend less than mobilized capital or may have other sources such as interbank loans, securities issuance, etc Lower mobilization makes the liquidity of banks increased (Golin, 2011) Previous studies by Aspachs et al (2003); Bonfim and Kim (2011); India (2004); Golin (2001) all show a negative correlation between the loan-to-deposit ratio and the bank's liquidity Therefore, the author hypothesizes H6: Loan-to- deposit ratio and liquidity have an inverse relationship.
GDP variable: Economic growth The annual real GDP growth rate is used to measure the GDP variable The economic situation has a close relationship with businesses in general and banks in particular Theoretically, banks should keep more liquidity during economic downturns, when lending is riskier; Conversely, during periods of economic growth, banks tend to reduce liquidity reserves so that they can lend more, while deposits may decrease, thereby increasing liquidity risk (Chung-Hua) Shen et al., 2009) Dinger (2009) states that holding liquid assets is negatively related to economic growth In addition, Khemais et al (2017) conclude that economic growth has a negative impact on the liquidity of Tunisian banks The increase in Tunisian household income has allowed them to sign up for financial services such as consumer loans, home loans, etc This increases bank lending and increases the bank's liquidity risk From there, the author hypothesizes H7: Economic growth has a negative impact on the liquidity of banks.
INF variable: Inflation This is an index that is measured by the annual inflation inflation is at all expected, banks can adjust interest rates to increase interest income faster than interest expenses As a result, banks can increase lending, while due to competitive pressure, funding activities may decrease, thereby increasing liquidity risk Research by Vodová (2011), Malik (2013), Truong Quang Thong, and Pham Minh Tien (2014) also shows that the degree of inflation change has a negative impact on liquidity risk When the economy suffers from inflation, banks tighten credit As a result, banks lend less, gradually reducing long-term investments and increasing liquid assets Therefore, the author hypothesizes H8: Inflation has a positive effect on bank liquidity.
Dependent variable LIQ Total liquid assets/
CAP Equity/Total assets Equity ratio H2: +
Total non – performing loan (group 3, 4, 5)/Total outstanding loans
Net interest income/Average earning assets
BS Log(Asset) Bank size H5: +
LDR Total outstanding loans/Total deposits
The annual inflation rate through the CPI
Data analysis methods and techniques
The research analysis method used is a regression with panel data The regression method with panel data has been implemented in many previous studies such as the study of Dietricha and Wanzenried (2011), Ali et al (2011), Alper and Anbar (2011), Nguyen Cong Tam and Nguyen Minh Ha (2012) The study used descriptive statistics to preliminarily analyze the basic information from the sample, analyze the phenomenon of multicollinearity, autocorrelation To determine the correlation between the independent variables and the dependent variables, the study estimates the regression parameters for the model of factors affecting the liquidity of commercial banks with the ordinary least squares ( OLS), impact model with fixed effects model ( FEM), and random effects model ( REM) to get the best equation showing the relationship of factors The research paper will be implemented in the following order:
Based on data collection, the data of variables are calculated and coded on EXCEL software, then entered into STATA 14 software to perform descriptive statistics The content of descriptive statistical analysis is a collection of characteristics of data that reflect in general the number of observed samples, mean value, maximum value,minimum value, standard deviation, It can be seen that a more general view of the liquidity of commercial banks in Vietnam today. in the model, considering the correlation between the independent and dependent variables, as well as between the dependent variables The study also determined the importance of each factor because they were used simultaneously in the model and removed the relationship between the factors if necessary The study applied correlation analysis to determine the relationship between explanatory factors This analysis is based on the correlation matrix, the main purpose is to examine the change of the independent variables to the dependent variable and ensure a multicollinear relationship between the independent variables.
(iii) Verification of multicollinearity phenomenon:
Multicollinearity is a phenomenon in which the independent variables in the model are linearly correlated with each other, which means that each variable contains some information about the dependent variable and that information is contained in another independent variable The phenomenon of multicollinearity in the model will be checked by the correlation coefficient between the independent variables and the variance exaggeration factor (VIF) If the correlation coefficient between the independent variables is greater than 0.8 (the comparison standard according to Farrar & Glauber (1967) is 0.8), it will lead to multicollinearity,but this criterion is often inaccurate and there are cases The correlation coefficient is quite low, but multicollinearity still occurs Therefore, to limit the error as well as ensure the stability of the model, the research will conduct further testing by analyzing the variance magnification factor (VIF) using the "vif" command in STATA If in the test results, the VIF of the variable greater than 10 is detected (Gujarati, 2003), the study will remove the independent variables greater than 10 After that, the study will continue to execute the "collin" command in STATA with another independent variable, check the VIF and continue to remove the variable until the VIF of all
After completing descriptive statistics and analyzing the correlation between variables, the study will estimate the model This is an important step in the data processing The author runs ordinary least squares regression (OLS) However, the estimates of the OLS regression method will be affected if there are defects, individual features, entities, and thus it will strangle the actual relationship between the dependent variable and the independent variable, the estimated results may be biased Therefore, the research uses two more estimation methods: fixed effects model (FEM) and random effects model (REM).
This is an OLS model that estimates each observation with equal importance But an estimator feeds this information into the model, and so there are cases when it can provide the least biased linear estimates for the model (BLUEs) Transforming the variables initially satisfying the assumptions of the classical model and then applying the OLS method to them is known as the ordinary least squares (OLS) estimation method In summary, the OLS for the variables was transformed to satisfy the standard least squares hypotheses.
(vi) Fixed effects model (FEM):
According to Gujarati (2003), the FEM model assumes that each entity (banks) has its own (time-constant) characteristics, which can independently affect the variables,and that there is a correlation between them rest of each entity (containing its characteristics) with independent variables FEM can control and isolate the effect of these discrete features (time constants) from the dependent variables so that the real impact of the independent variables on the dependent variables can be estimated.These distinct characteristics are unique to one entity and do not correlate with the
The difference between REM and FEM is expressed in the volatility of the entities.
In REM, the volatility of entities is assumed to be random and uncorrelated with the independent variables Therefore, if the differences between entities affect the dependent variable, then FEM will be more appropriate than REM In REM, the remainder of each entity (which is not correlated with the explanatory variable) is treated as a new explanatory variable.
(viii) Check the heteroskedasticity test:
The study uses the Breusch and Pagan Lagrangian Test with the "xttest0" statement to test the autocorrelation with the hypothesis:
H 0 : The model has no change in variance
H 1 : The model has a phenomenon of variance.
If the test results show that the P-value < 0.05, the hypothesis H 0 is accepted and the REM model is selected as the most suitable model If P-value > 0.05, the hypothesis H 0 is rejected, the model has autocorrelation defects and the research will choose the OLS model as the model for regression However, if the REM model is chosen, the error variance phenomenon has self-destructed.
(ix) Testing of regression hypotheses of the Hausman test:
The purpose of the Hausman test for the research model is to select the appropriate model The choice of model depends on testing to see which model is more suitable for the research data sample To choose the REM or FEM model, the study uses the Hausman test to show whether it exists a similar correlation between εi, t and thei, t and the independent variables with the hypothesis pair:
When the P-value < 0.05, we reject H 0 , then i, t, and the independent variable are correlated and a fixed effects model will be applied In contrast, the study used the random effects model as the final model for regression.
Finally, discussing research results: The research hypotheses will be tested through the research data of the built regression equation The test standard uses t- statistics and p-value (Sig.) respectively, the reliability is taken according to the standard 95%, the p-value will be compared directly with the value of 0.05 to conclude acceptance agree or reject the research hypothesis To consider the data fit and the model fit, we use R-square coefficient, t-statistic and F-statistic to test To evaluate the importance of factors, it is considered that the corresponding Beta coefficient (β) in the regression) in the regression equation built from research data.
From the experimental results of the research model, the author will discuss and make comments on the impact of independent variables on the of commercial banks in Vietnam on the basis of theory, the views of the previous research.
In this chapter, the researcher presented the sources of data collected The researcher used data of 27 commercial banks in Viet Nam in this study In this chapter, the researcher also discussed the method used to analyse the data and to determine the measurement testing to provide the empirical results of the study The next chapter will discuss the data analysis, present the results of the regression model,discuss major findings and make recommendations for future research.
Table 2 The results of the descriptive statistics of the variables in the research model
Variable Obs Mean Std Dev Min Max
Source: Processing research data by using Stata 14.0
The dependent variable of the model is liquidity (LIQ), with a mean value of 0.3629, which shows that total liquid assets over total deposits of sampled banks are about 36.29% In addition, the minimum liquidity value is 0.0891 And the maximum value recorded is 0.9125 In general, the liquidity of banks in Vietnam is considered to be relatively high compared to banks in India in the study by Al- Homaidi et al (2019) and banks in Ethiopia in Assfaw's study (2019).
For factors inside the bank, the variable equity efficiency (ROE) has an average value of 0.0070 The smallest value is a negative number, namely -0.0551 in TPB
(2011), while the largest value is 0.0299 in TCB (2020) Also, the standard deviation is 0.0071.
The equity Ratio (CAP) has an average value of 0.1320; in which the highest value is 0.3837 belongs to ABB (2011), the smallest value is 0.0262 belongs to TPB (2012). The average bad debt variable (NPL) is 2.11%; the lowest value of 0% belongs to
10 banks in different years due to not providing enough information in the annual report Meanwhile, the highest value of 8.80% belongs to SHB (2012).
Profitability (EA) averaged 2.89%; in which the smallest value is -0.75% (TPB,
2011) and the highest value is 8.77% (VPB, 2019).
The variable bank size (BS) has an average value of 14,1109, the largest value is 15.1953 belongs to AGR (2020).
The LDR ratio is the loan-to-deposit ratio, averaging 0.8582; in which BAB reached the maximum value of 1.8050 (2011).
EMPIRICAL RESULTS AND DISCUSSIONS
Regression results
Table 5: Regression results of research model LIQ
ROE β) in the regression- coefficient 3.9445** 2.7732** 2.8754*** t-Statistic 2.32 2.48 2.6
CAP β) in the regression- coefficient -0.1452 0.0383 0.0143 t-Statistic -0.81 0.22 0.09
NPL β) in the regression- coefficient -1.0919 -0.5736 -0.5973 t-Statistic -1.63 -1.25 -1.32
EA β) in the regression- coefficient -1.1968 -1.6849** -1.5960* t-Statistic -1.19 -1.98 -1.94
BS β) in the regression- coefficient 0.0168** 0.0056 0.0067 t-Statistic 2.47 1.09 1.33
GDP β) in the regression- coefficient -0.9578 -1.2596 -1.1983 t-Statistic -0.53 -1.08 -1.04
INF β) in the regression- coefficient 1.6612*** 1.5159*** 1.5303*** t-Statistic 8.15 11.57 11.82
Coefficient β) in the regression- coefficient 0.4145 0.4784 0.4698 t-Statistic 2.68 4.53 4.39 n 270 270 270
Source: Processing research data by using Stata 14.0
Table 5 is the result of a regression model explaining the impact of factors affecting liquidity of Vietnamese commercial banks as measured by LIQ respectively by OLS,FEM, REM methods The study will examine the defects of the OLS model to draw
(i) The results of the regression model explain the effects of factors affect the liquidity of Vietnamese commercial banks as measured by the corresponding LIQ according to the OLS method :
The results of the OLS model presented in Table 5 show the statistical significance between efficiency of equity (ROE), bank size (BS), loan-to-deposit ratio (LDR) and inflation (INF) on liquidity (LIQ) in the research scope of Vietnam's commercial banking industry from 2011 - 2020.
LIQ i,t = 0.4145 + 3.9445ROE i,t + 0.0168BS i,t – 0.2970LDR i,t + 1.6612INF i,t + εi, t and the i,t
Regarding LIQ model result, the variable ROE (efficiency of equity), BS ( bank size) and INF (Inflation) have a positive impact on LIQ and have statistical significance at 5% and 1% level of significance It means that when the variable efficiency of equity (ROE), bank size (BS) and inflation (INF) increases by 1%, LIQ will increase by 394.45%, 1.68% and 166.12% respectively.
Variables LDR ( loan-to-deposit ratio) has the negative effect on LIQ and have statistical significance at 1% level of significance It means that loan-to-deposit ratio (LDR) increase by 1% , LIQ will reduce by 29.70%.
Variables CAP ( equity ratio), NPL ( bad debt), EA ( profitability) and GDP ( economic growth) have p- value of 0.417, 0.103, 0.233 and 0.594 respectively more than 10% level of significance, so CAP ( equity ratio), NPL ( bad debt), EA
( profitability) and GDP ( economic growth) have no statistical significance the impact on the change of LIQ.
(ii) The results of the regression model explain the effects of factors affect the liquidity of Vietnamese commercial banks as measured by the corresponding LIQ significance between efficiency of equity (ROE), profitability (EA), loan-to-deposit ratio (LDR) and inflation (INF) on liquidity (LIQ) in the research scope of Vietnam's commercial banking industry from 2011 - 2020.
LIQ i,t = 0.4784 + 2.7732ROE i,t – 1.6849EA i,t – 0.1740LDR i,t + 1.5159INF i,t + εi, t and the i,t
Regarding LIQ model result, the variable ROE (efficiency of equity) and INF (Inflation) have a positive impact on LIQ and have statistical significance at 5% and 1% level of significance It means that when the variable efficiency of equity (ROE) and inflation (INF) increases by 1%, LIQ will increase by 277.32% and 151.59% respectively.
Variables EA ( profitability) and LDR ( loan-to-deposit ratio) have the negative effect on LIQ and have statistical significance at 5% and 1% level of significance It means that, profitability (EA) and loan-to-deposit ratio (LDR) increase by 1% , LIQ will reduce by 168.49% and 17.40% respectively.
Variables CAP ( equity ratio), NPL ( bad debt), BS ( bank size) and GDP
( economic growth) have p- value of 0.823, 0.211, 0.278 and 0.283 respectively more than 10% level of significance, so CAP ( equity ratio), NPL ( bad debt), BS ( bank size) and GDP ( economic growth) have no statistical significance the impact on the change of LIQ.
Source: Processing research data by using Stata 14.0 (Note: YES, NO represent for having and without defects respectively)
Table 6, checking the autocorrelation phenomenon of the OLS model, the coefficient is Prob less than 0.05, so it is concluded that the model has autocorrelation defects.
Table 7 Breusch and Pagan Largrangian test to check the heteroskedasticity and choose OLS or REM
Source: Processing research data by using Stata 14.0
(Note: YES, NO represent for having and without defects respectively)
The study will use Breusch and Pagan Lagrangian Test to check the heteroskedasticity and select one of two models, OLS and REM According to the results in Table 7, the coefficient is Prob less than 0.05, the study decided to choose the REM model instead
However, the model has defects in autocorrelation and heteroskedasticity ( Prob less than 0.05), which will render the estimates obtained by conventional regression on panel data ineffective and the tests invalid The study will compare and select one of two models, FEM and REM to overcome the defects to ensure that the obtained estimates are stable and effective.
Table 8 Hausman test to select REM or FEM
Source: Processing research data by using Stata 14.0
Next, the study will use the Hausman test to choose one of the remaining two models, FEM and REM According to Table 8, the Prob coefficient is greater than 0.05 (accepting the hypothesis Ho), the study will use the REM model as a research model to assess the impact of factors affecting the liquidity of commercial banks inVietnam.
Source: Processing research data by using Stata 14.0 (*, **, *** significance at 10%, 5% and 1% respectively)
Table 9 presents the regression results of the model measuring the relationship of factors affecting the liquidity of commercial banks according to the REM estimation method. The results of the defect test will be presented in the appendix.
The estimated results of the model presented in Table 9 show the statistical significance between efficiency of equity (ROE), profitability (EA), loan-to- deposit ratio (LDR) and inflation (INF) on liquidity (LIQ) in the research scope of Vietnam's commercial banking industry from 2011 - 2020.
LIQ i,t = 0.4698 + 2.8754ROE i,t – 1.5960EA i,t – 0.1862LDR i,t + 1.5303INF i,t + εi, t and the i,t
Regarding LIQ model result, the variable ROE (efficiency of equity) and INF (Inflation) have a positive impact on LIQ and have statistical significance at 1% level of significance It means that when the variable efficiency of equity (ROE) and inflation (INF) increases by 1%, LIQ will increase by 287.54% and 153.03% respectively.
CONCLUSION AND MANAGEMENT INTERPRETATION 61 5.1 Conclusion
Recommendations
The SBV needs to seriously and further strengthen credit management at Vietnamese commercial banks Specifying the ratio of loans to total mobilized capital, and the problem of bad debt handling also needs to be dealt with more drastically This issue has been concretized by Circular 36/2014/TT-NHNN issued by the State Bank of Vietnam on November 20, 2014, to stipulate limits and ratios of capital adequacy in the operation of financial institutions credit bureau The SBV must strengthen the inspection and supervision of the provisioning for risks, the compulsory reserve of liquidity reserve, to ensure that the bad debt of some banks does not affect the entire system and the whole economy as a whole economy.
Diversify forms of mobilization and lending: Actively source financial resources,limit imbalances in borrowing and borrowing activities, and limit liquidity risks.The SBV needs to further diversify its refinancing tools, and at the same time encourage commercial banks to increase the development of derivative financial products to help commercial banks reduce concentrated profits from credit and attract capital deposits from residents and other institutions For small commercial banks,which do not have enough valuable papers or are not able to compete in the open market, the SBV provides support through refinancing tools This support from theState Bank is very short-term and commercial banks are required to adjust their capital structure and use capital sources accordingly.
Increase the investment rate for highly liquid assets, narrow credit when the bank falls into a weak liquidity situation and other sources of liquidity are inaccessible Or granting credit in cases where the bank has too many liquid assets and the bank itself incurs excessive opportunity costs from these assets.
Balance the structure between deposit mobilization and lending Avoiding the case where too much mobilized capital is not found, will greatly affect the bank's liquidity.
In addition, banks need to choose a credit growth policy that corresponds to the mobilization capacity and deposit term to reduce the term difference between deposits and loans.
Regarding the increase of charter capital, banks need to develop a balanced policy in the process of distributing financial results to pay dividends to shareholders and retain an appropriate portion of profits to supplement equity.
The Board of Directors of commercial banks should perform well in the management of liquidity differences and issues related to interest rate risk. Commercial banks need to improve regulations related to deposits and loans (especially deposits and medium and long-term loans) It is necessary to find a scientific solution to prevent customers from depositing and withdrawing money before maturity when market interest rates are high or when other competitors offer higher interest rates that are more attractive to customers In addition, managing the maturity mismatch between a bank's liabilities and assets is an important part of effective liquidity management.
Strengthening the liquidity risk management capacity of the bank's staff: Focusing on human resource training, in-depth training with core staff, build a team of good experts, etc. selecting strategic shareholders at home and abroad to sell shares; Bank leaders also need to pay attention to the issue of using financial leverage.
Building a centralized credit risk management model; (ii) Developing a risk management strategy: setting up and using provisions to deal with risks throughout the system; (iii) Implement well the credit management process: including the stages of appraisal and inspection before, during, and after lending the strict implementation and management of the process have helped commercial banks. Timely detect, correct, limit, and prevent bad debts, thereby building the most effective credit process
Set up a risk management department in the bank: Reasonable calculation of liquidity criteria and forecast of liquidity needs to be proactive in supply.
Limitations
Although many efforts have been made to complete the study, this study still has many limitations:
Firstly, the data set has many shortcomings, because some banks provide incomplete information, and some banks that have just been established in recent years do not have previous information.
Second, the study did not consider data lag, the relationship of non-linearity.
Third, the study has not considered some other factors that can affect liquidity such as interest rate, liquidity risk provision, dependence on external funding sources, M2 money supply, etc.,
Fourth, there is no basis to propose specific measures to improve the credit risk situation of banks Therefore, a qualitative study on credit risk mitigation measures should be conducted for each bank, thereby suggesting ideas for further studies of this
In summary, by using the REM model, the study has identified the factors affecting the liquidity of commercial banks in Vietnam, including: efficiency in using equity,profitability, lending rate borrowing on deposits and the inflation rate In addition,the author also gives some recommendations to improve liquidity for commercial banks for reference Finally, the author also points out some limitations in the research process.
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Va riabte Obs Mea n std
Source: Processing research data by using Stata 14.0 13
L EA BS LDR GDP INF
ScurcE ss df MS Number of obs = 270 £ lo, ^ t X
LIQ Coef Std Err t p> 111 [95% Conf Interval]
Source: Processing research data by using Stata 14.0
Source: Processing research data by using Stata 14.0
Appendix 5 White’s test for heteroscedasticity iihitE ' s test ±CE Ho: hoinos k edas ti City against Ha : utirestriữted hetercskedasticity chi2í44) = 104.08
Prób > chi2 = 0.0000 ữamercn & Irivedi's decotạpasitian af IM-test
Source: Processing research data by using Stata 14.0 autaccrrelation
Source: Processing research data by using Stata 14.0
Source: Processing research data by using Stata
Fixed-effects 111 [95% Conf Interval]
65247703 (íraction of variance due to u_i)
Source: Processing research data by using Stata 14.0
R-sq: Obs per group within - 0.4405 min = 10 between = 0.1713 avg = 10.0 overall = 0.2610 max = 10
5 (fracti on of variance due to u i) tem rem Difíerence 3.2
INF 1.51595 1.530304 -.0143541 0200362 b = consistent under Ho and Ha; cbtained from xtreg
B = inccnsistent under Ha, efficient under Ho; cbtained from xtreg Test: Ho: difference in coeííicients not systeinatic
Source: Processing research data by using Stata 14.0
Appendix 10 Breusch and Pagan Largrangian test
Breusch and Pagan Lagrangian multiplier test for randcm effects
LIQ[BẪNK,t] = Xb + u[BMK] + e[BẪNK,t]
Appendix 12 List of joint stock commercial banks in Vietnam in the sample
No Stock code Banks’ name
1 ABB An Binh Commercial Joint Stock Bank
3 AGRIBANK Vietnam Bank for Agriculture and Rural Development
4 BAB Bac A Commercial Joint Stock Bank
5 BVB Viet Capital Commercial Joint Stock Bank
6 BID Joint Stock Commercial Bank For Investment And Development
7 CTG Vietnam Joint Stock Commercial Bank for Industry and Trade
8 EIB Vietnam Commercial Joint Stock Export Import Bank
9 HDB Ho Chi Minh City Development Joint Stock Commercial Bank
0 KLB Kien Long Commercial Joint Stock Bank
1 LPB LienViet Post Joint Stock Commercial Bank
2 MBB Military Commercial Joint Stock Bank
3 MSB Vietnam Maritime Commercial Join Stock Bank
4 NAB Nam A Commercial Joint Stock Bank
5 NVB National Citizen Commercial Bank
6 SCB Saigon Commercial Joint Stock Bank
7 SSB Southeast Asia Commercial Joint Stock Bank
8 SGB Saigon Bank For Industry And Trade
9 SHB Saigon Hanoi Commercial Joint Stock Bank
0 STB Sai Gon Thuong Tin Commercial Joint Stock Bank
1 TCB Vietnam Technological and Commercial Joint Stock Bank
2 TPB Tien Phong Commercial Joint Stock Bank
3 VCB Bank for Foreign Trade of Vietnam
4 VIB Vietnam International Commercial Joint Stock Bank
5 VIETABANK Vietnam Asia Commercial Joint Stock Bank
6 VBB Vietnam Thuong Tin Commercial Joint Stock Bank
7 VPB Vietnam Prosperity Joint Stock Commercial Bank
Appendix 13 Liquidity reserve of Vietnam commercial banks period 2011
20.6 9 Source: Synthesizing from VietstockFinance, annual reports of commercial banks and processing data in Excel