LIST OF ACRONYMS 1 OLS Pooled OLS regression model 2 FEM FEM Fixed Effects Model 3 REM Random Effect Model 4 FGLS Feasible generalized least squares model 5 NIM Net interest margin
INTRODUCTION
RESEARCH PROBLEM
Liquidity risk is one of the most common risks in banking activities A bank facing payment risk will entail risks related to other activities In addition, liquidity risk can originate from one bank and be transmitted to another bank or from one business organization to banks due to economic activities of the market or the current cross-ownership situation Today, with the current banking restructuring situation, the content of liquidity or liquidity risk is one of the urgent and important content for the banking system Therefore, joint stock commercial banks today focus on building policies and operating activities to ensure the bank's liquidity as well as manage liquidity-related risks
The operation of the banking system is a decisive factor in helping to develop the economy In particular, liquidity is a key factor determining the existence and development of a bank Liquidity risk is the most dangerous risk among banking risks, it not only threatens the safety of each commercial bank, but is also related to the safety of the entire system (Eichberger & Summer, 2005) The global financial crisis of 2007 - 2008 and the mass collapse of financial institutions around the world have shown shortcomings in liquidity management of financial institutions, leading to alarming about liquidity shortages at banks Since the crisis, liquidity risks at banks have gradually received great attention from policymakers and researchers around the world In particular, the beginning of 2023 saw the collapse of four banks and one bank on the brink of bankruptcy Although the incident only occurred in 11 days, including Silicon Valley Bank - the second largest bank in the US, the cause of the collapse was loss of liquidity Specifically, SVB's failure stemmed from risk management problems, especially liquidity risk management, and overconfidence in interest rate predictions During the period of low market interest rates, SVB had abundant capital and actively invested in US government bonds along with long-term deposits at other banks When interest rates increased, SVB's bond investments fell into a loss situation, causing depositors to worry and spread word of mouth to withdraw money massively, causing the bank to have to sell a lot of assets (bonds) at a loss to pay debt, leading to pressure on bank liquidity In Vietnam, a similar incident recently occurred - the SCB bank incident where people competed to withdraw money However, the issue worth mentioning here is that this incident is completely different from 15 years ago since the 2008 crisis because now there are many financial risk management tools in place and many control barriers resulting in the liquidity of the entire system being much stronger than in history Therefore, the potential risk of liquidity risk of Vietnamese Commercial Banks is still quite high as well as the liquidity risk monitoring problem of the State Bank of Vietnam is still not as expected
To ensure the safety of a bank's operations and the stability of the entire system, analyzing factors affecting bank liquidity risks is an issue of constant concern
The author has chosen the topic: "Factors affecting the liquidity risk of Vietnam
Commercial Banks in Vietnam" as a research topic, in order to find out the main factors affecting the liquidity risk of commercial banks in Vietnam and give a more general overview of this issue
Relating to the research topic on the impact of liquidity risk on commercial banks, it has received the attention of many domestic and foreign scholars, as researched by (Ha, D T., Hang, H T T., Huy, T T., & Phung, N T K ,2022) using Machine learning research method in Python language with 28 commercial banks in Vietnam during the period 2009 to 2020, (NGUYEN, H C., 2022) using GMM regression model method with 26 Vietnamese commercial banks from 2008 to 2018, (Bhati, S S., De Zoysa, A., & Jitaree, W., 2019) foreign research sample is commercial banks in India between 1996 and 2016 research uses random effects panel data regression model and (Alim, W., Ali, A., & Metla, M R., 2021) validation study in commercial banks in Pakistan from 2006 to 2019 using panel data regression model
Based on previous research, the author analyzes factors affecting banks' liquidity risks in Vietnam and other countries around the world Most previous studies have not updated the data to date The results of this author's research contribute to the updated scientific literature with additional data up to the most recent year and conducted on 32 Vietnamese commercial banks, thereby discovering new results that create new contributions with many different aspects Firstly: The research will make certain contributions to perfecting the theoretical framework of banking liquidity risk in Vietnam Second: Research to identify factors affecting bank liquidity risks in Vietnam Third: On the basis of inheriting the previous research model and adjusting the research variables to suit the Vietnamese situation, the project selected variables with strong positive correlations among the variables inherited from previous studies to evaluate the impact on liquidity risk at Vietnamese commercial banks In practical terms, the results of the study help bank managers have a method to approach and measure the basic factors affecting the bank's liquidity risk At the same time, researching and selecting variables with a strong positive correlation according to the latest data is the basis for bank managers to update and complete the policy framework for managing and operating the banking system in both aspects of banking management and administration agencies with the goal of well controlling liquidity risks for the current banking system.
RESEARCH OBJECTIVES
The general research objective of thine study is to estimate the level of impact of factors affecting liquidity risk of Vietnamese commercial banks
To achieve the general research objective above, the study carries out specific objectives as follows:
Firstly, determining factors have impacts on liquidity risk on Vietnamese commercial banks
Secondly, estimating the level of impact of these factors on liquidity risk of Vietnamese commercial banks.
RESEARCH QUESTIONS
To achieve the proposed research objectives, the study will answer the following research questions:
Research question 1: Which factors have impacts on liquidity risks of Vietnamese commercial banks?
Research question 2: To what extent do these factors affecting the liquidity risk of Vietnamese commercial banks?
RESEARCH SCOPE
The research object of the study is factors that affects liquidity risk of Vietnamese commercial banks
Scope of spatial research: The study focuses on data of 32 Vietnamese commercial banks
The study was conducted in the period 2011 - 2022 This study was chosen for a number of reasons: first, a period of more than ten years, although not too long, is enough for the business to stabilize its operations, and at the same time This is the period when businesses gradually recovered after the global financial crisis in 2009; as well as the period when Vietnamese commercial banks encountered the COVID-
19 pandemic At the same time, this is also the stage where the author collects enough data for the necessary research.
Contributions of the study
The research has both academic and practical significance as follows:
Firstly, the study provides an overall analysis of the level and direction of factors that affect the liquidity risk of commercial banks and helps Banks pay attention to preventing liquidity crises
Secondly, the research results contribute to helping state banks and especially commercial banks have more data to consider and take measures effective and appropriate solutions in adjusting policies to minimize liquidity risks in Vietnamese commercial banks.
RESEARCH STRUCTURE
This chapter presents an overview of the research paper including the following contents: reasons for choosing the topic; research problem; objectives of the study; research question; object and scope of the study; research significance; research paper structure.
LITERATURE REVIEW
THEORETICAL FRAMEWORK
The concept of liquidity followed by Duttweiler (2011) refers to ability to fulfill financial obligations on time, the ability to turn assets into cash quickly and the market's acceptance of those assets Liquidity plays an important part in commercial banks, because they must meet the capital needs of customers and business activities such as withdrawals, loans, payments and capital transactions (BIS, 2009 ; Praet & Herzberg, 2008)
The concept of liquidity was introduced by Keynes (1930) and Fisher (1930) in monetary theory According to them, money is the most liquid asset, but if you keep too much money, you will lose profitable investment opportunities Therefore, commercial banks that want to increase profits will tend to invest in high-risk assets, reducing the ratio of liquid assets and weakening liquidity (Nguyen, 2016) In contrast, highly profitable banks are often concerned with safety and credit control, as well as enhancing liquid assets to avoid default risk (Bunda & Desquilbet, 2008; Chatterjee & Eyigungor, 2009; Rychtarsik, 2009) Wilson, Casu, Girardone & Molyneux (2010) said credit is the main business activity of banks For banks, using short-term capital to provide commercial loans and finance businesses' current assets is an effective solution to maintain liquidity
However, according to Smith (1776), credit activities can encounter great difficulties during periods of financial crisis when banks face sudden withdrawals of customer deposits due to the influence of psychology crowd management Moulton (1918) states that liquidity is the ability of a commercial bank to minimize liquidity risk by allocating assets to investments that are highly liquid or can be converted into cash at a reasonable price certain rate (Toby, 2006) as well as the ability to generate profits and retain profits for reinvestment On the contrary, in theory, credit activities will lose liquidity when a large number of depositors decide to withdraw money
Prochnow (1949) believes that income from assets is formed from one-time and regular debt repayments throughout the life of the asset, this source of income will increase the liquidity of the asset This theory has made important contributions to the study of term structure, expected return or profitability of assets as a factor in assessing liquidity Profitability is the most suitable index to evaluate a bank's performance and health (Lopez & Saurina, 2007)
According to BIS (2009), liquidity risk is one of the main causes of bank weakness, when banks cannot meet customers' withdrawal needs This can lead to a liquidity trap (Jeanne & Svensson, 2007), when banks have to look for other sources of capital at high costs or rely on the intervention of the central bank or the interbank market (Diamond & Rajan, 2005)
Liquidity risk can be divided into two types: financial liquidity risk and market liquidity risk (Decker, 2000; Pham, 2019; Gomes & Khan, 2011) Market liquidity risk is the risk that banks cannot sell assets on the market quickly and at low cost Financial liquidity risk is the risk that a bank will not be able to pay debt obligations when due due to being unable to liquidate assets or lacking capital These two types of risks affect each other through contagion effects in the financial system (Diamond
Another cause of liquidity risk is macroeconomic factors and the bank's financial, operating and management policies (Ali, 2004) From the perspective of bank liquidity management, both surplus and deficit reflect bank imbalances Liquidity surplus occurs when the economy lacks effective investment projects, capital is not used due to credit appraisal capacity or overdeveloped capital On the contrary, liquidity deficit is when banks do not have enough capital to operate, leading to loss of business opportunities, loss of customers, loss of market and reduced reputation from the public (Truong, 2012; Brunnermeier & Yogo, 2009; Falconer, 2001; Plochan, 2007; Ahmed & Duellman, 2013; Goodhart, 2008; Goddard & Wilson, 2009)
2.1.2 Liquidity supply, demand, and net liquidity position
Liquidity becomes an issue when banks face withdrawal demands from customers At that point, banks need to balance not only the demand for withdrawals with the available funds but also with the ability to mobilize further capital Therefore, assessing a bank's liquidity position needs to consider its dynamic state, meaning it must be examined in the context of the supply-demand relationship of available capital in each specific phase
Liquidity supply refers to the amount of funds available or potentially available for a bank to use in the short term This inflow is generated from various sources:
These are considered the most important source of liquidity supply for banks
To increase this demand, thus increasing liquidity supply for banks, measures such as adjusting deposit interest rates can be implemented In conditions where other investment opportunities become less attractive, these deposit funds can also be increased
This is considered the second most important source of liquidity supply Lending activities are the primary function of banks, providing the largest source of revenue for banks but also carrying high inherent risks, affecting a bank's ultimate repayment ability If all loans are repaid on time, not only is business efficiency ensured, but it also becomes a significant source of liquidity supply for banks
• Borrowing in the money market
Banks can increase their liquidity supply by borrowing in the money market, including new loans, extensions, and revolving loan repayments Transactions occur between banks themselves or with the central bank."
To meet liquidity needs, banks can convert a portion of liquid assets into cash Revenue from service provision
Income for banks in providing services to customers such as guarantee fees, letter of credit opening fees
• Issuance of shares in the market
The issuance of shares by banks into the market is also a significant source of liquidity supply for banks
Liquidity demand reflects the need to withdraw funds from banks at various times This demand depends on the following factors:
This is a frequent and immediate liquidity demand, including demand deposits, current deposits, term deposits at maturity, and premature withdrawals Among these, demand deposits and current deposits require banks to maintain a reserve to meet payment demands from these accounts Factors contributing to this liquidity demand may include fluctuations in inflation in the economy, significant differences in deposit interest rates among banks, and differential returns on investment opportunities (stocks, real estate, gold, foreign currencies) compared to depositing funds in banks
This also strongly influences liquidity demand for banks This demand is affected by factors such as business investment demand, competitive lending rates of banks compared to other banks, and other sources of capital becoming less accessible "
This refers to the funds that banks must repay for borrowings from economic organizations, individuals, other financial institutions, or the central bank
• Service provision costs and interest expenses
These are expenses related to interest payments on mobilized funds, interest payments on issued securities that have matured, which banks must pay to customers
This is the money that banks must pay to their shareholders
The repurchase of issued shares by banks also impacts the liquidity demand of banks
Although these various liquidity demands play very different roles, they all contribute to the liquidity demand of banks However, in practice, sometimes due to the high risk nature of the first demand (customer deposit withdrawal demand) to the safety of the bank's operations, this factor is often the most closely watched when discussing the liquidity of a bank In addition, the demand from borrowing customers and fulfilling the obligations of the bank also creates liquidity demand for the bank The only difference is that if banks are not allowed to refuse demands from depositors, then demands from borrowing customers can be refused However, the reputation of the bank will be diminished if it consistently rejects borrowing customers due to liquidity reasons, as this implies that the bank is losing profitable investment opportunities
In terms of time, the liquidity demand of a bank includes both short-term and long-term aspects
Short-term liquidity demand is immediate or nearly so Transactional deposits, term deposits at maturity, and instruments mobilized in the money market fall within the scope of short-term liquidity demand Meeting this type of liquidity demand requires banks to maintain a relatively large amount of highly liquid assets (cash in hand, deposits at the central bank and other financial institutions, government securities )
EMPIRICAL RESEARCH
The financial crisis that lead to bank’s collapse from 2008 has negatively impacted on the real economy Therefore, a paying particular attention to the consequences of Vietnam economy through the study of Ha, D T., Hang, H T T.,
Huy, T T., & Phung, N T K., (2022) assessing the impact of internal and external factors on the liquidity of Vietnamese commercial banks in the period from 2009 to
2020 collected from 28 commercial banks with using Machine learning on the python platform for observational data Model results and regression coefficients show that profitability on liquidity of commercial, equity ratio, credit risk provision ratio, inflation rate have a negative impact and bank size, the ratio of loan outstanding, growth rate have a positive impact on the liquidity of Vietnamese commercial banks
The study NGUYEN, H C (2022) uses the audited financial statements of 26 Vietnamese commercial banks listed on the Ho Chi Minh City Stock Exchange (HOSE) and Hanoi Stock Exchange (HOSE) during the 2008–2018 period to estimate the system GMM model, which provides empirical evidence on the effect of the variables of customer deposit to total assets (DEPO) ratio, loan to assets (LTA) ratio, liquidity of commercial banks (LIQ), credit development (CRD) ratio, external funding (EFD) ratio, and credit loss provision (LLP) ratio on liquidity risk The study confirms that commercial banks’ internal factors play the most important role, and there is no empirical evidence on macro variables that affect liquidity risk Finally, in accordance with the theoretical framework, the study uses an estimation method with the R language and the bootstrap methodology to give empirical proof of the nonlinear correlation and U-shaped graph between commercial bank size and liquidity risk The importance of commercial bank size in absorbing and moderating the effects of liquidity shocks is demonstrated, however, excessive growth in commercial bank size would increase liquidity risk in commercial bank operations
The recent economic crisis caused by COVID – 19 epidemic Hao, N T N., & Wong, W K (2021) focused on bank's internal and macroeconomic variables on affecting liquidity risk on the performance of commercial banks in Vietnam in the period 2010 - 2020 They assumed that rising income from interest increases liquidity risk There is not enough evidence to assess the risk of dummy variables - shocks during the study period COVID-19 in influencing liquidity risk Total loans on total assets ROA, ROE, and NIM have positively impact on the bank performance In addition, we find that liquidity risk could have the opposite effect
Tran, T T., Nguyen, Y T., & Long, T (2019) determined the factors affecting the liquidity of commercial banks in the Vietnamese banking system in the period
2010 – 2015 and used the measurement method by four ratios to evaluate bank's liquidity, which is current assets to total assets, asset liquidity to short-term deposits and mobilized capital, total outstanding loans to total assets and total outstanding short-term loans, mobilized capital and deposits The independent variables are divided into two groups: internal factor and external factor, including: size of the bank, ratio of liquid reserves to total assets of a bank, dependence on capital of the bank, equity ratio, loan to capital ratio, credit risk reversal ratio to total loans, and economic growth rate, Money supply
Research article Nguyen, H T V., & Vo, D V (2021) examined the factors that determine the liquidity of 17 commercial banks listed on the Vietnam Stock Exchange, HOSE, HNX and UPCoM The study used quarterly audited financial statements from the first quarter of 2006 to the first quarter of 2020; it includes 496 observations The author collected GDP and inflation data compiled from the International Monetary Fund and the General Statistics Office of Vietnam Then use the unbalanced panel data method The results show that the size of total assets, return on total assets and credit growth have a positive correlation with the liquidity of listed banks; Meanwhile, the interaction between bank size and return on total assets has a negative impact on the liquidity of commercial banks listed on HNX, HOSE, UPCoM
The research by Alim, W., Ali, A., & Metla, M R (2021), the author used panel data for Ordinary least squares analysis to determine liquidity risk on financial performance of commercial banks in Pakistan conducted from 2006 to 2019 using archived data State Reserve on Bank of Pakistan website The results of the study show that higher liquidity increases bank performance in commercial banks of
Pakistan This conclusion is consistent with several studies and available literature This study can become a good reference for future policy decisions regarding the minimum liquidity requirements of banks in the region
The study of Golubeva, O., Duljic, M., & Keminen, R (2019) investigated the impact of liquidity on bank profits after implementation of Basel III regulations and assume that liquidity ratios have different effects on bank profits, depending on specific indicators and macroeconomics of banking The study makes 180 observations for the period 2014-2017 and 37 observations for 2018 analyzed in a population of 45 European banks The author uses multiple proxies for bank liquidity, including the Liquidity Coverage Ratio, a new measure inspired by the Basel III framework, and the loan-to-deposit and financing spreads.
FACTORS AFFECTING LIQUIDITY RISK
Symbol Variable Measurement Method Reference
LIQR Liquidity risk financing gap ratio NGUYEN, H C
(2022) LIQ Liquidity Short term assets/ Short term liabilities
NGUYEN, H C (2022) LIQ Liquidity ratio Current assets/ Current liabilities
CRD Credit growth ratio Increase (decrease) of loan balance during the year/ Total loan balance at the beginning of the year
DEPO Term deposit The ratio of customer deposits to total assets - the size of deposits
LLP The loan loss provisions to loan ratio
The loan loss provisions to loan ratio
LTA Loans to total assets NGUYEN, H C
EFD (The ratio of external funding to total liabilities Interbank loan + Loan from other credit institutions)/ Total capital
ROA Return on asset Net income/ Total assets NGUYEN, H C
D T., Hang, H T T., Huy, T T., & Phung, N T K., (2022) ROE Return on equity Net income/ shareholder equity
(Total interest income - total interest expense)/
Cash Cash status index variable
(Cash + Deposits in State Bank + Deposits in Financial institution)/
LDR Credit to capital mobilization ratio
Hao, N T N., & Wong, W K (2021) FGAP Funding gap (Credit balance - mobilized capital) / Total assets
ETA Equity to Total assets ratio
T K., (2022) DEP Customer deposit to asset ratio
Cash in hand + State Bank Vietnam balances + T-bills and bonds -
Balances due to other banks
The ratio of non - performing loans to total gross advances
CRE Loan rate Loan/ Toatal capital Nguyen, H T V., &
Vo, D V (2021) CGR Loan growth rate (Loan year t - loan year t-1)/ Loan year t-1
Vo, D V (2021) FCRER Fund for credit risk Provisions/ Loans Nguyen, H T V., &
Interaction between the size of total assets and the rate of return on total assets
NPM Net profit margin Net income/ Revenue Golubeva, O.,
Duljic, M., & Keminen, R (2019) EBTDA Earnings before taxes, depreciation and amortization
(EBITDA + interest income - interest expense)/ Revenve
Golubeva, O., Duljic, M., & Keminen, R (2019) LCR (Loan cover ratio), a measure inspired by Basel III rules to
High quality liquid assets/ total net cash
Golubeva, O., Duljic, M., & Keminen, R (2019) estimate risks from potiential liquidity shortages outflows expected within
LTD (Loan to deposit) an alternative liquidity ratio
Net loans/ total deposits Golubeva, O.,
T K., (2022) FGR (Financing gap ratio) an alternative liquidity ratio
(Net loans - total deposits)/ total assets
Golubeva, O., Duljic, M., & Keminen, R (2019) NPLL Provisions established for possible defaults by customers on loans from banks (a proxy for a credit risk of a bank)
Loan loss provision/net loans
Golubeva, O., Duljic, M., & Keminen, R (2019) , Tran, T T., Nguyen,
SS Securities gains and losses, a realised net (as a measure of a bank's credit risk)
Securities gains - securities losses/Total bank revenue
EFD Dependence on external financing source the proportion of to tal interbank loans to total capital
LTR Long-term lending interest rate
Y T., & Long, T (2019) SIZE Size of total assets of the banks (The large scale increases the power in the market and improves technology efficiency at low cost)
Logarithm of bank's total assets to proxy size
M2 Money supply World Bank datasets Tran, T T., Nguyen,
Y T., & Long, T (2019) INF Inflation rate World Bank datasets Tran, T T., Nguyen,
T K., (2022) GDPG Economic growth World Bank datasets Tran, T T., Nguyen,
In this chapter, the study will first present the theoretical basis of liquidity and liquidity risk, then present the factors affecting liquidity risk, which collected from previous study Finally, order and choose factors from previous analysis studies to make analytical models for future chapter.
RESEARCH METHOD
RESEARCH MODEL
The model of this paper is mainly based on the inheritance of previous authors (Ha, D T., Hang, H T T., Huy, T T., & Phung, N T K., 2022) Similar to the previous research, in this study, the authors have selected the dependent variable representing the liquidity of commercial banks which is the variable "The ratio of liquid assets to total assets" And the independent variables that the authors use in the model are bank size (SIZE) and equity ratio (CAP) as well as macro-independent variables including economic growth rate (GDPG) and inflation rate (INF) as factors affecting the liquidity of commercial banks and credit risk provision ratio (LLD) and profitability (ROA) and ratio of loan oustanding balances to total deposits (LDR) At the same time, many studies show that the loan loss provisions to loans ratio affects the liquidity risk of commercial banks such as (NGUYEN, H C., 2022), ect Therefore, in order to increase the accuracy and stability of the research model, the author has selected 1 more variable including the loan loss provisions to loans ratio (LLP) is independent variable in the model to analyze their impact on liquidity of commercial banks in Vietnam In this study, data was collected from 32 commercial banks in Vietnam from 2011 to 2022 The experimental research model is as follows:
LIQRit = β0 + β1ROAit + β2NIMit + β3SIZEit + β4CAPit + β5LLDt + β6LDRt + β7GDPGit + β8INFit + ɛi
Where: β0: Intercept β1, … β8: The individual regression coefficents of the independent variables i: Represents for banks t: Represents for years ɛ: Represents the error of the model
LIQ: Ratio of Liquid assets to Total assets (as a percentage)
ROA: Return on total assets, representing profitability (calculated as a percentage)
NIM: Net interest margin of the commercial banks in Vietnam (calculated as a percentage)
SIZE: Represents the size of the bank (calculated in base 10 logarithms of total assets)
CAP: Represents equity ratio (calculated as a percentage)
LLD: Represents the provision for credit losses (as a percentage)
LDR: Ratio of outstanding loans to total deposits (calculated as a percentage) GDPG: Represents GDP growth rate (as a percentage)
INF: Represents Vietnam's inflation rate (as a percentage)
MESUREMENT OF RESEARCH VARIABLES
The liquidity risk of banks is usually measured by the LIQ The calculation of LIQ was proposed by (Tran, T T., Nguyen, Y T., & Long, T., 2019) and (NGUYEN,
H C., 2022) The lower the LIQ, the more stable the bank because it is related to the bank's insolvency ratio
LIQ = Liquid assets/ Total assets
ROA is used to measure the profitability of the banks and measured as the net income to total assets High ROA shows that the financial position of the banks is stable, and they are not interested in investing in risky loans because of less pressure to generate income ROA’s formula is presented by the following formula:
ROA = Net income/ Average Total Assets
The net interest income ratio is a percentage difference between interest income and the bank's interest expenses payable, indicating how much the banks benefit from the interest rate difference between mobilization and credit investment
NIM = (Interest income – interest expense)/ Total assets
Bank size is measured by taking the logarithm of the bank's total assets, the SIZE factor shows the total assets that the bank currently has, and also represents the bank's liquidity, through the following formula:
CAP: Equity ratio is measured by equity divided by total assets, this ratio shows the capital adequacy and financial strength of a bank A low ratio of this index indicates that the bank uses a lot of financial leverage leading to high risk, which can reduce the bank's profitability when the cost of capital decreases Research on this factor has high significance for liquidity This ratio has the formula:
Provision ratio for credit risk is measured by provision for credit losses on total value of loans Provision is calculated on bad debts in group 3, group 4 and group 5 according to the regulations of the State Bank, so the higher this ratio, the higher the credit risk
LLD = (Provision for credit losses)/(Total outstanding loans)
The ratio of total outstanding loans divided by total deposits assesses the extent to which customer loans are financed by customer deposits This variable can reflect the liquidity position of the bank
LDR = ( Total outstanding loans) / (Total deposits)
The economic growth rate is one of the common factors, related to many problems occurring in the economy GDP shows the development of the economy over the years, and at the same time can see the development trend of the economy, from which it is possible to forecast new opportunities and challenges for economic development
The inflation rate is one of the common and important factors, measured by the consumer price index The INF shows the rate of change in commodity prices over the years, thereby predicting the trend of the inflation rate so that the central bank can develop economic policy in accordance with the trends of the exchange rate inflation rate.
RESEARCH HYPOTHESIS
Return on assets (ROA): Return on assets is measured by net income after tax to total assets Muharam and Kurnia (2013); Rahman and Banna (2015); Alzoubi (2017); İncekara and Çetinkaya (2019) showed that ROA has a positive impact on liquidity risk
H1: ROA has a positive impact on liquidity risk
Net interest margin (NIM): Net interest margin measures the gap between what the bank pays savers and what the bank receives from borrowers Due to
Delécha, Henao, Muthoora and Vtyurina (2012), NIM positively affects liquidity assets
H2: NIM has positively associated with liquidity assets
Bank’s size (SIZE): Bank size is measured as the natural logarithm of the total assets Moussa (2015); Cucinelli (2013); Alzoubi (2017); Mahmood, Waheed and Arif (2019) revealed Bank’s size positive with liquidity risk
H3: Banksize ratio is positively related to commercial banks of Vietnam
Equity ratio (CAP) According to (Vodová, 2013), profitability of many banks declined quite substantially, liquidity remains almost at the same level or slightly decreased This demonstrates the positive link between profitability and liquidity H4: CAP has positive impact on commercial banks in Vietnam
LLD (Provision ratio for credit risk): has significantly impact on liquidity
When the provision for credit risks of banks tends to increase, the bank's liquidity decreases This is also consistent with the previous experimental studies of Sufian Chong (2008), Vong Chan (2009), Inoca Minaryeanu (2012)
H5: Provision ratio for credit risk has negative impact on commercial banks in Vietnam
LDR (Loans-to-Deposits Ratio): The ratio of total outstanding loans divided by total deposits assesses how customer deposits finance customer loans The total capital mobilized is mainly short-term and the bank uses a lot for credit activities, less liquid assets will be financed and liquidated (Vu Thi Hong, 2015) Therefore, the loans to deposits ratio and liquidity are expected to have negative relationship
H6: Loans to deposits ratio has has negative impact on commercial banks in Vietnam
Economic growth (GDPG): The economic growth index is one of the macro factors affecting all business activities across all economic sectors, calculated by the annual 4 economic growth index The Paper of Moussa (2015); Vodova (2011); İncekara, and Çetinkaya(2019) indicated that GDP negatively with liquidity risk H7: GDP has negative impact on commercial banks of Vietnam
Inflation (INF): The variable inflation rate is calculated by the inflation rate of the year of observation, showing the trend of the economy and serving as an indicator for the State Bank to adjust economic policy in line with the economic trend in that period Vodova (2011); Moussa (2015); İncekara and Çetinkaya (2019) revealed INF has a negative effect on liquidity risk Hua Shen et al (2009); Cucinelli, D (2013) H8: INF is inversely related to commercial banks of Vietnam
Table 3.1 Statistics of expected signs of variables in the model
Muharam and Kurnia (2013); Rahman and Banna (2015); Alzoubi, T (2017); İncekara and Çetinkaya (2019)
“(Interest income-interest expense)/ Total assets”
“The natural logarithm of Total Assets” +
Moussa (2015); Cucinelli (2013); Alzoubi, T (2017); Mahmood, Waheed and Arif (2019)
5 LLD Provision ratio for credit risk
“Provisions for credit losses / Total outstanding loans”
Sufian Chong (2008), Vong Chan (2009), Inoca Minaryeanu (2012)
“Total loans / Total short-term deposits”
The annual growth in real GDP in year t (World bank database)
Moussa (2015); Vodova (2011); İncekara, and Çetinkaya(2019)
The annual inflation rate in year t (World bank database)
Vodova (2011); Moussa (2015); İncekara and Çetinkaya (2019) revealed INF has a negative effect on liquidity risk Chung Hua Shen et al (2009); Cucinelli, D (2013)
The data source of the study was taken from the audited financial statements of
32 Vietnamese commercial banks listed on Ho Chi Minh City Stock Exchange (HOSE) and Hanoi Stock Exchange (HNX) during 2011–2022 period including the following joint stock commercial banks: “An Binh Commercial Joint Stock Bank (ABB), Asia Commercial Joint Stock Bank (ACB), The Vietnam Bank for Agriculture and Rural Development (AGR), Bac A Commercial Joint Stock Bank (BAB), Bao Viet Commercial Joint Stock Bank (BaoVietBank), Bank for Investment and Development of Vietnam (BIDV), Viet Capital Commercial Joint Stock Bank (BVB), Vietnam Joint Stock Commercial Bank for Industry and Trade (CTG), Vietnam Export Import Commercial Joint-Stock Bank (EIB), Ho Chi Minh City Development Joint Stock Commercial Bank (HDB), Kien Long Bank (KLB), LienViet Post Joint Stock Commercial Bank (LPB), Military Commercial Joint Stock Bank (MBB), Vietnam Maritime Commercial Joint Stock Bank (MSB), Nam A Commercial Joint Stock Bank (NAB), National Citizen Commercial Joint Stock Bank (NVB), Orient Commercial Joint Stock Bank (OCB), Petrolimex Group Commercial Joint Stock Bank (PGB), Saigon Commercial Joint Stock Bank (SCB), Saigon Bank For Industry And Trade (SGB), Saigon-Hanoi Commercial Joint Stock Bank (SHB), Southeast Asia Commercial Joint Stock Bank (SSB), Sai Gon Thuong Tin Commercial Joint Stock Bank (STB), Vietnam Technological, Commercial Joint Stock Bank (TCB), Tien Phong Commercial Joint Stock Bank (TPB), Joint Stock Commercial Bank for Foreign Trade of Vietnam (VCB), Vietnam International CJS Bank (VIB), Vietnam –
Asia Commercial Joint Stock Bank (VAB) and Vietnam Prosperity Joint Stock Commercial Bank (VPB), Ocean Commercial One Member Limited Liability Bank (OceanBank), Vietnam Thuong Tin Commercial Joint Stock Bank (VBB) and Vietnam Public Joint Stock Commercial Bank (PvcomBank)” 1Second data source levels of macro - research variables are collected by the author at The World Bank's website
The research topic will apply the panel data method to fully represent the contents of the micro and macro research variables of each Vietnamese commercial bank in the period from 2011 to 2022, multicollinearity rarely occurs, more degrees of freedom, more efficient results After collecting enough data, the author will put it into STATA analysis software to analyze the above research model.
RESEARCH RESULTS AND DISCUSSION
DESCRIPTIVE STATISTICS
The thesis applies a descriptive statistical method of research variables through SUM command in STATA software to get an overview of research variables such as the total number of observations, mean value, standard deviation, minimum value, and maximum value Secondary data collected from 32 Vietnamese commercial banks and The World Bank in the period from 2011 to 2022 is shown through the research variables in the following statistical table
Table 4.1 Statistics of variables used in the research model
(Source: Analysis results from STATA software)
Statistical results show that there are a total of 350 observations from 32 Vietnamese commercial banks in the period 2011 - 2022, the average value of liquidity risk (LIQ) of 32 Vietnamese commercial banks at 18.36%, and the lowest LIQ is 2.97% and the highest is 198,7%, which shows that Vietnamese commercial
Variable Obs Mean Std.dev Min Max
INF 350 0.0471262 0.0432771 0.006312 0.1867773 banks in particular and the banking system in general always maintain a good liquidity statement and control the situation in which the liquidity risk is not too high
This first dependent variable is return on asset (ROA) has an average value of 1.74%, the highest is about 29.55% and the lowest is -0.38%, which shows that during this period Vietnamese commercial banks have an independency heavily on external sources of capital
Secondly, net interest margin (NIM) has 3.21% on average and the bank’s highest value is 70.49% while lowest value is -0.64%, this ratio shows the gap between what the bank pays savers and what the bank receives from borrowers Equity ratio (CAP) averaged 9.81%, highest at 110.5% and lowest at 0.02%
Provision ratio for credit risk (LLD) has the average statistical result at 1.36%, the highest value at 3.27%, and the lowest at 0.54
Loan to total deposit ratio (LDR) has an average value of 122.56%, the highest at 835.15% and the lowest at 10.53%, this proves that 32 Vietnamese commercial banks in this period are concentrating on lending activities based on customer deposits is very high
The size of the bank (SIZE) has an average statistical value of 812.65%, with the highest value at 932.65% and the lowest at 665.21%, proving the total asset size of each bank holding is growing
The GDP growth rate (GDPG) has an average value of 6.03%, the highest value is 8.02% and the lowest value is 2.56% Inflation rate (INF) statistics show that the average value is 4.71%, the highest value is 18.67% and the lowest is 0.63%.
CORRELATION MATRIX
A correlation matrix is a statistic that measures the correlation relationship between two variables The correlation coefficient can range from -1 to +1, with -1 indicating a perfect negative correlation, +1 indicating a perfect positive correlation
A variable that is correlated with itself will always have a correlation coefficient of
1 A correlation coefficient of 0 (or close to 0) means that the two variables have nothing to do with each other If the value of the correlation coefficient is negative, it means that as x increases, y decreases (and conversely, when x decreases, y increases)
The thesis analyzes the correlation relationship between the dependent variable LIQ with the research variables in turn: Net interest margin; Equity ratio; The ratio of loan outstanding to total deposit;Provision ratio for credit risk; Bank size; Return on assets; Economic growth rate; Inflation rate The results of the correlation analysis are presented in the following table:
Table 4.2 Correlation coefficients between research variables2
LIQ ROA NIM SIZE CAP LLD LDR GDPG INF LIQ 1
2 “***Correlation is significant at the 0.05 level, * Correlation is significant at the 0.1 level”
(Source: Analysis results from STATA software)
The results of the correlation matrix of Table 4.2 show that the relationship between the variables is at an acceptable level because most of values of the correlation coefficients of the variables are less than 0.8 However, there is 1 variable – NIM, which value more than 0.8 Variables NIM, SIZE and GDPG are negatively correlated with the dependent variable LIQ; while the remaining variables are positively correlated with LIQ The highest correlation is between INF and liquidity risk of Vietnamese commercial banks, that is 0.2194 Evidence of strong correlation amongst the variables taken into the study is not found
The variable ROA has a positive correlation with liquidity risk ratio of 0.0577 indicating a positive relationship between ROA and LIQ, so the higher the return on assets, the higher liquidity risk
The variable NIM has a negative correlation with bank stability of 0.0265 indicating a negative relationship between NIM and LIQ, so the higher the net interest margin ratio, the lower liquidity risk
The variable SIZE has a negative correlation with bank stability of 0.2441indicating a negative relationship between SIZE and LIQ, so the higher the bank size, the lower liquidity risk
The variable CAP has a positive correlation with liquidity risk ratio of 0.1538 indicating a positive relationship between CAP and LIQ, so the higher the equity ratio, the higher liquidity risk
The variable LLD has a positive correlation with liquidity risk ratio of 0.0099 indicating a positive relationship between LLD and LIQ, so the higher the provision ratio for credit risk, the higher liquidity risk
The variable LDR has a positive correlation with liquidity risk ratio of 0.0031 indicating a positive relationship between LDR and LIQ, so the higher the ratio of loan outstanding to total deposits, the higher liquidity risk
The variable GDPG has a negative correlation with bank stability of 0.0466 indicating a negative relationship between GDPG and LIQ, so the higher the economic growth rate r, the lower liquidity risk
The variable INF has a positive correlation with liquidity risk of 0.2194 indicating a positive relationship between INF and LIQ, so the higher the inflation rate, the lower liquidity risk.
CHECK FOR MULTICOLLINEARITY
To check whether the research model has independent variables or not by testing the multicollinearity in the research model through the VIF command in STATA software The results obtained VIF < 10 means that the research model does not have multicollinearity and vice versa if the research results obtain a VIF index > 10, it means that the research model has multicollinearity
Table 4.3: Results of multicollinearity test
(Source: Analysis results from STATA software)
After applying the VIF command in STATA software, the independent variables all have VIF indexes below 10 Moreover, almost indicators below 4 meaning that there is no multicollinearity between these independent variables.
ESTIMATION RESULTS
4.4.1 Comparison between Pooled OLS model and Fixed Effects Model (FEM)
To choose a suitable model, the author tests the Pooled-OLS and FEM models to find a more suitable model The test hypothesis is:
H0: The Pooled-OLS model is more suitable for the research variables
H1: The FEM model is more suitable for the research variables
After processing the research data and putting it into the STATA analysis software, the analysis results of the two models are respectively:
Table 4.4: Results of Pooldes - OLS Model
LIQ Coef Std.Err t P > |t| [95% Conf.Interval]
(Source: Analysis results from STATA software)
Table 4.5: Results of Fixed Effects Model
Fixed-effects (within) regression Number of Obs 350
Group variable: id Number of groups 32
R - squared Obs per group within = 0.0994 min 3 between = 0.1549 avg 10.9 overall = 0.1066 max 12
LIQ Coef Std.Err t P > |t| [95% Conf.Interval]
(fraction of variance due to u_i) sigma_e 0.14336623 rho 0.33769606
(Source: Analysis results from STATA software)
The test results show that Prob > F = 0.0000 is less than 0.05, showing that reject hypothesis H0, which means that the FEM model is more suitable for research variables compared with the Pooled-OLS model
4.4.2 Comparison between FEM model and REM model
After choosing the FEM model between the two Pooled models - OLS and FEM, author continue to choose two models to choose the appropriate Research model for the next steps, the thesis applies the Hausman test to compare with the Fix Effect Model (FEM) and Random Effect Model (REM)
With Hausman test, the research hypothesis is posed as:
H0: There is no correlation between the independent variables and the residuals, which means that the REM model is more suitable
H1: There is a correlation between the independent variables and the residuals, which means that the FEM model is more suitable
Test: H0: Difference in coefficients not systematic
(Source: Analysis results from STATA software)
Based on the analysis results from STATA software, it shows that P-value 0.9789 > 0.05, so the hypothesis H0 is accepted, which means that the REM model is more suitable
4.4.3 Test for the phenomenon of heteroskedasticity
After selecting the REM model as the most suitable model, the author will test the heteroskedasticity through the Breusch and Pagan Lagrangian test for Random Effects Model
H0: The model have no first-order autocorrelation
H1: The model occurs with variable variance
Table 4.7: Breusch and Pagan Lagrangian multiplier test for random effects
(Source: Analysis results from STATA software)
After choosing the REM model, the research model is more suitable than the FEM model The model, the author applied the test of variance phenomenon, obtained the result Prob > chibar2 = 0.0000 < 0.05, so the author concluded the research model has variable variance phenomenon
To check whether the research model has autocorrelation or not by applying Wooldridge Test, the research test hypothesis is:
H0: Research model has no autocorrelation
H1: The research model has autocorrelation
If the test results are obtained with Prob > F > 5%, the research model accepts the hypothesis H0, that is, the research model has no autocorrelation phenomenon
Wooldridge test for autocorrelation in panel data
(Source: Analysis results from STATA software)
After applying Wooldridge Test, the author's research model obtained P-value
= 0.0000 < 0.05, the research model rejected the hypothesis H0, meaning that the results of the research model have a phenomenon autocorrelation.
FEASIBLE GENERALIZED LEAST SQUARED ESTIMATION
Through analysis and model testing, it was found that the REM model is the most suitable, but the research model encounters autocorrelation and variable variance through Wooldridge and Modified Breusch and Pagan Lagrangian tests
Therefore, to overcome these two phenomena, the author continues to apply the feasible generalized least squares model (FGLS) method in the research model
Table 4.9: FGLS model troubleshooting results
Cross-sectional time-series FGLS regression
Obs per group min 3 avg 10.9375 max 12
LIQ Coef Std.Err z P > z [95% Conf.Interval] ROA 2.206931 0.3806784 5.80 0.000 1.460815 2.953047
(Source: Analysis results from STATA software)
In the FGLS research model, the variables with research significance are ROA, NIM, LDR and INF, in which the variables NIM has a negative impact on LIQ, the other variables have statistical significance In addition, variables that are not statistically significant in the model are LLD,and GDPG
From the estimated results obtained, the model studies the factors affecting the liquidity risk of Vietnamese commercial banks in the period 2011- 2022:
LIQ = 0.437*** + 2.207***ROA - 0.949***NIM - 0.0298*SIZE+ 0.0787*CAP - 0.126LLD + 0.0129***LDR - 0.115GDPG + 0.488***INF + ɛi
CONCLUSIONS AND RECOMMENDATIONS
CONCLUSIONS
Bank liquidity is a very important indicator used to evaluate the safety and efficiency of banks' operations Based on panel data of 350 observations of 32 Vietnamese commercial banks in the period 2011 - 2022, the data is processed and analyzed through STATA software Thereby, the research results are presented in the form of descriptive statistics, correlation analysis, estimation methods such as Pooled-OLS, FEM, REM, FGLS and especially the method of regression model FGLS method, in addition, the research results are included in the test to give the best and most reliable research Experiencing estimation and testing methods, the thesis has given the conclusion of the impact of liquidity risk on Vietnamese commercial banks in the period 2011 - 2022 The thesis has determined the impact of liquidity risk on Vietnamese commercial banks in the period 2011 - 2022 through 32 joint stock commercial banks The final research results of the model show that in the model with the dependent variable, 6 variables have statistical significance which are ROA, CAP, LDR, NIM, SIZE and INF In which the group of internal factors that have a negative impact on the bank's liquidity are NIM, SIZE The factors ROA, CAP, LDR and INF have a positive impact on the liquidity risk of the bank The remaining variables are not statistically significant in the model.
RECOMMENDATIONS
5.2.1 Declining in bank size and liquidity balance
Research results in the model of bank size (SIZE) have a negative impact on liquidity Although, in fact in Vietnam, banks with large asset scale such as BIDV, VietinBank, VietcomBank, AgriBank have had the advantage in gaining the trust of customers and easily access loans from SBV, credit institutions or on the interbank market At the same time, commercial banks with large capital scale have a wide network, so they have many advantages in mobilizing capital and transferring capital internally to ensure liquidity However, in Vietnam, most banks often rely on the interbank market or liquidity support from the last lender (State Bank) to ensure liquidity for the whole system In general, the large scale might not reduce liquidity risk, and vice versa Moreover, when most banks depend on government intervention in liquidity shortages that partly lead to increasing the bank’s liquidity risk Besides, large-scale banks often use high financial leverage, meaning they borrow more to invest and grant credit However, increased leverage also increases liquidity risks, especially when financial markets experience volatility Therefore, commercial banks need to have effective risk management systems to assess, monitor and manage liquidity risks This includes measuring and monitoring liquidity indicators, assessing the bank's solvency in emergency situations, and developing contingency plans to deal with unexpected situations Banks can reserve a portion of funding or reserve capital for use in case of need, to ensure short-term solvency Furthermore, Diversifying capital supply is a way to minimize liquidity risk Banks can mobilize capital from many different sources such as deposits, bonds, equity capital, or sponsorship from international financial institutions
5.2.2 Handling bad loans and improving credit quality
Research results show that the issue of outstanding loans over total deposits plays a very important role, having a strong impact on the liquidity of Vietnamese commercial banks Therefore, banks need to focus on strengthening risk management capabilities in accordance with their constantly increasing scale over time Strengthen internal supervision to prevent the generation and accumulation of bad debt in the future, by ensuring that banks avoid excessive lending, maintain appropriate credit standards to ensure quality loan amount Vietnamese Commercial Bank needs to build a long-term strategy from remote bad debt prevention measures such as perfecting credit policies in accordance with international standards as a prerequisite to ensure consistent application of credit policies and strict in banking
In addition, banking departments need to focus on performing their functions and tasks well in identifying, controlling and minimizing risks At the same time, make full credit risk provisions and classify debts according to the nature of the debt to provide specific solutions to respond when problems arise with those debts, thereby having a way to manage them more effective When provisions are adequate, debt handling will be easier and avoid affecting the bank's profits and liquidity
Finally, to improve liquidity, banks can consider using the deposit interest rate tool to increase capital mobilized from customer deposits, especially medium and long-term capital to always meet customer needs promptly withdraw money from the bank at a reasonable cost
5.2.3 Decreasing in equity size (CAP) and using efficiently capital
The research results, equity increases similiarly with the liquidity ratio Of all the sources of capital, equity is the most flexible and the bank has the most autonomy However, banks need to use equity effectively to improve their liquidity management capacity Banks can increase their equity in many ways: raising capital from internal sources from undivided profits, raising capital from external sources such as issuing common shares, issuing preferred shares, converting debt securities into shares
Reducing equity capital also comes with narrowing the scale of bank operations, by reducing ineffective or high-risk business activities This can help focus resources on core activities and reduce the risk of liquidity risks In addition, part of the equity capital can be used to optimize the bank's capital structure , including investing in assets that are easily converted into cash when needed This helps enhance capital efficiency and the ability to meet payment needs
5.2.4 Complying with regulations and ensuring safety in liquidity
Commercial banks need to strictly comply with regulations on liquidity assurance as well as other regulations related to banking activities Maintaining a liquidity ratio at a level higher than the minimum requirement of the management agency is necessary to enhance the ability to prevent risks from unforeseen fluctuations in business A violation of the law, even from just one individual on the board of directors, can have serious consequences for the bank's liquidity safety Therefore, internal supervision and inspection need to be conducted periodically to promptly detect and correct errors, ensuring banking operations run smoothly and comply with the law.
LIMITATIONS OF THE TOPIC
While the study has yielded certain insights, it is constrained by the data collection scope pertaining to Vietnamese joint stock commercial banks To enhance the robustness of the findings, it is recommended to broaden the sample size and advocate for complete transparency in the disclosure practices of these banks The current research encompasses data from only 32 joint stock commercial banks over the period from 2011 to 2022, which narrows the study's breadth and precludes a holistic understanding of the variables influencing the banks' financial stability An inclusive and unbiased perspective on the liquidity risks within Vietnam's banking sector necessitates an examination of the entire system This system is comprised of various entities including State Banks, Joint Stock Commercial Banks, wholly foreign-owned banks, Joint Venture Banks, Banks for Social Policies, Associations, and 56 Cooperative Banks The primary shortcoming of this thesis lies in its focus on a subset of 32 joint stock commercial banks Future research endeavors should therefore aim to encompass the full spectrum of the Vietnamese banking system to procure a more comprehensive assessment of its financial stability.
FURTHER RESEARCH DIRECTIONS
The scope of the research topic should be broadened to encompass a more extensive time frame, which will facilitate a more accurate prediction of the trends affecting liquidity risk over various periods Additionally, it is crucial to incorporate a diverse array of financial systems into the study, extending beyond the joint-stock commercial banks in Vietnam to include other banking systems This comprehensive approach will provide a more holistic understanding of the financial landscape and its associated risks
From the regression results obtained in chapter 4, this chapter makes general conclusions to suggest policy implications to help joint stock commercial banks to limit a liquidity risk in order to help regulators, managers, and researchers concentrate more on those variables to strengthen the liquidity risk role of banks Besides, the author also points out the limitations of the topic and proposes future research directions
Ahmed, A S., & Duellman, S (2013) Managerial overconfidence and accounting conservatism Journal of Accounting Research, 51(1), 1–30
Alim, W., Ali, A., & Metla, M R (2021) The Effect of Liquidity Risk Management on Financial Performance of Commercial Banks in Pakistan
Ali, I (2004) Historical impacts on political economy in Pakistan Asian Journal of Management Cases, 54, 191
Alrwashdeh, N N F., Ahmed, R., Danish, M H., & Shah, Q (2023) Assessing the factors affecting the liquidity risk in Jordanian commercial banks: a panel data analysis International Journal of Business Continuity and Risk Management, 13(1), 84-99
Alzoubi, T (2017) Determinants of liquidity risk in Islamic banks Banks & bank systems, (12,№ 3), 142-148
Bhati, S S., De Zoysa, A., & Jitaree, W (2019) Factors affecting the liquidity of commercial banks in India: A longitudinal analysis
Brunnermeier, M K., & Yogo, M (2009) A note on liquidity risk management
Bunda, I., & Desquilbet, J B (2008) The bank liquidity smile across exchange rate regimes International Economic Journal, 22(3), 361–386
Cucinelli, D (2013) The determinants of bank liquidity risk within the context of the Euro area Interdisciplinary Journal of Research In Business, 2(10), 51–64
Chatterjee, S., & Eyigungor, B (2009) Maturity, indebtedness, and default risk
Chen, Y K., Shen, C H., Kao, L., & Yeh, C Y (2018) Bank liquidity risk and performance Review of pacific basin financial markets and policies, 21(01),
Decker, P A (2000) The changing character of liquidity and liquidity risk management: A regulator’s perspective Chicago, IL: Federal Reserve Bank of
Delécha, C., Henao, C., Muthoora, P., & Vtyurina, S (2012) The Determinants of Banks' Liquidity Buffers in Central America
Diamond, D W., & Rajan, R G (2005) Liquidity shortages and banking crises
Duttweiler, R (2011) Managing liquidity in banks: A top-down approach Hoboken: John Wiley & Sons
Eichberger, J., & Summer, M (2005) Bank capital, liquidity, and systemic risk
Journal of the European Economic Association, 3(2-3), 547-555
Falconer, B 2001 Structural liquidity: the worry beneath the surface Balance
Ferrouhi, E M (2014) Bank liquidity and financial performance: Evidence from Moroccan banking industry Verslas: teorija ir praktika, 15(4), 351-361
Fisher, R A (1930) The genetical theory of natural selection Oxford:
Goddard, J., & Wilson, J (2009) Competition in banking: A disequilibrium approach Journal of Banking & Finance, 33, 2282–2292
Gomes, T., & Khan, N (2011) Strengthening bank management of liquidity risk: The Basel III liquidity standards Bank of Canada Financial System Review, 5, 35-42
Goodhart, C (2008) Liquidity risk management Financial Stability Review, 11(6), 39–44
Golubeva, O., Duljic, M., & Keminen, R (2019) The impact of liquidity risk on bank profitability: some empirical evidence from the European banks following the introduction of Basel III regulations Journal of Accounting and Management Information Systems, 18(4), 455-485
Ha, D T., Hang, H T T., Huy, T T., & Phung, N T K (2022) Factors Affecting Liquidity Of Commercial Banks In Vietnam Proceedings on Engineering, 4(2), 115-
Hao, N T N., & Wong, W K (2021) Does Bank Liquidity Risk Lead to Bank's Operational Efficiency? A Study in Vietnam Advances in Decision Sciences, (4), 1-
Hong,V T (2015) Factors affecting the liquidity of Vietnamese commercial banks Journal of Development and Integration, 23(33), 32- 49
Incekara, A., & Çetinkaya, H (2019) Credit risk management: A panel data analysis on the Islamic banks in Turkey Procedia Computer Science, 158, 947-954
Jeanne, O., & Svensson, L O (2007) A credible commitment to optimal escape from a liquidity trap: The role of the balance sheet The American Economic Review,
Keynes, J M (1930) Economic possibilities for our grandchildren New York: Harcourt Brace
Saurina Salas, J., Jiménez, G., & Lopez, J A (2007, September) How Does Competition Impact Bank Risk Taking? In EFA 2007 Ljubljana Meetings Paper, FRB of San Francisco Working Paper (No 2007-23)
Mahmood, H., Khalid, S., Waheed, A., & Arif, M (2019) Impact of macro specific factor and bank specific factor on bank liquidity using FMOLS approach Emerging Science Journal, 3(3), 168-178
Mahmood, Z (2019) An emerging economy perspective on corporate sustainability reporting: Main actors’ views on the current state of affairs in Pakistan,
Asian Journal of Sustainability and Social Responsibility, 8, 5–11
Moussa, M A B (2015) The determinants of bank liquidity: Case of Tunisia International journal of economics and financial issues, 5(1), 249-259
Muharam, H., & Kurnia, H P (2013) Liquidity risk on banking industry: Comparative study between islamic bank and conventional bank in Indonesia Al-
Iqtishad: Jurnal Ilmu Ekonomi Syariah, 5(2)
Munteanu, I (2012) Bank liquidity and its determinants in Romania Procedia
Nguyen, H C (2022) Factors affecting liquidity risks of joint stock commercial banks in Vietnam The Journal of Asian Finance, Economics and Business, 9(4), 197-
Nguyen, H T V., & Vo, D V (2021) Determinants of liquidity of commercial banks: Empirical evidence from the Vietnamese stock exchange The Journal of
Asian Finance, Economics and Business, 8(4), 699-707
Nguyen, T M L (2016) Factors affecting liquidity ratio at Vietnamese commercial banks Journal of Banking, 9, 22–26
Pham, T A (2019) Government spending and economic growth: Theory and empirical evidence in Vietnam Journal of Economics and Development, 5, 11–15
Plochan, P (2007) Risk management in banking [Master thesis] Slovakia:
Praet, P., & Herzberg, V (2008) Market liquidity and banking liquidity: linkages, vulnerabilities and the role of disclosure, Financial Stability Review, 11, 95–
Prochnow, H V (1949) Term loans and theories of bank liquidity New York: Prentice-Hall
Rahman, M L., & Banna, S H (2015) Liquidity risk management: a comparative study between conventional and Islamic banks in Bangladesh Journal of Business and Technology (Dhaka), 10(2), 18-35
Rychtarsik, S (2009) Liquidity scenario analysis in the Luxembourg banking sector (BCL Working Paper No 41) Luxembourg City: Central Bank of Luxembourg
Singh, A., & Sharma, A K (2016) An empirical analysis of macroeconomic and bank-specific factors affecting liquidity of Indian banks Future Business Journal, 2(1), 40-53
Sufian, F., & Chong, R R (2008) DETERMINANTS OF BANK PROFITABILITY IN A DEVELOPING ECONOMY: EMPIRICAL EVIDENCE FROM THE PHILIPPINES Asian Academy of Management Journal of Accounting
Toby, A (2006) An empirical study of liquidity risk management in Nigerian Banks Journal of Financial Management and Analysis, 3(5), 51
Tran, T T., Nguyen, Y T., & Long, T (2019) The determinants of liquidity risk of commercial banks in Vietnam Banks and Bank Systems, 14(1), 94
Trenca, I., Petria, N., & Corovei, E A (2015) Impact of macroeconomic variables upon the banking system liquidity Procedia Economics and Finance, 32, 1170-1177
Truong, Q T (2012) The SCP Paradigm in banking sector – The case of Vietnam, Edition Universitaire Europeenne
Vodová, P (2011) Liquidity of Czech commercial banks and its determinants
International Journal of Mathematical Models and Methods in Applied Sciences, 6(5), 1060–1067
Vodová, P (2013) Determinants of commercial bank liquidity in Hungary E-
Vong, P I., & Chan, H S (2009) Determinants of bank profitability in Macao Macau Monetary Research Bulletin, 12(6), 93-113
Hong, V T (2015) Các yếu tố ảnh hưởng đến thanh khoản của các ngân hàng thương mại Việt Nam https://www.uef.edu.vn/newsimg/tap-chi-uef/2015-07-08- 23/6-so-23.pdf
Wilson, J O., Casu, B., Girardone, C., & Molyneux, P (2010) Emerging themes in banking: Recent literature and directions for future research The British Accounting Review, 42(3), 153–169
(Source: Analysis results from STATA software)
(Source: Analysis results from STATA software)
APPENDIX B RESULTS OF POOL-OLS, FEM, REM AND FGLS
Table B 1 Results of Pool-OLS model
(Source: Analysis results from STATA software)
Table B 2 Results of FEM model
(Source: Analysis results from STATA software)
Table B 3 Results of REM model
(Source: Analysis results from STATA software)
Table B 4 Results of FGLS model
(Source: Analysis results from STATA software)
(Source: Analysis results from STATA software)
(Source: Analysis results from STATA software)
(Source: Analysis results from STATA software)
Table C 3 Breusch and Pagan Lagrangian multiplier test for random effects
(Source: Analysis results from STATA software) Table C 4 Wooldridge test
(Source: Analysis results from STATA software)
Ticker Year LIQ ROA SIZE CAP LLD LDR GDPG INF NIM ABB 2011 0,22 0,01 7,62 0,11 1,60% 1,24 0,06 0,19 0,05