Microsoft Word TCNH KLTN đợt 4 NH 20 21 Nguyễn Mỹ Khánh 030805170081 docx HO CHI MINH CITY, 2021 MINISTRY OF EDUCATION AND TRAINING THE BANK OF VIET NAM BANKING UNIVERSITY OF HO CHI MINH CITY NGUYEN M[.]
INTRODUCTION OF THE STUDY TOPIC
STUDY NECESSARY
One of the most specific risks besides credit risk, interest rate risk, market risk, operational risk,… in bussiness banking is liquidity risk This type of risk doesn’t get mentioned frequently when it comes to banking operation, however it can be seen as a very important link in the whole activity chain of commercial banks If a bank has to deal with liquidity risk, it is predictable to have its usual operations being harmed and get exposed to the threat of the entire system breakdown Ever since the global financial crisis occured, financial institutions and banks had experienced some difficulties because they failed to manage liquidity in prudent manners Therefore, liquidity risk management of commercial banks should always be a matter of special concern by state agencies to carry out management or supervision activities.
Over the past two decades, the Vietnamese banking system has implemented major reform process, leading to new development in both quantity and quality However, liquidity risk was never being given proper attention, or even being considered as a real threat to commercial banks A bank with good liquidity, or in other words, a bank without liquidity risk, is when they always make sure to have available expendable fund storage to meet up with the needs of customers, both requested in advance or pop-up. This should also be the duty of the bank managers, to help ensure adequate liquidity for their working banks Commercial banks might be insolvent, lose its reputation, or worse, having little to no inner resources left to maintain the bank’s operation due to the backfire of being listless to such crucial issue.
This has proven that liquidity is an important factor to the economic welfare of banks Banking sector can be vulnerable if the asset price bubbles, hence exacerbating the current finance situation, leading to many unexpected consequences It is essential
2 to understand the relationships between econometric specifications and liquidity With the mentioned reasons above, author has chosen the topic “Factors affecting the liquidity of commercial banks in Vietnam 2010-2020” to carry out research.
THEORITICAL FRAMEWORKS AND EMPIRICAL STUDIES
The basic groundworks of bank liquidity, risks and causes of liquidity risk.
Authors from foreigner countries has collaged and pointed out some of the main issues that affect liquidity in commercial banks Some of the studies are: The management of Liquidity risk in Islamic Banks: The case of Indonesia (Ismal Rifki, 2010); Liquidity risk management by Zimbabwean Commercial Banks (Laurine Chikoko, 2012); Determinants of Banks Liquidity: Empirical Evidence on Turkish Banks (Amer Mohamad, 2016) Some even bring out the specific case study such as: Managing liquidity risk in bank – Case study Rural investment credit bank Cameroon (Anye Paul Tsi, 2018) or Effect of liquidity management on the performance of commercial banks – A case of Stanbic Bank Uganda Limited (Hillary Businge, 2017) and many more.
According to the study of Anye Paul Tsi (2018), one main reason that affects liquidity so much is through its management The author suggested that due to the unprofessional workers being recruited led to the failure in liquidity management.Therefore, it is unable for banks to meet up with customers liquidity needs Bank shouldn’t just focus on loans in particular sectors, they need to have a general control over every issue to satisfied customers’ needs, especially liquidity needs It would be better in his opinion that rural investment credit banks create legit risk management departments that recruited and train their managers properly to analyze and prevent liquidity risks beforehand.
On the other hand, Businge Hillary (2017), with the case of Stanbic Bank Limited's liquidity management practices, stated that cash storage, supply or withdrawal of liquidity consistent desired level of reserve money from market, daily analysis of liquidity conditions, alongside with detailed reports of daily cash flow The study also discovered that liquidity is a requirement for daily operations and may cause banks to miss out on incentives provided by credit, service, and goods suppliers.
Through Laurine Chikoko (2012)’s research, whom idea also pointed out that banks were lacking of the liquidity management frameworks Besides, Laurine mentioned that Banks in Zimbabwe during the 2000-2008 era relied mainly on internal factors to maintain the liquidity risk under warning states With high inflation rates at the time, customers were in high demand of cash withdrawals, which puts local banks in more danger than foreigner banks Banks relied mainly on cash reserves to manage depositor liquidity, hence use it to calculate and analyze withdrawal patterns.
With domestic research, Tran Thi Lien (2018) mentioned in her master's thesis in economics using quantitative research method, panel data to analyze the factors Pooled OLS Regression and the model runs 2 effects FEM and REM on STATA application.
To overcome the phenomenon of variable variance and autocorrelation, the thesis uses the FGSL method The results show that the factors affecting liquidity were: equity ratio, return on assets, bank market share, operating cost efficiency, inflation rate and economic crisis.
By using data from 20 joint stock commercial banks in Vietnam in the period from
2008 to 2017, Vo Nguyen Thao Quynh (2018) uses the ratio of liquid assets to total assets (LAR) to measure liquidity risk in the period 2008-2017 This is the period when the Vietnamese banking system has many fluctuations since the economy was witnessing the first stage of the fundamental restructuring process in Vietnam's banking system The estimated results of the regression model are as follows: loan-to-total assets ratio, marginal interest income The author also came to a conclusion that economic
4 growth rate has a positive impact on liquidity risk, but inflation rate has a negative impact on liquidity risk.
Through Nguyen Thi Mong Bao (2018)’s analysis, alongside with her correlation and regression of panel data, the study found the impact of a number of factors on the liquidity of commercial banks Equity ratio, asset size, loan ratio, economic growth rate,are reported to positively correlated with liquidity On the contrary, profitability ratio,inflation rate has negative correlation with bank's liquidity However, this study did not find the effect of bad debt ratio, credit provision ratio on liquidity The research not only helps to objectively identify the factors affecting liquidity but also help bank and the governments to make effective management policies for the banking system.
OBJECTIVES
Identify the causes affecting liquidity at 24 Vietnamese joint stock commercial banks listed on the stocks to make appropriate solutions and recommendations.
Based on the research purposes and problematics, alongside with the research basis of previous studies, the specific objectives are:
- Determining the factors affecting the liquidity of commercial banks in Vietnam.
- Among the identified factors, classify micro and macro factors.
- Measure the influence of those factors.
- Find out the correlation between factors.
Based on the following questions to achieve the research objectives initially set out
Question 1 : What factors affect the liquidity of Vietnamese commercial
Question 2: How are the above factors related to liquidity?
Question 3: How much do these factors affect liquidity at commercial banks?
RESEARCH SUBJECTS AND SCOPE
The thesis mainly studies the factors affecting liquidity and their influence on the operation of commercial banks.
- Identification space: The research collects econometric specifications from 24 Vietnamese joint stock commercial banks listed on the stocks market: BIDV, Vietinbank, Vietcombank, VPBank, Eximbank, HDBank, MB, Sacombank, Techcombank, TPBank, VIB, ACB, LienVietPostBank và MSB, SHB, NCB, ABB, Bắc Á Bank, Kienlongbank, Vietbank, VietcapitalBank, Saigonbank, Nam Á Bank,PGBank The author collected data, secondary data, micro and macro factors from the financial statements of these commercial banks.
RESEARCH METHODOLOGIES
Author uses a combination of various types of research methods to be able to complete the thesis:
Method of observation, synthesis, analysis, and comparision between the contrast and similarity of the specific variables appeared within economic events and financial fluctuations that affected liquidity of banks during the observation period within the 24
From the financial statements of 24 commercial banks in Vietnam during the period 2010 to 2020, the author collects necessary data to support the determination of internal factors such as liquidity (LIQ), asset size (LOGAS), capital adequacy ratio(CA), deposits (DP), asset quality (AQ), asset management (AM), return on asset (ROA), operational efficiency (OPEF), non-interest income (NII), net interest margin (NIM) On the other hand, external factors such as GDP growth rate (GDP), inflation rate (INF), exchange rate (EXCH), interest rate (INTRT) were collected from the official website of the World Bank Moreover, based on the scientific studies on liquidity of commercial banks belongs to authors and scientists globally and nationwide, the author was able to inherited their results to carry further scientific research and come up with an appropriate research model for accurate analysis.
The required data will be retrieved from consolidated financial statements, then transform into variables for further calculation After that, calculated variables will be input into econometric models for classifications (choosing between Pooled-OLS, FEM, REM) using software STATA 14.
From the data collected from the financial statements, author then synthesizes the above data in the form of panel data, after that based on the original model from previous studies to analyze this data table To check for correlation of the variables, the author will first use the correlation analysis method Then carry out the F-test to choose between Pool-OLS and FEM/REM estimation methods Next, continue to use Hausman test to choose between FEM and REM for the final suitable model It is essential to run tests on multicollinearity, autocorrelation and heteroscedasticity to avoid false results.Finally,
7 the above defects are overcome by using the Feasible Generalized Least Squares
CONTRIBUTION OF THE TOPIC
The thesis has applied the theories of financial statement analysis, doing research about financial indicators in modern commercial banking management to describe the criteria affecting the liquidity of Vietnam commercial banks listed on stock exchanges in the period from 2010 to 2020.
Based on the previous studies, with a change in space and time, providing a more complete, up-to-date overview of liquidity in commercial banks in Vietnam and thereby help financial practices to understand more about the factors affecting the liquidity of commercial banks in Vietnam.
Moreover, this thesis should work as one of the guidance to help the Government,the State bank and relevant agencies to have timely and effective solutions to the management of liquidity hence hedging any kind of related risk to the topic.
STUDY LAYOUT
CHAPTER 1: INTRODUCTION TO THE TOPIC
1.4 Subjects and scope of research
CHAPTER 2: THEORETICAL BASIS AND OVERVIEW OF
2.1.1 Overview of the commercial banking system in Vietnam
2.1.2 Overview of liquidity of commercial banks in Vietnam
2.2 Overview of research related to liquidity at Vietnamese commercial banks
CHAPTER 3: RESEARCH METHODS AND DATABASE
5.2 Policy implications and suggestions for further adjustments
5.3 Limits of the study and directions for further research
This study examines the impact of bank-specific factors and macro-specific factors on liquidity in Vietnamese commercial banks, for the period of 2010 to 2020, right after the US economy crisis affection from 2007-2009 The thesis also emphasizes the importance of understanding and constructing comprehensive liquidity frameworks as a means of mitigating liquidity stress by using univariate regression model with the dependent variable representing the bank's liquidity and the independent variables are the significant indicators of banks to conduct safety and timely liquidity The thesis was written based on general research opinion from most of the authors, hence inherits and selects the regression model that show clear results that bank liquidity depends not only on factors inside the banking system such as the size of total assets, capital ratio, profitability but also affected by macroeconomic variables such as economic growth, inflation rate, interest rate and exchage rate.
The factors affecting the liquidity of commercial banks are said to be very diverse and impossible to make the most accurate statistics Depending on many different conditions, dissimilar environments and opposed circumstances that lead to unalike results In addition to the result of the data analysis, we can understand thoroughly the existed significant relationship between liquidity, bank specific factors and external factors belong to the economy After that, the state of reserve liquidity will be easier to forecast, hence suitable financial solutions to the growth of commercial banks and the entire economy in general are made.
THEORETICAL BASIS ON FACTORS AFFECTING
Concept
According to the Federal Reserve, the central bank of the United States, liquidity refers to the amount of cash and other assets that banks have on hand to pay bills and meet short-term commercial and financial obligations timely Furthermore, assets like central bank reserves and government bonds that can be swiftly converted to cash when financial obligations are requested are called liquid assets In order to stay afloat, a financial institution needs enough liquid assets to cover depositor withdrawals and other short-term obligations.
❖ The concept of liquidity risk
Liquidity risk normally happens under the influence of these 2 cases For starters, unwanted events might occur between borrowers and banks, such as borrowers were unable to afford the planned terminated loans from bank Secondly, the bank’s relationship with depositors might not goes well when under the circumstance that depositors decide to redeem their savings but the bank is unable to do so In reality, banks frequently discover asset-liability mismatches (gaps) that must be adjusted to balance because banks receive liquid liabilities for banking activities but most of their investment are illiquid assets.
Liquidity risk is primarily dependent on the particular individual features of financial organizations, but under extreme circumstances, it might jeopardize the financial system's liquidity The bank collects a yield that is tied to its profitability since it operates to transform maturities, which are vulnerable to these risks A larger scale of matching assets can decrease liquidity risks and benefit profitability simultaneously On the contrary, this relationship also works in the reverse direction: when loans are in a desperate state, they have an influence on both profitability and liquidity since predicted cash flows do not materialize Furthermore, there is a link between capital and solvency:more capital lowers liquidity creation but provides greater resilience in the case of financial crises (Manish Kumar,2013)
Types of liquidity risks
There are two sorts of liquidity risk: financing liquidity risk and market liquidity risk (European Central Bank, 2002) The financing liquidity risk is triggered by a maturity imbalance between cash inflows and outflows, as well as the immediate and unscheduled cash requirements owing to unforeseen circumstances On the other hand, the inability to liquidate assets at or near market value can manifest as a price drop, is known as market liquidity risk (Brunnermeier and Pedersen, 2009) In this thesis, we will discuss promptly about the affection of the both mentioned risks on the liquidity in commercial banks, as well as analysing the micro and macro factors that directly impact on the ability or inability to fulfill monetary obligation.
Affections from liquidity risks of commercial banks
The Basel Committee on Banking Supervision in February 2008 has mentioned in its study that even when liquidity was becoming more of a major issue to banking activities, many banks still failed to consider a number of basic concepts of liquidity risk management, which in the end led to unsatisfied contingent obligations These financial intermediaries thought severe and long-term liquidity disruptions were unlikely to happen, therefore they didn't run stress tests that took into account the probability of market-wide stress, which created a huge disruption and lead to unexpected consequences Banks by then will be unable to fulfill any prior lending obligations,formally or informally When a sudden need for funds develops and the bank has no liquid funds on hand but only assets, unprofitable asset sales are unavoidable Moreover, the amount of the default risk premium that the bank is required to pay for funds may increase Therefore, liquidity will reap the benefits of the central bank's discount window to borrow money Efficient liquidity management are essential to restrict the use of these facilities Besides, the fact that inflation rates will arise in the economy can’t be ignored.
More importantly, in order to attract reasonable cost funds, banks must create its own safe and sound reputation Reputation risk is more likely to happen in time with liquidity risk, or just right after, which makes it harder for banks to reach out to fund investors The lack of raising proper money fund on time exposes banks to other serious risks such as bankruptcy and government bailout (Holmstrom and Tirole, 2000) That is why it’s very crucial to do research about factors that influence greatly to liquidity,helps strengthen the banking operation over time to hedge risks This will allow the banks to cope with liquidity pressure, avoid the collapse of the whole banking system in specific and generally let the economy under stable control.
THEORY OF LIQUIDITY MEASUREMENTS AND FACTORS AFFECTING
Efficient solutions to reduce liquidity risk and measurements of the liquidity occurrence probability are carry out by analyze the following factors, which include the business model indicators of the bank and the macroeconomic conditions data. According to Eissa A Al-Homaidi, Mosab I Tabash, Najib H Farhan and Faozi A.
(2019), liquidity endures strong affection from both internal and external factors. Whereby bank-specific determinants comprise of: assets size, capital adequacy ratio, deposits, asset quality, profitability, operational efficiency, non-interest income, on the other hand, macroeconomic factors are consist of: GDP growth rate, inflation rate, interest rate, and exchange rate.
In this thesis, liquidity in commercial banks is the main topic to have research on the correlation of related factors In the banking field, liquidity is understood as the ability to meet the withdrawal needs of customers Normally, liquid assets all have a ready and open market for trading This means that all these assets are widely traded all around the world in different exchanges with stable prices As for assets that are illiquid, they are usually not traded on public exchanges but are more often traded privately This means that the prices of liquid assets can change by a large margin and can take a significant amount of time to complete Essentially, the harder it is to turn an asset into cash, the less liquid it is Some central banks in the world have applied quantitative methods in liquidity risk analysis of the commercial banking system to detect and warn about the risk of liquidity of the whole banking system To do this, researchers have built a set of liquidity indicators of the commercial banking system, and these indicators are considered as one of the warning standards to help policy makers as well as bank governance to have timely response to help prevent liquidity crisis from occurring and spreading Analysis of these liquidity risk in the banking system are being carried through: Stress test model, early warning systems, system liquidity index according to Basel, index analysis of systemic-adjusted liquidity, the developments in the currency market.
Asset size is closely related to the performance of banks because when banks do well and make a profit, expansion will open up new opportunities to recruit consumers, enhancing the bank's liquidity by allowing more deposits to be mobilized Nonetheless, if the bank's operational activities are unproductive, it will be exposed to significant risks when it is unable to meet the bank's requirements for deposit settlement or payment of past-due obligations As a result, depending on the business state of the bank, increasing the size of the bank might have a favorable or negative influence on liquidity risk.
Various research on the impact of asset size on bank liquidity risk have given conflicting conclusions However, the author believes that this particular factor has a broad impact on liquidity even though it’s not crucial, which is the same idea with Donjeta Morina (2021)’s research Due to how large the scale of the bank is, the price of fund will adjust accordingly, leading to easier access to the available source of wealth, causing less concern to the liquidity Therefore, the following hypothesis will be:
Hypothesis H 1 : Asset size has a positive correlation with liquidity in commercial banks.
This is an economic indicator in which reflects the relationship between equity and risk-adjusted assets of a commercial bank closely The lower this ratio indicates, the higher chances that banks use financial leverage, which contains a lot of risk and can reduce the bank's profit with high cost of loan (Munteanu, 2012) As a result, opportunity to obtain interest income from loans low, which leads to the amount of credit extended is relatively low, and then end up with low liquidity Research by Tang
My Sang (2018) has shown that CA coefficient is proportional with LIQ, so it changes dependently with the indicator Therefore, author expect the capital adequacy ratio to be positively correlated with the bank's liquidity Hence, the following hypothesis will be:
Hypothesis H 2 : Capital adequacy ratio has a positive correlation with liquidity in commercial banks.
Liquidity risk in banking has been related to transaction deposits and their ability to trigger issues in banking activities , according to some study of Singh and Sharma
(2016), Rashid and Jabeen (2016), Sopan and Dutta (2018), which passively blamed depositor for the bank's struggles Arif and Anees (2012) also raised a concern that banks have to deal with liquidity problems when deposits in these financial institutions are withdrawn out of the blue It is assumed that an inverse relationship is relevant between deposits and bank’s liquidity, leading to a reduction in liquidity once deposits witnessed a rise, Dinger (2009) Meanwhile, Gatev and Strahan (2009) also showed that banks employ transaction deposits to minimize the risk of unused loan commitments, which later on turn into a liquidity concern Low-level transaction deposits bank normally will have their stock-return volatility increases with unused commitments In times of limited liquidity, deposit lending strategy becomes significantly stronger Their findings contradict the traditional view of bank liquidity risk Nevertheless, deposits indicator in the bank usually have negative influence on liquidity With that being discussed, the hypothesis will be:
Hypothesis H 3 : Deposits has a negative correlation with liquidity in commercial banks.
The asset quality of the banks was carry on research in the liquidity risk model, which can be describe as a ratio of Loan over Total Assets, in which a slight reduction in loan rate can lead to a disturbing decrease in asset quality of banks Furthermore, it can even slow down bank’s ability to generate incomes Therefore, more risks are created which slow down the liquidity process (Sopan and Dutta, 2018) Banks should put more money in liquid assets with high quality if their funding costs rise, as a way of saying that banks should not depend on interbank funds if their liability costs increase, but rather invest in liquid assets that will contribute considerably to liquidity When banks have sufficient liquid assets with great quality, they become less reliant on other external sources of funding Considering to be an explanatory variable, therefore asset quality mainly has positive effect on the liquidity risk in banks, according to Munteanu
(2012) The higher the quality, the more valuable the asset is Hence, it is easier to approach liquidity in banking activities This will lead to the hypothesis:
Hypothesis H 4 : Asset quality has a positive correlation with liquidity in commercial banks.
To measure precisely profitability of the 24 banks, ROA is an important indicator while carry out the regression model In Moussa (2015)’s idea, the ratio reflects the efficiency of the bank's management in generating assets As a backup in case of sudden withdrawals, banks often reserve liquid assets at a certain level This factor presents its positive affection towards the bank’s liquidity, and has been pointed out by some of the previous studies (Singh and Sharma, 2016; Vodova, 2013) Research by Bordeleau, É., and Graham, C (2010) also stated that the profitability of the Bank of Canada is improved greatly when the bank captures a necessary amount of liquid assets Higher
ROA reflects a high flow of capital to fulfill liquidity requirements, whereas lower ROA shows less excess capital, which may cause liquidity volatility during demand deposits. Besides which, a bank with low profitability ratio prevents potential lenders from providing funds because of the likelihood of the bank to be solvent.
Moreover, it is recommended that ROE and ROA indicators should be combine. The combination of this pair of indicators will not only evaluate the efficiency of production activities, but also have a better view of the financial structure of the business However, while running tests, a lot of problems occurred leading to multicollinearity so author selected only ROA indicator for this thesis ROA indicator throughout many research has stated its positive affection to liquidity in commercial banks This will lead to the hypothesis:
Hypothesis H 5 : ROA has a positive correlation with liquidity in commercial banks.
Operational efficiency in financial institutions is often focused on optimizing the transaction costs associated with an investment In the study of Berger and Mester
(1997), operational efficiency of commercial banks is reflected in the relationship between output revenue and the cost of using input resources, or the ability to turn input resources into the best outputs in business activities of commercial banks Specifically, it is effective once commercial banks create the largest output revenue with the smallest input resource value In the framework of this study, commercial banks are considered to be efficient when they achieve the greatest output through the use of the same amount of input resources as other commercial banks but the lowest usage costs You can tell if a bank is under good condition or maintain good position in the field based on how high its operational efficiency The study of Rashid and Jabeen (2016) depicts positive and significant association between banks operational efficiency and banks liquidity It is no doubt that operational efficiency has a positive affection accordingly with the liquidity in commercial banks because with good level of operational efficiency, banking operation tends to work well, letting liquidity process to occur smoothly As a result, the hypothesis will be:
Hypothesis H 6 : Operational efficiency has a positive correlation with liquidity in commercial banks.
Net interest income (NII) is the difference between interest income from the use of productive asset and cost of the use of debt and also one of the main performance metric for most financial institution In each bank, NII varies accordingly, especially when it comes to interest rate implementation, such as employing a floating rate, flat rate, or sliding rate NII can be positive or negative depending on whether the asset is sold for a profit or a loss This indicator has been taken as an important attributes and measures of bank specifics Therefore, NII has a close correlation with the formation of factors affecting the bank's liquidity Which then leads the author to this hypothesis:
Hypothesis H 7 : NII has a positive correlation with liquidity in commercial banks.
This is the percentage difference between interest income generated by a bank's earning assets (loans and investments) and its main expenses - interest paid to depositors The net ratio between interest earned and interest paid to customers is a key measure of a bank's profitability In developing countries like Vietnam, with scarce capital markets, individuals and businesses tends to access capital mainly through bank loans with limited information Hence, too much dependence on banks is shown, in which resulted in high NIM when banks lend money A huge increase in cost of loan reduces private investment, inhibits economic development (Martinez Peria and Mody,
DATABASE
5.2 Policy implications and suggestions for further adjustments
5.3 Limits of the study and directions for further research
This study examines the impact of bank-specific factors and macro-specific factors on liquidity in Vietnamese commercial banks, for the period of 2010 to 2020, right after the US economy crisis affection from 2007-2009 The thesis also emphasizes the importance of understanding and constructing comprehensive liquidity frameworks as a means of mitigating liquidity stress by using univariate regression model with the dependent variable representing the bank's liquidity and the independent variables are the significant indicators of banks to conduct safety and timely liquidity The thesis was written based on general research opinion from most of the authors, hence inherits and selects the regression model that show clear results that bank liquidity depends not only on factors inside the banking system such as the size of total assets, capital ratio, profitability but also affected by macroeconomic variables such as economic growth, inflation rate, interest rate and exchage rate.
The factors affecting the liquidity of commercial banks are said to be very diverse and impossible to make the most accurate statistics Depending on many different conditions, dissimilar environments and opposed circumstances that lead to unalike results In addition to the result of the data analysis, we can understand thoroughly the existed significant relationship between liquidity, bank specific factors and external factors belong to the economy After that, the state of reserve liquidity will be easier to forecast, hence suitable financial solutions to the growth of commercial banks and the entire economy in general are made.
CHAPTER 2 THEORETICAL BASIS ON FACTORS AFFECTING LIQUIDITY OF COMMERCIAL BANKS
2.1 THEORIES ON FACTORS AFFECTING LIQUIDITY OF
According to the Federal Reserve, the central bank of the United States, liquidity refers to the amount of cash and other assets that banks have on hand to pay bills and meet short-term commercial and financial obligations timely Furthermore, assets like central bank reserves and government bonds that can be swiftly converted to cash when financial obligations are requested are called liquid assets In order to stay afloat, a financial institution needs enough liquid assets to cover depositor withdrawals and other short-term obligations.
❖ The concept of liquidity risk
Liquidity risk normally happens under the influence of these 2 cases For starters, unwanted events might occur between borrowers and banks, such as borrowers were unable to afford the planned terminated loans from bank Secondly, the bank’s relationship with depositors might not goes well when under the circumstance that depositors decide to redeem their savings but the bank is unable to do so In reality, banks frequently discover asset-liability mismatches (gaps) that must be adjusted to balance because banks receive liquid liabilities for banking activities but most of their investment are illiquid assets.
Liquidity risk is primarily dependent on the particular individual features of financial organizations, but under extreme circumstances, it might jeopardize the financial system's liquidity The bank collects a yield that is tied to its profitability since it operates to transform maturities, which are vulnerable to these risks A larger scale of matching assets can decrease liquidity risks and benefit profitability simultaneously On the contrary, this relationship also works in the reverse direction: when loans are in a desperate state, they have an influence on both profitability and liquidity since predicted cash flows do not materialize Furthermore, there is a link between capital and solvency: more capital lowers liquidity creation but provides greater resilience in the case of financial crises (Manish Kumar,2013)
There are two sorts of liquidity risk: financing liquidity risk and market liquidity risk (European Central Bank, 2002) The financing liquidity risk is triggered by a maturity imbalance between cash inflows and outflows, as well as the immediate and unscheduled cash requirements owing to unforeseen circumstances On the other hand, the inability to liquidate assets at or near market value can manifest as a price drop, is known as market liquidity risk (Brunnermeier and Pedersen, 2009) In this thesis, we will discuss promptly about the affection of the both mentioned risks on the liquidity in commercial banks, as well as analysing the micro and macro factors that directly impact on the ability or inability to fulfill monetary obligation.
2.1.3 Affections from liquidity risks of commercial banks
The Basel Committee on Banking Supervision in February 2008 has mentioned in its study that even when liquidity was becoming more of a major issue to banking activities, many banks still failed to consider a number of basic concepts of liquidity risk management, which in the end led to unsatisfied contingent obligations These financial intermediaries thought severe and long-term liquidity disruptions were unlikely to happen, therefore they didn't run stress tests that took into account the probability of market-wide stress, which created a huge disruption and lead to unexpected consequences Banks by then will be unable to fulfill any prior lending obligations,formally or informally When a sudden need for funds develops and the bank has no liquid funds on hand but only assets, unprofitable asset sales are unavoidable Moreover, the amount of the default risk premium that the bank is required to pay for funds may increase Therefore, liquidity will reap the benefits of the central bank's discount window to borrow money Efficient liquidity management are essential to restrict the use of these facilities Besides, the fact that inflation rates will arise in the economy can’t be ignored.
More importantly, in order to attract reasonable cost funds, banks must create its own safe and sound reputation Reputation risk is more likely to happen in time with liquidity risk, or just right after, which makes it harder for banks to reach out to fund investors The lack of raising proper money fund on time exposes banks to other serious risks such as bankruptcy and government bailout (Holmstrom and Tirole, 2000) That is why it’s very crucial to do research about factors that influence greatly to liquidity, helps strengthen the banking operation over time to hedge risks This will allow the banks to cope with liquidity pressure, avoid the collapse of the whole banking system in specific and generally let the economy under stable control.
2.2 THEORY OF LIQUIDITY MEASUREMENTS AND FACTORS AFFECTING THE LIQUIDITY OF COMMERCIAL BANKS
Efficient solutions to reduce liquidity risk and measurements of the liquidity occurrence probability are carry out by analyze the following factors, which include the business model indicators of the bank and the macroeconomic conditions data. According to Eissa A Al-Homaidi, Mosab I Tabash, Najib H Farhan and Faozi A.
(2019), liquidity endures strong affection from both internal and external factors. Whereby bank-specific determinants comprise of: assets size, capital adequacy ratio, deposits, asset quality, profitability, operational efficiency, non-interest income, on the other hand, macroeconomic factors are consist of: GDP growth rate, inflation rate, interest rate, and exchange rate.
In this thesis, liquidity in commercial banks is the main topic to have research on the correlation of related factors In the banking field, liquidity is understood as the ability to meet the withdrawal needs of customers Normally, liquid assets all have a ready and open market for trading This means that all these assets are widely traded all around the world in different exchanges with stable prices As for assets that are illiquid, they are usually not traded on public exchanges but are more often traded privately This means that the prices of liquid assets can change by a large margin and can take a significant amount of time to complete Essentially, the harder it is to turn an asset into cash, the less liquid it is Some central banks in the world have applied quantitative methods in liquidity risk analysis of the commercial banking system to detect and warn about the risk of liquidity of the whole banking system To do this, researchers have built a set of liquidity indicators of the commercial banking system, and these indicators are considered as one of the warning standards to help policy makers as well as bank governance to have timely response to help prevent liquidity crisis from occurring and spreading Analysis of these liquidity risk in the banking system are being carried through: Stress test model, early warning systems, system liquidity index according to Basel, index analysis of systemic-adjusted liquidity, the developments in the currency market.
Asset size is closely related to the performance of banks because when banks do well and make a profit, expansion will open up new opportunities to recruit consumers, enhancing the bank's liquidity by allowing more deposits to be mobilized Nonetheless, if the bank's operational activities are unproductive, it will be exposed to significant risks when it is unable to meet the bank's requirements for deposit settlement or payment of past-due obligations As a result, depending on the business state of the bank, increasing the size of the bank might have a favorable or negative influence on liquidity risk.
Various research on the impact of asset size on bank liquidity risk have given conflicting conclusions However, the author believes that this particular factor has a broad impact on liquidity even though it’s not crucial, which is the same idea with Donjeta Morina (2021)’s research Due to how large the scale of the bank is, the price of fund will adjust accordingly, leading to easier access to the available source of wealth, causing less concern to the liquidity Therefore, the following hypothesis will be:
Hypothesis H 1 : Asset size has a positive correlation with liquidity in commercial banks.
This is an economic indicator in which reflects the relationship between equity and risk-adjusted assets of a commercial bank closely The lower this ratio indicates, the higher chances that banks use financial leverage, which contains a lot of risk and can reduce the bank's profit with high cost of loan (Munteanu, 2012) As a result, opportunity to obtain interest income from loans low, which leads to the amount of credit extended is relatively low, and then end up with low liquidity Research by Tang
My Sang (2018) has shown that CA coefficient is proportional with LIQ, so it changes dependently with the indicator Therefore, author expect the capital adequacy ratio to be positively correlated with the bank's liquidity Hence, the following hypothesis will be:
Hypothesis H 2 : Capital adequacy ratio has a positive correlation with liquidity in commercial banks.
Liquidity risk in banking has been related to transaction deposits and their ability to trigger issues in banking activities , according to some study of Singh and Sharma
DESCRIPTION OF RESEARCH METHOD
The thesis uses theoretical basis as the foundation for analyzing the liquidity of joint-stock commercial banks, following by adopted theories of financial statement analysis and financial indicators in modern banking administration In addition, this research exclusively studies the criteria normally being used to measure and evaluate liquidity inside commercial banks, as indicated in the subject and scope of research. Beside using qualitative method to analyze and synthesize, author also incorporated quantitative method by using correlation analysis, panel regression technique to compare the contrast of the related indicators to liquidity risks and determine the liquidity in banks Different methodologies and concept models are used to identify the impacts, which include: Pooled-OLS, REM, FEM, and FGSL.Furthermore, to draw the conclusion that the models are suitable, the author has employed a variety of defect testing methodologies to assess the model's applicability.
RESEARCH MODEL
Through studying the theory of liquidity and factors affecting liquidity along with review of previous studies, the author finally picked out the research model belongs to author Eissa A Al-Homaidi, Mosab I Tabash, Najib H Farhan and Faozi A (2019) as the original model to build a proposed research model and apply specifically to this study In addition, the reference model is suitable since it has similar reflection with the actual operating situation of the Vietnamese economy as well as the commercial banking system.
The data was subjected to multiple regression analysis in order to identify the impact of various characteristics of liquidity risk on the performance of Vietnamese commercial banks In order to match domestic conditions, the model of this thesis is adjusted and built based on the study of Chowdhury and Rasid (2017) and Masood and Ashraf (2012), Eissa A Al-Homaidi, Mosab I Tabash, Najib H Farhan and Faozi A. (2019):
LỉQ it = p 0 + p i LOGAS it + p 2 CA it + p 3 DP it + p 4 AQ it + p s ROA it
+ p 6 OPEF it + p 7 Nỉỉ it + p s NỉM it +p g GDP it
+ p ÍO ỉFR it + p^EXCH it + p^ỉNTRT it + £ it
In this regression model, the author uses 1 independent variable, which is liquidity(LIQ) and 12 other dependent variables which comprise of: Assets size (LOGAS), capital adequacy ratio (CA), deposits (DP), asset quality (AQ), return on assets (ROA),operational efficiency (OPEF), non-interest income (NII), net interest margin (NIM) alongside with macroeconomic factors such as: GDP growth rate (GDP), inflation rate(INF), exchange rate (EXCH), interest rate (INTRT).
DESCRIPTION OF VARIABLES
In this study, the dependent variable which is liquidity (LIQ) can be defined by using liquid assets/total assets with the calculation of other inner and outter bank-related indicators According to Pushpa Raj Ojha (2017), this ratio provides a general information about a bank's liquidity This explains the percentage of liquid assets available in the total assets of the bank A high ratio means the bank's liquidity is very good.
The size of a bank has a significant impact on liquidity The author uses the natural logarithm to calculate the asset size using total assets data, thereby determining the relationship between total assets and liquidity risk (Munteanu, 2012).
Asset size it = Natural logarithm of total assets
Basel’s capital adequacy ratio is measured by using equity divided to total assets.Capital adequacy refers to the share capital available to help the business of banks fluctuate smoothly The amount of this capital measured by the ratio of net capital to total assets enables the bank to absorb any sudden shocks that may occur suddenly.
Based on the prior studies of Singh and Sharma (2016), Moussa (2015), Rashid and Jabeen (2016), the author has concluded that deposits in this research will be calculated by using the equation of deposits over total assets.
The findings of the study suggested that asset quality had an influence on the liquidity deliberately This indicator is calculated by using the ratio of loans over total assets In the findings of Sopan and Dutta (2018)’s research, it is believed that the higher this ratio indicates a bank is loaned up and its liquidity is low The higher the ratio, the more risk a bank may deal with.
Author calculated return on assets (ROA) by dividing net operating income by total assets By using this ratio, it will be easier to show bank’s intentions, therefore explaining why they choose to participate in high-risk ventures in order to boost profits. (Moussa,2015)
A ratio between net interest income and total asset to measure the efficiency in business banking operation Rashid and Jabeen (2016) said that with the result of this equation, the efficiency of profit earned from operating costs in business banking
Total Assets it function is clearly represented, which dedicates a positive correlation towards liquidity in commercial banks.
The differences between any profits (before taxes are applied) and trade commissions or fees is net investment income (NII) By using net profit over total asset ratio, we can calculate a useful value for investors to gauge how much revenue exceeds a company's expenses, hence prediction on handiness liquidity is made easier.
Net interest margin is computed by dividing net interest income by total earning assets Raising capital from customer deposits and lending is the main revenue- generating activity for the bank However, there are still other activities that bring profits to the bank such as trading in securities, derivatives, foreign exchange, guarantee Profitable assets are those that bring profits to the bank such as loans to customers, investments, deposits at the SBV, etc Thus, when a bank has the best ability to allocate assets to yielding assets, the net interest income for the best period will result in a high NIM (ratio of interest margin) Depending on the credit cycle and regulatory policies of the State Bank or the lending policy of each bank, NIM ratios will vary from period to period and from bank to bank As a results, high NIM indicator will leads to high liquidity in commercial banks.
GDP growth rate is a financial indicator of the health of a country's economy The
Nll it Non — Interest Income it
NIM it Net Interest Income it
Total Assets it author collected the real annual GDP growth rate for the research in order to observes its positive affection towards liquidity in commercial banks GDP growth rate tends to increase, though unevenly, over the study period However, in 2020, Vietnam's GDP witnessed a strong downfall To explain this sharp decline, the COVID-19 epidemic was the main cause of the outbreak and appeared in Vietnam in early 2020, causing a stagnation of the economy.
Within this study, the author collected the annual inflation rate over the observation period The inflation rate of a country through many studies has shown its own close relationship and the liquidity of commercial banks But this effect is still very much debated when some argue that its negative impact on bad debt ratio cannot be denied, others reject the above idea by giving the opposite argument through other empirical studies However in this research, the author belive that the opposite effect of
INF shows that firms hold less liquid assets in response to rising inflation, as higher inflation rate leads to erosion of the purchasing power of money.
By measuring the FX market globally, Banti et al (2012) has stated that a relationship between exchange rate and liquidity does exists Moreover, the author also belived that liquidity risk is priced in the cross section of currency returns Strong common movements in liquidity across currencies was also found by Mancini et al.
(2013 ) in the findings of Mancini’s research Subsequently, the author can confirm that liquidity and exchange rate had strong impact on each other when collecting the average exchange rate during observation period.
We have set the interest rate as an important indicator of monetary policy The interest rate is considered as an important factor affecting banking activities The lending interest is collected If the interest rate raises, the demand for credit falls which means a decrease in illiquid assets, but if there is an increase in interest rates on deposits the creation of liquidity by liabilities will increase.
LIQ Liquidity Liquid Asset bc Total
LOGAS Size of the bank Natural logarithm of total assets H 1 (+)
Equity it Total Assets it H 2 (+) Munteanu
Loan bc Total Assets bc H 4 (+)
Net profit it Total Assets bc
Total Operating Expense bc Total Assets bc H 6 (+)
Descripti on Formula Estimate d effect Reference
Annual real GDP growth rate H 9 (+) Munteanu
INF Inflation rate Annual inflation rate H 10 (-)
EXCH Exchange rate Average in a year H 11 (+)
INTRT Interest rate Lending interest H 12 (+)
(Source: Compiled by the author) 3.4.1 Model OLS, FEM, REM, FGLS
This study employs quantitative and regression methods by using Stata 14.0 software As a result, a regression model with 1 dependent variable and 12 independent variables was developed The author then uses the variance magnification factor (VIF - Variance Inflation Factor) to test for multicollinearity.
PTER 4: RESEARCH RESULTS
4.1 PRACTICAL SITUATION OF DETERMINE FACTORS IN
4.1.1 Practical situation of liquidity in commercial banks in Vietnam 2010- 2020
Liquidity in commercial banks has a slight downward trend during the period 2010- 2020 We have witnessed a peak in 2011 at a rate of 28.17%, following by a drastic drop over the year Especially, in 2016, the liquidity seemed to have reached the bottom with the rate 13.21% Afterwards, there has been a small incline in the next 4 years, but the percentage are only about half of the highest rate in 2011 (15.19% in 2020).
Figure 4.1 Liquidity on total asset ratio during 2010-2020
As of 2010, the banking industry has revealed a number of problems as a result of the global market's impact as well as the impacts of the previous explosive growth.
Credit quality had worsened, system liquidity was chaotic, and there was a threat of malfunction in business banking.
This led to the result in early 2012, the commercial banking system began the restructuring process in accordance with the Scheme on System Restructuring in Financial Institutions for the Period 2011-2015 (Project 254) After 5 years of implementation, the project has largely met its objectives, dealt with weak commercial banks, and maintained overall system stability Therefore, the Bank Restructuring Project Phase 2 (2016 - 2020) was approved by the Government in Decision No 1058/ QD-TTg, with specific objectives: Continue to reduce the bad debt ratio in an effective manner, reduce the number of weak commercial banks; pulling lending interest rates down to the average level of developing countries, which is about 5%/year; ensure 70% of commercial banks fully implement Basel II by 2020 Up to now, after nearly 3 years, this process has achieved initial results However, the system still has potential problems of concern about high level of bad debt as well as poor financial capacity of commercial banks All resulted in the decline of the liquidity over the 11 years period.
4.1.2 Practical situation of independent factors determine liquidity in commercial banks in Vietnam
During the period 2010-2020, the asset size in banks is quite stable The asset size’s value is a lot higher than liquidity in banks The bank’s asset size mainly stays between 16-17% Even though the incline rate is quite small, it is clear to see that the asset size in banks is increasing overtime.
Figure 4.2 Liquidity and asset size during 2010-2020
(Source: Calculated by the author)
It is estimated that in the past decade, the total assets of the entire banking system have at least doubled its original size In January 2020, there were 4 banks with total assets exceeding 1 million billion VND: BIDV, Agribank, VietinBank, Vietcombank. The total assets of these four banks currently account for about 40% of the entire system (cafef.vn) According to this study of Delechat et al (2012), a bank's size has a negative impact on its liquidity Large banks can raise funds from sources elsewhere outside, whereas small banks must try hard to maintain adequate liquidity This explain why the bigger the asset size, the smaller the liquidity buffer gets, as the great distance between liquidity and asset size rate shown above.
❖ Liquidity and Capital adequacy ratio
Drastic changes have occurred through the years during 2010-2020 In 2012, the capital adequacy ratio of bank has the highest value among the years (11.17%) Ever since 2012, the ratio kept sliding down and varies between 6% Small reduction has been detected during 2017-2020, and it might take a while to reach the top again.
Figure 4.3 Liquidity and capital adequacy ratio during 2010-2020
(Source: Calculated by the author)
In order to manage the business activities of commercial banks, the State Bank of Vietnam has used capital adequacy ratio (CA) as a management tool to ensure that the business activities of banks are not exposed to great risks such as bad debt, liquidity risk that jeopardize business in banks Therefore, the ratio of liquidity and capital adequacy ratio is relatively correlation.
According to the author's statistics, the deposits of commercial banks tends to increase steadily over time However, in 2011 while the ratio was at the bottom of the analysis table with at rate 48.48%, a breakthrough incline occurred right in the following year The deposits ratio was 82.87% in 2012, nearly double from previous year But from 2013 to 2020, the growth rate dropped by 20% and performed insignificant ratio but still maintains this increase at a slow speed.
(Source: Calculated by the author)
To explain for the sudden change in 2012, the deposit and loan interest rates in this particular year had many big changes 2012 was a very difficult year for the Vietnamese economy at many different levels However, the Government and the State
Figure 4.4 Liquidity and deposits during 2010-2020
Bank also have timely policies and decided to have 6 reductions in deposit and lending interest rates In 11/6/2012, the interest rate ceiling for deposit in VND has been reduced from 11%/year to 9%/year In addition, according to Circular 19/2012/TT- NHNN issued on June 8, 2012, the State Bank allowed commercial banks to decide on their own interest rates for long-term deposits (from 12 months or more) This is a reasonable step of the SBV, helping commercial banks to balance their term deposit structure.
Asset quality ratio seems to increase steadily through times The average ratio lies between 87% to 92% during 11 years record On the other hand, an unexpected incline in 2019 with the value of 129.89% has caught the attention of the author.
Figure 4.5 Liquidity and asset quality during 2010-2020
(Source: Calculated by the author)
According to the consolidated financial report and expert comments, in 2019, the banking industry achieved many positive results, although credit growth was rather modest (less than 14%), many banks reported to have extremely profitable profits This came from the great decline in bad debt ratio of most banks as a result of the flexible management of SBV in issuing a series of interest rate adjustment policies, especially with the implementation of Circular 22/2019/TT-NHNN which stipulating safety limits and ratios in banking operations, ensuring objectives of money market management.
In which, Vietcombank reached the milestone of USD 1 billion when the profit reached VND 23,155 billion, up 26.7% compared to the previous year and exceeding 13% of the year plan Along with state-owned banks such as Agribank, VietinBank, BIDV also had a positive business year Agribank reported a profit of VND 12,700 billion, BIDV reached VND 10,768 billion and VietinBank reached VND 11,780 billion.
The return on asset ratio has been quite stable throughout the years It is clear to see that the peak was at 1.51% (2011) and the drop didn’t stop until the lowest point was reached at 0.05% (2015) Later on, the indicator seems to have a slight incline but doesn’t make much impact compare to the highest point.
Figure 4.6 Liquidity and ROA during 2010-2020
(Source: Calculated by the author)
During the period from 2010 to 2020, commercial banks are increasingly expanding in both size and number Furthermore, commercial banks also aggressively implemented defensive work for the purpose of controlling bad debts, improving credit quality, so that the bad debt ratio accounted for less, as well as a raise in the liquidity. Therefore, we can see the positive movement between LIQ ratio and ROA under the total asset measure.
Same goes as other bank-specific indicators, the indicator operational efficiency doesn’t seem to have much changes during the year and maintain a stable rate under2%.
However, in 2014, there has been a change in the pattern when the value occurred at 2.44%, twice the value compares to its lowest in 2010 (1.3%).
(Source: Calculated by the author)
According to Duong Nguyen Thanh Tam (2020), the global economic crisis that broke out in 2007-2008 left a global recession and also caused a stagnation in the Vietnamese economy after this struggling time The financial performance of banks in general and the commercial banking system in Vietnam in particular has caused a serious decline Faced with that situation, the Project on restructuring the system of credit institutions for the period 2011 - 2015 was issued together with the Prime Minister's Decision No 254/QD-TTg dated March 1, 2012 and the Prime Minister's Decision No The project “Restructuring the system of credit institutions associated with bad debt settlement for the period 2016-2020” in Decision No 1058/QD-TTg dated July 19, 2017 was issued, which then brought Vietnam into two restructuring phases From 2012-2015, which is the first restructuring journey, the operational efficiency at commercial banks has not yet recovered significantly By the second stage
Figure 4.7 Liquidity and operational efficiency during 2010-2020
0.01 0 0.0180 0.020 0.0181 0.0244 0.0165 0.016 0.0174 0.0180 0.0178 0.0170 of the restructuring process, the operational efficiency at commercial banks had a better growth at 17.8% before falling again due to the epidemic situation in 2020.
The NII ratio is an important indicator when it comes to business in banking system.
The number is rather small with a consistent growth rate, mostly lies beneath 1%.
Figure 4.8 Liquidity and NII during 2010-2020
(Source: Calculated by the author)
RESEARCH RESULTS
The summary of the significant relationship between factors affecting the liquidity of Commercial banks in Vietnam is described by descriptive statistics The Sum
(Source: Calculated by the author)
Figure 4.13 Liquidity and interest rate during 2010-2020 function in the statistical software Stata 14 was used to summarize the characteristics of the data Descriptive statistics analyze common criteria such as mean, standard deviation, minimum value, maximum value This dataset collected from 24 banks with the value of 12 objects from 2010-2020 and presented in the following panel:
Variable Obs Mean Std Dev Min Max
Note: This table reports the variable statistic of the panel regression result of 24 Joint stock commercial banks in Vietnam in the period 2010-2020 The dependent variable is LIQ , which is the liquidity of commercial banks LOGAS is the asset size, CA is the capital adequacy ratio, DP is the deposits, AQ is the asset quality, ROA is the return on assets, OPEF is the operational efficiency, NII is the non-interest income, NIM is the net interest margin, GDP is the economic growth rate, INF is the inflation rate, EXCH is the exchange rate, INTRT is the interest rate The definition of the variables is presented in Table 4.1
(Source: Calculated by the author)
Table 4.1 presents the statistics of variables used in the research model with data belongs to 256 observations from 24 joint stock commercial banks listed on the stock exchanges in the period from 2010 to 2020.
Statistical results show that the average value of liquidity (LIQ) of commercial banks is 18%, while the lowest LIQ level belongs to TP Bank (1%) in 2019 and the highest level is LienViet Post Bank (105,1%) in the year 2012 Thus, it can be view clearly that liquidity in Vietnamese commercial banks is quite good A positive result in liquidity implies that joint-stock commercial banks are always under stable control of liquidity.
Asset size (LOGAS): 16.73 is the average value for asset size, which is fairly the highest rate in all variable due to its enormous dataset value Saigon Bank has got the minimum value at 13.34 in 2010, in contrast with maximum value of Vietcombank in
2017 (19.63) It is no surprise to see Vietcombank holds the maximum percentage in asset size due to its large scale in banking business and its popularity for creditability while ranking 1 st in the top 10 most trustworthy commercial banks in Vietnam.
Capital adequacy ratio (CA): This factor average value is 8%, alongside with 1.5% for the minimum value of VPBank in 2019 and TPBank holds 29.9% for the maximum value in 2012.
Deposits (DP): The average value is 67.23% with the minimum value is 25.1% belongs to TPBank in 2011 and maximum value is 528.7% belongs to Viet Capital Bank in 2012 Viet Capital Bank has proved that with good monetary policies, you can earn customer’s trust, which will lead to higher chance of having more quality liquidity assets even though their popularity wasn’t on top Therefore, its deposits in 2012 was at a massive rate compares to others.
Asset quality (AQ): This indicator show an average value at 97.75%, while its minimum value at An Binh Bank is 63.2% in 2014 and maximum value at PG Bank is 911.5% in 2019 A positive asset quality ratio which describe a correlation relationship with high liquidity rate is a good result for any banking business.
Return on assets (ROA): The average value of ROA is 0.87% While Vietnam Thuong Tin Bank – VBB owns it lowest spot at -0.4% in 2015, the top spot belongs to
Operational efficiency (OPEF): 17.58 is the average value of this indicator with the minimum value is -1.25% belongs to TP Bank in 2016 On the opposite, Techcombank has the maximum value of 18.81% in 2014 due to its excellent money management services, trade finance and risk management capabilities in foreign exchange business.
Non-interest income (NII): The average value is 0.62%, with the minimum value is 0.0018% belongs to ACB in 2016 and the maximum value is 38.7% belongs to Saigon Bank in 2010 The higher the NII, the more profit a bank earns from selling an asset, which leads to higher liquidity rate.
Net interest margin (NIM): The average value stays at 3.25% TP Bank in 2011 occurred the lowest NIM rate at -0.9%, while VP Bank in 2019 has got the highest rate of NIM at 9.5%.
GDP growth rate (GDP): 6.0% is the average value of this indicator In Vietnam particularly, we have witnessed the lowest rate of GDP in 2020 at 3% While the highest rate occurred just 2 years before the drop at 7.1% (2018).
Inflation (INF): With the average value of 5.72%, the minimum value of inflation rate was 0.9% in 2015 and the maximum value of inflation rate in Vietnam was 18.7% in 2011.
Exchange rate (EXCH): This indicator has a pretty high rate with 102.7% as the average value, 99.3% was it minimum value in 2015 and the maximum value was 113.3% in 2011.
Interest rate (INTRT): Comparing to the exchange rate, interest rate seems to have smaller percentage when the average value is 4.5% The minimum and maximum value are respectively -0.4% (2011) and 9% (2010)
Table 4.2 Correlation coefficient between liquidity and independent variables
Variable LIQ LOGAS CA DP AQ ROA OPE
NII NIM GDP INF EXCH INTRT
Note: This table reports the variable statistic of the panel regression result of 24 Joint stock commercial banks in Vietnam in the period 2010-2020 The dependent variable is LIQ , which is the liquidity of commercial banks LOGAS is the asset size, CA is the capital adequacy ratio, DP is the deposits, AQ is the asset quality,
ROA is the return on assets, OPEF is the operational efficiency, NII is the non-interest income, NIM is the net interest margin, GDP is the economic growth rate, INF is the inflation rate, EXCH is the exchange rate, INTRT is the interest rate The definition of the variables is presented in Table 4.2.
(Source: Synthesize from research dataset from Stata 14 statistical analysis software)
The correlation coefficient of the variable LOGAS has an outcome of +0.252, which indicates a positive correlation with the dependent variable LIQ It is clear that the coefficients of asset size are statistically significant and comes with a positive sign, letting us know that the size of banks significantly increases their liquidity holdings. After the crisis from 2007-2009, the scale of banks is relatively high, increase a huge amount of total assets which have partly helped banks to cope with difficulties in liquidity This means it is consistent with Hypothesis H 1
RESULT DISCUSSION
Table 4.10 Results summary Variable Hypothesis Estimated effect
Note: This table reports the 9 independent variables that influence significantly on LIQ as a result of the FGLS regression model of 24 Joint stock commercial banks in Vietnam in the period 2010-2020 The 9 significant variables are: LOGAS is the asset size, CA is the capital adequacy ratio, ROA is the return on assets, NII is the non-interest income, NIM is the net interest margin, GDP is the economic growth rate, INF is the inflation rate, EXCH is the exchange rate, INTRT is the interest rate The variables that don’t have any significant impact on LIQ are: DP is the deposits, AQ is the asset quality, OPEF is the operational efficiency The definition of the variables is presented in Table 4.9
Asset size of the bank (LOGAS) is the first variable with statistical significance and has a positive impact on the ratio of liquidity, this relationship is statistically significant at the 1% level This result is consistent with the hypothesis set out inChapter 3 According to hypothesis H 1 : Asset size has a positive effect on LIQ ratio.This result is also consistent with the results that the authors Eissa, Mosab, Najib and Faozi A. (2019); Moussa (2015), Munteanu (2012) have studied In addition, larger banks will have more resources and experience in handling and analyzing adverse selection and moral hazard problems.
Capital adequacy ratio (CA) is the second variable with statistical significance and has a positive impact on the ratio of liquidity, this relationship is statistically significant at the 1% level This result is consistent with the hypothesis set out: Hypothesis H 2 : There is a positive correlation between capital adequacy ratio and liquidity in commercial banks In order to manage the business activities of commercial banks, the State Bank of Vietnam has used capital adequacy ratio (CA) as a management tool to ensure that the business activities of banks are not exposed to great risks such as bad debt, liquidity risk that jeopardize business in banks Therefore, the ratio of liquidity and capital adequacy ratio is relatively correlation.
Return on assets (ROA) is the third variable that is statistically significant and has a positive impact on liquidity According to the proposed hypothesis H 5 : ROA has a positive impact on liquidity, which is said to be quite consistent with the research results Singh and Sharma (2016), Moussa (2015), Rashid and Jabeen (2016), Sopan and Dutta (2018), Gatev and Strahan (2009), Moussa (2015) also show results that are similar to the results of this study The reason given is that when banks bring in a large enough profit, achieving the outlined goals, their strategy will often be less reckless to participate in risky projects and can reduce the source of bad debt, ensure the liquidity of assets.
NII is the fourth variable with statistical significance and has a negative impact on the ratio of liquidity, this relationship is statistically significant at the 1% level This result is the opposite with hypothesis H 7 set out in chapter 2, in which the author supposed that there is positive correlation between non-interest income and liquidity in commercial banks On the other hand, findings from foreign research of Sopan and Dutta (2018); Munteanu (2012); Moussa (2015) also had the same negative result with
NII indicator Calomiris and Nissim (2014) also explained that noninterest income has become less valuable to banks since the financial crisis occurred in 2007-2009, particularly because of its variability This variable reflects the difference between the revenue generated from a bank's interest-bearing assets and the expenses associated with paying on its interest-bearing liabilities So, when the revenue of the investors grows, they tend to invest more, which then leads to a decline in the amount of deposits and minimize the ability of liquidity Therefore, NII and liquidity has a negative relationship with each other.
NIM is the fifth variable with statistical significance and has a negative impact on the ratio of liquidity, this relationship is statistically significant at the 1% level This result is inconsistent with the set out hypothesis H 8 that attest to the fact that there is a positive correlation between NIM and liquidity in commercial banks in Vietnam Also, it is a contrary to what theory and other empirical studies revealed Since we can understand that a high NIM increases the profit of the lender, so therefore it is not necessary to retrieve more revenue from the bank, which leads to the decline in liquidity While a negative NIM indicates that the lender has been able to make good use of the asset, as their returns produced by investments has failed to offset interest expenses, debts grow larger than the assets or the payments are too large for the company to afford, the owners will liquidate their assets to remain stable operations As a results, NIM and LIQ has negative correlation towards each other.
The sixth variable, GDP, has a positive impact on liquidity of banks at 5% significance level, consistent with hypothesis H 9 of the model If GDP increases by 1 unit, the liquidity will increase by 0.378 units This result is accurate, especially under a developing economy and good production in business activities, borrowers tends to repay their bank loans better which help contributing to the growth of the economy. Hence, huge increase in the liquidity of commercial banks exists.
Through the results of the model, which has overcome the defects, the seventh significance variable INF has a positive effect on the liquidity The estimated coefficient for inflation is statistically significant at 1% significance level It is therefore in contrast with the hypothesis H 10 : Inflation rate has a negative impact with liquidity The researcher then acknowledge that inflation is one of the macro-economic factors that affects positively the liquidity of commercial banks The findings of this research are consistent with Tseganesh (2012) who revealed that inflation rate has a positive influence on the liquidity in banking operations Moreover, according to Truong Quang Thong and Pham Minh Tien (2014)’s research, when the economy is suffering from high inflation rate, the banks tends to tighten the credits As a results, banks minimize the amount of loans, lessen the long-term investments and increase the liquidate assets. Therefore, if INF rate goes up, LIQ also goes up and vice versa.
Exchange rate (EXCH) is the eighth variable with statistical significance and has a negative impact on the ratio of liquidity, this relationship is statistically significant at the 1% level This result is inconsistent with the hypothesis H 11 , in which the researcher expected a positive correlation between exchange rate and liquidity in Vietnamese commercial banks Liquidity is usually experienced in terms of the volatility of price movements A highly liquid market will tend to see prices move very gradually and in smaller increments A less liquid market will tend to see prices move more abruptly and in larger price increments Therefore its no doubt that exchange rate and liquidity correlate negatively.
Interest rate (INTRT) is the ninth variable with statistical significance and has a positive impact on the ratio of liquidity, this relationship is statistically significant at the 1% level This result is consistent with the hypothesis H 12 Interest rates on loans continued to decline as a result of the improvement of the overall business environment in the past few year, also prove the efficiency of the implementation of theGovernment’s Circulars The decline in interest rates on loans is the main factor that causes the growth of credit applications and as a result of this will decline the liquidity risk, therefore increase the liquidity obligations of the banking sector INTRT and LIQ variables have proven its positive correlation with each other.
By using the software STATA 14.0 for statistically analysis and conducted regression respectively for these models Pooled-OLS, FEM, REM, FGSL, the author was able to estimate the models and evaluate the affection of factors that determine specifically on the liquidity in commercial banks in Vietnam In addition, other tests were also carried out in order to discover the most appropriate estimated model based on the collected dataset the author originally extracted from 24 banks’ consolidated financial reports Therefore, the author has built a model to measure the effects on liquidity in commercial banks (LIQ) including these factors: asset size (LOGAS), capital adequacy ratio (CA), return on assets (ROA), GDP growth rate (GDP), non-interest income (NII), net interest margin (NIM), inflation rate (INF), exchange rate (EXCH), interest rate (INTRT).
The Wald test and Hausman test were also conducted with the purpose to detect endogenous variables in a regression model, finding defects such as autocorrelation and heteroscedasticity As a result, an appropriate regression model for factors determine liquidity in commercial banks in Vietnam was built: LIQ = - 0.690 + 0.06LOGAS +
0.594CA + 1.302ROA - 2.977NII – 1.299NIM + 0.378GDP + 1.095INF – 0.258EXCH + 6.034INTRT + ε it The results claimed that: asset size (LOGAS), capital adequacy ratio (CA), return on assets (ROA), GDP growth rate (GDP), inflation rate (INF), interest rate (INTRT) all have a positive correlation with the LIQ variable On the other hand, a negative correlation can be seen between liquidity in banks and non- interest income (NII), net interest margin (NIM), exchange rate (EXCH).
In short, the result from this research have helped the author to visualized a clear picture of the factors affecting liquidity in commercial banking With this in mind,proper recommendations and suggestions will be discussed in chapter 5 to improve liquidity issue in joint stocks commercial banks in Vietnam.
CONCLUSION, PROPOSAL AND LIMITATIONS
SUMMARY OF RESEARCH RESULTS
Through the theoretical basis of previous research around the world on factors determine liquidity in commercial banks, a common among all of the authors is that they divided the independent variables into two groups of micro and macro Hence, depending on the specific characteristics of space and time, different conclusions are made.
This thesis used regression method to analyze the liquidity on total assets within
24 commercial banks in Vietnam during 2010-2020 The conclusion shows that the variables of asset size, capital adequacy ratio, NII, NIM, inflation rate, exchange rate and interest rate are the factors that have an impact on the existence of a good or bad liquidity In which, only the NII, NIM and exchange rate variables have negative impact on the liquidity Others indicators remain a positive affection.
This result is expected to contribute not only to the board of directors and managers at commercial banks but also to economic researchers in Vietnam in order to have a broad view of liquidity, thereby orienting appropriate monetary bank policies to help develop the economy and banking system in Vietnam, forecast any risks in liquidity matters.
PROPOSAL
Business activities in commercial banks listed on the stock exchange are currently facing with many risks, in which liquidity risk is always the top concern of banks.Through the regression method to analyze the factors affecting liquidity, the author would like to suggest and make some proposals to improve the future effectiveness of liquidity risk management at commercial banks in Vietnam According to some of the proposal in these research: Anh P and Loan L (2018), Shah, Khan, Shah and Tahir
(2018), Tuan Nguyen (2020) that the author was able to came up with overview solutions for this thesis.
First and foremost, it is critical to expand the bank scale In order to do so, commercial banks must have a suitable strategy for banking development, increase productivity, ensure scale expansion is under control, thus improve liquidity ability. Banks should consider increase the volume of highly liquid assets when continue to expand their scale to hedge against risks Subsequently, ensure capital adequacy ratio according to Basel II standards also helps broadening bank scale According to SBV, the roadmap in which apply the standard capital adequacy ratio, is divided into 2 phases and 10 chosen banks were the pilot for this scheme until 2020, but only 7 of the 10 selected banks implemented this This implementation means that banks will calculate the CA coefficient according to Circular 41 from January 1, 2019, adding more capital and allocate assets to both ensure a safe zone and optimize capital resources Therefore, it is essential for the SBV and commercial banks to keep pushing forward with this scheme Effective operational efficiency will happen and help expand the bank scale more easily, in addition to better liquidity in commercial banks.
Secondly, commercial banks need to avoid pushing races in deposit interest rates and unfair competition on prices This competitive method will mostly harm the commercial banks themselves Chaotic created during residential crowdfunding market while the system was suffering from economy stress has also left bad consequences.Therefore, instead of blindly chasing profit, policies in commercial banks can have adjustments to compete through such as improving product quality, strengthening relationships with customers, etc As a result, if banks can take good care of the customers and not too focus on competing against other banks, they will stand a high chance of being trusted when liquidity risks happen Customers will choose to stay with the bank since they know their benefits are the top priority, and the bank ought to make sure that the customers are aware of their preference positions through daily customer services and good valuable products.
Thirdly, commercial banks must fully comply with regulations on ensuring safety in liquidity and business activities in banks Commercial banks need to maintain a higher level of safety than the minimum prescribed by authorities for liquidity, even in practice This will help commercial banks have more opportunities to avoid risks from anomalous business factors As for compliance with regulations on business activities, legal violations, sometimes one person can sabotage the whole system and seriously affect the liquidity safety of the whole bank Therefore, supervision and inspection activities in the bank need to be carried out regularly From there, it is possible to promptly detect errors and make adjustments.
The State Bank of Vietnam (SBV) is the institution that enforces national monetary system and regulates the quantity of funds fluctuation in the economy The State Bank must not only focus on the development of criteria for each commercial bank, it is essential to establish a common warning index system for the entire banking system An early warning system for liquidity will play a critical role in alerting upcoming liquidity stress Only monitor the safety of the system based on ensuring that the coefficients of each commercial bank won’t be enough to meet the safety requirements Sometimes, commercial banks will depend mainly on the characteristics of the financial market, that’s when a general indicator of the market will have a great effect in alerting the State Bank to anticipate in the financial market system and timely market regulation policies will be promulgate in accordance with Basel regulations.
Building a legal framework satisfactory for the commercial banking system is a major task that must be performed Furthermore, system monitoring and notification, as well as the coordination of dealing with information catastrophes during stressful times, are critical It is crucial to enhance the legal system in order to have an effective legal structure that provides equal opportunities and safety for all organizations operating inVietnam's region in general, and particularly in commercial banking services Besides this, the government must ensure fairness and transparency between domestic and foreign credit institutions in order for banks to compete fairly, ensuring the banking system's safety and efficiency Around the same time, the law is being converted into a tool for the government to control and dictate competitive market Thus, the liquidity in commercial banking depends fairly on the enacted laws of the government, so it’s a must to promulgate quality monetary policies for the commercial banks in Vietnam.
LIMITATIONS
Through synthesis, research and estimation, the article then added conclusions to support the proceeding economy growth and reduction of bad liquidity ratio Although the study has achieved the set objectives, but due to the limitations of research time, research data and research methods, the topic still cannot avoid the following shortcomings.
To begin with, the research period during period 2010-2020 is not reliable enough when analyzing the relationship between liquidity status and performance of Vietnamese commercial banks through the FGSL model Whereby the dataset is not guaranteed to be correct for all periods or circumstances These are aggregated data from commercial banks in Vietnam, so the proven influencing factors can only have the most approximate results when applied to domestic commercial banks Besides, the collected database is only temporary, in which is potent for definite time.
Moreover, in some aspects the concept might not be true to real condition due to differences in specific elements Commercial banks have some elements in common, however they do not clearly classify either it is for state commercial banks or private commercial banks.
Lastly, the built-in macro factors are limited, including interest rate, exchange rate and inflation rate, not including other factors such as economic growth rate or unemployment rate Speaking of macro factors, it was unexpected because at the end of
2019 and the beginning of 2020, an outbreak of epidemics affected the economy badly and left consequences to the economy and created new evaluating aspect to factors determined the liquidity in banks.
In chapter 5, the research has made proposals and solutions based on the evaluation of the previous chapters in measuring affection of factors determine liquidity in Vietnamese commercial banks The author also discussed about the relationship between liquidity status and efficiency of bank specific factors to serve as a basis for providing solutions and recommendations for future improvements in commercial banks operating activities The conclusion in this thesis implies that macroeconomic stability will contribute to better increase in liquidity at Vietnamese commercial banks in the near future based on the research during 2010-2020 Recommendations to limit liquidity risk for commercial banks in Vietnam were made not only for the bank owner but also the competent authorities Improving the efficiency of safe operations and maintaining a reasonable structure of assets and capital are reasonable solutions to hedge risks, boosting strong mechanism in bank when liquidity is in need.
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RESEARCH DATA
S CA DP AQ ROA OPE
F NII NIM GDP INF EXCH INTR
REGRESSION RESULT WITH STATA 14
1 Panel data description encode SHORTCUT, gen (BANK) xtaet BANK YEAR panel variable: BANK (unbalanced) time variable: YEAR 2010 to 2020 delta: 1 unit sunmarize LIQ LOGAS CA DP AQ RŨA OPEF NII HIM GDP 1NF EXCH INTRT, separator(13)
Variable Obs Mean Std Dev Min Max
AQ RŨA OPEF NII NIM GDP INF EXCH INTRT
AS CA DP AQ ROA OPEF
3 Pooled-OLS regression reg LIQ LOGAS CA DP AQ
GCP INF EXCH INTRT.beta
Source ss í d MS Number of obs =
Fixed-effect3 (within) regression Number of obs =
Group variable: BANK Number of grcupa = 256 24
R-sq: Obs per group: within = 0.6111 min = 5 betueen = 0.0690 avg = overall = 0.2120 max = 11
Q Coeí- Std Err_ t p>|t| [95% Gonf- Intervall
77555723 (Ễracticn of variance due to ui)
Random-effect3 GLS regression Number of cbs = 256
Group variable BANK Number of groups = 24
R-sq: Obs per group: within 0.5520 min
Q Coef Std Err z p>|z| [95% Conf Interval]
_cons -.9711956 2648011 -3.67 0.000 -1.490196 -.4521949 sigma_u 03929269 sigma_e 06740251 ch o 25364091 (fracticn of variance due co u_i)
T 10.87298 6.762552 4.110431 b = consistent under Ho and Ha; obtained from xtreg
B = incongistent under Ha, efficient under Ho; obtained from xtreg
Test: Ho: difference in coefficientg not systematic chi2(12) = (b-B)'[(V_b-V_B)*(-!)]chi2 = 0.0000(V_b-V_B is not pogitive deíinite)
EP AQ RŨA OPEE NII NIM GDP INF EXCH
Modiíied Wald test for groupwise heteroskedasticity in fixed effect regression mcdel HO: sigma(i) A 2 = sigma*2 for all i
xtserial LIQ LOGAS CA DP AQ RŨA OPEF NII NIU GEP INF EXCH INTRT
Wooldridge test for autocorrelation in
Cross-sectional time-series FGLS regression
Coefficient9: generalized least squarea Panels: heterosiedaatic
Ccrrelation: common AR(1) coefficient for all panels