INTRODUCTION
INTRODUCTION
As liquidity problems of some banks during global financial crisis re-emphasised, liquidity is very important for functioning of financial markets and the banking sector
Liquidity in commercial banks refers to their ability to fund asset growth and fulfill obligations without incurring significant losses, as defined by the Basel Committee The viability of these banks is heavily reliant on their liquidity position, which was first highlighted by Diamond and Dybvig Maintaining an optimal level of liquidity is crucial for effective banking operations; inadequate liquidity can result in insolvency, while excessive liquidity may lead to reduced profitability Both scenarios can ultimately harm shareholder value and pose risks to other banks due to contagion effects, potentially increasing the likelihood of bankruptcy.
Research on bank liquidity across various economies reveals that factors influencing it can be categorized into two main groups: endogenous and exogenous factors Endogenous factors include bank size, capital ratio, credit risk, net interest margin (NIM), deposit ratio, return on assets (ROA), and return on equity (ROE) In contrast, exogenous factors encompass inflation (INF) and economic growth (GDP).
This research investigates the factors affecting bank liquidity in Vietnam from 2010 to 2018 The findings highlight key influences on liquidity levels and provide actionable recommendations to enhance the liquidity of Vietnamese commercial banks moving forward.
THE RECOMMENDATION OF RESEARCH
Bank liquidity is crucial for the stability of commercial banks, as a lack of it can lead to their collapse The 2007 subprime lending crisis in the US significantly impacted both the national and global economies, with the Basel Committee on Banking Supervision (BCBS, 2004) identifying liquidity issues as a key factor Banks that depend on short-term money markets for funding often face severe liquidity challenges The surge in subprime lending contributed to an imbalance in global credit capital, exacerbated by increasing credit growth from loose monetary policies and a declining demand for capital mobilization following major financial scandals This resulted in an oversupply of capital that the market could not effectively utilize Subprime lending emerged as a temporary solution to this surplus, ultimately leading to a loss of credit quality control and triggering the 2007 credit crisis.
Following the 2007 financial crisis, global commercial banking systems, particularly in Vietnam, experienced significant challenges, including a critical shortage of bank liquidity While improvements have been noted since 2012, numerous hidden risks persist within the banking sector Consequently, the research titled "Bank Liquidity and Its Determinants in Vietnam" aims to enhance bank liquidity and ensure profitable outcomes for commercial banks in the future.
OBJECTIVES OF THE STUDY
This research aims to analyze the factors influencing the liquidity of commercial banks in Vietnam and proposes practical solutions to improve their liquidity systems.
This research is taken to achieve the particular goals followed by:
Finding out and examining the determinants of liquidity of Vietnam commercial banks
Proposing practical recommendations and policies for improving liquidity system of Vietnam commercial banks.
RESEARCH QUESTIONS
The research will answer some questions as follows:
- What factors affect the liquidity of commercial banks in Vietnam?
- What is the impact level of these factors? What factors affect the most liquidity affecting the banking system?
- What solutions can improve the liquidity of Vietnamese commercial banks?
RESEARCH’S SUBJECT AND RANGE
The object of the research is the bank liquidity of the commercial banks in Vietnam
The sample of the research was collected from financial report Vietnamese commercial banks in the period from 2010-2018
LIQ = β 0 + β 1 *ROA + β 2 *SIZEi,t + β 3 *TLAi,t + β 4 *CEAi,t+ β 5 *T DESi,t
- LIQ = total liquid assets / total asset
LIQ depicts the bank’s ability to absorb liquidity shocks In theory, the higher liquidity ratio indicates that the bank is in a better position to meet its stochastic withdrawals (Chagwiza, 2014)
The Return on Assets (ROA) metric illustrates how banks can generate income from their assets (Chin, 2011) This ratio is frequently utilized in various studies to evaluate and compare the financial performance of banks By using ROA as a dependent variable, researchers can effectively compare their findings with those of other studies.
4 findings in this literature It reflects the ability of the banks to use the financial data and real estate resources to generate profits (Khrawish, 2011; Ongore and Kusa, 2013)
- SIZE = size of the bank = natural logarithme of total assets
Size can show the economies of scale The large banks benefit from economies of scale which reduces the cost of production and information gathering (Boyd and Runkhle,
- TLA= total loans / total assets
TLA shows the percentage of loans in relation to total assets
- CEA=operating expenses / total assets
Operating expenses including personal expenses and other expenses CEA shows the weight of operating expenses compared to total assets
- T deposit = total deposits / total assets
Deposits include demand deposit and term deposits T deposits show the share of deposits compared to total assets
- GDP =growth rate of gross domestic product
GDP shows the growth of economic activity in the country (Ayadi and Boujelbène,
INF shows the increase in the price index.
RESEARCH METHODOLOGY
- Qualitative method: Using the method of description, synthesis, comparison, analysis to raise the factors affecting the liquidity of commercial banks and build a research model
- Quantitative method: Implementing the multivariate regression model According to Brooks (2008), the regression process uses multivariate regression analysis for panel data including:
- Implementation of Pooled OLS, FEM, REM and FGLS models
- Perform tests to choose the most appropriate model
- Examining the defects of the selected model and overcoming the model's defects (if any)
- Analyze the results of the final model after testing and overcoming defects
RESEARCH CONTENT
To analyze the determinants of liquidity of commercial banks in Vietnam, this search is organized by separating into five chapters
Chapter1: The introduction of research
Chapter 2: Overview of Vietnam commercial banks and empirical study
Chapter 3: Estimated model and methodology
Chapter 4: The result of estimation
MAJOR THEMES OF THE RESEARCH
In addition to the introduction, conclusion, list of abbreviations, list of tables, list of references, appendices, the content of the thesis consists of 5 chapters:
This paper introduces the significance of the research topic, outlining its necessity in the current academic landscape It establishes clear research objectives and poses pertinent research questions that guide the inquiry The scope and subject of the research are defined to ensure a focused analysis Furthermore, the methodology employed is verified, highlighting its relevance and rigor Ultimately, the study aims to contribute valuable insights to the field, underscoring its importance and potential impact.
LITERATURE REVIEW
THEORETICAL FOUNDATIONS OF BANK LIQUIDITY
2.1.1 The definition of bank liquidity 1
Liquidity is a crucial factor for banks and the overall banking system It refers to the availability of cash or assets that can be quickly converted into cash, rather than being tied up in investments or real estate.
2.1.2 The role of bank liquidity for Vietnamese commercial banks
Liquidity is a crucial aspect of the banking system, ensuring timely access to funds for operations High liquidity indicates a bank's robust performance and strong reputation in the market Consequently, global banks prioritize liquidity and actively seek effective strategies to improve it for commercial banking.
2.1.3 Supply and demand of liquidity and net liquidity status2
Bank liquidity is crucial when a financial institution faces customer withdrawal demands or must fulfill its commitments During such times, banks must balance withdrawal requests with their available funds while also considering future capital mobilization Therefore, assessing bank liquidity requires a dynamic perspective, focusing on the relationship between the supply and demand of the bank's available capital over specific periods.
1 http://economics.about.com/cs/economicsglossary/g/liquidity.htm
2 Keynes, John Maynard A Treatise on Money 2 tr 67
The liquidity supply is a source of liquidity for banks, which includes sources of capital that increase the bank’s solvency, such as:
Deposit amounts: This is considered to be the main source of liquidity of banks
To increase the liquidity supply, banks can take some methods like: to adjust deposit rates, create good services (such as promotion, reward), professional stylist, bank reputation
Credits refunded represent fully paid credits that help secure a bank's capital When all credit balances are paid on time, it not only ensures the security of banking operations but also serves as a primary source of liquidity for banks.
All of receivables from service of customers such as guarantee fees, L/C open fees, transferred fees, etc
Market loans are essential for enhancing liquidity, as banks can borrow funds in the monetary market from other commercial banks or central banks, particularly during times of liquidity crises The interbank market plays a crucial role in addressing solvency challenges swiftly.
Selling assets: When the bank meets liquidity demand, it can exchange a partial asset into money
Issuing stocks allows banks to enhance their liquidity sources, but the revenue generated is typically allocated for development initiatives aimed at expanding market share or restructuring equity, rather than directly addressing liquidity needs.
Demand for liquidity reflects the need to withdraw money from the bank at different times This demand depends on the following factors:
Customers often require immediate access to their funds, which includes various types of deposits such as non-term deposits, payment deposits, and term deposits It is essential for banks to maintain sufficient reserves to fulfill withdrawal requests from these accounts, particularly for non-term and payment deposits, ensuring that customer demands are met promptly.
The primary focus of the bank is to extend credit to customers by utilizing deposit capital for lending purposes Customer loan demand significantly affects the bank's liquidity requirements, which are influenced by factors such as investment needs, prevailing high lending interest rates, and regulatory conditions for loans.
Self-liquidating loans: This is the amount of money the bank must repay for borrowings from other economic organizations, individuals, credit institutions or the SBV
Administration and service expenses: This is all of expenses of banking operating such as salaries, bonuses and other outside services including electricity, water, advertising, etc
Interest expense: These are the costs of interest payment of deposits, interest payment of valuable papers
Dividend payment: Dividend payments to shareholders
Share repurchase, also known as buying back shares, refers to the expenditure incurred by a company to acquire its own treasury shares that were previously issued This strategy is often employed to stimulate demand, boost stock prices, enhance earnings per share (EPS), or reward employees.
The analysis indicates that the supply and demand for liquidity vary significantly, with the difference between total supply and demand at any given time reflecting the net liquidity status, as demonstrated by the formula.
-NLP = Total supply of liquidity - Total demand of liquidity There are three cases occurring when identifying net liquidity status:
- NLP = 0, liquidity status is balanced, (this is almost impossible in practice)
When the NLP is greater than zero, it indicates a liquidity surplus, meaning that the total supply of liquidity exceeds the total demand In this scenario, the bank manager must identify investment opportunities to capitalize on this excess liquidity for profit maximization.
When the NLP is less than zero, indicating a liquidity deficit, it is essential for bank managers to assess when and where to boost the supply of additional liquidity This situation arises because liquidity demand operates independently of the bank's intentions, making it impossible for the bank to voluntarily reduce this demand Thus, addressing the surplus or deficiency in liquidity becomes a critical focus for effective financial management.
Liquidity imbalances in banks can manifest as either a surplus or a shortage A liquidity surplus often indicates an inefficient economy with limited investment opportunities, stemming from a bank's inability to access markets or fully utilize profitable assets This surplus can also arise when a bank's capital grows too quickly relative to its operational capacity To address this surplus, banks may consider purchasing government securities or engaging in interbank lending Conversely, a liquidity shortage occurs when banks lack sufficient capital to operate, leading to severe consequences such as lost business opportunities, diminished customer trust, and market share decline To mitigate liquidity shortages, banks can sell secondary reserves, borrow overnight in the interbank market, or rediscount loans with the central bank.
FACTORS AFFECTING ON BANK LIQUIDITY
Bank liquidity is influenced by several key factors, including the size of the bank, reliance on external funding, and profitability ratios Additionally, the interest rate differential between lending and mobilization, provisions for credit risk, and levels of bad debt play significant roles The number of customers and whether a bank is listed or unlisted also impact liquidity External factors such as GDP growth rates, inflation, money supply, compulsory reserve ratios, unemployment rates, and political stability further affect a bank's liquidity position.
2.2.1 Group of factors inside the bank
Commercial banks, which often depend on external funding, frequently turn to the interbank market to address temporary liquidity shortages, especially during liquidity crises This method is considered one of the quickest and simplest sources of liquidity While some empirical studies, such as Lucchetta (2007), indicate that external funding dynamics (EFD) positively impact liquidity, research by Inoca Munteanu (2012) and others suggests that EFD may have a negative effect on liquidity (Chung-Hua Shen et al.).
2009), (Inoca Munteanu, 2012) This shows that EFD has a relationship (positive or opposite) with bank liquidity depending on the country, the economic context, the research period
2.2.1.2 Equity to Total Assets (ETA)
The equity to total assets (ETA) ratio is a key indicator of a bank's financial health, safety, and adequacy It serves as an internal measure of liquidity supply, offering a simplified alternative to the minimum capital adequacy ratio, particularly when data for calculating total risk-weighted assets is lacking, as noted by Vodova (2013) Equity acts as a crucial buffer, providing essential protection against various risks, including liquidity risks faced by banks.
2.2.1.3 The the weight of operating expenses compared to total assets (CEA)
Operating expenses including personal expenses and other expenses CEA shows the weight of operating expenses compared to total assets
2.2.1.4 Loans loss provision to total loans (LLPTL)
Allowance for credit losses is a reserve established to mitigate potential losses from customers who may default on their debt obligations This credit risk provision is crucial for assessing asset quality and serves as a preemptive measure, ensuring that banks are shielded from capital loss risks By making provisions for credit losses, banks accurately represent the true nature of their asset quality, reflecting the residual value of assets on their balance sheets.
Chung-Hua Shen et al (2009) examine how the provision for credit losses relative to the total loan balance influences bank credit risk, which subsequently affects profitability and liquidity within the banking sector.
Deposits include demand deposit and term deposits T deposits show the share of deposits compared to total assets
The coefficient is calculated by assessing after-tax profits relative to total equity, indicating the bank's management efficiency in utilizing equity Banks primarily generate profits through traditional operations, specifically the interest rate spread between loans and capital mobilization Consequently, a bank's profitability tends to decrease as its asset holdings for liquidity purposes increase, and vice versa (Aspachs et al).
The Return on Assets (ROA) measures a bank's ability to generate income from its assets (Chin, 2011) This ratio is frequently utilized in various studies to assess and compare the financial performance of banks By using ROA as a dependent variable, researchers can effectively align their findings with existing literature It serves as an indicator of how well banks leverage their financial data and real estate resources to generate profits (Khrawish, 2011; Ongore and Kusa, 2013).
Size can show the economies of scale The large banks benefit from economies of scale which reduces the cost of production and information gathering (Boyd and Runkhle, 1993)
Net Interest Margin (NIM) reflects the efficiency of financial intermediation by measuring the difference between interest receivables from borrowers and interest expenses paid by banks to creditors and depositors According to Hamadi and Awdeh (2012), a higher NIM indicates better financial performance in managing interest income and expenses.
2.2.2 Group of factors outside the bank
In the context of the most difficult liquidity of Vietnamese commercial banks
Between 2008 and 2011, there were differing views on the liquidity supply of banks in Vietnam Some experts argued that the limited liquidity capacity of the developing economy was a primary factor, while others believed that the government's fiscal and monetary policies played a significant role in influencing liquidity issues.
Between 2007 and 2012, monetary easing, while beneficial, placed excessive emphasis on public sector investment and spending, which, according to research by M Lucchetta (2007), has indirectly impacted the liquidity of the banking system.
The relationship between GDP and bank liquidity varies under different economic conditions During a recession, banks typically increase their liquid asset holdings due to the perceived risks associated with lending Conversely, in times of robust economic growth, banks often decrease their liquidity reserves to enhance lending capabilities, as individuals are more likely to invest in higher-return opportunities beyond traditional bank deposits.
Research from 60 studies indicates that during periods of economic growth, banks tend to hold fewer liquid assets This trend can be attributed to two main factors: first, liquidity shocks are infrequent in strong economic conditions; second, heightened loan demand during these periods raises the opportunity cost of maintaining liquid assets Conversely, some experts argue that businesses operate more efficiently in a robust economy, leading to increased financial resources and improved debt repayment capabilities, which ultimately enhances the liquidity supply for banks.
The relationship between inflation and bank liquidity is complex and widely debated Perry (1992) suggests that if inflation is fully anticipated, banks can adjust interest rates to enhance interest income more quickly than their expenses, allowing for increased lending despite potential declines in capital mobilization due to competitive pressures, which may reduce liquidity Conversely, Munteanu (2012) indicates that inflation negatively impacts liquidity, while Laurine (2013) finds a positive correlation between inflation and liquidity, and Cucinelli (2013) concludes that inflation has no significant effect on bank liquidity.
LITERATURE REVIEW
According to analysis from the article in the Financial Magazine, Period 1 - May
In 2019, bank liquidity was primarily influenced by two major factors: endogenous factors, including profit, equity, bad debt ratio, loan-to-total assets ratio, bank size, and provisions for credit losses, and exogenous factors, such as economic growth rate, inflation rate, and central bank refinancing interest rates Analysis of data from 21 commercial banks revealed that internal factors like bank size and profitability positively affected liquidity, while bad debt and equity ratios had a negative impact on it.
A study by Vu Thi Hong (2015) employed FEM quantitative methods to analyze the factors influencing the liquidity of 35 joint stock commercial banks in Vietnam from 2006 to 2011 The findings revealed a positive correlation between the equity ratio, bad debt ratio, and profit ratio, while indicating an inverse relationship with the loan ratio.
14 deposits is negatively correlated with liquidity However, this research did not find the effect of provisions on credit risk and bank size on bank liquidity
Research by Nguyen Thi My Linh (2016) indicates that scale negatively affects bank liquidity, which contrasts with the findings of Truong Quang Thong and Pham Minh Tien (2014), who assert that scale positively influences bank liquidity This discrepancy highlights the lack of consensus regarding the impact of bank size on liquidity Additionally, Truong Quang Thong and Pham Minh Tien identify various exogenous factors that also influence liquidity.
In 2014, it was analyzed that banks maintain high liquidity reserves during economic recessions, primarily due to tightened lending practices and reduced liquidity in developed economies This trend occurs as banks hold more risky assets and increase their lending activities Additionally, evidence of inflation indicates an opposing effect on liquidity within commercial banking systems.
Research by Moussa (2015) highlights the significance of bank liquidity as a crucial variable within the banking system Analyzing a sample of 18 Tunisian banks from 2000 to 2010, the study employed two liquidity measures: liquid assets to total assets and total loans to total deposits Utilizing both static and dynamic panel methods, the findings revealed that factors such as financial performance, capital to total assets, operating costs to total assets, GDP growth rate, inflation rate, and delayed liquidity significantly impact bank liquidity Conversely, variables like bank size, total loans to total assets, financial costs to total credits, and total deposits to total assets were found to have no significant effect on bank liquidity.
Research by Distinguin et al (2013) examined the relationship between bank liquidity and capital from 2000 to 2006 in the US and Europe, finding that banks tend to reduce capital to enhance liquidity or when facing illiquidity In contrast, Singh and Sharma (2016) identified a positive and significant correlation between bank capital and liquidity Additionally, Berger and Bouwman (2009) demonstrated that higher capital levels facilitate liquidity creation, as ample bank capital enhances risk absorption Overall, the literature indicates that bank capital significantly influences liquidity.
According to a research by Munteanu, I (2012) showed that recently, the global crisis has proven that the lack of bank liquidity was the main trigger of all the negative
This research paper investigates the factors influencing bank liquidity in Romania, utilizing a multiple regression model across a panel of commercial banks It highlights the challenges faced by profitable banks in managing their funds due to a misunderstanding of liquidity risk The study analyzes both common and distinct determinants of two liquidity rates, aligning with existing literature It distinguishes between the pre-crisis years and the crisis period from 2008 to 2010, revealing that the Z-score, a key indicator of bank stability, significantly affects liquidity during the crisis Additionally, the research establishes a conceptual and empirical framework aimed at developing effective liquidity management tools, addressing the complexities of stress testing this critical variable.
The research conducted by Aspachs et al (2005) offers an in-depth analysis of the factors influencing bank liquidity policies in the UK It explores the interplay between macroeconomic policies and the Central Bank's role in managing liquidity support, particularly during economic cycles The study emphasizes the Central Bank's critical function as a lender of last resort (LOLR) during liquidity crises, providing essential capital support Utilizing quarterly balance sheet and income statement data from 1985 to 2003, this research highlights the significance of liquidity buffers in maintaining financial stability.
Valla & Escorbiac (2006) build upon the findings of Aspachs et al (2005) by exploring the determinants of banking liquidity and macroeconomic factors affecting the liquidity of British banks Their research indicates that bank liquidity is influenced by several key factors, including the likelihood of receiving final lending support, the growth of lending, GDP growth, and short-term interest rates Additionally, they found that bank profit has a negative correlation with liquidity, while the relationship between bank size and liquidity can vary, showing both positive and negative correlations.
Bonfim & Kim (2011) conducted research that diverges from earlier studies on banks in Europe and North America, examining two distinct periods: before and during the financial crisis (2002-2009) Their findings highlight the significant impact of both internal and macroeconomic factors on the liquidity of these banks.
16 ensure the ability to manage risks The best bank liquidity risk, most banks often ignore external factors without knowing that these are important support factors for liquidity
Unlike Aspachs et al (2005), Lucchetta (2007) does not explore the impact of central bank funding or macroeconomic policies on the interbank market, inflation rates, business cycles, and financial crises in relation to bank liquidity Furthermore, Lucchetta's research does not establish a connection between bank size and liquidity.
In conclusion, numerous studies, both domestic and international, have explored bank liquidity and its determinants in Vietnamese commercial banks However, many of these studies overlook certain factors and lack empirical validation The significant influence of both endogenous and exogenous factors underscores the necessity of this research.
In Chapter 2, the author discusses the theoretical foundations of bank liquidity and its significance within Vietnamese commercial banks The analysis covers the liquidity situation of these banks from 2010 to 2018, highlighting various factors that influence their liquidity.
CHAPTER 3: SAMPLE SELECTION, VARIABLES AND ECONOMETRIC MODELS ABOUT FACTORS AFFECTING ON LIQUIDITY OF
PROPOSING A RESEARCH MODEL OF THE FACTORS AFFECTING ON
This chapter outlines a research model, detailing the variables involved and their expected signs Additionally, it presents the available research data and methodologies for implementing the research model effectively.
3.1 Proposing a research model of the factors affecting on liquidity of commercial bank Viet Nam
Based on the baseline model of the authors
According to Vu Thi Hong (2015), the study reveals a positive correlation between equity ratio, bad debt ratio, and profit ratio, while indicating a negative correlation between the loans-to-deposits ratio and liquidity However, the research did not establish a significant impact of provisions on credit risk or bank size concerning bank liquidity.
Research by Truong Quang Thong and Pham Minh Tien (2014) indicates that scale positively influences bank liquidity, although the relationship between bank size and liquidity remains inconclusive Their analysis reveals that during economic recessions, banks tend to maintain higher liquidity reserves due to tightened lending practices, while liquidity in developed economies decreases as banks hold riskier assets and increase lending Additionally, evidence suggests that inflation exerts a contrasting effect on liquidity within commercial banking systems.
In his 2015 study, Moussa evaluated two liquidity measures: liquid assets relative to total assets and total loans compared to total deposits He discovered that factors such as financial performance, capital to total assets ratio, operating costs to total assets, GDP growth rate, inflation rate, and delayed liquidity significantly influence bank liquidity Conversely, variables like bank size, total loans to total assets ratio, financial costs to total credits, and total deposits to total assets ratio were found to have no significant impact on bank liquidity.
LIQ = β 0 + β 1 *ROA i,t + β 2 *SIZE i,t + β 3 *TLA i,t + β 4 *CEA i,t + β 5 *TDES i,t
DESCRIPTION OF VARIABLES
The liquidity indicator (LIQ) reflects a bank's capacity to withstand liquidity shocks, with a higher liquidity ratio suggesting a stronger ability to manage unpredictable withdrawals According to Chagwiza (2014), this metric is crucial for assessing a bank's financial stability The formula for calculating bank liquidity is essential for understanding this relationship.
LIQ = total liquid assets / total asset
3.2.1.2 The return on asset (ROA)
Return on Assets (ROA) measures a bank's ability to generate income from its assets (Chin, 2011) This ratio is frequently utilized in various studies to evaluate the financial performance of banks By using ROA as a dependent variable, researchers can effectively compare their findings with existing literature It indicates how well banks leverage financial data and real estate resources to produce profits (Khrawish, 2011; Ongore and Kusa, 2013) The formula for calculating Return on Assets is:
Larger banks experience economies of scale, which lead to reduced production and information gathering costs (Boyd and Runkhle, 1993) This demonstrates how bank size can influence operational efficiency and cost-effectiveness.
SIZE= natural logarithme of total assets
3.2.1.4 The percentage of loans in relation (TLA)
TLA shows the percentage of loans in relation to total assets The formula TLA is:
TLA= total loans / total assets
3.2.1.5 The weight of operating expenses (CEA)
Operating expenses including personal expenses and other expenses CEA shows the weight of operating expenses compared to total assets The formula of CEA is:
CEA = operating expenses / total assets
3.2.1.6 The share of deposites (TDES)
Deposits include demand deposit and term deposits T deposits show the share of deposits compared to total assets The formula of T deposits is:
TDES = total deposits / total assets
GDP shows the growth of economic activity in the country (Ayadi and Boujelbène, 2012)
The formula of the growth economy is:
GDP = growth rate of gross domestic product
3.2.1.8 The rate of inflation ( INF)
INF shows the increase in the price index.The formula of the rate of inflation is:
LIQ depicts the bank’s ability to absorb liquidity shocks In theory, the higher liquidity ratio indicates that the bank is in a better position to meet its stochastic withdrawals (Chagwiza,
2014) total liquid assets / total asset
SIZE Size can show the economies of scale natural logarithme of total assets
TLA TLA shows the percentage of loans in relation to total assets total loans / total assets -
Operating expenses including personal expenses and other expenses CEA shows the weight of operating expenses compared to total assets operating expenses / total assets
Deposits include demand deposit and term deposits T deposits show the share of deposits compared to total assets total deposits / total assets + Moussa (2015)
GDP shows the growth of economic activity in the country (Ayadi and
Boujelbène, 2012) growth rate of gross domestic product -
INF shows the increase in the price index rate of inflation
RESEARCH METHODOLODY
This study analyzes data from 26 commercial banks in Vietnam, covering the period from 2010 to 2018 Financial information was sourced from the official websites of the Vietnamese banking association during this timeframe.
Macroeconomic data are collected from site of central bank of Viet Nam and national statistic institution
The research scope: 26 commercial banks include State-owned banks and Urban Joint-Stock Commercial banks in Vietnam
Table 3.2 List of commercial banks in the research sample period 2010-2018
1 ACB Asia Commercial Joint Stock Bank
2 ABB An Binh Commercial Joint Stock Bank
3 BIDV Joint Stock Commercial Bank for Investment and Development of
4 CTG Vietnam Joint Stock Commercial Bank for Industry and Trade
5 EIB Joint Stock Vietnam Export Import Commercial Joint Stock Bank
6 HDB Ho Chi Minh City Development Joint Stock Commercial Bank
7 KLB Kien Long Commercial Joint – Stock Bank
8 LPB LienViet Post Joint Stock Commercial Bank
9 MSB Maritime Commercial Joint Stock Bank
10 MBB Military Commercial Joint Stock Bank
11 NAB Nam A comercial Join Stock Bank
12 NCB National Citizen Commercial Joint Stock Bank
13 OCB OCEAN Commercial Joint Stock Bank
14 PGB Petrolimex Group Commercial Joint Stock Bank
15 STB Thuong Tin Commercial Joint Stock Bank
17 SEA Southeast Asia Commercial Joint Stock Bank
18 SGB Saigon Bank for Industry and Trade
19 SHB Saigon – Hanoi Commercial Joint Stock Bank
20 TCB Vietnam Technology and Commercial Joint Stock Bank
Information relating to the sample:
As of June 30, 2018, Vietnam's financial sector comprised a diverse array of institutions, including 31 commercial banks, 4 state-owned banks, 2 social policy banks, 1 cooperative bank, 2 joint venture banks, and 9 wholly foreign-owned banks Additionally, there were 48 branches and 49 representative offices of foreign banks operating in the country, along with 16 financial companies, 11 financial leasing firms, 4 micro-finance institutions, and 1 credit fund system, according to the State Bank of Vietnam.
Some typical events relating to the merger, acquisition and renaming of commercial banks during the study period include:
July 27, 2010, Joint-stock Non-state-owned enterprises commercial banks changed their name to Vietnam Prosperity Joint Stock Commercial Bank
On December 15, 2011, three commercial banks—Saigon Commercial Joint Stock Bank, Vietnam Tin Nghia Commercial Joint Stock Bank, and First Commercial Joint Stock Bank—merged to form a single entity, Saigon Commercial Joint Stock Bank, which commenced operations on January 1, 2012.
November 23, 2013: Dai A Joint stock commercial bank and Bank of Investment and Development of Vietnam were merged
On January 23, 2014, Nam Viet Commercial Joint Stock Bank officially changed its name to National Joint Stock Commercial Bank
21 TPB Tien Phong Commercial Joint Stock Bank
22 VCB Joint Stock Commercial Bank for Foreign Trade of Vietnam
23 VIB Vietnam International Commercial Joint Stock Bank
24 VAB Viet A Commercial Joint Stock Bank
25 VAC Viet Capital Commercial Joint Stock Bank
26 VPB Vietnam Prosperity Joint Stock Commercial Bank
To analyze data, we must use two primary method including Panel Data and Generalized
Panel data, also referred to as longitudinal data, combines both time series and cross-sectional elements, allowing for in-depth historical event and cohort analysis (Brook, 2008).
In this case, is dependent variable, is intercept, is a vector of parameters to be estimated on the explanatory variables, and is a vector of observations on the explanatory variables, t = 1,…., T; i = 1,…,N 2
Panel data offers several advantages over cross-sectional data, enabling researchers to explore a wider array of issues and address more complex problems than what is achievable with solely time-series or cross-sectional data.
We can remove the impact of certain forms of omitted variables bias in regression esults by structuring the model in an appropriate way
Panel data can effectively address multicollinearity issues that may occur when analyzing time series data individually By utilizing pure time-series data, researchers often need extensive datasets to conduct meaningful hypothesis tests However, integrating cross-sectional and time series data enhances the number of degrees of freedom, thereby increasing test power This approach leverages the dynamic behavior of multiple entities simultaneously, providing richer insights and more robust statistical analysis.
3.3.2.2 Techniques used for panel regression
There are three predominant approaches, consisting of Pooled OLS, FEM (Fixed Effect Model) and REM (Random Effect Model)
Simplest method is just to estimate by OLS with a sample of NT observations, not reconizing panel structure of data This can be specified like:
Where is the dependent variable of observation i, is a 1 x k vector of observations on the explanatory variables, t = 1,….,T, i = 1,… N 2
The technique has limitations in its applicability, as it requires that the number of time series observations (T) per cross-sectional unit (i) be at least equal to the total number of units Consequently, the fixed effects model (FEM) and random effects model (REM) are more frequently utilized in practice.
The fixed effects model – FEM
Fixed effects models (FEM) enable the intercept in regression analysis to vary across different cross-sections while remaining constant over time, whereas the slope estimates are fixed across both dimensions As noted by Brooks (2008), FEM can be formally specified to capture these characteristics.
In the analysis, the dependent variable for observation i at time t is influenced by the independent variable for the same observation and time Each research unit has a specific intercept, while the slope is determined by various factors, including x.
According to Brooks (2008), there is two main methodologies used to estimate this model is: (i) OLS of the dummy regression model; (ii) OLS using the entity demeaned data
While this model addresses the limitations of Pooled OLS, it does have some drawbacks One key consideration is the number of degrees of freedom in the regression, as this factor is crucial to understanding the implications of our analysis.
When constructing demeaned variables, we lose one degree of freedom for each of the N explanatory variables for which we estimate the mean Additionally, the inclusion of multiple dummy variables can introduce multicollinearity due to imprecise estimation A significant drawback of this model is the inability to assess the impact of all variables that influence the outcome but remain constant over time.
The random effects model – REM
The difference between the random effects model and the fixed effects model proposes different intercept terms for each entity If there is a votality among entities
The Random Effects Model (REM) is preferred over the Fixed Effects Model (FEM) when there are differences among entities that influence dependent variables, as REM accounts for random changes among these entities and does not correlate with explanatory variables.
The random effects model can be written as:
In contrast to the fixed model, the Random Effects Model (REM) assumes that the intercepts for each cross-sectional unit are derived from a common intercept, C, which remains consistent across all units and time periods, along with a random variable that varies between units but stays constant over time.
Where having zero mean measures the random deviation of each entiry’s intercept term plus variance This can be written as: hay và
Where is the new cross-sectional error term (the various difference of firms) and is independent of the individual observation error term
Relative to the Fixed Effects Model (FEM), the Random Effects Model (REM) is often more suitable when addressing the significant drawbacks associated with FEM However, it's essential to note that REM assumes the entities in the sample are uncorrelated with the independent variables; if this assumption does not hold true, the REM approach becomes inapplicable.
To evaluate between POLS and FEM models, which model is more suitable for research, we do F-test, with:
The hypothesis Ho: POLS model is a suitable model for research
The hypothesis H1: FEM model is a suitable model for research
If Prob> F = 0.0000 (p-value / t / [95% Conf Interval]
Source: Extract data from Stata
Previous studies assumed that unobserved factors in the model remained constant across different objects and over time The findings are illustrated in the Pooled OLS regression model shown in Table 4.4 The results indicate that an increase in TDES and INF correlates with a rise in liquidity, while an increase in TLA by commercial banks tends to decrease liquidity.
4.3.3.2 Estimation results of FEM model
Table 4.5 Estimation results of FEM model
LIQ Coef Std Err T P> / t / [95% Conf Interval]
Source: Extract data from Stata
Unobserved factors in the model can vary between objects and over time, making the simple regression model POLS unsuitable for evaluating research results Consequently, the fixed effects model (FEM) results, as shown in Table 4.5, are comparable to those of the POLS regression model However, the FEM regression analysis did not reveal any significant relationships between return on assets (ROA), capital expenditure (CEA), GDP, and liquidity.
4.3.3.3 Estimation results of REM model
Table 4.6 Estimation results of REM model
LIQ Coef Std Err Z P> / z/ [ 95% Conf Interval]
Source: Extract data from Stata
The results from the Random Effects Model (REM) align closely with those of the simple regression model (POLS), while the Fixed Effects Model (FEM) results are deemed unsuitable Key factors positively influencing bank liquidity include SIZE, TDES, and INF, whereas the TLA variable exerts a negative impact on bank liquidity.
4.3.4 Choose between two models POLS and FEM
To evaluate between POLS and FEM models, which model is more suitable for research, we do F-test, with:
The hypothesis Ho: POLS model is a suitable model for research
The hypothesis H1: FEM model is a suitable model for research
Results: Prob> F = 0.0000 (p-value chi2= 0.3436 (p-value > 0.05) so, it accepts the Ho hypothesis
The REM model is a suitable model for research
4.3.6 Test for the errors of REM
The author uses the Wooldridge test for the research model with the following test hypothesis:
H0: There is no sequence autocorrelation
H1: There is a sequence correlation phenomenon
The result is that Prob>F= 0.0000 (p-value>0.05) so, it accepts the Ho hypothesis and rejects H1 hypothesis
There is no sequence autocorrelation
The author uses the Breusch and Pagan Lagrangian multiplier tests that the variance of variance changes after estimating the REM model with the following test hypothesis:
H0: There is no variance change phenomenon
H1: There is a phenomenon of variance of error of change
The result is that: Prob>chibar2= 0.000 (p-value< 0.05) so, it accepts the H1 hypothesis and rejects Ho hypothesis
There is a phenomenon of variance of error of change
The initial findings indicate that the REM model is appropriate for the study; however, it exhibits variance change To address this issue, the author employs the FGLS model, which accounts for both autocorrelation and variance change The results of the FGLS regression are presented in Table 4.8.
LIQ Coef Std Err Z P> /z/ [95% Conf Interval]
Source: Extract data from Stata
The analysis reveals that the variables ROA, SIZE, TLA, CEA, TDES, and INF are statistically significant at the designated significance levels, while the GDP variable does not demonstrate statistical significance in the model This finding is consistent across the three models examined: Pooled OLS, FEM, and REM.
The study analyzed traditional models for table data, such as Pooled OLS, Fixed Effects Model (FEM), and Random Effects Model (REM) To address the limitations of the REM model, the study employed the Feasible Generalized Least Squares (FGLS) model, which proved to be suitable as it demonstrated that all independent variables significantly influence the dependent variable.
DISCUSS THE RESEARCH RESULTS
The analysis indicates that factors positively influencing the liquidity of commercial banks include Return on Assets (ROA), Capital to Asset Ratio (CEA), Total Deposits (TDES), bank size (SIZE), and inflation (INF) Conversely, Total Loans to Assets (TLA) is identified as a variable that negatively impacts liquidity.
4.4.1 The correlation between ROA and LIQ
Figure 4.2 shows the correlation between ROA and LIQ
The regression analysis presented in Table 4.8 indicates a positive relationship between Return on Assets (ROA) and liquidity (LIQ), with a coefficient of 0.0208 This finding aligns with both expectations and the observed trends in Figure 4.2, which illustrates that while ROA and liquidity move in the same direction, the correlation is not particularly strong Notably, liquidity has shown a gradual decline from 2011 to 2018 This suggests that effective operations within commercial banks lead to increased profits, thereby enhancing the stability of the liquidity system and ultimately raising the liquidity rate.
4.4.2 The correlation between TLA and LIQ
Figure 4.3 shows the correlation between TLA and LIQ
The regression analysis in Table 4.8 reveals a statistically significant negative coefficient of TLA (-0.6246) at the 1% level, indicating that TLA adversely affects liquidity (LIQ) This finding aligns with expectations and is supported by Figure 4.3, which illustrates the negative correlation between TLA and LIQ, consistent with Vu Thi Hong's 2012 study High levels of commercial bank debts can lead to non-performing loans, as customers struggle to repay, ultimately resulting in a decline in the liquidity rate Consequently, when debts are not sufficiently recovered, banks face challenges in meeting maturity deposits and accommodating sudden withdrawals.
4.4.3 The correlation between SIZE and LIQ
Figure 4.4 shows the correlation between SIZE and LIQ
The regression analysis presented in Table 4.8 indicates a statistically significant negative coefficient of SIZE (-0.1145) at the 10% level, suggesting that SIZE adversely affects LIQ Additionally, Figure 4.4 illustrates a negative correlation between SIZE and LIQ Notably, three regression models demonstrate a positive effect of SIZE on LIQ, aligning with the expectations and findings of previous studies by Moussa (2015) and Truong Quang Thong and Pham Minh Tien (2014).
4.4.4 The correlation between CEA and LIQ
Figure 4.5 shows the correlation between CEA and LIQ
The regression analysis presented in Table 4.8 indicates a positive and statistically significant coefficient for CEA (0.1141) at the 10% level, suggesting that CEA positively influences liquidity (LIQ) This finding aligns with the observed data in Figure 4.5, which highlights a significant change in CEA from 2011 to 2012, while LIQ experienced only minor fluctuations Both CEA and LIQ exhibit a similar directional effect, with CEA contributing to an increase in the liquidity rate by 0.0027 units This implies that a secure liquidity system, supported by banks effectively managing their budgets, enhances liquidity rates Additionally, the inflation rate (INF) is shown to increase the liquidity rate by 0.1968 units, indicating that rising inflation prompts commercial banks to tighten credit, resulting in a decrease in loans to customers and an overall increase in liquidity rates.
4.4.5 The correlation between TDES and LIQ
Figure 4.6 showa the correlation between TDES and LIQ
The regression analysis presented in Table 4.8 indicates a positive and statistically significant coefficient of TDES at 0.2210, confirming its positive impact on liquidity (LIQ) at a 1% significance level This finding aligns with expectations and is visually supported by Figure 4.6, which illustrates a positive interaction between TDES and LIQ, consistent with the research conducted by Moussa (2015) Consequently, an increase in TDES is associated with a rise in liquidity by 0.7844 units.
When customer deposits rise, commercial banks increase their reserves, which in turn enhances liquidity.
4.4.6 The correlation between INF and LIQ
Figure 4.7 shows the correlation between INF ànd LIQ
The regression analysis presented in Table 4.8 indicates a positive and statistically significant regression coefficient of INF (0.3885) at the 1% level, confirming that inflation positively impacts liquidity (LIQ) This finding aligns with both theoretical expectations and empirical evidence, as illustrated in Figure 4.7, which further supports the positive correlation between inflation and liquidity These results are consistent with prior research conducted by Vodová (2011), Malik (2013), and Truong Quang Thong and Pham Minh Tien.
In 2014, it was observed that during periods of high inflation, banks tended to tighten credit, leading to reduced lending and long-term investments while increasing their liquid assets This behavior aligns with historical economic patterns.
4.4.7 The correlation between GDP and LIQ
Figure 4.8 shows the correlation between GDP and LIQ
The regression analysis presented in Table 4.8 reveals a statistically significant negative coefficient of GDP (-0.1444) at the 5% level, indicating that GDP adversely affects liquidity (LIQ) This finding aligns with both theoretical expectations and the visual data depicted in Figure 4.8, which illustrates the negative correlation between GDP and liquidity.
In Chapter 4, we selected the FGLS model based on quantitative research after addressing the errors among all models We then estimated the data, discussed the results, and will present our conclusions and suggestions in the subsequent chapter.
CONCLUSION ABOUT THE IMPACT OF THE MICRO AND MACRO
Chapter 5 will focus on two key areas: first, it will conclude the analysis of how seven factors—Return on Assets (ROA), Size, Total Loans to Assets (TLA), Capital to Equity Ratio (CEA), Total Deposits, Inflation (INF), and Gross Domestic Product (GDP)—affect bank liquidity, along with recommendations for enhancing the liquidity of Vietnamese commercial banks Second, it will highlight the limitations of the current study and suggest directions for future research.
The findings from the Pooled OLS, FEM, REM, and FGLS models align closely with both local and global studies, indicating that Return on Assets (ROA) positively influences liquidity While ROA has been underexplored in Vietnam, previous research by Nguyen Thi My Linh and Truong Quang Thong primarily examined the effects of Return on Equity (ROE) on bank liquidity Interestingly, the Total Liabilities to Assets (TLA) variable exhibits a contrasting effect on liquidity within Vietnamese commercial banks compared to the findings of Vu Thi Hong (2012) This study is pioneering in Vietnam by investigating the impact of Cost Efficiency Assessment (CEA) and Total Deposits (TDES), revealing that CEA positively affects liquidity, which diverges from Moussa's (2015) conclusions, while TDES also shows a positive correlation with liquidity, consistent with Moussa's findings Additionally, macroeconomic factors play a significant role, with inflation (INF) positively impacting liquidity, corroborating the results of Vodová (2011), Malik (2013), and Truong Quang Thong and Pham Minh Tien (2014) During periods of high inflation, banks tend to tighten credit, leading to reduced loans and long-term investments while increasing liquid assets Furthermore, Gross Domestic Product (GDP) positively affects bank liquidity, highlighting a critical yet under-discussed factor in Vietnamese banking research.
SUGGESTING SOLUTIONS TO IMPROVE BANK LIQUIDITY FOR
Based on the research results in chapter 4, the dissertation gives some proposes, in order to increase bank liquidity in Vietnamese commercial banks
5.2.1 Increasing the quality of business activities
Firstly, commercial banks need to focus on building a good liquidity management strategy, improving the quality of business activities to create high profits for the bank
The Return on Assets (ROA) significantly influences bank liquidity, indicating that effective operations lead to increased profits, which in turn enhance the quality of a bank's liquidity system To manage liquidity effectively, banks must develop a comprehensive liquidity management strategy that anticipates changes in deposits, loans, and profits Improving business operations is essential for effective liquidity management, necessitating a focus on enhancing human resources and technology Additionally, banks should conduct thorough market analysis and risk assessments for each business process to implement timely preventive measures Strengthening connections among commercial banks is also crucial to ensure payment safety and foster a competitive environment.
Secondly, commercial banks should improve their operation quality, creating a strong banking size
Larger total assets significantly enhance a bank's liquidity, as the size of its assets directly impacts both costs and liquidity status Theoretically, banks with greater total assets enjoy improved liquidity, and they also benefit from better access to the interbank market and support from the "Lender of Last Resort" (Vodova, 2013b).
5.2.3 Setting provision for credit risks
Thirdly, commercial banks need to set provision for credit risks to reduce outstanding loans, which will help banks be more active and reduce shock when liquidity risks occur
Historically, banks have limited their provisions for risks, often neglecting to account for bad debts to maintain higher reported profits and attract investors This practice has led to the concealment of bad debts or their restructuring to avoid necessary provisions To address this issue, the State Bank must implement policies to closely monitor the bad debt situations of individual banks, ensuring they accurately reflect bad debts and make appropriate credit risk provisions This approach will facilitate comprehensive measures to effectively manage and resolve bad debts.
5.2.4 Increase costs that are beneficial to the operation of the bank and cut down unnecessary expenses
Commercial banks must enhance their risk management strategies to minimize the substantial costs associated with risk provisioning Additionally, it is crucial to implement stringent measures to reduce management expenses, particularly by eliminating unnecessary non-operational costs.
Each bank may set a goal of reducing 3-4% of management expenses compared to the estimate in 2019
Analyze and provide an optimal cost structure and funding source for the bank in each period
- Establish a policy of dividing costs and reasonable profits for the bank
- Control the use of all assets in the company, avoiding wasteful and misuse
5.2.5 Increasing capital mobilization from deposits of individual and corporate customers
Fifth, commercial banks need to increase capital mobilization from deposits of individual and corporate customers
An increase in customer deposits prompts banks to raise their reserves, subsequently enhancing their liquidity ratios To optimize capital mobilization and lending, banks should restructure short-term loans into medium-term loans, utilizing short-term funds for longer-term financing Additionally, issuing valuable papers and adjusting lending structures in sensitive sectors like securities, real estate, and consumer finance is essential Completing regulations related to deposits is also necessary to ensure a stable banking environment.
To maintain customer loyalty and prevent premature deposits or withdrawals, offering 50 loans at competitive medium and long-term market interest rates is essential This strategy ensures that clients remain committed even when market interest rates increase or competitors present more appealing offers.
Inflation is positively correlated with liquidity, as supported by previous studies (Vodová, 2011; Malik, 2013; Truong Quang Thong & Pham Minh Tien, 2014) Historically, during high inflation periods, banks have tightened credit, leading to reduced lending, decreased long-term investments, and increased liquidity assets To foster economic growth, commercial banks must operate efficiently, minimizing liquidity risks Additionally, research indicates that as GDP rises, liquidity tends to increase; during economic growth, banks hold fewer liquid assets due to the infrequency of liquidity shocks and heightened loan demand, which raises the opportunity cost of maintaining liquid assets.
LIMITATIONS OF THE TOPIC AND THE NEXT RESEARCH
This thesis aims to investigate the factors influencing bank liquidity in commercial banks, with a particular emphasis on understanding how these factors interact and proposing legal measures to enhance liquidity in Vietnam's banking sector While the study has met its primary objectives, it acknowledges limitations related to time constraints, data availability, and research methodologies.
The research is constrained by limited data, as it only analyzes the annual financial statements of 26 commercial banks over a span of ten years from 2010 to 2018 Additionally, the study does not segment the timeframe into distinct periods to assess the influence of independent variables on the dependent variable during each stage.
The research has not explored the objectives from various perspectives, limiting the comparison of effective research methods By broadening the approach, the study could yield valuable proposals and practical solutions to enhance bank profitability.
Future research will focus on broadening the study's scope to include not only Vietnamese commercial banks but also comparable banks in the region Additionally, the PMG estimation method will be utilized, and the research will be divided into distinct stages This approach aims to enhance empirical studies on the factors influencing bank liquidity in Vietnamese commercial banks.
Chapter 5 concludes the analysis of seven key factors—Return on Assets (ROA), Size, Total Liabilities to Assets (TLA), Capital to Equity Ratio (CEA), Time Deposits, Inflation (INF), and Gross Domestic Product (GDP)—on bank liquidity It offers recommendations for enhancing the liquidity of Vietnamese commercial banks while also addressing the study's limitations and suggesting directions for future research.
REFERENCE LIST OF VIETNAMESE DOCUMENTS
1 Hoàng Trọng và Chu Nguyễn Mộng Ngọc, “Phân tích dữ liệu nghiên cứu với SPSS” Nhà xuất bản Thống kê, 2005
2 Nguyễn Thị Mỹ Linh (2016), “Các yếu tố tác động đến tỷ lệ thanh khoản tại các ngân hàng thương mại Việt Nam”, Tạp chí Ngân hàng, số 9/2016;
3 Trần Hoàng Ngân & Phạm Quốc Việt (2016), “Mối quan hệ giữa quản trị công ty và thanh khoản của các ngân hàng thương mại Việt Nam”, Tạp chí Ngân hàng, số
4 Trịnh Quốc Trung và Nguyễn Văn Sáng, “Các yếu tố ảnh hưởng đến hiệu quả hoạt động của các ngân hàng thương mại Việt Nam”, Tạp chí Công nghệ ngân hàng, trang 11-15, Số 85 –Tháng 4/2013
5 Trương Quang Thông &Phạm Minh Tiến (2014), “Các nhân tố tác động đến rủi ro thanh khoản của hệ thống ngân hàng thương mại Việt Nam”, Tạp chí Phát triển kinh tế, số 276, 50-62;
6 Vũ Thị Hồng (2012), “Các yếu tố ảnh hưởng đến thanh khoản của các Ngân hàng thương mại Việt Nam”, Đại học Thủy Lợi
7 Vũ Thị Hồng, “Các yếu tố ảnh hưởng đến thanh khoản của các ngân hàng thương mại Việt Nam”, Tạp chí Phát triển & Hội nhập, Số 23 (33) - Tháng 07- 08/2015
8 Website của 31 ngân hàng thương mại Việt Nam
9 Website của của ngân hàng Nhà nước: https://www.sbv.gov.vn
10 Website Tổng cục Thống kê: https://www.gso.gov.vn
1 Adraian, T., Shin, H.S (2008) Liquidity and financial contagion Financial stability Review , Banque de France
2 Aspachs, O., Nier, E., Tiesset, M (2005), “ Liquydity, Banking Regulation and macroeconomics” Proof of shares, bank liquydity from a panel the bank’s
Ukresident, Bank of England working paper
3 Ayadi, N., Boujelbène, Y (2012) “ The determinants of the profitability of the Tunisian deposit banks” IBIMA Business Review , 1-21
4 Basel Committee on Banking Supervision, 1988 Basel I: “International Convergence of Capital Measurement and Capital Standards”, Bank for International Settlements
5 Basel, “A Framework for Measuring and Managing Liquidity”, http://www.bis.org [Online], 1992
6 Berger, A N., & Bouwman, C H (2009) Bank liquidity creation The review of financial studies, 22(9), 3779-3837
7 Blundell, R., Bond, S (1998) Initial conditions and moment restrictions in dynamic panel data models Journal of Econometrics, 87, 115-143
8 Bonfim, D., Kim, M (2008), “Liquydity risk in banking: Is there herding?”, International Economic Journal, vol 22, no 3, pp 361-386
9 Bourbonnais (2009) “Econométrie , manuel , et exercices corrigés “ Bunda, I., Desquilbet, J.B (2008) The bank liquidity smile across exchange rate regimes International Economic Journal, 22(3), 361-386
10 Boyd J H., Runkhle D E (1993) Size and performance of banking firms testing the prediction of theory, Journal of Monetary Economics, February, vol 31, 1, p47-67
11 Bunda, I., Desquilbet, J.B (2008) The bank liquidity smile across exchange rate regimes International Economic Journal, 22(3), 361-386
12 Cucinelli, D (2013) The determinants of bank liquidity risk within the context of euro area Interdisciplinary Journal of Research in Business, 2(10), 51-64
13 Chagwiza, W (2014) Zimbabwean commercial bank liquidity and its determinants International Journal of Empirical Finance, 2(2), 52-64
14 Choon, L.K, Hooi, L.Y, Murthi, L, Yi, T.S, Shven, T.Y (2013) The determinants influencing liquidity of Malaysia commercial banks, and its implication for relevant bodies: evidence from 15 malaysian commercial banks.http://eprints.utar.edu.my
15 Diamond, D W., & Dybvig, P H (1983) Bank runs, deposit insurance, and liquidity Journal of political economy, 91(3), 401-419
16 Distinguin, I., Roulet, C., & Tarazi, A (2013) Bank regulatory capital and liquidity: Evidence from US and European publicly traded banks Journal of Banking & Finance, 37(9), 3295-3317
17 Gorton, G., & Huang, L (2004) Liquidity, efficiency, and bank bailouts American Economic Review, 94(3), 455-483
18 Gorton, G., & Pennacchi, G (1990) Financial intermediaries and liquidity creation The Journal of Finance, 45(1), 49-71
19 Gorton, G., & Winton, A (2017) Liquidity provision, bank capital, and the macroeconomy Journal of Money, Credit and Banking, 49(1), 5-37
20 Gul, S., Irshad, F., Zaman, K (2011) Factors affecting bnak profitability in Pakistan The Romanian Economic Journal , 39, 61-87
21 Hamadi, H., Awedh, A (2012) The determinants of bank net interest margin: evidence from the lebanese banking sector “ Journal of Money , Investment and Banking, 23, 85-98
22 Khrawish, H.A (2011) Determinants of commercial bank performance: Evidence from Jordan International Research Journal of Financial and Economics, 5(5), 19-
23 Laurine, C (2013) Zimbabwean commercial banks liquidity risk determinants after dollarisation Journal of Applied Finance and Banking, 3(6), 97
24 Lucchetta, M 2007, What Do Data Say About Monetary Policy, BankLiquidity and Bank Risk Taking?, Economic Notes by Banca Montedei Paschi di Siena SpA, vol 36, no 2, pp 189-203
25 Malik, V S., Pan, A., Willett, W C., & Hu, F B (2013) Sugar-sweetened beverages and weight gain in children and adults: a systematic review and meta- analysis The American journal of clinical nutrition, 98(4), 1084-1102
26 Moussa, M (2015) The Determinants of Bank Liquidity: Case of Tunisia International Journal of Economics and Financial Issues , 5 (1) , 249-259
27 Munteanu, I (2012) Bank liquidity and its determinants in Romania Procedia Economics and Finance, 3, 993-998
28 Ongore, V O., & Kusa, G B (2013) Determinants of financial performance of commercial banks in Kenya International journal of economics and financial issues, 3(1), 237-252
29 Ongore, V.O, Kusa, G.B (2013) Determinants of financial performance of commercial banks in Kenya Internationl Journal of Economics and Financial Issues, 3(1), 237-252
30 Panwar, N., Sharma, S., & Singh, A K (2016) A survey on 5G: The next generation of mobile communication Physical Communication, 18, 64-84
31 Perry, J., & Davids, J R (1992) Gait analysis: normal and pathological function Journal of Pediatric Orthopaedics, 12(6), 815
32 Poorman Jr, F., & Blake, J (2005) Measuring and modeling liquidity risk: new ideas and metrics Financial Managers Society Inc White Paper
33 Rauch, C., Steffen, S., Hackethal, A.and Tyrell, M., 2009, Determinants of Bank Liquidity Creation, Working Paper, available at http://ssrn.com/abstract43595 or http://dx.doi.org/10.2139/ssrn.1343595
34 Shen, C H., Chen, Y K., Kao, L F., & Yeh, C Y (2009) Bank liquidity risk and performance: A cross-country analysis Department of Finance, National Taiwan
35 Umar, M., & Sun, G (2016) Determinants of different types of bank liquidity: evidence from BRICS countries China Finance Review International, 6(4), 380-
36 Valla, N & Saes-Escorbiac, B & Tiesset, M., (2006), “Bank liquidity and financial stability,” Financial Stability Review, Banque de France, issue 9, pages
37 Valla, N and B Saes-Escorbiac, 2006, Bank liquidity and financial stability, Banque de France Financial Stability Review, pp 89-104 National Bank of
Romania Website www.bnro.ro The Statistical Office of the European Union, Eurostat ec.europa.eu/eurostat/
38 Vodova, P (2011) Liquidity of Czech commercial banks and its determinants International Journal of mathematical models and methods in applied sciences, 5(6), 1060-1067
39 Vodová, P (2013) Determinants of commercial bank liquidity in Hungary Finansowy Kwartalnik Internetowy e-Finanse, 9(4), 64-71
40 Yilmaz, A.A (2013) Profitability of banking system: evidence from emerging market WEI International Academic Conference, Antalya, Turkey, p105-111
APPENDIX APPENDIX A: DESCRIPTIVE STATISTIC AND CORRELATION
APPENDIX B: RESULT OF POOLED OLS, FEM, REM AND GLS
Table B.1 Result of Pooled OLS
Table B.5 Compare result from using estimated models
APPENDIX C: TESTING ERRORS OF ESTIMATED MODELS
Table C.3 Breusch and Pagan Lagrangian multiplier test
YEAR BANK ROA TLA CEA ROE CAP SIZE TDES LIQ