INTRODUCTION
Background of study
In banking operations, factors like credit risk, exchange rates, and interest rates often exhibit delays, while bank liquidity is immediate and rarely balanced The total demand for liquidity within banks matches the total supply, leading to frequent encounters with liquidity deficits or surpluses Prolonged capital shortages can harm a bank's market reputation, diminishing its capital-raising ability and profitability A more critical scenario arises when depositors withdraw funds en masse without sufficient available capital, potentially pushing commercial banks towards bankruptcy, sale, or merger, which could ultimately jeopardize the stability of a country's banking and financial system.
Liquidity is crucial for the safety of credit institutions, as all banks face liquidity risk It is essential for bank managers to continuously monitor and measure liquidity to make timely adjustments Furthermore, the authorities of the State Bank must also understand liquidity risk to implement policies that stabilize the entire banking system.
The 2008 global financial crisis highlighted significant weaknesses in the financial system, particularly the liquidity challenges faced by commercial banks Despite being profitable, many banks struggled with effective asset and capital management, leading to liquidity crises The Basel Committee on Banking Supervision (BCBS, 2008) identified these liquidity issues as a root cause of the financial turmoil, which had been largely overlooked in prior years The crisis further exacerbated bank liquidity problems, revealing that institutions reliant on short-term money markets for financing their operations were particularly vulnerable.
In the wake of recent financial crises, liquidity has become a critical focus for commercial banks, particularly in Vietnam Over the past twenty years, the Vietnamese banking system has undergone significant reforms, leading to substantial growth in both the number and quality of commercial banks However, despite these advancements, the importance of liquidity management remains underappreciated within the sector.
This study aims to analyze the impact of various factors on the liquidity of commercial banks in Vietnam from 2011 to 2020 Liquidity (LIQ) is the dependent variable, while independent variables include equity utilization efficiency, equity ratio, bad debt, profitability, bank size, debt-to-deposit ratio, inflation rate, and economic growth The research focuses on understanding how these elements influence the liquidity of the banking sector in Vietnam over the specified period.
This study examines the factors influencing the liquidity of Vietnam's banking sector over a decade, from 2011 to 2020, focusing on all 27 commercial banks listed on the Vietnam stock exchange Utilizing a disclosure approach and multiple linear regression models, the research analyzes comprehensive data to highlight the significant positive relationships between liquidity (LIQ) and various independent factors The findings are presented through tables and narrative descriptions, underscoring the robust growth of the banking sector during this period.
Therefore, the study of liquidity in the banking system is extremely necessary
Strong liquidity in banks is crucial for stabilizing financial markets and supporting economic growth, particularly in Vietnam's current landscape As liquidity challenges are a primary focus at the start of each year, effective management of these issues is essential for maintaining a well-functioning economy This significance has led the author to select this topic for exploration.
The liquidity of commercial banks is influenced by various determinants, which are crucial for understanding and managing liquidity risks By identifying these factors, the author provides valuable policy implications aimed at mitigating liquidity risks and enhancing the stability of banking operations Implementing these strategies can contribute to a more resilient financial system.
Objectives
The general objective is to determine the factors affecting the liquidity of commercial banks in Vietnam
From there, propose some governmance implications to improve the liquidity and increase the competitiveness of Vietnamese commercial banks
This paper will explore the theoretical foundations of commercial bank liquidity, drawing insights from prior research conducted in Vietnam and globally Additionally, it will develop an analytical model to examine the factors influencing the liquidity of commercial banks during the period from 2011 to 2020.
Thirdly, the paper will determine specific impact of factors and the results of the empirical evaluation model of liquidity for the commercial banking in Viet Nam over the period 2011-2020
This paper outlines key factors influencing the financial performance of commercial banks and offers targeted solutions and management strategies for the board of directors By enhancing liquidity and competitiveness, these recommendations aim to support the ongoing development of the banking system in the years ahead.
Research questions
What factors affect the liquidity of commercial banks in Vietnam?
How are the factors affecting the liquidity of joint-stock commercial banks in Vietnam?
Proposing necessary policies or solutions to manage the liquidity of commercial banks in Vietnam in the most effective way?
Significant of the study
The financial system is primarily driven by commercial banks, which play a crucial role in the national economy Serving as the backbone of modern business, these banks act as financial intermediaries that connect various economic sectors, making them essential for the smooth operation of all departments within the economy In many countries, the banking industry is closely monitored to ensure economic stability, particularly in developing nations like Vietnam, where it receives significant governmental attention and scrutiny.
The Vietnamese banking system is currently grappling with significant challenges, including global crises and weaknesses within the national economy To navigate the volatile money market, Vietnamese commercial banks must enhance efficiency across all operations while ensuring immediate liquidity The key concern for these banks is to maintain stable liquidity levels and pursue sustainable development amid international integration Additionally, the evolving Covid-19 pandemic presents both complications and an opportunity for banks to reevaluate their liquidity issues and identify effective solutions.
In recent years, the banking industry has garnered significant attention from various economic sectors, experiencing substantial growth and an increase in capital value This expansion provides banks with greater opportunities to issue large loans; however, it also introduces potential risks, particularly liquidity risks Such risks can severely impact not only the stability of individual banks but also the overall financial system of a country In nations like Vietnam, where the capital market is underdeveloped, the commercial banking system serves as the primary source of capital for the economy A bank with strong liquidity can meet market demands effectively, but a lack of necessary capital may lead to insolvency, loss of credibility, and systemic failure Additionally, the influx of foreign banks and heightened competition can challenge the capital mobilization efforts of smaller banks, further influencing their liquidity.
This study enhances and builds upon existing research by identifying key internal and external factors that influence the liquidity of commercial banks in Vietnam Internal factors include capital efficiency, equity ratio, profitability, and bank size, while external factors encompass GDP and inflation By analyzing these elements, the research provides a comprehensive understanding of the determinants of bank liquidity.
This study expands on previous research by examining the effects of bad debt and the loan-to-deposit ratio, alongside other factors Spanning a decade from 2011 to 2020, this extended study period enhances the accuracy and reliability of the findings compared to earlier studies.
The research identifies key factors influencing the liquidity of Vietnamese commercial banks and assesses their impact This serves as a valuable reference for banks to enhance liquidity in accordance with Vietnam's economic conditions, along with recommendations for improving their liquidity management strategies.
Objects and Scope of study
The author compiles and analyzes tabular data sourced from the Vietnam Stock Exchanges and annual consolidated financial statements of Vietnam's Joint Stock Commercial Banks This analysis utilizes financial results from 27 joint stock commercial banks, encompassing both state-owned institutions like Vietcombank and Vietinbank, as well as non-state banks such as Sacombank and TPbank, over a decade from 2011 to 2020 Due to insufficient data availability from the Vietnam Stock Exchanges, the research sample comprises 270 observations.
Structure of paper
Beside table of contents, appendices and bibliography, research structure consists of 5 chapters:
Chapter 2: Theoretical framework and literature review:
Chapter 4: Empirical result and discussion
Chapter 5: Conclusion and management interpretation
This research report is structured into five chapters, beginning with an overview that covers the research background, the history of Vietnam's banking system, the problem statement, objectives, questions, methods, scope, significance, and overall structure of the study The second chapter delves into the concept of liquidity, its measurement, and a review of literature regarding the determinants of bank liquidity The third chapter outlines the research methodology and presents the findings and discussions The final chapter concludes the report with recommendations, suggestions for future research, and includes references and appendices.
THEORETICAL FRAMEWORK AND LITTERATURE REVIEW
Theoretical framework
As defined by the Bank for International Settlements (2008), “A bank's solvency is the ability of a bank to quickly raise capital and meet maturing needs without incurring losses”
The Basel Committee on Banking Supervision defines liquidity as the capability to consistently utilize available capital for various business activities, including managing payment deposits, loans, and capital transactions.
Liquidity reflects the capacity to fulfill payment obligations promptly and in the designated currency For banks, liquidity is assessed from three perspectives: the liquidity of assets, the liquidity of capital, and the overall liquidity of the institution.
Liquidity refers to how quickly and cost-effectively an asset can be converted into cash An asset is considered highly liquid if it can be transformed into cash with minimal time and expense involved.
Capital liquidity refers to a bank's capacity to generate and increase its capital, which is assessed by the time and expense involved in mobilizing that capital when necessary Higher liquidity is indicated by a reduced time frame and lower costs associated with raising capital.
A bank's liquidity refers to its capacity to fulfill financial obligations promptly and cost-effectively For commercial banks, this involves meeting payment requests, processing withdrawals, and approving new loans based on valid credit applications A bank is deemed to possess strong liquidity if it can adequately address payment needs at the right time and at reasonable costs, ensuring customer and partner satisfaction.
Duttweiler (2009) identifies two key aspects of liquidity: natural liquidity and artificial liquidity Natural liquidity refers to cash flows from assets or liabilities with a defined repayment period, where customer transactions are often predictable, whether they involve consistent amounts or varying sums This predictability applies to both assets and liabilities in the banking sector In contrast, artificial liquidity arises from the ability to convert assets into cash before their due date This conversion is facilitated when there is demand in the market for specific securities, allowing for transactions to occur smoothly.
From the viewpoint of bank managers, liquidity refers to a bank's capacity to swiftly and completely fulfill its financial obligations, which include paying deposits, processing loans, handling accounting payments, and managing various financial transactions.
Liquidity refers to the ease of accessing assets and capital to cover expenses promptly when needed Highly liquid funds have low costs and quick deposit times, while highly liquid assets can be converted into cash swiftly and at minimal cost Examples of highly liquid assets include treasury bonds, commercial papers, and bills of exchange, in contrast to illiquid assets like real estate, production chains, and machinery.
Liquidity refers to the capacity to swiftly convert assets into cash with minimal costs It encompasses the accessibility of assets and capital at a reasonable expense, enabling banks to effectively meet their various operational needs.
An asset is considered highly liquid when it can be converted to cash quickly and at a low cost Similarly, capital is deemed highly liquid when the expenses associated with raising it are minimal and the time required to obtain it is short.
Liquidity is not defined by a specific amount or rate; rather, it reflects a bank's capacity to meet its payment obligations Conversely, a lack of liquidity indicates a bank's inability to fulfill these obligations Therefore, liquidity serves as a qualitative measure of a bank's financial strength.
Liquidity supply refers to the funds that a bank can access for short-term use, encompassing various components such as customer deposits, service revenue, credit repayments, asset sales, and money market borrowing The primary source of liquidity comes from additional customer deposits, followed by refundable credit sources and service revenue.
Liquidity demand refers to the immediate or short-term cash requirements of a bank, encompassing factors such as customer deposit withdrawals, credit extensions, loan repayments, operational expenses, taxes, and cash dividends The primary sources of liquidity demand are customer withdrawals and the need to extend credit to clients.
The Net Liquidity Position (NLP) is the difference between the total supply and the total demand for liquidity at any given time:
NLP > 0: The bank has to deal with a liquidity surplus, ie an interest-free cash surplus, the bank needs to determine whether it should be productively invested in this surplus
NLP < 0: The bank is facing a liquidity deficit, i.e short of cash for payment, the bank needs to identify additional liquidity sources
NLP = 0: When liquidity demand equals liquidity supply and demand, the bank reaches liquidity equilibrium But in reality, this is a rare case
Evaluating liquidity is crucial for commercial banks, as the supply and demand for liquidity often do not align, leading to periods of excess or shortage Bank managers must continuously assess liquidity to make informed decisions that maximize profits from surplus capital or secure timely funding for illiquid assets at reasonable costs Effective liquidity assessment and prompt management decisions enable banks to optimize idle capital for profit generation, enhance liquidity, and bolster their reputation, ultimately mitigating the risk of liquidity crises.
Liquidity risk refers to the potential for a commercial bank to become insolvent or face high costs when mobilizing capital to meet payment obligations This risk can stem from various factors, including subjective reasons that lead to insolvency, resulting in operational slowdowns, financial losses, damage to credit reputation, and the possibility of bankruptcy (Duttweiler, 2009).
Based on nature and demand, liquidity risk is divided into the following four groups:
Measurement of liquidity
Liquidity refers to a bank's capacity to finance asset growth and fulfill obligations as they arise, without incurring significant losses Liquidity risk stems from banks' essential function of converting short-term deposits into long-term loans and encompasses two main types: funding liquidity risk and market liquidity risk Funding liquidity risk involves the potential inability of a bank to meet both anticipated and unforeseen cash flow and collateral requirements without disrupting its operations In contrast, market liquidity risk arises when a bank struggles to liquidate or adjust a position at prevailing market prices due to inadequate market depth or disruptions.
Liquidity, as defined by The Basel Committee on Banking Supervision, refers to the ability to utilize available capital for business operations, including deposit payments, loans, and capital transactions Duttweiler (2009) characterizes liquidity as a qualitative measure of a bank's financial strength, encompassing both natural and artificial liquidity Natural liquidity arises from cash flows generated by assets and liabilities over a specific timeframe, such as customer repayments and service revenue, while artificial liquidity is achieved by converting assets into cash prior to their maturity Rose (2002) notes that liquidity is sustained through cash flows from various sources, including customer deposits, short-term securities issuance, and interbank borrowing, as well as asset sales and customer payments, ultimately reflecting the capacity to meet all payment obligations as they come due.
According to Aspachs, Nier, and Tiesset (2005), banks can mitigate liquidity crises through three key mechanisms First, they maintain a buffer of liquid assets, such as cash and government-issued securities, to reduce liquidity needs that could jeopardize their stability Second, banks can borrow from the interbank market to address liquidity shortages, although this approach is subject to market liquidity risks Lastly, central banks serve as Lenders of Last Resort, offering emergency liquidity assistance to struggling institutions and ensuring overall liquidity during systemic shortages.
Moulton (1918) introduced Possibility Theory, which posits that commercial banks can mitigate liquidity risk by incorporating more liquid assets into their portfolios This theory highlights that loans can create fundamental contradictions leading to liquidity challenges It emphasizes that commercial lending alone does not secure a bank's liquidity during crises; instead, factors such as profit generation, capital accumulation, and asset convertibility are essential for maintaining liquidity.
The Basel Committee on Banking Supervision (1997) defines liquidity risk as the inability of banks to raise capital from assets or liabilities at the lowest cost Brunnermeier (2009) highlights that inadequate management of liquidity risk can lead banks to experience liquidity shocks, forcing them to sell illiquid assets and curtail lending, which negatively impacts the economy The liquidity challenges faced by individual commercial banks and the entire banking system were largely overlooked by policymakers and bank administrators until the global financial crisis of 2007-2009 Consequently, it is crucial to measure and provide warnings regarding the potential for systemic liquidity risk within the commercial banking sector.
Liquidity risk can be assessed through two primary methods: the liquidity gap and liquidity ratios The liquidity gap measures the difference between current and future assets and liabilities, where a positive difference indicates a deficit (Bessis, 2009) However, a significant drawback of this method is that few banks reveal their liquidity shortfalls in annual reports, limiting the ability to compare multiple banks On the other hand, liquidity ratios serve as balance sheet indicators that highlight essential liquidity trends, ensuring that banks maintain sufficient capital at minimal cost in the short term This may involve holding easily sellable assets such as cash reserves and government securities, maintaining substantial stable debt, particularly from retail deposits, or securing lines of credit with other financial institutions.
Numerous authors, including Andries (2009), Aspachs et al (2005), and Ghosh (2010), have proposed various liquidity ratios This research will evaluate the liquidity positions of Vietnamese commercial banks using four specific liquidity ratios.
The liquidity ratio L1 indicates a bank's capacity to absorb liquidity shocks, with liquid assets including cash, balances with central banks, and government-issued securities Generally, a higher ratio of liquid assets to total assets signifies a greater ability to withstand liquidity challenges, as market liquidity remains consistent across banks However, an excessively high ratio may suggest inefficiency, as liquid assets typically yield lower income, resulting in significant opportunity costs for banks Thus, it is crucial to balance liquidity and profitability effectively.
L2 = Liquid assets / (Deposits + Short-term borrowing)
The liquidity ratio L2 assesses a bank's sensitivity to specific funding sources, including deposits from households, businesses, and financial institutions, as well as capital from debt securities This ratio highlights the bank's vulnerability to these funding types, with a higher L2 value indicating a greater capacity to absorb liquidity shocks The formula for L3 is liquid assets divided by deposits.
The L3 liquidity ratio closely resembles the L2 ratio but focuses exclusively on household and business deposits Unlike the L2 ratio, the L3 ratio assesses a bank's liquidity under the assumption that it cannot borrow from other banks during liquidity shortages This stringent measure of liquidity helps identify a portion of the market's liquidity risk A bank is considered capable of meeting its capital obligations when the L3 ratio is 100% or higher, while a lower ratio signifies heightened vulnerability to deposit withdrawals.
L4 = Loans / ( deposits + short term capital)
The last liquidity ratio L4 relates illiquid assets with liquid liabilities the higher this ratio the less liquid the bank is
Liquidity ratios have a notable limitation as they often fail to fully reflect liquidity risk Despite this drawback, these ratios remain widely utilized due to their reliance on publicly available data from bank balance sheets and their straightforward interpretability.
Specific factors affecting commercial banks’ liquidity
Gross domestic product (GDP) serves as a key indicator of economic health, influencing banks' liquidity management During recessions, banks typically maintain higher liquidity levels to mitigate lending risks, while in times of economic growth and elevated interest rates, they tend to decrease liquidity to enhance lending capabilities, leading to a reduction in liquid assets Research by Fola (2015) and Bunda and Desquilbet (2008) indicates a positive correlation between economic growth and liquidity, contrasting with findings from Valla et al (2006) and Vodova.
(2011, 2012) find a negative relationship between these two variables
Inflation significantly influences a bank's liquidity, as highlighted by Perry (1992), who noted that banks adjust interest rates based on their inflation expectations When inflation is anticipated to rise, banks tend to increase interest income more rapidly than expenses However, inaccurate forecasts can lead to higher costs and diminished net profits, complicating capital acquisition Research by Bunda and Desquilbet (2008), Vodova (2011, 2012), and Fola (2015) further emphasizes a positive correlation between inflation and liquidity risk.
ROE reflects the efficiency of the bank's use of equity Previous research has shown a positive relationship between this index and liquidity position (Bunda,
2008) Some studies such as Lucchetta (2007), Valla et al (2006), and Vodova
Research indicates a complex relationship between bank profitability and liquidity While some studies (2011, 2012) highlight a positive correlation between net profit margin and liquidity, others (Aspachs et al., 2005; Lucchetta, 2007; Vodová, 2011) reveal a negative impact of Return on Equity (ROE) on bank liquidity Specifically, as banks boost lending or invest in high-profit assets, ROE rises, yet these investments often correspond to lower liquidity Consequently, findings suggest that higher ROE is associated with diminished liquidity in banks.
Banks primarily hold capital to mitigate various risks, such as liquidity risk and operational risks, with credit risk being the most significant While the traditional view emphasizes capital's role in risk absorption, emerging theories indicate that bank capital may also influence a bank's capacity to generate liquidity However, these theories present conflicting predictions regarding the relationship between capital levels and liquidity creation.
Gorton and Winton (2000) suggest that increased capital ratios can lead to reduced liquidity due to deposit overcrowding They contend that deposits provide more effective liquidity protection for dealers compared to investments in bank equity, as deposits are either fully or partially insured and can be withdrawn at face value In contrast, bank capital is not insured and has a fluctuating value based on the bank's condition and stock market liquidity Consequently, a higher capital ratio reallocates investor funds from more liquid deposits to less liquid bank capital, resulting in lower overall liquidity for the bank.
The "risk absorption" hypothesis posits that increased capital in banks enhances their ability to create liquidity, linking risk transformation to liquidity generation This concept is supported by two perspectives: one highlights that while creating liquidity can expose banks to risks, as noted by Diamond and Dybvig (1983) and Allen and Gale (2004), the potential for significant losses arises when banks must liquidate illiquid assets to satisfy client liquidity demands Conversely, other studies, such as those by Repullo (2004) and Thaised (2004), suggest that higher bank capital enables institutions to absorb risk and increase their risk tolerance Together, these insights suggest that higher capital ratios may empower banks to produce greater liquidity.
Bad debts (NPLs) are loans where a bank's customers fail to meet their contractual obligations for principal or interest payments over 90 days (Ghafoor,
Bad debts negatively impact banks and hinder economic development, contributing significantly to financial distress within the banking sector A high bad debt ratio can restrict credit availability, diminish customer confidence, and trigger mass withdrawals due to concerns over the bank's liquidity, resulting in substantial liquidity demands Previous studies by Barr et al (1994), Bloem and Gorter (2001), Lucchetta (2007), Vong and Chan (2009), and Sabri et al highlight these critical issues.
(2020) show a negative relationship between bad debt ratio and bank liquidity
Profitability significantly influences banks' risk tolerance and liquidity conversion capabilities, as highlighted by Rauch et al (2008) and Shen et al (2010) It is noted that prolonged loan volumes can enhance interest income, thus increasing profitability potential for commercial banks However, banks with substantial loan volumes also encounter elevated liquidity risks, necessitating a careful balance between liquidity and profitability Conversely, maintaining high liquidity comes with the opportunity cost of forgoing potentially lucrative investments (Kamau 2009) The inherent trade-off between return and liquidity risk is illustrated by the shift from short-term to long-term securities, which can boost profitability while simultaneously heightening liquidity risk Consequently, a high liquidity ratio typically signifies a bank that is less risky but also less profitable (Hempel et al 1994).
Research indicates that bank size significantly influences liquidity, often negatively affecting smaller banks' access to capital markets As noted by Kiyotaki and Moore (1997), Holmstrom and Tirole (1998), and others, smaller commercial banks typically hold more liquid assets due to these challenges This trend is supported by findings from Vodová (2011) and Anamika and Anil (2016), highlighting the disadvantages faced by smaller institutions in securing funding compared to their larger counterparts.
Research by Lucchetta (2007) and Vodova (2011, 2012) indicates that larger total assets correlate with lower liquidity risk, as big banks can more easily raise capital, lend in the interbank market, and receive central bank support These large commercial banks often benefit from hidden advantages and lower capital costs, allowing them to invest significantly in high-risk, high-return projects Interestingly, despite their size, large banks may experience higher liquidity risk compared to smaller counterparts In the context of Vietnam, a positive correlation between bank size and liquidity has been observed.
2.3.2.6 Lending on the deposit interest rate
Lending constitutes a crucial asset for banks, representing the largest share of their financial statements and generating significant revenue, despite being an illiquid asset According to Vu's research (2012), banks that allocate a greater portion of their short-term funds to lending tend to finance less liquid assets Furthermore, commercial banks primarily list customer loans on their balance sheets, indicating that a higher loan-to-fund ratio negatively impacts the bank's liquidity.
Between April 2012 and October 2015 in Vietnam, a negative correlation was observed between the Loan-to-Deposit Ratio (LDR) and bank liquidity Bank liquidity was assessed by analyzing the difference between deposits and credit operations, revealing a high LDR of 95% in April 2012, indicating poor liquidity across the banking system However, from April 2012 to April 2013, the LDR significantly decreased to 86%, suggesting an improvement in liquidity, which inversely aligned with the LDR trends.
Figure 1: Specific factors affecting commercial banks’ liquidity
Literature review
The study by Aspachs et al (2005) offers a comprehensive analysis of the liquidity policy determinants for UK banks, examining the interplay between macroeconomic policies—particularly Central Bank policy—and the business cycle It highlights the Central Bank's crucial role in sustaining liquidity, as it can act as a lender of last resort during a commercial bank's liquidity crisis The research is grounded in data from balance sheets and quarterly income statements, providing valuable insights into liquidity buffers and their implications.
Valla and Escorbiac (2006) presented findings that align with the study by Aspachs et al (2005), focusing on internal and macroeconomic factors influencing the liquidity of UK banks Their research identifies key determinants of bank-specific liquidity, including the probability of final loan support, loan growth, GDP growth, short-term interest rates, and highlights a negative correlation between bank profitability and liquidity Additionally, the relationship between bank size and liquidity can vary, showing either a negative or positive correlation.
Luchetta (2007) highlights that the risk-free rate of monetary policy adversely impacts banks' liquidity and lending decisions in the interbank market, while the interbank rate has a positive influence Analyzing unbalanced data from 5,066 European banks between 1998 and 2004, the study explores the relationship between investment and lending amid interest rate fluctuations, using data from the European Central Bank (ECB) The research primarily focuses on how monetary policy, through interest rate tools, affects banks' risk tolerance and liquidity management Key factors influencing lending decisions include interbank market pricing—shaped by liquidity supply and demand—and the liquidity ratio The findings indicate that monetary policy interest rates negatively affect banks' liquidity holdings and lending behaviors in the interbank market.
In 2011, Bonfim and Kim conducted a study that diverged from earlier research focused on banks in Europe and North America by dividing the analysis into pre-crisis and crisis periods to assess the impact of internal and macroeconomic factors on bank liquidity Their findings revealed that many banks tend to overlook external factors, which are crucial for effective liquidity risk management The study underscores the significance of financial institutions in mitigating liquidity risk while identifying key factors that influence liquidity Utilizing data from Bankscope covering 2002-2009, the research included only commercial banks with consolidated financial statements, resulting in 2,968 observations, predominantly from banks in Canada, France, Germany, Italy, the Netherlands, the Russian Federation, the United Kingdom, and the United States.
In 2011, Vodová conducted a study analyzing the determinants of liquidity in Czech commercial banks from 2001 to 2009 The regression analysis revealed a positive relationship between bank liquidity and factors such as capital adequacy ratio, bad debt, lending rates, and interbank market interest rates Conversely, a negative correlation was found between liquidity and the inflation rate, business cycle, and financial crises Additionally, the study indicated that the link between bank size and liquidity remains ambiguous The selection of variables was informed by relevant prior research, with the author intentionally excluding factors like political recession, economic reform impacts, and exchange rate regimes to focus on elements specifically influencing bank liquidity in the Czech Republic.
Bonfim and Kim (2012) highlight that the increasing complexity of financial intermediaries has led to liquidity challenges, as banks often allocate their limited resources to loans for businesses and consumers These loans are typically financed through short-term deposits, creating a timing mismatch that exposes banks to liquidity risk (Diamond and Dybvig, 1983) To mitigate this risk, banks can manage liquidity through a structured balance sheet that includes a liquidity buffer However, maintaining liquid assets comes with an opportunity cost, as it can reduce profitability Consequently, while banks have incentives to hold liquid asset buffers such as cash and government bonds, ensuring liquidity safety remains a significant challenge in banking operations management (Bonfim and Kim, 2012).
Bunda and Desquilbet (2008) utilize the ratio of liquid assets to total assets to assess bank liquidity in emerging economies, revealing that larger banks experience increased liquidity risk Their findings indicate that a higher equity-to-asset ratio, which reflects capital adequacy, positively influences liquidity risk Additionally, they highlight that during liquidity crises, banks face significant shortages The study also notes that banks operating under floating exchange rate regimes are less vulnerable to liquidity risk compared to those in other financial systems.
A study by Hackethal, Rauch, Steffen, and Tyrell (2010) analyzed a dataset of 1,107 commercial banks across 36 emerging economies from 1997 to 2006 to identify factors influencing liquidity The findings indicate that macroeconomic elements, particularly tight monetary policy, adversely affect bank liquidity through the interest rate channel Additionally, while stimulating job demand via credit growth serves as a capital injection to support economic health, it simultaneously increases liquidity risk The researchers concluded that only macroeconomic and monetary policy factors are significantly linked to liquidity risk, whereas bank-specific factors such as size and performance show no correlation.
Umar's research, conducted by M & Sun G (2016), examines the impact of the Financial Crisis on bank liquidity in BRICS emerging economies—Brazil, Russia, India, China, and South Africa—over the period from 2002 to 2014 The study highlights that the financial crisis significantly influences the liquidity created by banks when assessed through balance sheet data, while off-balance-sheet liquidity remains unaffected However, the research is limited to listed public banks, omitting unlisted banks, which restricts a comprehensive evaluation of liquidity issues across these nations Additionally, the findings indicate that the size of bank assets in these ten countries does not impact liquidity levels.
In Vietnam, the banking system has undergone significant reforms over the past two decades, leading to advancements in both the quantity and quality of commercial banks However, liquidity risk remains a critical issue that has not received adequate attention Research on liquidity risk is limited, primarily consisting of qualitative studies that focus on liquidity risk management or the identification of its underlying causes.
Dang Quoc Phong (2012) on factors affecting the liquidity of commercial banks in Vietnam This study is analyzed only for the period 2007-2012 and for
This study examines the liquidity of 37 joint-stock commercial banks in Vietnam by analyzing the impact of various internal factors, including bank size, equity ratio, profit margin, and bad debt ratio, alongside external influences like inflation and economic growth rates Liquidity is measured using the ratio of Current Assets to Total Assets.
Truong Quang Thong (2013) used data of Vietnamese commercial banks from
Between 2002 and 2011, a study was conducted to identify the causes of liquidity risk in the Vietnamese commercial banking system The findings indicate that liquidity risk is influenced by both macroeconomic factors and specific bank characteristics Notably, the research reveals a nonlinear relationship between Total Assets and liquidity risk; initially, as total assets increase, liquidity risk decreases, but beyond a certain threshold, further increases in total assets lead to heightened liquidity risk.
Vu Thi Hong (2015) utilized the quantitative FEM method 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 equity ownership ratio, bad debt ratio, and payback ratio with liquidity, while the loan-to-deposit ratio exhibited a negative correlation Notably, the study found no significant impact of the provisioning ratio or bank size on liquidity.
In his 2017 study, Vo Xuan Vinh investigates the connection between liquidity risk and credit risk in Vietnamese commercial banks using data from annual reports spanning 2007 to 2015 Utilizing a Vector Autoregression (VAR) model, the findings indicate no direct relationship between liquidity risk and credit risk However, the analysis reveals that lagged variables of both risks do influence current credit risk The study also notes that the author did not fully explore all variables impacting liquidity risk within the theoretical framework and did not address the endogenous issues present in the research model.
Vo Xuan Vinh and Mai Xuan Duc (2017) conducted a study on the impact of foreign ownership on liquidity risk in 35 Vietnamese commercial banks from 2009 to 2015 Their findings indicate that an increase in foreign ownership correlates with heightened liquidity risk and reduced capital levels in these banks This research provides valuable empirical evidence highlighting the influence of foreign shareholders on liquidity risk management and other operational aspects within Vietnamese commercial banks.
From empirical studies on the liquidity of some countries in the world and Vietnam, the author can comment as follows:
DATA AND METHODOLOGIES
Data collection methods and techniques
To analyze the liquidity of the banking system in Vietnam and identify the influencing factors, a quantitative research method was employed This study utilized a theoretical model and gathered actual survey data from 27 Vietnamese Joint Stock Commercial Banks Due to insufficient data from the Vietnam Stock Exchange, the research sample comprised 270 observations.
The paper utilizes a combination of secondary and primary data sources to analyze bank performance Secondary data is gathered from financial reports, bank websites, academic journals, theses, and documents from the State Bank, Ministry of Finance, and Tax Department For quantitative research, primary data is collected through direct surveys and questionnaires administered to bank employees, as well as consultations with bank managers.
This study primarily utilizes data sourced from the Vietnam Stock Exchanges, annual consolidated financial statements, and annual reports of banks, specifically focusing on the Vietnam Joint Stock Commercial Bank during the period from 2011 onwards.
2020 The final dataset includes observations for 27 banks This process is repeated for each year, 2011-2020
This study analyzes internal variables using secondary data sourced from the consolidated financial statements and annual reports of 27 commercial banks from 2011 to 2020 Key indicators include equity capital, total assets, total loans, deposits, and bad debts (groups 3, 4, and 5), along with liquid assets such as cash, deposits at the State Bank, trading securities, and government bonds The research calculates critical variables, including return on equity, equity ratio, bad debt, profitability, bank size, loan-to-total deposit ratio, economic growth index, and inflation index, resulting in a comprehensive dataset comprising 270 observations.
Research Models
The author proposes a foundational model to address the components of payment risk faced by commercial banks, grounded in the preceding theory and analysis.
LIQ i,t = a o + β 1 ROE i,t + β 2 CAP i,t + β 3 NPL i,t + β 4 EA i,t + β 5 BS i,t + β 6 LDR i,t + β 7 GDP i,t + β 8 INF i,t +ε i,t
The author utilizes LIQ as a key indicator of commercial banks' liquidity, calculated by dividing total liquid assets by total deposit balance When interest rates increase, depositors often withdraw their funds to pursue better returns, resulting in a diminished deposit balance for banks This withdrawal can create a capital shortfall, hindering the banks' ability to fulfill loan credits Over time, a sustained lack of liquidity can damage a bank's reputation and decrease its profitability.
The Return on Equity (ROE) variable measures the efficiency of equity utilization, specifically assessing its impact on bank liquidity through the ratio of profit after tax to total equity Bank profits primarily stem from traditional operations, particularly the interest rate differential between lending and capital raising Consequently, a bank's liquidity needs necessitate holding more assets, which can diminish profit-generating capacity Conversely, when lending rates increase, banks are inclined to retain less capital to invest in profitable ventures Thus, the study posits hypothesis H1: a higher efficiency in equity utilization correlates with reduced bank liquidity.
The equity ratio, measured by the equity to total assets ratio, plays a crucial role in assessing a bank's financial capacity Banks with an equity ratio above the industry average demonstrate superior capabilities in mobilizing resources, lending, and maintaining solvency Given the numerous risks faced by the banking sector, a robust equity position is essential for mitigating potential losses and preventing bankruptcy In instances of insolvency, a bank's capital may be utilized to settle customer obligations Research indicates a significant inverse relationship between the equity ratio and liquidity risk, suggesting that an increase in the equity ratio effectively reduces liquidity risk for commercial banks.
Hypothesis H2 is: Equity ratio has a positive effect on bank liquidity
The impact of bad debt on a bank's liquidity is assessed using the ratio of total bad debt, categorized as groups 3, 4, and 5, to total outstanding loans According to the United Nations Statistics Office, debt is classified as bad when it is 90 days past due in interest or principal, or when interest payments are overdue for 90 days or more, regardless of whether principal payments have been made, refinanced, or deferred This classification raises concerns about the borrower's ability to repay the loan in full Consequently, bad debt poses significant risks to both creditors and banks, potentially leading to substantial capital losses, as highlighted in previous studies by Lucchetta (2007), Iqbal (2012), and Vong and Chan.
Research indicates a negative correlation between bad debt ratios and bank liquidity (2009), while Vodová (2011) presents evidence of a positive relationship between non-performing loans (NPL) and liquidity Consequently, Hypothesis H3 posits that the NPL ratio is negatively correlated with bank liquidity.
The author evaluates the impact of profitability on a bank's liquidity using the ratio of net interest income to average interest-bearing assets According to the Pecking Order theory, highly profitable commercial banks tend to retain earnings to bolster their capital, which limits their reliance on external funding sources and subsequently enhances their liquidity This finding aligns with the results of previous studies by Mahmood et al (2019), Cuinelli (2013), and Vodová (2013) When a bank generates substantial profits while maintaining strong liquidity, it lays a solid foundation for increasing asset value for its owners Therefore, the author posits the hypothesis H4: Profitability has a positive relationship with bank liquidity.
Bank size, measured using the base 10 logarithm of total assets, significantly influences liquidity, with larger banks often experiencing reduced liquidity despite their extensive resources While large banks can leverage the interbank market and support from the State Bank of Vietnam (SBV) to manage liquidity risks, they also face challenges associated with maximizing profits through substantial lending Research by Akhtar et al (2011) and Ahmed et al (2011) indicates a positive correlation between bank size and liquidity, suggesting that increased total assets can mitigate liquidity risk Conversely, Abdullah and Khan (2010) argue that larger banks may encounter heightened liquidity risks In the Vietnamese context, larger banks typically enjoy improved liquidity, leading to the hypothesis that bank size positively affects liquidity.
The Loan-to-Deposit Ratio (LDR) is a critical metric that reflects a bank's lending capacity relative to its deposit interest rate, calculated by dividing total loans by total deposits A higher LDR indicates that a bank has a greater proportion of its capital tied up in loans, which can lead to liquidity challenges during difficult times, as it may struggle to meet lending demands and attract smaller capital sources Conversely, a lower LDR suggests that a bank is lending less than its mobilized capital, potentially utilizing alternative funding sources such as interbank loans or securities issuance, thereby enhancing its liquidity position.
Previous research, including studies by Aspachs et al (2003), Bonfim and Kim (2011), India (2004), and Golin (2001), indicates a negative correlation between the loan-to-deposit ratio and bank liquidity Consequently, the author proposes the hypothesis H6, suggesting that there is an inverse relationship between the loan-to-deposit ratio and liquidity.
Economic growth, measured by the annual real GDP growth rate, significantly influences the banking sector During economic downturns, banks are expected to maintain higher liquidity due to increased lending risks, whereas in times of growth, they typically reduce liquidity reserves to enhance lending capabilities, which can elevate liquidity risk as deposits may decline (Shen et al., 2009) Dinger (2009) highlights a negative correlation between liquid asset holdings and economic growth, while Khemais et al (2017) find that economic growth adversely affects the liquidity of Tunisian banks The rise in household income in Tunisia has led to increased uptake of financial services, such as consumer and home loans, further boosting bank lending and associated liquidity risks Consequently, it is hypothesized that economic growth negatively impacts bank liquidity.
Inflation, measured by the annual inflation rate through the CPI index, significantly influences bank performance and liquidity According to Perry (1992), banks can adjust interest rates to boost interest income when inflation is anticipated, leading to increased lending despite competitive pressures that may reduce funding activities and heighten liquidity risk Research by Vodová (2011), Malik (2013), and others indicates that fluctuations in inflation negatively affect liquidity risk, as banks tend to tighten credit during inflationary periods Consequently, this results in decreased lending, reduced long-term investments, and an increase in liquid assets Therefore, it can be hypothesized that inflation positively impacts bank liquidity.
Table 1 Summary of variables in the research model
Variables Measures Variable name Hypotheses
Total liquid assets/Total deposits
ROE After-tax profit/Equity
CAP Equity/Total assets Equity ratio H2: +
Total non – performing loan (group 3, 4, 5)/Total outstanding loans
Net interest income/Average earning assets
BS Log(Asset) Bank size H5: +
Total outstanding loans/Total deposits
GDP Real GDP annual growth rate
The annual inflation rate through the CPI
Data analysis methods and techniques
The research employs a regression analysis method utilizing panel data, a technique previously implemented in studies by Dietricha and Wanzenried (2011), Ali et al (2011), Alper and Anbar (2011), and Nguyen Cong Tam and Nguyen Minh Ha (2012) Descriptive statistics were used for an initial examination of the sample's basic information, alongside an analysis of multicollinearity and autocorrelation phenomena.
This study aims to analyze the relationship between independent and dependent variables by estimating regression parameters that influence the liquidity of commercial banks Utilizing Ordinary Least Squares (OLS), Fixed Effects Model (FEM), and Random Effects Model (REM), the research seeks to identify the most accurate equation that represents these factors The research will be conducted in a structured manner to ensure comprehensive results.
Data collection and coding were conducted using EXCEL software, followed by the entry of this data into STATA 14 for descriptive statistical analysis This analysis encompasses key characteristics of the data, including the number of observed samples, mean value, maximum and minimum values, and standard deviation Overall, these results provide a comprehensive overview of the liquidity of commercial banks in Vietnam today.
(ii) Analysis of correlation matrix between variables:
Correlation analysis provides insights into the relationships among variables in a model, focusing on both independent and dependent variables This study assesses the significance of each factor by analyzing their interactions and, if needed, removing certain relationships Utilizing a correlation matrix, the analysis aims to explore how changes in independent variables affect the dependent variable while ensuring that multicollinearity among the independent variables is addressed.
(iii) Verification of multicollinearity phenomenon:
Multicollinearity occurs when independent variables in a model are linearly correlated, meaning they share information about the dependent variable To detect multicollinearity, researchers examine the correlation coefficient and the Variance Inflation Factor (VIF) A correlation coefficient above 0.8, as suggested by Farrar & Glauber (1967), indicates potential multicollinearity; however, this threshold can sometimes be misleading Therefore, to enhance model stability, further analysis using the "vif" command in STATA is conducted If any variable has a VIF greater than 10, as noted by Gujarati (2003), it will be removed from the model The process continues with the "collin" command in STATA until all variables have a VIF of less than 10, ensuring that the model effectively addresses multicollinearity issues.
After conducting descriptive statistics and analyzing variable correlations, the study proceeds to model estimation, a crucial phase in data processing The author employs ordinary least squares regression (OLS), but acknowledges that OLS estimates can be biased due to defects, individual characteristics, and entity variations, potentially obscuring the true relationship between dependent and independent variables To address these concerns, the research incorporates two additional estimation methods: the fixed effects model (FEM) and the random effects model (REM).
The Ordinary Least Squares (OLS) model estimates each observation with equal weight, allowing for the possibility of generating the Best Linear Unbiased Estimates (BLUEs) in certain scenarios By transforming the variables to meet the assumptions of the classical linear model, the OLS estimation method can be effectively applied In essence, the OLS technique involves adjusting the variables to comply with the standard least squares hypotheses, ensuring accurate and reliable estimations.
(vi) Fixed effects model (FEM):
The Fixed Effects Model (FEM), as described by Gujarati (2003), posits that each entity, such as banks, possesses unique time-constant characteristics that can independently influence various variables while also exhibiting correlation with the independent variables This model effectively isolates and controls the impact of these distinct characteristics on the dependent variables, allowing for a clearer estimation of the true effects of the independent variables Importantly, these unique characteristics are specific to each entity and do not correlate with those of other entities.
(vii) Random Effects Model (REM):
The key distinction between Random Effects Model (REM) and Fixed Effects Model (FEM) lies in how they handle entity volatility REM assumes that entity volatility is random and uncorrelated with independent variables, making it suitable when differences between entities do not significantly influence the dependent variable Conversely, FEM is more appropriate when these differences do impact the dependent variable, as it accounts for the unique characteristics of each entity In REM, the residuals of each entity, which are uncorrelated with the explanatory variables, are treated as new explanatory variables.
(viii) Check the heteroskedasticity test:
The study uses the Breusch and Pagan Lagrangian Test with the "xttest0" statement to test the autocorrelation with the hypothesis:
H0: The model has no change in variance
H1: The model has a phenomenon of variance
When the P-value is less than 0.05, the null hypothesis (H0) is accepted, indicating that the Random Effects Model (REM) is the most appropriate choice for the analysis Conversely, if the P-value exceeds 0.05, the null hypothesis is rejected, revealing autocorrelation issues, which necessitates the use of the Ordinary Least Squares (OLS) model for regression It is important to note that selecting the REM model implies that the issue of error variance has been effectively addressed.
(ix) Testing of regression hypotheses of the Hausman test:
The Hausman test is utilized in research to determine the most appropriate model for analysis, specifically between the Random Effects Model (REM) and the Fixed Effects Model (FEM) This test assesses the suitability of each model by examining the correlation between the error term (εi, t) and the independent variables, thereby guiding researchers in selecting the model that best fits their data sample.
H0: i, t, and independent variable are not correlated with each other
H1: i, t, and independent variable are correlated with each other
When the P-value is less than 0.05, we reject the null hypothesis (H0), indicating a correlation between the independent variable and the dependent variable, which typically leads to the application of a fixed effects model However, this study ultimately employed a random effects model as the final regression approach.
The research hypotheses will be evaluated using the regression equation derived from the research data, with t-statistics and p-values (Sig.) serving as the testing standards A reliability threshold of 95% will be applied, comparing the p-value directly to 0.05 to determine whether to accept or reject the hypotheses To assess data and model fit, we will utilize the R-square coefficient, t-statistic, and F-statistic for comprehensive testing.
To evaluate the importance of factors, it is considered that the corresponding Beta coefficient (β) in the regression equation built from research data
Based on the experimental results of the research model, this article analyzes the influence of independent variables on commercial banks in Vietnam, drawing on theoretical frameworks and insights from previous studies.
This chapter outlines the data sources utilized in the study, focusing on 27 commercial banks in Vietnam It details the analytical methods employed to assess the data and establish measurement testing, which underpins the empirical findings The subsequent chapter will delve into data analysis, present the regression model results, highlight key findings, and offer recommendations for future research.