Literature review
Foreign research
Previous research on the impact of liquidity on bank performance is limited, with most studies concentrating on the determinants of banking performance, including liquidity factors The relationship between liquidity and banking performance is complex, with some studies indicating a positive correlation, while others suggest a negative connection Additionally, several studies have found no significant link between liquidity status and bank performance.
A study by Kutsienyo (2011) investigated the factors influencing the needs of Ghanaian banks by analyzing data from 26 commercial banks between 2000 and 2009 using the GLS estimation technique The research focused on two dependent variables, Return on Assets (ROA) and Return on Equity (ROE), alongside five independent variables related to both bank-specific and macroeconomic factors The findings revealed that liquidity negatively affected the demand for banking services as measured by ROA.
Ahmad Aref Almazari (2014) examines the internal factors influencing bank profitability in Saudi Arabia and Jordan from 2005 to 2011 The study reveals a significant positive correlation between Return on Assets (ROA) and liquidity risk, highlighting the importance of liquidity management in enhancing bank profitability.
Poposka and Trpkoski (2013) analyze the factors influencing the profitability of Macedonian banks between 2001 and 2012, utilizing return on assets (ROA) and return on equity (ROE) as key indicators Their findings reveal a significant positive correlation between liquidity and bank profitability, highlighting the essential role of liquidity and capital in enhancing bank performance Similarly, Mahshid Shahchera's research focuses on the impact of liquidity on bank profitability in Malaysia, while Étienne Bordeleau and Christopher Graham contribute to the discourse on this critical financial aspect.
A 2010 study investigated the relationship between liquidity and bank profitability in the USA and Canada, revealing a positive correlation between the two factors.
A study by Kosmidou et al (2005) revealed that liquidity risk positively impacts Return on Assets (ROA) by analyzing business activity factors in Greece from 1990 to 2002, utilizing data from 23 banks The findings indicate that banks with lower liquidity experience reduced ROA, aligning with earlier research by Bourke (1989).
A study by Sayedi (2014) analyzed the factors influencing bank profitability in Nigeria from 2006 to 2014, utilizing Return on Assets (ROA) and Return on Equity (ROE) as key profitability indicators The research identified that market power and liquidity have a positive effect on bank profitability, while interest rates negatively impact ROA.
Zaphaniah Akunga Maaka (2013) investigated the impact of liquidity risk on the financial performance of commercial banks in Kenya, using profit before tax (PBT) as the dependent variable The study identified key independent variables including deposits, cash, liquidity gap, non-performing loans (NPL), and leverage ratio Findings revealed that an increased liquidity gap and leverage ratio negatively impact bank profits To mitigate liquidity risk, banks should focus on maintaining sufficient cash reserves, enhancing their deposit base, and minimizing both the liquidity gap and bad debt.
Domestic research
In Vietnam, there are some studies mainly focus on analyzing the determinants on banking performance, which is not directly assessing the relationship between liquidity risk and bank's performance
A study conducted by Ngo Phuong Khanh in 2013 reveals a negative correlation between the liquidity assets ratio and the profitability of joint-stock commercial banks in Vietnam during the period from 2007 to 2011.
In her 2019 research on "The Determinants of Bank Performance," Vu Thi Thuy Kieu analyzed the impact of various factors on bank profitability using Return on Assets (ROA) and Return on Equity (ROE) as dependent variables The study found that the deposit-to-asset ratio negatively affects bank profitability, while bank liquidity, size, and loans-to-assets ratio positively influence it Additionally, the research indicated that capital does not exhibit a linear relationship with bank performance.
Ho Thanh Thuy (2017) demonstrated that liquidity risk adversely affects the business performance of banks during the period from 2008 to 2015 The study also identified additional factors influencing the performance of commercial banks in Vietnam, including the ratio of customer deposits to total assets (DEP), cash status index (CASH), non-performing loan ratio (NPL), and bank size (SIZE).
Nguyen Thanh Phong (2020) conducted a study analyzing banking performance using return on assets (ROA) and return on equity (ROE) as dependent variables, while examining liquidity risk (LGAP), equity to assets (ETA), nonperforming loans (NPL), economic growth rate (GDP), inflation rate (INF), and annual unemployment rate (UEP) as independent variables The findings indicate that liquidity risk (LGAP) significantly influences the performance of commercial banks, suggesting that higher investments in high-risk assets can lead to increased returns Additionally, operational efficiency is impacted by factors such as ETA, NPL, GDP, and UEP.
Nguyen Cong Tam and Nguyen Minh Ha (2012) conducted a study on bank performance in Southeast Asia, focusing on five domestic commercial banks from each of the following countries: Indonesia, Vietnam, Malaysia, the Philippines, Thailand, and Singapore, based on total assets.
2007 - 2011 The study results show that capital adequacy (CAR) has a negative effect on the ROE while the LDR ratio representing liquidity has the opposite impact on ROE
Table 1: Summary of previous studies
The determinant of profitability of banks in ghana
The Impact of Liquidity Asset on Iranian Bank Profitability
The Impact of Liquidity on Bank
Impact of internal and external factors on profitability of Bank in Nigeria
Determinants of profitability ofdomestic UK commercial banks
The determinants affecting the profitability of joint stock commercial banks in Vietnam
The determinants affecting the bank performance in Vietnam
The impact of liquidity risk on commercial bank performance in Vietnam
Discuss about liquidity risk and commercial banks performance of Vietnam
Research on factors affecting the efficiency of banks in Southeast Asia
This research aims to examine how liquidity affects the performance of commercial banks in Vietnam, while also suggesting strategies to enhance liquidity and boost the overall performance of these banks.
The subject and scope of the study
- Study subjects: 25 commercial banks in Vietnam
+ Spatial scope: research on the relationship between liquidity and Vietnamese commercial banks performance
+ Time scope: research on the relationship between liquidity and Vietnamese commercial banks performance in the period from 2010 to 2020.
Method of research
The study uses descriptive statistical methods, analysis, comparison to analyze the relationship between liquidity and the commercial bank's performance in Vietnam from 2010 to 2020
This thesis employs quantitative research methods to construct a panel data regression model, assessing the impact of liquidity on the performance of commercial banks in Vietnam Utilizing STATA software, the study analyzes secondary data sourced from the financial statements and annual reports of Vietnamese commercial banks spanning from 2010 to 2020.
Research structure
Chapter 1: Theoretical basis of liquidity impacts on commercial bank performance Chapter 2: Research methods and research data
Chapter 3: Model of testing the impacts of liquidity on commercial bank performance
Chapter 4: Conclusion and some recommendations on liquidity management and enhancing commercial banks performance
THEORETICAL OVERVIEW OF LIQUIDITY IMPACTS ON
Liquidity in Commercial Banks
In the financial sector, the term "liquidity" is used in many different areas
Liquidity refers to the ability to quickly convert assets into cash without incurring discounts or transaction costs It highlights the ease with which an asset can be sold immediately after purchase, ensuring a seamless transition between assets and cash.
Liquidity is defined as the ability to meet payment obligations when they are due, primarily influenced by cash flows (Duttweiler, 2011) A highly liquid asset possesses two key characteristics: it can be easily converted into cash through an active market and maintains stable prices unaffected by transaction volume or timing (Rose, 2011) The measurement of liquidity is based on the time and cost required to convert an asset into cash Assets deemed highly liquid, such as treasury bills and certificates of deposit, can be quickly and inexpensively transformed into cash In contrast, assets like real estate and machinery exhibit low liquidity due to longer conversion times and higher costs.
From a banking perspective, liquidity refers to the ability to promptly utilize available capital for business operations, such as deposit payments, loan repayments, and capital transactions (Le Phuc Minh Chuyen, 2014) The Basel Committee on Banking Supervision defined liquidity in 2008 as a bank's capacity to fund asset increases and meet obligations as they arise, without facing unacceptable losses Consequently, a bank's liquidity is essential for its overall success.
Elliot (2015): "measure of bank's ability to find cash, short-term creditworthy securities, government bills, etc., which can be converted into cash"
Liquidity refers to a bank's capacity to quickly and completely meet its financial obligations, including deposits, loans, and other transactions Unlike solvency, which pertains to a bank's overall capital to cover expenses, liquidity focuses on the timely availability of funds A bank can be solvent yet still face liquidity issues if it cannot repay debts on time, highlighting the distinction between having sufficient capital and the immediate ability to fulfill financial commitments.
1.1.2 The role of liquidity for commercial banks:
Liquidity is vital for the business of tradable assets, providing significant advantages when effectively managed In commercial banks, high liquidity protects the interests of both customers and banks by ensuring fair transactions through enhanced asset circulation Conversely, illiquidity can lead to reduced lending, harming the bank's reputation and decreasing transactions High liquidity maintains a balance between supply and demand, safeguarding the interests of all participants Additionally, liquidity contributes to market stabilization; banks facing liquidity issues risk manipulation by competitors, which can result in customer loss and diminished market position Furthermore, liquidity facilitates faster transaction times, as it is measured by the efficiency of converting assets to cash, ultimately leading to quicker and more convenient processes, especially in fluctuating transaction environments.
Banks serve as secure repositories for both short-term and long-term customer deposits, necessitating a strong commitment to meet customer demands Maintaining liquidity is crucial for commercial banks to uphold their financial stability and credibility with stakeholders Adhering to appropriate liquidity ratios is essential to prevent funding issues that could arise from imbalances Effective liquidity management also supports the bank's structural integrity and addresses complexities related to its size To ensure compliance with liquidity requirements, banks often rely on dependable sources of liquidity while prioritizing low-cost options in their operations.
Liquidity risk arises when a bank faces a deficit, making it unable to meet its financial obligations This situation can lead to severe consequences, such as increased savings interest rates and higher lending rates, which hinder loan availability and can result in losses for the bank Additionally, a lack of liquidity complicates the withdrawal process for customers, further depleting the bank's capital for lending activities According to Truong Quang Thong (2012), insufficient liquidity can jeopardize a bank's survival, leading to lost business opportunities, customers, and public trust, while also triggering other risks like credit and systemic risks The interconnectedness of banks means that the failure of one can lead to a domino effect, threatening the entire financial system Addressing liquidity challenges incurs real and potential costs, including interest on borrowed funds and lost future profits from liquidating assets, which negatively impacts profitability and operational efficiency To manage liquidity effectively, banks must accurately assess their liquidity needs, establish a balanced investment portfolio with easily convertible securities, and maintain asset stability to prevent large-scale withdrawals Additionally, forecasting customer withdrawal needs is crucial for ensuring timely capital availability.
1.1.3 Measurement of liquidity of commercial bank
Liquidity can be measured in three different ways: (1) funding gap method
(2) net liquidity method and (3) liquidity ratios
Saunders & Cornett (2006) introduced the "Funding Gap" concept as a tool for assessing a bank's liquidity status Typically, a bank's liquidity manager focuses on two key balance sheet components: the average balance of core deposits and the overall liquidity position.
In banking operations, the average credit balance is crucial as most assets are financed through deposits Since the majority of current deposits can be withdrawn at any time, this leads to a liquidity gap, resulting in liquidity risk.
Conventional loans have low liquidity, so large and unpredictable withdrawals can lead to a loss of bank liquidity (Bonin et al., 2008) MS Advisory
The funding gap, defined as the difference between the average loans offered and the average core deposits, serves as a critical indicator of a bank's liquidity As noted by Dang Van Dan (2015), the funding gap method is highly effective in quantitative analysis, providing insights into the bank's liquidity status This gap acts as a warning signal, highlighting potential liquidity risks that a bank may face in the future.
A positive funding gap indicates that a bank's loans exceed its average deposits, prompting a reduction in cash reserves and liquid assets To manage liquidity, the bank may resort to additional borrowing in the money market, which consequently heightens its liquidity risk.
When the funding gap is negative, it indicates that loans are generally lower than deposits, resulting in excess funds for the bank This allows the bank to enhance its liquidity reserves by acquiring highly liquid assets or engaging in interbank lending While increasing liquidity reserves can secure future liquidity needs, it incurs a capital cost due to holding less profitable assets, which may diminish bank profits Conversely, investing in alternative assets can benefit the bank but also introduces higher risks.
Commercial banks evaluate their liquidity status using net liquidity (NLP), as noted by Peter S Rose (2004) Research by Madzivire and O'Brien (2018) indicates that non-performing loans significantly affect a bank's liquidity.
Liquidity supply refers to the capital that enhances a bank's solvency, primarily derived from customer deposits, which serve as the main source of liquidity It also includes revenue generated from services, the recovery of granted credits, the sale of trading assets, and the issuance of securities.
Liquidity demand refers to a bank's need for capital to support its operations, encompassing the withdrawal of customer deposits, loan disbursements, and repayments of loans and interest Additionally, it includes costs associated with management and services, as well as expenses related to acquiring securities and paying dividends.
- If NLP is higher than 0: surplus in liquidity It occurs when banks do ineffective business, inefficient use of capital and lack of market access
Bank performance
1.2.1 The concept of bank performance
Perspectives on performance are diverse, depending on the purpose of the research can consider performance in different aspects
Performance encapsulates the impact of technological advancements, effective resource allocation, workforce skills, and management quality It highlights the intricate relationship between economic returns and the associated costs required to achieve those outcomes.
When evaluating the performance of an enterprise, it could be referred to two criteria: absolute performance and relative performance
Absolute performance is defined as the difference between business performance and the costs incurred to achieve those results This metric indicates the size, volume, and profit generated under specific conditions, times, and locations However, it can be challenging to compare this indicator across businesses of similar size, as it does not effectively demonstrate the resource utilization levels in relation to comparative business performance among organizations.
Relative performance: due to the measurement based on the comparison ratio between the outputs and inputs The relative business performance is determined as follows:
Assessing bank performance is essential for comparing organizations of varying sizes, geographical scopes, and timelines According to Peter S Rose (2002), this evaluation is rooted in the theoretical framework of business performance assessment while considering the unique characteristics of commercial banks Narrowly defined, a bank's operational efficiency reflects its profit generation capability alongside operational safety Broadly, it encompasses not only profit generation but also the rational structure of liabilities and assets, ensuring stable profit growth Key resources, including labor, facilities, and financial assets related to core activities like deposits, loans, and investments, are critical in determining efficiency levels and influencing overall bank performance.
Truong Quang Thong (2011) said that performance can be seen as the result of the bank's operating efficiency over a certain period from the business perspective of the bank
Operational efficiency, as defined by the European Central Bank (2010), refers to the capacity to generate sustainable profits These profits are initially utilized to cover unexpected losses, enhance capital positions, and foster improved future returns by investing in retained earnings.
In the realm of commercial banking, performance is analyzed through system theory, focusing on two key aspects: first, the efficiency in transforming inputs into outputs, enhancing profitability, and minimizing costs to boost competitiveness against other financial institutions; second, the likelihood of maintaining safe and secure operations within the bank.
Commercial banks can define their business performance as the optimal integration of resources to minimize operational challenges, ultimately maximizing output This performance is assessed by comparing results against their marginal production line.
Banking performance is a multifaceted concept that varies based on research objectives In this context, it is evaluated through an operational lens, focusing on the profitability of banks while ensuring stable commercial banking operations and minimizing risks.
1.2.2 Criteria for evaluating the performance of commercial banks
There are several studies in the world have evaluated banks' performance through indicators such as ROA, ROE, NIM, including Bennaceur & Goaied
(2008), Chung Hua Shen et al (2009), Vong & Chan (2009), Khalid et al (2011)
Return on Assets (ROA) is a key indicator of a bank's profitability, reflecting its efficiency in utilizing assets to generate net profit after taxes, irrespective of whether these assets are funded through borrowed capital or equity.
The Return on Assets (ROA) index measures a bank's efficiency in managing and utilizing its assets, which primarily consist of loans and owner equity This metric indicates how effectively a bank generates profits from its assets, with a higher ROA signifying better asset management and conversion into net profit According to Mark and Ilse (2008), "The ROA provides information about how much profits are generated on average by each unit of assets." Additionally, ROA is instrumental in assessing the effects of financial leverage, aiding in informed capital mobilization decisions.
Return on Equity (ROE) is a key metric for assessing a commercial bank's performance, as highlighted by Rudra (2009) This important measure appeals to investors and shareholders alike, as it indicates the rate of income generated for shareholders from their investments in the bank.
A high Return on Equity (ROE) indicates that a business effectively generates profit from shareholders' investments, reflecting its ability to balance shareholder and borrowed capital to leverage competitive advantages Efficient investment mobilization and lending processes contribute to this performance Conversely, a relatively low ROE compared to peers may hinder a bank's capacity to attract capital.
The Bank's operational efficiency is evident in its marginal income ratios, particularly the net interest rate margin This key indicator measures the difference between interest income and interest expenses, highlighting the Bank's ability to optimize profitable assets while managing low-cost capital effectively.
The trend of international integration is reshaping banks' income structures, leading to a growing reliance on non-interest sources in a highly competitive environment Research indicates that increasing interest income can reduce risk for banks, as evidenced by DeYoung et al (2001), who found that US banks benefit from long-term relationships with borrowers, promoting sustainability In contrast, non-interest income remains volatile, influenced by customer preferences Furthermore, Lee et al (2014) highlighted that rising interest rates can elevate overall and specific risks, particularly during periods of economic instability or crisis.
1.3 The impact of liquidity and other factors on commercial banks performance
1.3.1 The impact of liquidity on bank performance
Numerous theories and studies have explored the relationship between liquidity and bank profitability, yielding varied conclusions As a result, liquidity can influence bank profitability in different ways, exhibiting negative, positive, or even nonlinear effects.
According to Rose (1998), there exists a trade-off between liquidity and profitability in banking; specifically, higher liquidity typically results in lower expected profitability Commercial banks serve as intermediaries, mobilizing surplus capital from depositors to lend to borrowers for profit maximization However, banks must remain prepared to meet customer demands for withdrawals and loans, which can incur costs if liquidity is insufficient Failure to respond promptly to these needs, particularly in unexpected circumstances, can erode customer trust and hinder customer acquisition, especially amidst intense competition Moreover, a decline in liquidity, if not promptly addressed, can have systemic repercussions (Ali Sulieman Alshatti, 2015) Thus, maintaining an optimal level of liquidity is crucial for enhancing a bank's profitability.
On the other hand, according to Bordeleau and Graham (2010), Shahchera
RESEARCH METHODS AND RESEARCH DATA
Actual situation of commercial banks in Vietnam from 2010 to 2020
The banking industry plays a crucial role in the economic landscape, being the first to feel the impact during economic crises while also leading the recovery efforts that stabilize the economy In Vietnam, significant transformations have occurred within the commercial banking system, driven by modernization and integration efforts.
The evolution of Vietnam's commercial banking system has seen significant growth in both quantity and quality since its inception, marked by an expansion in service offerings and enhanced customer service professionalism The entry of fully foreign-owned banks and the gradual easing of operational restrictions have intensified competition, compelling domestic banks to restructure for sustained growth.
Diagram 2.1: Total assets growth in banking industry, 2010 - 2019
Source: The State Bank of Vietnam, IMF
Firstly, the growth of size witnessed a fluctuation through the period shown
In 2010 reached the highest rate among the period shown at 40.36%; therefore, all
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 banks have positive growth rates This significant growth explained by in the period
Between 2009 and 2010, the economy and stock market began to recover from the 2008 global financial crisis, leading to increased borrowing and investment in profitable assets However, by 2012, total asset growth sharply declined to a low of 5.40%, primarily due to the implementation of Circular 21 in September 2012, which imposed stringent qualifications on interbank deposits converted into loans, thereby limiting asset creation Additionally, trust activities slowed down in late 2011 and early 2012, further reducing the industry's overall asset size Although there were signs of potential recovery in asset growth, the trend continued to decline in 2014, with a notable resurgence in asset growth from 2015 to 2017, before experiencing a significant downturn.
In 2019, the total assets of the credit institution system reached VND 12,578,812 billion, reflecting a 13.30% increase compared to the end of 2018, following the State Bank of Vietnam's credit tightening policy that limited loan growth at banks in 2018.
Diagram 2.2: Credit and deposit growth in banking industry, 2010 - 2019
Source: The State Bank of Vietnam, IMF
Secondly, the growth of activities was shown by the growth of 2 main activities, namely credit and deposit After a period of fast credit growth in 2009 -
From 2010 to 2013, credit growth rates declined significantly from 31.19% to 12.52%, with a low of 8.91% in 2012, primarily due to the government's tightening of monetary policy aimed at reducing dollarization In 2011, deposit growth sharply fell after the State Bank imposed a ceiling deposit interest rate of 14% per year However, growth resumed in March 2012 following signals from the State Bank about a gradual reduction of this ceiling rate Between 2013 and 2017, the economy showed signs of recovery, particularly during 2016-2017, when credit and deposit growth peaked, driven by the effective implementation of decision 1572/QD/NHNN for comprehensive restructuring of credit institutions By 2019, credit growth reached 12.10%, reflecting high credit quality and adherence to credit control policies that helped manage inflation and support economic growth, while deposits increased by 2.76% compared to 2018.
The average ratio of liquid assets to total assets across three bank groups stands at 14.93% over 11 years, showing minimal fluctuation This ratio peaked at 29.03% in 2010 but experienced a sharp decline the following year Nguyen Ngoc Thang, the Standing Deputy Director of the State Bank of Vietnam, noted that deposits at credit institutions significantly dropped after September 7, 2011, when new regulations limited banks' ability to mobilize funds beyond the amount deposited This decline has contributed to liquidity challenges for smaller banks, particularly in attracting deposits from organizations and individuals at joint-stock commercial banks.
2011 has decreased by nearly 41,000 billion VND (10.5%) Besides, a part of deposits has also been transferred to state-owned commercial banks
Diagram 2.3 Liquidity assets to total assets in banking industry, 2010 – 2019
Source: The State Bank of Vietnam, IMF
Around 2012, commercial banks faced a tightening liquidity situation, with the liquidity assets to total assets ratio dropping to its lowest point in 11 years at just 11.32% in 2018 This decline was attributed to the high-risk, profitable nature of the stock market, which experienced significant fluctuations in 2018, leading to a decrease in the liquidity of investment securities Consequently, commercial banks reduced their holdings in securities and shifted towards more attractive investment options, such as the gold market Although the liquidity ratio slightly improved to 11.66% in 2019, it remained relatively low.
Diagram 2.4: Non-performing loans growth in banking industry, 2010 – 2019
Source: The State Bank of Vietnam
The significant increase in bad debt during this period can be traced back to the previous phase of rapid credit growth, where many banks engaged in substantial lending without adequate risk management Consequently, this has led to the accumulation of various risks within banking operations, revealing vulnerabilities that are increasingly apparent across the entire banking system, particularly concerning non-performing loans.
Between 2010 and 2015, the rapid increase in non-performing loans was attributed to inadequate quality and risk provisioning by banks The banking sector's bad debt ratio peaked at 4.86% in 2012, subsequently declining but stabilizing around 3% in the following years State Bank Governor Nguyen Thi Hong addressed these concerns during a Government Press Conference.
In 2014, the rise in non-performing loans (NPLs) was primarily attributed to the challenging business operations faced by enterprises, leading many to struggle with debt repayment Concurrently, the economy's limited capital absorption capacity restricted credit expansion As bad debts surged, threatening the stability of the banking system and economic activities, the Government responded by issuing Decision 843/2013/QD-TTG to address the issue.
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 approving the Project "Decision on framework of NPL disposal" and the project
The establishment of the Asset Management Companies of Vietnam Credit Institutions (VAMC) has led the State Bank to direct credit institutions to actively implement measures for regulation and reorganization As a result, over the past three years, the Vietnamese banking sector has seen a significant improvement in bad debts, with the non-performing loan (NPL) ratio dropping to approximately 1.89% This restructuring and handling of bad debts within the credit institutions system has resulted in notable positive changes, enhancing the overall operational efficiency of the sector.
Diagram 2.5: ROA and ROE growth in banking industry, 2010 – 2019
Source: The State Bank of Vietnam
Return on Assets (ROA) and Return on Equity (ROE) are key indicators of financial efficiency, allowing analysts to assess the risks associated with banks During 2010 and 2011, ROE ratios were notably high at 20.06% and 21%, respectively, while ROA remained low at 1.40% for both years This period reflects the bank's effective business operations, resulting in substantial profits However, starting in 2012, the trends began to shift.
Between 2011 and 2015, the banking sector experienced a significant decline in return on equity (ROE) and return on assets (ROA) due to rising non-performing loans (NPLs), with ROE dropping from over 21% to 5.98% and ROA reaching a low of 0.49% in 2015 This decline indicated a decrease in banks' effectiveness and profitability However, following the implementation of a decision to address bad debt in 2016, both ROE and ROA began to recover By 2019, ROE had increased by 15.29% year-on-year, and ROA saw a growth of 1.08%, marking the highest growth rate for ROA during the observed period.
Research model
This study aims to examine the impact of liquidity on bank performance, building on theoretical foundations and expanding upon previous research conducted by Vu Thi Thuy Kieu (2019), Pham Nguyen Anh Long (2019), and Ho Thanh Thuy.
2019), the research selected and used variables that suitable for Vietnamese commercial banks The study proposes the following model
The dependent variable Y i,t is a measure of bank performance of commercial banks, corresponding to ROA it , ROE it
This study analyzes the influence of two sets of factors on Vietnamese commercial banks: micro-factors, including liquidity status ratio (LIQ), bank size (SIZE), non-performing loan ratio (NPL), deposit to asset ratio (DTA), equity ratio (ETA), and loans to assets (LTA); and macroeconomic variables such as inflation rate (INF) and GDP growth (GDP).
The model is rewritten as follows:
Table 2.6: Dependent variables and independent variables in the study
Notation Variable name Measurement formula Expected
INF Inflation rate Data collection from WB +
GDP GDP growth Data collection from WB
2.2.1 Dependent variables a Return on Asset ratio (ROA)
Return on Assets (ROA) is a widely recognized metric for assessing bank profitability, frequently utilized in numerous research studies, including those by Vong and Chan (2009), Chung-Hua Shen et al (2009), Pham Nguyen Anh Long (2019), Ahmad Aref Almazari (2014), and Nguyen Cong Tam and Nguyen Minh Ha (2012).
Return on Assets (ROA) is a key financial metric that indicates the rate of return generated from a bank's total assets, reflecting its efficiency in profit generation It demonstrates how effectively a bank manages its assets to yield profits, as highlighted by Dietrich and Wanzenried (2011).
ROE and ROA are essential metrics for assessing a bank's performance in generating income and interest from equity While both indicators measure efficiency, ROE specifically considers the cost of raising capital, unlike ROA.
Several studies have utilized Return on Equity (ROE) as a key indicator to assess bank efficiency, including research by Pham Nguyen Anh Long (2019), Mohammad Hossein Khadem Dezfouli et al (2014), Pham Thi Hang Nga (2011), Saleh Taher Alzorqan (2014), Chung-Hua Shen et al (2009), and Dietrich and Wanzenried (2011).
2.2.2 Independent variables and research hypotheses: a Liquidity status (LIQ)
Research indicates a positive correlation between liquidity ratios and efficient banking operations, particularly during financial crises Banks that maintain a substantial amount of highly liquid assets can effectively manage unexpected liquidity risks and reduce borrowing costs from external sources Studies conducted in Vietnam and various Asian regions have also highlighted the beneficial relationship between liquidity and banking performance, reinforcing the importance of liquid assets for financial stability and operational efficiency.
Research indicates a negative correlation between liquidity ratios and bank performance, as holding liquid assets often results in lower interest rates for long-term loans (Goddard et al., 2004; Lee and Hsieh, 2013) While maintaining liquidity can mitigate bankruptcy risk by lowering costs and boosting profits, excessive liquid asset holdings may adversely impact profitability (Bordeleau and Graham, 2010) Additionally, Anbar and Alper (2011) found no significant relationship between liquidity ratios and bank profits.
In the volatile landscape of the Vietnam capital market, characterized by numerous fluctuations and challenges, prior research suggests that liquidity plays a crucial role in enhancing bank performance.
H1: Liquidity status has a positive impact on Bank's performance b Bank size (SIZE)
The size variable plays a crucial role in analyzing the impact of bank size on profitability Economic theories indicate that larger banks can improve their competitiveness by leveraging their reputational advantages and offering lower-cost services through economies of scale, ultimately leading to higher profits Numerous studies, including those by Chung Hua Shen et al (2009), Nguyen Cong Tam and Nguyen Minh Ha (2012), Mohammad Hossein Khadern Dezfouli et al (2014), and Anbar and Alper (2011), have demonstrated a positive correlation between bank size and profitability.
H2: The bank size has a positive impact on the Bank's performance c Non-performing loans (NPLs)
The NPL ratio is a crucial indicator of asset quality and loan-related risks A significant rise in the Bank's NPL ratio reflects a decline in credit quality and an escalation in credit risk, which subsequently heightens liquidity risk.
The study anticipates that an increase in the Non-Performing Loans (NPLs) ratio will negatively impact the bank's profitability Previous research, including findings by Ho Thanh Thuy (2017), Ahmed Arif and Ahmed Nauman Aness (2012), and Nguyen Cong Tam and Nguyen Minh Ha (2012), supports the negative correlation between NPLs and bank returns.
H3: Non-performing loans to assets has a negative impact on Bank's performance d Equity structure (ETA)
The capital index is a key indicator of a bank's strength and market position, reflecting its ability to withstand and recover from economic shocks A higher equity-to-total-assets ratio is associated with greater profitability, as banks with substantial capital require less external funding, which in turn reduces borrowing costs and enhances financial stability Additionally, increased capital levels lead to lower leverage and risk Research by Shen et al (2009), Vong and Chan (2009), and Poposka and Trpkoski (2013) supports the notion that banks with a higher equity ratio tend to be more profitable and secure.
H4: Equity to assets has a positive impact on the Bank's profitability e Deposit to assets (DTA)
Deposits represent idle funds placed by individuals and organizations in banks to utilize their non-cash payment services, and the deposit-to-asset ratio serves as a key indicator of capital efficiency Banks rely heavily on these deposits, using them to finance loans, which makes them vulnerable to liquidity shortages A higher deposit-to-asset ratio indicates lower capital costs for financing credit operations, thereby enhancing bank profitability Research, including studies by Ahmed Arif and Ahmed Nauman Anees (2012) and others, shows a positive correlation between customer deposits and total assets, suggesting that this ratio significantly influences bank performance Consequently, a robust deposit ratio is anticipated to positively affect a bank's overall performance.
H5: Deposit to asset has a positive impact on the Bank's performance f Loan to assets (LTA)
Asset composition, measured by the ratio of total loans to total assets, serves as a key indicator of liquidity in banking operations, highlighting the allocation of assets to less liquid forms such as loans This metric provides insights into a bank's governance capacity, as noted by Isik and Hassan (2003), where effective loan management can reduce operating costs and enhance efficiency Additionally, the Loan-to-Asset (LTA) ratio assesses a bank's ability to fulfill credit demands backed by its asset base (Abdullah, 2003), emphasizing that lending activities are the primary profit drivers for commercial banks.
H6: Loans to assets ratio has a positive impact on the Bank's performance g Inflation growth rate (INF)
Inflation is a macro factor affecting the Bank's performance Vong and Chan
Research methodology
Given the temporal and spatial nature of the data in this study, the panel data regression method is the most appropriate for analysis Three distinct models can be employed for regression analysis utilizing panel data.
The Pooled OLS Model fails to account for the unique characteristics of individual banks, as it does not consider spatial and temporal variations By applying a uniform estimation approach, it assumes that coefficients β0 and βi are identical across all observations This assumption is flawed, given that each bank exhibits distinct traits and circumstances that can change annually Consequently, using this estimation method may lead to inaccurate results, undermining the model's ability to accurately depict the relationship between dependent and independent variables.
The Fixed Effects Model (FEM) is an advanced version of the Pooled OLS model, specifically designed to account for unique characteristics among banks This model addresses the correlation between residuals and independent variables, enhancing the accuracy of the analysis.
The REM Model, or Random Effects Model, is an advanced statistical approach derived from the Pooled OLS model It provides enhanced control for the unique characteristics of banks while ensuring that there is no correlation between the model's residuals and the independent variables.
The study will perform regression of all three models: Pooled OLS, FEM and REM.
Data Description
This article analyzes the financial statements of 25 commercial banks in Vietnam from 2010 to 2020, focusing on microeconomic factors such as inflation and GDP growth rates Data for these factors is sourced from the World Bank and the Vietnam General Statistics Office The gathered information is processed using Excel software to perform calculations and establish research variables.
The research utilized panel data, integrating both spatial and time-series information, focusing on the period from 2010 to 2020 This timeframe was chosen for its relevance and currency, providing a solid foundation for assessing the current situation and offering valuable recommendations for the future.
Variable Observation Mean Std Dev Min Max
Source:Calculation results on STATA
The table summary statistics for the variables indicates that in the 25 banks surveyed for twelve years
The profitability of Vietnamese banks is reflected through key metrics such as Return on Assets (ROA) and Return on Equity (ROE) The average ROA stands at 0.87%, with a peak of 4.8% recorded by BIDV in 2018 and a low of -5.99% by TPBank in 2011, accompanied by a standard deviation of 0.80% Meanwhile, ROE has an average of 10.02%, reaching a maximum of 29.57% (VIB in 2020) and a minimum of -56.33% (TPBank 2011), with a standard deviation of 8.44% These averages indicate that the profit margins of Vietnamese banks are at a medium level compared to global standards, although there has been a consistent improvement in recent years Notably, the ROE is approximately ten times greater than the ROA, indicating a significantly higher growth rate of profit after tax relative to total assets.
The analysis of the independent variables influencing bank performance reveals that liquidity (LIQ) has an average of 18.65% and a standard deviation of 8.66% The liquidity values range significantly, with a minimum of 3.54% recorded by TPBank in 2011 and a maximum of 61.10% by PVBank in the same year This substantial volatility indicates notable differences in liquidity levels among various banks.
In terms of bank size, the mean is 18.4629, a standard deviation of 1,5379 The smallest figures for bank size in the data sheet belonged to SaigonBank in
2016, and the largest is BIDV in 2020
Nonperforming loans (NPL) with an average of 2.12%, the standard deviation of 1.08%, the maximum value of 34.13% (ACB in 2011), and a minimum of 0.02% (TPBank 2010)
The average equity to total assets (ETA) ratio stands at 9.91%, with a standard deviation of 0.0600 In 2020, SaigonBank recorded the lowest capital value at 2.67%, whereas AnbinhBank achieved the highest ETA rate of 47.94% in 2011.
The average ratios of deposits to total assets (DTA) and loans to total assets (LTA) among banks show minimal variation, with DTA at 69.84% and LTA at 54.76%.
The two indicators of inflation rate (INF) and economic growth rate (GDP) are 6.04% and 6.01% The maximum value of the two indicators INF and GDP, is 2.91% in 2011 and 0.72% in 2019, respectively
This chapter provides a comprehensive analysis of the business performance of Vietnamese commercial banks, focusing on key indicators such as total assets, credit growth, capital mobilization, bad debt levels, and the ROA and ROE ratios Additionally, it outlines the research model, methodology, and data description used in the study.
This chapter outlines the research context and model, which will serve as the foundation for conducting regression testing in Chapter 3 Additionally, it will present potential solutions to enhance liquidity and improve the performance of commercial banks in Vietnam, discussed in Chapter 4.
MODEL OF TESTING THE IMPACTS OF LIQUIDITY ON
Analysis of autocorrelation
To determine the relationship between the variables in the model, the author uses the correlation coefficient analysis between the independent and dependent variables
Table 3.1: Autocorrelation matrix among variables
ROA LIQ SIZE NPL ETA DTA LTA INF GDP ROA 1.0000
ROE LIQ SIZE NPL ETA DTA LTA INF GDP ROE 1.0000
Source:Calculation results on STATA
The correlation matrix indicates that the dependent variables show medium to low correlations with the independent variable, with all correlation coefficients below 0.8 The strongest correlation is 0.4365, observed between Return on Assets (ETA) and bank size (SIZE) However, this average coefficient suggests a minimal risk of multicollinearity in the regression model.
Table 3.2: Multi-collinearity VIF test
Source:Calculation results on STATA
The coefficients table indicates that all values are below 2, suggesting that the model's variables do not exhibit multi-collinearity This finding is a positive indicator for selecting an appropriate model.
Regression results
3.2.1 The results of the OLS, REM and FEM regression for model 1 (ROA)
Table 3.3: The results of OLS, REM and FEM regression for model 1
Coef P-value Coef P-value Coef P-value
NOTES: *, ** and *** represent statistical significance at 1%, 5%, and 10%, respectively
Source:Calculation results on STATA
The regression analysis indicates that seven out of eight variables—LIQ, SIZE, NPL, ETA, DTA, LTA, and INF—significantly influence Return on Assets (ROA), while GDP is not significant across three models Notably, NPL and DTA exhibit opposing effects on ROA, with statistical significance at 1% and 5% The remaining variables, particularly LIQ, ETA, SIZE, and LTA, demonstrate a strong impact on ROA, also at the 1% and 5% significance levels Consequently, the regression model can be reformulated based on these findings.
3.2.2 The results of the OLS, REM and FEM regression for model 2 (ROE)
Table 3.4: The results of the OLS, REM and FEM regression for model 2
Coef P-value Coef P-value Coef P-value LIQ 0.0139** 0.013 0.2192* 0.000 0.2707* 0.000
NOTES: *, ** and *** represent statistical significance at 1%, 5%, and 10%, respectively
Source:Calculation results on STATA
The most fundamental difference between model 1 and model 2 is that ETA,
Deferred Tax Assets (DTA) have a negligible effect on Return on Equity (ROE), only showing a slight impact at 10%, while exerting a strong influence on Return on Assets (ROA) The statistical significance of the variables—Liquidity (LIQ), Size (SIZE), Non-Performing Loans (NPL), Loan to Assets (LTA), and Inflation (INF)—aligns closely with ROA Notably, these variables demonstrate a more substantial effect on ROE compared to ROA Additionally, Gross Domestic Product (GDP) lacks statistical significance in both analyses.
Evaluation of the suitability of the regression model
Research using the data panel with the OLS, fixed effect model and the random effect model Therefore, F-test is to analyze what is more suitable between
OLS and FEM, and the Hausman test's study to selecting FEM or the REM
Table 3.5: F-test and Hausman test for model 1 Accreditation Pooled OLS and FEM FEM and REM
Table 3.6: F-test and Hausman test for model 2 Accreditation Pooled OLS and FEM FEM and REM
Source:Calculation results on STATA
The regression analysis conducted on the dependent variables Return on Assets (ROA) and Return on Equity (ROE) indicates that the Fixed Effects Model (FEM) is more optimal than the Pooled Ordinary Least Squares (OLS) model Furthermore, when comparing FEM and Random Effects Model (REM) options, Model 1 is deemed suitable for REM, while Model 2 is appropriate for FEM.
In the analysis of panel data across three models—Ordinary Least Squares (OLS), Random Effects Model (REM), and Fixed Effects Model (FEM)—the findings indicate that REM is applicable for model 1, while FEM is suited for model 2 These results align with the broader research on the influence of liquidity on the performance of commercial banks during the period from 2010 onward.
2020 Based on each variable in the regression model in the cases with significance level 1%, 5%, the regression model is rewritten as follows:
After identifying the REM model as the best fit for model 1 and the FEM model as appropriate for model 2, we can conduct a heteroscedasticity test for both models The presence of error variance may compromise the reliability of the regression results This study proposes the following hypotheses:
H1: The model does not have heteroscedasticity
Source:Calculation results on STATA
In model 1, prob is 0.3533 less than 0.5, so the study rejects hypothesis H1, accepts hypothesis H2
In model 2, prob is 0.0000 less than 0.05
Thus, in both of these models, the variable variance of variance occurs
Model using Wooldridge series cointegration test data The study offers the following hypotheses:
H1: The model does not have autocorrelation
H2: The model has an autocorrelation phenomenon
Table 3.8: Wooldridge serial correlation test
Source:Calculation results on STATA
The results showed in both models 1 and 2, prob equal to 0.0027 and 0.0029, which is less than 0.05 since the study accepts H2 and rejects hypothesis H1 Both models have the phenomenon of autocorrelation
3.3.3 Estimated according to the GLS method
The research model faced issues with variable variance and autocorrelation, leading to ineffective estimated results To address these challenges, the study employed the General Least Squares (GLS) estimation method, which effectively managed the violations related to autocorrelation and variable variance.
NOTES: *, ** and *** represent statistical significance at 1%, 5%, and 10% respectively
Source:Calculation results on STATA
Table 3.10: Summary of the results
Notation Variable name Expected Result Same result research
Dependent variables ROA Return on assets
Kosmidou et al., 2005; Poposka and Trpkoski, 2013; Shen et al., 2009; Ho Thanh Thuy, 2017; Ahmad Aref Almazari, 2014; Vu Thi Thuy
Shen et al., 2009; Nguyen Cong Tam and Nguyen Minh
Ha, 2012; Mohammad Hossein Khadern Dezfouli et al., 2014; Anbar and Alper;
Ho Thanh Thuy, 2017; Ahmed Arif and Ahmed Nauman Aness, 2012; Nguyen Cong Tam and Nguyen Minh Ha, 2012
Shen et al., 2009; Vong and Chan, 2009; Poposka and Trpkoski, 2013
Ahmed Arif and Ahmed Nauman Anees, 2012; Mohammad Hossein Khadem Dezfouli et al., 2014; Zaphaniah Akunga Maaka,
LTA Loans to Asset + + Isuk and Hassan, 2003; Pham
INF Inflation rate + + Vong and Chan, 2009
Poposka and Trpkoski, 2013; Sufian and Chong, 2008 a Result of regression model 1
The GLS regression results show that the P-value of 0.000 is less than 5%, so the model is evaluated as appropriate Research results are written in the form of new equations:
The regression analysis indicates that out of eight research variables, six significantly impact Return on Assets (ROA) at a 5% significance level: liquidity (LIQ), non-performing loans (NPL), equity to asset ratio (ETA), deposit to asset ratio (DTA), loans to assets ratio (LTA), and inflation (INF) Specifically, LIQ, ETA, LTA, and INF positively influence ROA, while DTA and NPL have negative effects This suggests that improved liquidity, higher equity to asset ratios, increased loans to assets, and rising inflation contribute to greater operational efficiency in banks, whereas an increase in non-performing loans and deposit to asset ratios leads to decreased bank performance.
Like model 1, P-value is evaluated appropriately for the model to use The model results in the form of equations:
In Model 2, liquidity (LIQ), size (SIZE), loans to assets (LTA), and inflation (INF) positively influence return on equity (ROE), while non-performing loans (NPL) and deposit to asset ratio (DTA) have a negative impact Notably, GDP and equity to total assets (ETA) do not exhibit a linear relationship with ROE The findings suggest that a decrease in non-performing loans and deposit ratios enhances bank performance, while improvements in liquidity, bank size, loans to assets, and inflation rates contribute positively to ROE.
Discuss the regression results
The study reveals that liquidity status (LIQ) positively influences both return on total assets (ROA) and return on equity (ROE) Specifically, at a 1% significance level, a 1% increase in liquidity status leads to a 2.11% rise in ROA and a substantial 19.16% increase in ROE, assuming other factors remain constant.
The findings align with the author's initial hypothesis and echo previous studies conducted in Vietnam and other Asian regions, including works by Vu Thi Thuy Kieu (2019), Pham Nguyen Anh Long (2019), Ho Thanh Thuy (2017), Shen et al (2009), and Kosmidou et al (2005) This suggests that banks maintain highly liquid assets, which reduces financial risk and stabilizes profits during liquidity shocks Additionally, the situation in Vietnam indicates a positive correlation between liquidity and the performance of commercial banks throughout the research period.
Between 2008 and 2011, many Vietnamese banks faced significant liquidity stress, leading to the forced restructuring of nearly 10 banks due to liquidity loss The repercussions of this crisis are still being felt today, highlighting the crucial link between liquidity and the performance of commercial banks in Vietnam During favorable capital market conditions, these banks can maintain minimal liquid assets to maximize profits However, in times of liquidity shortages, it becomes essential for banks to increase their liquid asset ratios to prioritize system safety and stability, ultimately enhancing their overall performance.
In unstable market conditions, particularly within a challenging capital market, banks struggle to mobilize capital to address liquidity shortages, often facing high borrowing costs from external sources Insufficient fast liquid assets can lead to increased expenses, negatively impacting bank profits This liquidity shortfall poses serious risks to banking operations, potentially resulting in bankruptcy and threatening the stability of the entire banking system Conversely, banks that maintain a robust portfolio of highly liquid assets can enhance profitability, improve competitiveness, and attract more customers, ultimately driving higher earnings.
The size of a bank (SIZE) has a positive effect on return on equity (ROE), showing a statistically significant increase of 1%, with a 1% rise in bank size correlating to a 0.62% increase in ROE However, no statistical significance was found between bank size and return on assets (ROA) These findings align with the expected hypothesis and are consistent with previous research conducted by Chung Hua Shen (2009), Ho Thanh Thuy (2017), and Nguyen Cong Tam and Nguyen Minh Ha (2012).
Large-scale banks demonstrate higher operational efficiency due to their ability to leverage economies of scale and strong reputations, enabling them to offer customers services at lower prices compared to smaller banks.
Research indicates that nonperforming loans significantly impair bank performance Specifically, at a 1% significance level, a 1% increase in the nonperforming loans ratio correlates with a 9.04% decrease in return on assets (ROA) and a staggering 108.61% decline in return on equity (ROE) These findings align with the initial hypothesis and corroborate previous studies conducted by Nguyen Cong Tam (2012), Ho Thanh Thuy (2017), and Ahmed Arif alongside Amed Nauman Aness (2015), as well as Mohamad Hossein Khadem Dezfouli et al (2014).
The increase in bad debt leads to heightened credit risk and other associated risks, such as liquidity and operational risks, ultimately diminishing operational efficiency This phenomenon aligns with the Vietnamese economy's landscape from 2010 to 2015, where rapid credit growth prompted commercial banks to lend extensively without adequately assessing credit quality As economic instability emerged, exacerbated by the global financial crisis, businesses faced significant challenges, resulting in a marked decline in bank asset volumes, an increase in non-performing loans (NPLs), and a surge in credit loss provisions Consequently, this situation severely impacted bank profits and operational efficiency, risking bankruptcy and threatening the stability of the entire banking system.
The regression analysis indicates that equity to assets (ETA) significantly influences return on assets (ROA) at a 5% significance level, with a 1% increase in ETA leading to a 12% rise in ROA However, ETA does not appear to affect return on equity (ROE), aligning with the author's hypothesis.
Capital indices reflect a bank's strength in the financial market and its resilience against economic shocks, with the equity-to-total-assets ratio influencing profitability Banks with higher capital are less susceptible to liquidity issues, require less external funding, and benefit from lower capital costs Conversely, increased capital leads to reduced leverage and risk, a finding supported by studies from Lee and Hsieh (2013), Poposka and Trpkoski (2013), Pham Nguyen Anh Long (2019), and Shen et al (2009).
The study's regression model reveals that the ratio of customer deposits to total assets (DTA) significantly negatively impacts business performance, indicated by a 5% statistical significance in the return on assets (ROA) and return on equity (ROE) Specifically, a 1% increase in the deposit-to-asset ratio results in a decrease of 0.38% in ROA and 4.86% in ROE, contradicting initial expectations This phenomenon may be attributed to the prevalence of short-term deposits, as noted by Antonina Davydenko (2011), suggesting that banks struggle to profit from deposits due to market competition Evidence from Vietnam (2008-2015) shows that from 2010 to 2014, capital mobilization outpaced credit growth, while high lending interest rates imposed by the State Bank hindered business operations, particularly under tight macroeconomic policies aimed at controlling inflation Despite an increase in mobilized capital, banks experienced decreased performance and negative credit growth, leading to a decline in ROE during this period These findings align with the research of Ho Thanh Thuy (2017) and Pham Anh Long (2019) but differ from the studies of Ahmed Arif and Ahmed Nauman Anes (2012) and Ameira Nur Amila Binti Sohaimi (2013).
The regression model results indicate that the loans to assets ratio positively influences both the return on assets (ROA) and return on equity (ROE) at a statistically significant level of 1% Specifically, a 1% increase in the loans to assets (LTA) ratio leads to a 0.94% rise in ROA and a 9.85% increase in ROE This finding supports the initial hypothesis that a higher loan to total assets ratio enhances lending activities, which are crucial for generating income for banks Consequently, increased lending results in higher interest income, thereby boosting the performance of commercial banks This positive correlation between the loans to assets ratio and bank performance is also corroborated by previous studies, including those by Bourke (1989), Moualhi et al (2016), and Francis (2013), as well as Trujillo-Ponce.
The regression analysis indicates that the inflation growth rate positively influences both return on assets (ROA) and return on equity (ROE) Specifically, a 1% increase in the inflation growth rate leads to a 2.49% rise in ROA and a 24.34% increase in ROE, aligning with the original author's hypothesis Ferrouhi (2014) highlights that inflation affects bank performance both directly, through rising costs for labor and equipment, and indirectly via changes in interest rates and asset values Unpredictable inflation can lead to rapid cost increases and reduced profits, particularly when banks lend for longer durations than their mobilization periods, as noted by Bordeleau and Graham (2010) Similar findings have been reported in studies by Vong and Chan (2009) and Pham Nguyen Anh Long (2019).
Research indicates that there is no correlation between economic growth rates and bank profitability, suggesting that economic performance does not significantly impact bank results This conclusion is supported by various studies, including those conducted by Anbar and Alper (2011), Ho Thi Hong Minh and Nguyen Thi Canh (2014), Poposka and Trpkoski (2013), Pham Nguyen Anh Long (2019), and Sufian and Chong (2008).
This chapter explores the research methodology, utilizing the Fixed Effect Model (FEM) based on the outcomes of the Hausman test, which indicated a preference for this model over the Random Effect Model (REM) However, subsequent tests revealed that the FEM model did not meet key regression assumptions, exhibiting issues such as heteroscedasticity and autocorrelation.
CONCLUSION AND SOME RECOMMENDATIONS ON
Conclusion
The research paper analyzes the effect of liquidity on the performance of 25 commercial banks in Vietnam from 2010 to 2020 Utilizing methods such as Pool OLS, Fixed Effect Model (FEM), and Random Effect Model (REM), the study reveals that the regression assumptions were violated due to heteroscedasticity and autocorrelation To address these issues, the author employed the Generalized Least Squares (GLS) method for more accurate results.
The regression analysis indicates that liquidity status significantly influences banking performance, with a positive correlation between the two Banks that maintain a substantial amount of highly liquid assets can effectively mitigate liquidity challenges Consequently, enhanced liquidity leads to improved banking performance, highlighting the importance for banks to prioritize liquidity management to boost their overall performance.
Research indicates that larger banks can leverage economies of scale to enhance their return on equity Additionally, the performance of Vietnamese commercial banks is influenced by factors such as the equity to asset ratio (ETA), loans to asset ratio (LTA), and the inflation growth rate (INF).
Research indicates that a high ratio of mobilized capital to total assets (DTA) and an increased nonperforming loans ratio (NPL) negatively impact bank efficiency during this period.
The study reinforces the significant impact of liquidity on bank performance, revealing that this relationship operates in opposite directions It effectively addresses two key questions: whether liquidity influences banking performance and the nature of this impact Furthermore, the research highlights that additional factors, including bank size, nonperforming loans, equity to asset ratio, and deposit to asset ratio, also play crucial roles in influencing bank performance.
Recommendation
Research indicates that enhancing a bank's capacity to hold highly liquid assets can significantly boost profitability, particularly during the challenging economic climate caused by the Covid-19 pandemic In 2020, credit growth sharply declined compared to 2019, prompting banks to actively implement programs aimed at reducing credit interest rates to support individuals and businesses impacted by the crisis Consequently, the pandemic affected enterprise operations, leading to increased overdue and bad debts, which in turn resulted in diminished profits for credit institutions compared to their targets This decline is largely due to the heavy reliance on credit revenue amid low credit growth and a slowdown in net interest income growth, exacerbated by interest rate cuts and heightened risk provisions Additionally, fluctuations in the capital market pose further challenges, making it difficult for banks to maintain liquidity without incurring high borrowing costs from external sources.
Bordeleau and Graham (2010) suggest that maintaining liquid assets is an effective strategy to mitigate bankruptcy risk by lowering costs and enhancing profitability To address immediate liquidity demands from customer fees, particularly in urgent situations, commercial banks must significantly boost their reserves of liquid assets.
High liquidity assets, such as cash and central bank deposits, provide essential financial stability for commercial banks but typically yield low returns Holding excessive liquid assets can lead to opportunity costs, as banks miss out on potentially higher profits from more profitable investments To balance liquidity needs while maximizing returns, banks must exercise flexibility in payment management, which relies on their forecasting and management capabilities Accurately analyzing and determining the necessary level of liquid assets is crucial, allowing banks to align their liquidity supply and demand effectively.
To assess liquidity demand, Vietnamese commercial banks can conduct quantitative analyses of customer groups and time periods throughout the year, using historical cash flow data to forecast future liquidity needs This analysis can be performed on various time frames—daily, weekly, monthly, annually, or over multiple years Additionally, banks can categorize sources based on payment frequency and average payment ratios, noting that funds with lower stability typically exhibit higher payout ratios, while more stable sources tend to have lower ratios Ultimately, after evaluating the factors influencing liquidity demand, bank managers will formulate a liquidity management strategy that establishes policies aimed at ensuring liquidity safety while maximizing profits.
To assess liquidity supply, commercial banks analyze treasury resources, utilizing cash reserves, deposits at State banks, and other credit institutions to fulfill customer payment needs When a customer with a savings deposit requests cash, the bank readily provides it Conversely, if a customer holds a checking account and issues a check or authorizes a payment to another bank, the bank will draw from its deposits at State banks or other credit organizations By effectively analyzing these resources, commercial banks can ensure timely payment and investment activities for their customers.
A robust equity base enables banks to maintain liquidity and mitigate bankruptcy risks, enhancing their competitiveness and profitability However, it's crucial to increase equity at a measured pace to prevent negative repercussions from rapid growth The expansion of owner's capital primarily occurs through three methods: increasing charter capital, issuing new shares, and boosting retained earnings.
Large-scale commercial banks typically increase their equity by raising charter capital, ensuring compliance with Basel II capital adequacy requirements To effectively implement this strategy, banks must create a comprehensive capital construction plan that optimizes the use of capital, prevents misuse, and promotes sustainable and efficient operations.
In the context of Vietnam's revitalizing stock market, commercial banks are actively issuing common stocks, presenting significant investment opportunities in banking stocks To streamline the process, banks can initially raise capital by issuing shares to their existing shareholders.
Thirdly, besides issuing shares, banks can increase equity by adding retained earnings to equity or by making stock dividends from existing shareholders
Fifthly, the bank could sell shares to strategic partners who are experienced domestic and foreign financial institutions, foreign investors
To effectively enhance equity, banks must develop strategies tailored to their unique strengths and circumstances, avoiding abrupt capital increases Additionally, it is crucial for banks to select capital-raising methods that not only achieve their capital growth objectives but also protect the interests of existing shareholders.
Research indicates that the ratio of deposits to total assets positively influences bank performance Deposits serve as a crucial funding source, representing a significant portion of a bank's cash reserves To enhance deposit acquisition in a competitive landscape and secure high-quality funds, banks have introduced and executed various deposit mobilization strategies.
Vietnamese commercial banks should focus on diversifying their product offerings to cater to the varied deposit needs of their diverse customer base By expanding their range of deposit products and savings accounts based on balance, banks not only enhance customer choice but also reduce operational costs As interest rates rise with higher balances, customers are more likely to consolidate their deposits, leading to fewer accounts per customer but larger account balances This consolidation allows banks to save on transaction costs, ultimately benefiting both the institution and its clients.
Secondly, commercial banks in Vietnam need to maximize convenience for customers by expanding their transaction network and developing modern technology
To enhance business efficiency and competitiveness, commercial banks must focus on improving the quality of their staff The qualifications, professionalism, dynamism, creativity, and service attitude of bank employees significantly impact customer satisfaction and are often the primary concerns raised by clients Investing in staff development is essential for meeting customer expectations and driving overall success in the banking sector.
In today's competitive landscape, banks must focus on enhancing their image and brand to stand out A strong brand and positive reputation are crucial for attracting customers and encouraging them to deposit money.
4.2.4 Expanding the size of bank assets
Research indicates that larger bank assets enhance profitability, a trend that contrasts with the negative correlation observed in Southeast Asian countries, as noted by Ho Thanh Thuy (2017) This highlights the unique characteristics of Vietnam's banking system, suggesting that banking expansion is crucial for increasing the profitability of commercial banks in the country A sufficiently large bank size allows for the exploitation of economies of scale, ultimately leading to improved profits.
To enhance the size of commercial banks in Vietnam, it is essential to boost both equity and deposits Key strategies for expanding asset size include restructuring or merging with other banks, issuing bonds and stocks, and investing in the establishment of new branches These measures aim to leverage economies of scale for greater efficiency and growth.
Research limitations and suggestions for future research
The study primarily relied on the bank's financial statements and reputable sources such as the World Bank and the General Statistics Office (GSO) due to the limited study period and challenges in data collection.
The study analyzed 275 observations from 25 commercial banks, which is limited compared to the total of 31 commercial banks and 4 state-owned banks Due to the absence of survey data from the population, the estimated relationships may lack accuracy, potentially leading to erroneous conclusions from hypothesis testing, including the acceptance of false hypotheses or the rejection of valid ones.
The author suggests that future studies on the impact of liquidity on commercial bank performance should broaden the research sample size Additionally, there is a need to investigate the specific liquidity threshold at which increased liquidity begins to negatively affect the performance of commercial banks in Vietnam, enhancing the comprehensiveness of the research model.
(1) Abdullah, F (2003), Manajemen Perbankan (Teknik Analisis Kinerja Keuangan Bank, Universitas Muhammadiyah Malang (UMM) Press, Malang
(2) Abreu and Mandes (2001), Commercial bank interest margins and profitability: evidence for some EU countries
(3) Almazari, A A (2014), Impact of Internal Factors on Bank Profitability:
Comparative Study between Saudi Arabia and Jordan, Journal of Applied
Finance & Banking, SCIENPRESS Ltd, vol 4(1), pages 1-7
(4) Arif A.& Anees, A N (2012), Liquidity risk and performance of banking system, Journal of Financial Regulation and Compliance, Vol.20 Iss: 2, page 182 – 195
(5) Ali, Sulieman Alshatti (2015), The effect of the Liquidity Management on Profitability in the Jordanian Commercial Bank, International Journal of
Business and Management, Vol 10, No 1; ISSN 1833 – 3850 E_ISSN 1833- 8119 Published by Canadian Center of Science and Education, page
(6) Amila, A N (2013), Liquidity Risk And Performance Of Banking System In
Malaysia, Journal of Mara University of Technology Johor Bank
(7) Anbar, A., & Alper, D (2011), Bank specific and macroeconomic determinants of commercial bank profitability: Empirical evidence from Turkey, Business and Economics Research Journal, 2(2), 139-152
(8) Athanasoglou, P P., Brissimis, S N., & Delis, M D (2008), Bank-specific, industry-specific and macroeconomic determinants of bank profitability,
Journal of international financial Markets, Institutions and Money, 18(2), 121-136
(9) Bank for international settlements (February 2008), Basel Committee on Banking Supervision: Liquidity Risk: Management and supervisory Challenges
(10) Bank for international settlements, Jannuary 2013 Basel III: The Liquidity Coverage Ratio and liquidity risk monitoring tools
(11) Bennaceur & Goaied (2008), The Determinant of Commercial Bank Interest Margin and Profitability: Evidence from Tunisia
(12) Bonfim & Kim (2014), Liquidity Risk in Banking: Is There Herding?
(13) Bonin, J., H Iftekhar & P Wachtel (2008), Banking in Transition Countries, BOFIT Discussion Paper, the Oxford Handbook of Banking
(14) Bordeleau, É., & Graham C (2010), The impact of liquidity on bank profitability: Bank of Canada
(15) Bunda and Desquilbet (2008), The Bank Liquidity Smile Across Exchange Rate Regimes
(16) Bunda, I., & Desquilbet, J.-B (2008), The bank liquidity smile across exchange rate regimes, International Economic Journal, 22(3), 361-386
(17) Chung-Hua Shen et al (2009), Banking Liquidity Risk and Performance
(18) Cucinelli, D (2013), The determinants of bank liquidity risk within the context of euro area, Interdisciplinary Journal of Research in Business,
(19) Delécha, C., Henao, C., Muthoora, P., & Vtyurina, S (2012), The Determinants of Banks' Liquidity Buffers in Central America
(20) DeYoung, R and Roland, K, (2001), Product Mix and Earnings Volatility at Commercial Banks: Evidence from a Degree of Total Leverage Model, J
(21) Douglas J Elliott (2015), Market Liquidity: A Prime, The Brookings
(22) Étienne Bordeleau & Christopher Graham (2010), The Impact of Liquidity on Bank Profitability, Staff Working Papers 10-38, Bank of Canada
(23) Ferrouhi, E M (2014), Bank Liquidity And Financial Performance: Evidence From Moroccan Banking Industry, Business: Theory & Practice,
(24) Ferrouhi, E., & Lahadiri, A (2014), Liquidity Determinants of Moroccan Banking Industry, International Research Journal of Finance and
(25) Goddard et al (2004), The Profitability of European Banks: A Cross- Sectional and Dynamic Panel Analysis
(26) K Poposka, M Trpkoski (2013), Efficiency of Macedonian banks: A DEA approach, Research Journal of Finance and Accounting 4 (6), 216-225
(27) Khalid et al (2011), The Impact of Liquidity Risk on Banking Performance:
Evidence from the Emerging Market
(28) Kosmidou, K., Zopounidis, C., 2005, Evaluating the performance of the Greek banking system, Operational Research: An International Journal, 5,
(29) Lawrence Kutsienyo (2011), The Determinant Of Profitability Of Banks In Ghana
(30) Lee, C-C, Yang S-J, Chang, C-H, (2014), Non-interest income, profitability and risk in banking industry: A cross-country analysis, North Am J Econ
(31) Mark and Ilse (2008), Modeling of Banking Profit via Return-on-Assets and
Return-on-Equity, World Congress on Engineering 2008, Vol II
(32) Dezfouli, M H K et al (2014), Inspecting the Effectiveness of Liquidity Risk on Banks Profitability, Kuwait Chapter of Arabian Journal of Business and Management Review, Vol.3, No.9
(33) MS Advisory (2020), Intercompany Loans: Shareholder Loans To Chinese
(34) Khan, M K R & Syed N A (2013), Liquidity Risk and Performance of the Banking System
(35) Madzivire O (2018), An investigation on effects of non-performing loans on liquidity risk in zimbabwean commercial banks: A case of cbz bank limited (2012-2017)
(36) Olagunju Adebayo et al (2011), Liquidity Management and Commercial Banks’ Profitability in Nigeri, Journal of Finance and Accounting
(37) P Perry (1992), Do Banks Gain or Lose from Inflation, Journal of Retail Banking, Vol 14, No 2, p 25-30
(38) Peter S.Rose (2011), Commercial Bank management
(39) Duttweiler R (2011), Managing liquidity in banks
(40) Rudra (2009), Why International Organizations Should Bring Basic Needs
(41) Alzorqan S T (2014), Bank Liquidity Risk and Performance: An empirical
Study of the banking system in Jordan, Research Journal of Finance and
(42) Saunders & Cornett, 2007, Financial Institutions Management: A risk Management Approach, UK: The MC Graw – Hill Education Chapter 17
(43) Sayedi,S.N.(2014), Impactsof internal and external factors on profitability of banks in Nigeria, International Journal of Social Sciences and Entrepreneurship, 1(9),1-21
(44) Shahchera , M (2012), The Impact of Liquidity on Iranian Bank Profitability, Journal of Money and Economy, Monetary and Banking
Research Institute, Central Bank of the Islamic Republic of Iran, vol 7(1), pages 139-160, October
(45) Sufian &Chong (2008), Determinants of Bank Profitability in a Developing
Economy: Empirical Evidence from the Philipinnes
(46) Trujillo-Ponce (2013), What determines the profitability of banks? Evidence from Spain
(47) 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
(48) Vong and Chan (2009), Determinants of Bank Profitability in Macao
(49) Zaphaniah Akunga Maaka (2013), The relationship between liquidity risk and financial performance of commercial banks in Kenya
(1) Annual report of the State Bank over the years 2010 – 2019
(2) Dan, D V (2015), Factors affecting liquidity risk of banks commercial banks in Vietnam, Financial magazine, 1st period, November 2015, pages
(3) Financial statements of commercial banks for the years 2010 - 2020
(4) Government, 2011 Resolution No 11//NQ-CP dated 21/02/2011, “Solutions to focus on controlling inflation, stabilizing the macro-economy, and ensuring social security”
(5) Thuy, T H (2017), The impact of liquidity risk on commercial bank performance in Vietnam
(6) Chuyen, M P L (2014), Discussion about bank lliquidity risk, < https://kqtkd.duytan.edu.vn/Home/ArticleDetail/vn/88/1601/ban-ve-rui-ro- thanh-khoan-cua-ngan-hang-phan-1>, accessed 10 April 2021
(7) Khanh, P N (2013), Factors affecting profitability of joint-stock commercial banks in Vietnam
(8) Phong, A N (2012), Impact of liquidity on profitability of stocks listed on Vietnam stock market, Economic development 2012, No 264, p.33-39
(9) Tam, C T & Ha M N (2012), Research on factors affecting the efficiency of banks in Southeast Asia
(10) Phong T N (2020), Discuss About Liquidity Risk And Commercial Banks Performance Of Vietnam
(11) Prime Minister (2013), "Establishment of Asset Management Companies of
(12) Prime Minister (2013), Decision 843/2013/QD-TTG, "Decision on framework of NPL disposal”
(13) State Bank of Vietnam, 2010 Circular No 13/2010/TT-NHNN dated 20/05/2010 stipulating prudential ratios in the operation of credit institutions
In 2010, the State Bank of Vietnam issued Circular No 19/2010/TT-NHNN on September 27, amending Circular No 13/2010/TT-NHNN from May 20, which outlines regulations regarding prudential ratios in credit institutions' operations.
(15) State Bank of Vietnam, 2011 Circular No 02/2011/TT-NHNN dated 03/03/2011 stipulating the maximum deposit interest rate in Vietnam Dong
The State Bank of Vietnam issued Circular No 30/2011/TT-NHNN on September 28, 2011, which establishes the maximum interest rate for deposits in Vietnamese Dong by organizations and individuals at credit institutions and foreign bank branches.
(18) Thong, Q T (2012), Commercial Banking Management, Economic Publishing House of Ho Chi Minh City
(19) Kieu, T T V (2019), The determinants of bank performance
APPENDIX Appendix 1: List of Vietnamese commercial banks in the sample
1 Asia Commercial Joint Stock Bank ABB
2 Asia Commercial Joint Stock Bank ACB
3 JSC Bank for Investment and Development of Vietnam BID
24 Vietcapital Commercial Joint Stock Bank BVB
4 Vietnam Joint Stock Commercial Bank for Industry and Trade CTG
5 Vietnam Joint Stock Commercia lVietnam Export Import Bank EIB
6 Ho Chi Minh City Housing Development Bank HDB
7 Kien Long Commercial Joint Stock Bank KLB
8 Joint stock commercial Lien Viet postal bank LPB
9 Military Commercial Joint Stock Bank MBB
10 Vietnam Maritime Joint – Stock Commercial Bank MSB
11 Nam A Comercial Join Stock Bank NAB
12 National Citizen Commercial Joint Stock Bank NCB
13 Orient Commercial Joint Stock Bank OCB
14 Joint Stock Commercia Petrolimex Bank PGB
17 Southeast Asia Commercial Joint Stock Bank SAB
15 Saigon Bank for Industry and Trade SCB
18 Saigon – Hanoi Commercial Joint Stock Bank SHB
16 Sai Gon Joint Stock Commercial Bank STB
19 Sai Gon Thuong Tin Commercial Joint Stock Bank STB
20 VietNam Technological and Commercial Joint Stock Bank TCB
23 Vietnam Asia Commercial Joint Stock Bank VAB
21 JSC Bank for Foreign Trade of Vietnam VCB
22 Vietnam International and Commercial Joint Stock Bank VIB
25 Vietnam Prosperity Joint Stock Commercial Bank VPB
Appendix 4: VIF test of multi-collinearity
Appendix 5: Ordinary Least Square Regression (OLS) for ROA/ROE
Appendix 6: Fixed Effect Regression (FEM) for ROA/ROE
Appendix 7: Random Effect Regression (REM) for ROA/ROE
Appendix 8: Hausman test for ROA/ROE
Appendix 11: Generalized Least Squares (GLS) Regression For ROA/ROE
Appendix 12: Datasets of 25 Commercial Banks in the research, 2010 -2020
NAME YEAR ROA ROE ETA DTA LTA NPL LIQ SIZE INF GDP
ABB 2010 0.0154 0.1085 0.1224 0.6170 0.5173 0.0116 0.2531 17.4535 0.1175 0.0642 ABB 2011 0.0077 0.0655 0.4794 0.7128 0.1137 0.0279 0.2223 17.5422 0.1812 0.0624 ABB 2012 0.0091 0.0803 0.4076 0.7071 0.1065 0.02826 0.2874 17.6444 0.0681 0.0524 ABB 2013 0.0027 0.0264 0.4103 0.6451 0.0997 0.06737 0.3114 17.8695 0.0604 0.0542 ABB 2014 0.0019 0.0204 0.3849 0.7176 0.0847 0.03966 0.3069 18.0271 0.0409 0.0599 ABB 2015 0.0014 0.0159 0.0899 0.7544 0.4802 0.02119 0.2160 17.9802 0.0063 0.0668 ABB 2016 0.0035 0.0419 0.0788 0.7747 0.5365 0.0270 0.1961 18.1219 0.0474 0.0621 ABB 2017 0.0062 0.0817 0.0724 0.7859 0.5669 0.0277 0.1875 18.2523 0.0353 0.0681 ABB 2018 0.0082 0.1101 0.0763 0.7389 0.5798 0.0189 0.3702 18.3153 0.0354 0.0708 ABB 2019 0.0104 0.1360 0.0765 0.6784 0.5467 0.0231 0.4084 18.4459 0.0279 0.0720 ABB 2020 0.0102 0.1334 0.0766 0.6231 0.5379 0.0226 0.2753 18.5723 0.0320 0.0291 ACB 2010 0.0166 0.2888 0.0555 0.5214 0.4216 0.0034 0.2376 19.1390 0.1175 0.0642 ACB 2011 0.0132 0.2749 0.0426 0.6296 0.3658 0.0086 0.3413 19.4539 0.1812 0.0624 ACB 2012 0.0034 0.0638 0.0716 0.7631 0.5832 0.0246 0.2020 18.9877 0.0681 0.0524 ACB 2013 0.0048 0.0658 0.0751 0.8641 0.6434 0.0302 0.0791 18.9311 0.0604 0.0542 ACB 2014 0.0055 0.0764 0.0690 0.8789 0.6476 0.0217 0.0641 19.0063 0.0409 0.0599
ACB 2015 0.0054 0.0580 0.0635 0.8787 0.6653 0.02595 0.0875 19.1211 0.0063 0.0668 ACB 2016 0.0061 0.0990 0.0602 0.8935 0.6992 0.0088 0.0770 19.2695 0.0474 0.0621 ACB 2017 0.0082 0.1410 0.0564 0.8917 0.6982 0.0070 0.0821 19.4656 0.0353 0.0681 ACB 2018 0.0167 0.2700 0.0638 0.8720 0.7000 0.0073 0.1116 19.6126 0.0354 0.0708 ACB 2019 0.0169 0.2464 0.0724 0.8034 0.6940 0.0054 0.1309 19.7649 0.0279 0.0720 ACB 2020 0.0186 0.2431 0.0797 0.7945 0.6941 0.0060 0.1382 19.9125 0.0320 0.0291 BID 2010 0.0097 0.1546 0.0601 0.6680 0.6795 0.0271 0.1924 19.5674 0.1175 0.0642 BID 2011 0.0083 0.1320 0.0601 0.5956 0.7244 0.0296 0.1713 19.8213 0.1812 0.0624 BID 2012 0.0058 0.1010 0.0547 0.6418 0.7012 0.029 0.1611 19.9992 0.0681 0.0524 BID 2013 0.0078 0.1377 0.0584 0.6378 0.7131 0.0226 0.1202 20.1225 0.0604 0.0542 BID 2014 0.0083 0.1515 0.0512 0.7054 0.6853 0.0192 0.1338 20.2930 0.0409 0.0599 BID 2015 0.0084 0.1666 0.0498 0.6890 0.7035 0.0209 0.1228 20.5615 0.0063 0.0668 BID 2016 0.0066 0.1412 0.0438 0.7544 0.7191 0.0196 0.1150 20.7296 0.0474 0.0621 BID 2017 0.0061 0.1460 0.0406 0.7378 0.7210 0.0161 0.1377 20.9075 0.0353 0.0681 BID 2018 0.0059 0.1423 0.0415 0.7705 0.7530 0.0190 0.1260 20.9956 0.0354 0.0708 BID 2019 0.0060 0.1266 0.0521 0.7478 0.7399 0.0174 0.1409 21.1220 0.0279 0.0720 BID 2020 0.0048 0.1340 0.0525 0.0525 0.7881 0.0154 0.1037 21.1398 0.0320 0.0291 BVB 2010 0.0098 0.0355 0.2527 0.3868 0.4409 0.0407 0.1605 15.9227 0.1175 0.0642
BVB 2011 0.0214 0.1004 0.1945 0.3083 0.2554 0.0270 0.1776 16.6469 0.1812 0.0624 BVB 2012 0.0108 0.0622 0.1580 0.4982 0.3729 0.0549 0.1444 16.8442 0.0681 0.0524 BVB 2013 0.0047 0.0318 0.1396 0.5222 0.4297 0.0401 0.1505 16.9535 0.0604 0.0542 BVB 2014 0.0066 0.0496 0.1285 0.5697 0.4984 0.0293 0.1415 17.0652 0.0409 0.0599 BVB 2015 0.0019 0.0161 0.1142 0.6418 0.5424 0.0100 0.1108 17.1835 0.0063 0.0668 BVB 2016 0.0001 0.0008 0.1022 0.7599 0.6427 0.0127 0.1021 17.2932 0.0474 0.0621 BVB 2017 0.0009 0.0101 0.0838 0.6772 0.6212 0.0133 0.1258 17.5019 0.0353 0.0681 BVB 2018 0.0022 0.0278 0.0739 0.7195 0.6305 0.0201 0.1407 17.6561 0.0354 0.0708 BVB 2019 0.0026 0.0351 0.0721 0.6798 0.6474 0.0251 0.1341 17.7631 0.0279 0.0720 BVB 2020 0.0028 0.0422 0.0637 0.6771 0.6426 0.0279 0.1310 17.9280 0.0320 0.0291 CTG 2010 0.0111 0.2215 0.0500 0.5600 0.6294 0.0066 0.3057 19.7228 0.1175 0.0642 CTG 2011 0.0151 0.2676 0.0619 0.6849 0.6371 0.0074 0.4031 19.9480 0.1812 0.0624 CTG 2012 0.0128 0.1981 0.0688 0.6138 0.6620 0.0146 0.4290 20.0372 0.0681 0.0524 CTG 2013 0.0107 0.1321 0.0938 0.6877 0.6529 0.01 0.3628 20.1723 0.0604 0.0542 CTG 2014 0.0092 0.1047 0.0832 0.7051 0.6652 0.0111 0.2752 20.3096 0.0409 0.0599 CTG 2015 0.0079 0.1025 0.0702 0.6881 0.6903 0.01055 0.1008 20.4741 0.0063 0.0668 CTG 2016 0.0078 0.1159 0.0636 0.7342 0.6979 0.0102 0.1092 20.6705 0.0474 0.0621 CTG 2017 0.0073 0.1198 0.0582 0.7502 0.7220 0.0113 0.1393 20.8141 0.0353 0.0681
CTG 2018 0.0480 0.0825 0.0579 0.7727 0.7428 0.0159 0.1822 20.8755 0.0354 0.0708 CTG 2019 0.0079 0.1307 0.0623 0.7196 0.7434 0.0116 0.2043 20.9390 0.0279 0.0720 CTG 2020 0.0106 0.1683 0.0637 0.7383 0.7475 0.0095 0.2391 21.0170 0.0320 0.0291 EIB 2010 0.0185 0.0193 0.1030 0.4435 0.4707 0.0142 0.2624 18.6916 0.1175 0.0642 EIB 2011 0.0193 0.2039 0.0888 0.6502 0.4067 0.0158 0.1723 19.0281 0.1812 0.0624 EIB 2012 0.0121 0.1332 0.0929 0.6054 0.4403 0.0132 0.1651 18.9522 0.0681 0.0524 EIB 2013 0.0039 0.0432 0.0864 0.6773 0.4908 0.0197 0.1651 18.9503 0.0604 0.0542 EIB 2014 0.0003 0.0039 0.0873 0.8127 0.5410 0.0246 0.1110 18.8975 0.0409 0.0599 EIB 2015 0.0003 0.0029 0.1053 0.8453 0.6789 0.0186 0.1608 18.6426 0.0063 0.0668 EIB 2016 0.0024 0.0232 0.1044 0.8344 0.6746 0.0295 0.1530 18.6738 0.0474 0.0621 EIB 2017 0.0059 0.0594 0.0954 0.8384 0.6783 0.0229 0.1584 18.8219 0.0353 0.0681 EIB 2018 0.0044 0.0453 0.0975 0.8468 0.6816 0.0185 0.1967 18.8437 0.0354 0.0708 EIB 2019 0.0054 0.0565 0.0940 0.8313 0.6696 0.0171 0.1561 18.9367 0.0279 0.0720 EIB 2020 0.0065 0.0657 0.1048 0.8347 0.6201 0.0255 0.2110 18.8934 0.0320 0.0291 HDB 2010 0.0101 0.1297 0.0686 0.4067 0.3386 0.0083 0.1817 17.3533 0.1175 0.0642 HDB 2011 0.0107 0.1444 0.0788 0.4240 0.3044 0.0163 0.2906 17.6227 0.1812 0.0624 HDB 2012 0.0067 0.0730 0.1022 0.8978 0.3970 0.0235 0.1987 17.7817 0.0681 0.0524 HDB 2013 0.0031 0.0311 0.0997 0.7235 0.5025 0.0342 0.1902 18.2725 0.0604 0.0542
HDB 2014 0.0051 0.0546 0.0924 0.6572 0.4171 0.014 0.1812 18.4159 0.0409 0.0599 HDB 2015 0.0050 0.0548 0.0924 0.7000 0.5245 0.01582 0.1067 18.4835 0.0063 0.0668 HDB 2016 0.0057 0.0746 0.0662 0.6873 0.5410 0.0152 0.1409 18.8281 0.0474 0.0621 HDB 2017 0.0103 0.1414 0.0780 0.6366 0.5458 0.0110 0.1835 19.0590 0.0353 0.0681 HDB 2018 0.0140 0.1800 0.0779 0.5927 0.5637 0.0136 0.1836 19.1911 0.0354 0.0708 HDB 2019 0.0162 0.1938 0.0888 0.5492 0.6306 0.0153 0.2656 19.2513 0.0279 0.0720 HDB 2020 0.0155 0.1885 0.0774 0.5472 0.5527 0.0093 0.2823 19.5811 0.0320 0.0291 KLB 2010 0.0194 0.0900 0.2554 0.5224 0.5501 0.011 0.1724 16.3514 0.1175 0.0642 KLB 2011 0.0259 0.1181 0.1936 0.7230 0.4708 0.0277 0.3811 16.6975 0.1812 0.0624 KLB 2012 0.0193 0.1017 0.1854 0.7076 0.5211 0.0278 0.2831 16.7377 0.0681 0.0524 KLB 2013 0.0157 0.0906 0.1626 0.6850 0.5675 0.0247 0.2334 16.8776 0.0604 0.0542 KLB 2014 0.0079 0.0514 0.1456 0.7937 0.5855 0.0195 0.1154 16.9555 0.0409 0.0599 KLB 2015 0.0068 0.0490 0.1332 0.8241 0.6405 0.011 0.0724 17.0472 0.0063 0.0668 KLB 2016 0.0043 0.0359 0.1105 0.8505 0.6491 0.0110 0.1560 17.2316 0.0474 0.0621 KLB 2017 0.0060 0.0346 0.0951 0.8838 0.6613 0.0098 0.1480 17.4352 0.0353 0.0681 KLB 2018 0.0058 0.0272 0.0886 0.8753 0.6966 0.0094 0.0744 17.5605 0.0354 0.0708 KLB 2019 0.0014 0.0179 0.0742 0.6442 0.6494 0.01020 0.0965 17.7493 0.0279 0.0720 KLB 2020 0.0023 0.0328 0.0684 0.7335 0.6009 0.0542 0.1011 17.8635 0.0320 0.0291
LPB 2010 0.0261 0.1721 0.1174 0.3520 0.2788 0.004 0.3371 17.3704 0.1175 0.0642 LPB 2011 0.0214 0.1826 0.1175 0.7589 0.2273 0.021 0.3561 17.8432 0.1812 0.0624 LPB 2012 0.0142 0.1242 0.1113 0.7602 0.3462 0.025 0.2863 18.0114 0.0681 0.0524 LPB 2013 0.0078 0.0772 0.0914 0.8746 0.3712 0.027 0.1957 18.1925 0.0604 0.0542 LPB 2014 0.0052 0.0636 0.0733 0.8764 0.4096 0.011 0.1954 18.4287 0.0409 0.0599 LPB 2015 0.0034 0.0467 0.0706 0.7858 0.5520 0.019 0.1880 18.4938 0.0063 0.0668 LPB 2016 0.0085 0.1334 0.0587 0.8384 0.5616 0.0113 0.1538 18.7704 0.0474 0.0621 LPB 2017 0.0090 0.1545 0.0574 0.8552 0.6157 0.0119 0.2065 18.9119 0.0353 0.0681 LPB 2018 0.0057 0.0980 0.0583 0.7824 0.6870 0.0144 0.1599 18.9808 0.0354 0.0708 LPB 2019 0.0085 0.1405 0.0623 0.6773 0.6869 0.0141 0.0965 19.1241 0.0279 0.0720 LPB 2020 0.0084 0.1389 0.0587 0.7202 0.7195 0.0108 0.1011 19.3059 0.0320 0.0291 MBB 2010 0.0192 0.2171 0.0889 0.5997 0.4384 0.0134 0.2767 18.5126 0.1175 0.0642 MBB 2011 0.0171 0.2296 0.0695 0.8371 0.4253 0.0161 0.2710 18.7488 0.1812 0.0624 MBB 2012 0.0147 0.2049 0.0733 0.8443 0.4241 0.0184 0.3141 18.9838 0.0681 0.0524 MBB 2013 0.0128 0.1625 0.0840 0.8108 0.4846 0.0245 0.2465 19.0106 0.0604 0.0542 MBB 2014 0.0130 0.1562 0.0826 0.8408 0.5016 0.0287 0.1925 19.1163 0.0409 0.0599 MBB 2015 0.0118 0.1256 0.1049 0.8372 0.5490 0.0266 0.1468 19.2139 0.0063 0.0668 MBB 2016 0.0120 0.1147 0.1038 0.8103 0.5882 0.0131 0.1196 19.3617 0.0474 0.0621
MBB 2017 0.0121 0.1232 0.0943 0.8186 0.5868 0.0135 0.1257 19.5645 0.0353 0.0681 MBB 2018 0.0181 0.1917 0.0943 0.7746 0.5925 0.0133 0.1980 19.7081 0.0354 0.0708 MBB 2019 0.0202 0.2113 0.0969 0.6627 0.6006 0.0116 0.1733 19.8353 0.0279 0.0720 MBB 2020 0.0182 0.1836 0.1012 0.6282 0.5938 0.0110 0.1192 20.0200 0.0320 0.0291 MSB 2010 0.0129 0.2342 0.0549 0.4216 0.2733 0.0187 0.2742 18.5634 0.1175 0.0642 MSB 2011 0.0069 0.1008 0.0831 0.7226 0.3301 0.0227 0.1949 18.5550 0.1812 0.0624 MSB 2012 0.0020 0.0244 0.0827 0.7023 0.2633 0.0265 0.1861 18.5153 0.0681 0.0524 MSB 2013 0.0030 0.0357 0.0879 0.6906 0.2559 0.0271 0.2138 18.4894 0.0604 0.0542 MSB 2014 0.0014 0.0151 0.0905 0.6731 0.2553 0.0261 0.2096 18.4634 0.0409 0.0599 MSB 2015 0.0011 0.0101 0.1305 0.6110 0.2693 0.0340 0.1767 16.4629 0.0063 0.0668 MSB 2016 0.0014 0.0103 0.1469 0.6618 0.3792 0.0217 0.1946 18.3439 0.0474 0.0621 MSB 2017 0.0012 0.0089 0.1223 0.6211 0.3226 0.0275 0.1727 18.5361 0.0353 0.0681 MSB 2018 0.0069 0.0631 0.1003 0.6199 0.3539 0.0301 0.1182 18.7411 0.0354 0.0708 MSB 2019 0.0071 0.0728 0.0947 0.5152 0.3995 0.0204 0.1990 18.8716 0.0279 0.0720 MSB 2020 0.0121 0.1267 0.0955 0.4953 0.4442 0.0198 0.1558 18.9900 0.0320 0.0291 NAB 2010 0.0109 0.0789 0.1499 0.3985 0.3617 0.0218 0.2352 16.4903 0.1175 0.0642 NAB 2011 0.0143 0.0879 0.1734 0.3386 0.3253 0.0284 0.2281 16.7619 0.1812 0.0624 NAB 2012 0.0103 0.0549 0.2047 0.5452 0.4234 0.0271 0.2246 16.5886 0.0681 0.0524
NAB 2013 0.0060 0.0413 0.1132 0.4753 0.3993 0.0148 0.2801 17.1753 0.0604 0.0542 NAB 2014 0.0057 0.0568 0.0893 0.5449 0.4213 0.014 0.4187 17.4343 0.0409 0.0599 NAB 2015 0.0053 0.0576 0.0963 0.6870 0.5828 0.0144 0.2790 17.3842 0.0063 0.0668 NAB 2016 0.0008 0.0096 0.0801 0.7953 0.5519 0.0153 0.1049 17.5733 0.0474 0.0621 NAB 2017 0.0049 0.0674 0.0674 0.7322 0.6521 0.0168 0.1186 17.8126 0.0353 0.0681 NAB 2018 0.0091 0.1497 0.3333 0.7219 0.6667 0.0112 0.1764 18.1338 0.0354 0.0708 NAB 2019 0.0086 0.1592 0.0524 0.7471 0.7050 0.0101 0.1682 18.3661 0.0279 0.0720 NAB 2020 0.0070 0.1384 0.0491 0.7315 0.6576 0.0132 0.1297 18.7157 0.0320 0.0291 NCB 2010 0.0081 0.0984 0.1010 0.5356 0.5315 0.0224 0.3025 16.8121 0.1175 0.0642 NCB 2011 0.0078 0.0635 0.1430 0.8134 0.5741 0.0229 0.1788 16.9289 0.1812 0.0624 NCB 2012 0.0001 0.0007 0.1476 0.5687 0.5970 0.0564 0.1182 16.8875 0.0681 0.0524 NCB 2013 0.0007 0.0058 0.1102 0.7679 0.4635 0.0607 0.1485 17.1854 0.0604 0.0542 NCB 2014 0.0002 0.0025 0.0872 0.7820 0.4517 0.0252 0.1148 17.4220 0.0409 0.0599 NCB 2015 0.0002 0.0020 0.0667 0.8754 0.4236 0.0214 0.1255 17.6915 0.0063 0.0668 NCB 2016 0.0002 0.0034 0.0468 0.8425 0.3674 0.0148 0.1273 18.0498 0.0474 0.0621 NCB 2017 0.0003 0.0068 0.0448 0.7878 0.4470 0.0221 0.1867 18.0900 0.0353 0.0681 NCB 2018 0.0005 0.0112 0.0446 0.7678 0.4926 0.0193 0.1546 18.0980 0.0354 0.0708 NCB 2019 0.0006 0.0114 0.0536 0.7351 0.4662 0.0151 0.1698 18.2025 0.0279 0.0720
NCB 2020 0.0000 0.0003 0.0476 0.8045 0.4448 0.0153 0.0946 18.3109 0.0320 0.0291 OCB 2010 0.0188 0.1113 0.1595 0.4412 0.5830 0.0205 0.2611 16.7956 0.1175 0.0642 OCB 2011 0.0134 0.0879 0.1475 0.6482 0.5445 0.028 0.1741 17.0514 0.1812 0.0624 OCB 2012 0.0087 0.0607 0.1393 0.6076 0.6286 0.028 0.1522 17.1269 0.0681 0.0524 OCB 2013 0.0080 0.0620 0.1209 0.8033 0.6153 0.029 0.1094 17.3058 0.0604 0.0542 OCB 2014 0.0061 0.0553 0.1028 0.7792 0.5490 0.03 0.1076 17.4815 0.0409 0.0599 OCB 2015 0.0047 0.0508 0.0855 0.8065 0.5601 0.0295 0.0560 17.7164 0.0063 0.0668 OCB 2016 0.0068 0.0865 0.0739 0.7667 0.6043 0.0265 0.0524 17.9715 0.0474 0.0621 OCB 2017 0.0110 0.1505 0.0728 0.7574 0.5716 0.0197 0.0452 18.2499 0.0353 0.0681 OCB 2018 0.0191 0.2358 0.0880 0.7160 0.5634 0.0229 0.0513 18.4203 0.0354 0.0708 OCB 2019 0.0237 0.2544 0.0974 0.5852 0.5955 0.0184 0.0739 18.5875 0.0279 0.0720 OCB 2020 0.0261 0.2443 0.1143 0.5715 0.5789 0.0171 0.0734 18.8429 0.0320 0.0291 PGB 2010 0.0163 0.1340 0.1327 0.6536 0.6583 0.0140 0.2935 16.6115 0.1175 0.0642 PGB 2011 0.0263 0.1873 0.1474 0.6214 0.6784 0.0210 0.6110 16.6824 0.1812 0.0624 PGB 2012 0.0130 0.0830 0.1659 0.6406 0.6997 0.0340 0.5211 16.7731 0.0681 0.0524 PGB 2013 0.0017 0.0119 0.1290 0.5572 0.5499 0.0300 0.4151 17.0294 0.0604 0.0542 PGB 2014 0.0052 0.0040 0.1295 0.6984 0.5560 0.0250 0.3749 17.0651 0.0409 0.0599 PGB 2015 0.0016 0.0122 0.1366 0.6833 0.6363 0.0280 0.2188 17.0216 0.0063 0.0668
PGB 2016 0.0050 0.0357 0.1408 0.7371 0.6993 0.0250 0.1769 17.0273 0.0474 0.0621 PGB 2017 0.0024 0.0183 0.1215 0.7809 0.7234 0.0299 0.2067 17.1930 0.0353 0.0681 PGB 2018 0.0043 0.0350 0.1233 0.7808 0.7300 0.0306 0.2070 17.2134 0.0354 0.0708 PGB 2019 0.0024 0.0200 0.1191 0.8041 0.7424 0.0316 0.1981 17.2678 0.0279 0.0720 PGB 2020 0.0050 0.0441 0.1087 0.7949 0.7039 0.0244 0.1821 17.4033 0.0320 0.0291 SAB 2010 0.0147 0.1121 0.1040 0.4488 0.3875 0.0214 0.1439 17.8272 0.1175 0.0642 SAB 2011 0.0016 0.0224 0.0548 0.8073 0.1943 0.028 0.1051 18.4315 0.1812 0.0624 SAB 2012 0.0006 0.0095 0.0744 0.7826 0.2224 0.0298 0.0949 18.1339 0.0681 0.0524 SAB 2013 0.0020 0.0268 0.0717 0.5711 0.2621 0.0284 0.1749 18.1958 0.0604 0.0542 SAB 2014 0.0011 0.0152 0.0709 0.7573 0.3999 0.0286 0.1580 18.1998 0.0409 0.0599 SAB 2015 0.0011 0.0016 0.0681 0.7984 0.5050 0.0285 0.1149 18.2553 0.0063 0.0668 SAB 2016 0.0012 0.0201 0.0569 0.8230 0.5707 0.0170 0.1343 18.4538 0.0474 0.0621 SAB 2017 0.0027 0.0506 0.0494 0.7899 0.5642 0.0093 0.1924 18.6439 0.0353 0.0681 SAB 2018 0.0037 0.0682 0.0591 0.7451 0.5973 0.0151 0.1657 18.7606 0.0354 0.0708 SAB 2019 0.0074 0.1143 0.0694 0.6082 0.6193 0.0231 0.0604 18.8743 0.0279 0.0720 SAB 2020 0.0081 0.1106 0.0759 0.6286 0.5980 0.0186 0.0505 19.0096 0.0320 0.0291 SGB 2010 0.0049 0.0589 0.0783 0.5836 0.5385 0.0191 0.2438 17.9129 0.1175 0.0642 SGB 2011 0.0189 0.0890 0.2151 0.6882 0.7278 0.0475 0.2722 16.5476 0.1812 0.0624
SGB 2012 0.0197 0.0869 0.2383 0.7353 0.7313 0.0293 0.2865 16.5137 0.0681 0.0524 SGB 2013 0.0117 0.0491 0.2384 0.7370 0.7266 0.0224 0.2285 16.5023 0.0604 0.0542 SGB 2014 0.0119 0.0518 0.2203 0.7496 0.7099 0.0208 0.1992 16.5770 0.0409 0.0599 SGB 2015 0.0026 0.0125 0.1911 0.7908 0.6542 0.0216 0.1765 16.6918 0.0063 0.0668 SGB 2016 0.0076 0.0404 0.1845 0.7978 0.6580 0.0228 0.1461 1.7625 0.0474 0.0621 SGB 2017 0.0027 0.0158 0.1603 0.8262 0.6616 0.0290 0.1353 16.8751 0.0353 0.0681 SGB 2018 0.0020 0.0122 0.1686 0.8163 0.6710 0.0220 0.1153 16.8297 0.0354 0.0708 SGB 2019 0.0030 0.0099 0.0290 0.7718 0.5827 0.0194 0.1249 20.1575 0.0279 0.0720 SGB 2020 0.0090 0.0324 0.0267 0.7372 0.5472 0.0146 0.1155 20.2682 0.0320 0.0291 SHB 2010 0.0126 0.1498 0.0820 0.5023 0.4723 0.014 0.3619 17.7480 0.1175 0.0642 SHB 2011 0.0123 0.1504 0.0821 0.7141 0.4108 0.0213 0.2939 18.0780 0.1812 0.0624 SHB 2012 0.0030 0.0034 0.0816 0.7989 0.4886 0.088 0.2344 18.5737 0.0681 0.0524 SHB 2013 0.0065 0.0856 0.0721 0.7166 0.5327 0.0406 0.1351 18.7827 0.0604 0.0542 SHB 2014 0.0051 0.0759 0.0620 0.8562 0.6158 0.0202 0.1416 18.9456 0.0409 0.0599 SHB 2015 0.0043 0.0732 0.0550 0.8458 0.6420 0.0304 0.1187 19.1371 0.0063 0.0668 SHB 2016 0.0042 0.0746 0.0566 0.8116 0.6941 0.0172 0.1492 19.2706 0.0474 0.0621 SHB 2017 0.0059 0.1102 0.0514 0.7978 0.6933 0.0193 0.1616 19.4715 0.0353 0.0681 SHB 2018 0.0055 0.1078 0.0505 0.7937 0.6712 0.0240 0.1754 19.5940 0.0354 0.0708
SHB 2019 0.0070 0.1388 0.0507 0.7097 0.7174 0.0183 0.1721 19.7161 0.0279 0.0720 SHB 2020 0.0067 0.1226 0.0582 0.7356 0.7323 0.0174 0.1166 19.8382 0.0320 0.0291 STB 2010 0.0146 0.1524 0.0920 0.5141 0.5359 0.0052 0.3029 18.8419 0.1175 0.0642 STB 2011 0.0141 0.1447 0.1028 0.6215 0.5693 0.0056 0.3322 18.7676 0.1812 0.0624 STB 2012 0.0068 0.0710 0.0901 0.7139 0.6333 0.0197 0.2113 18.8402 0.0681 0.0524 STB 2013 0.0142 0.1449 0.1057 0.8356 0.6851 0.0145 0.2619 18.8993 0.0604 0.0542 STB 2014 0.0126 0.1256 0.0952 0.8689 0.6745 0.0118 0.3081 19.0615 0.0409 0.0599 STB 2015 0.0027 0.0323 0.0756 0.8960 0.6366 0.01315 0.2509 19.4924 0.0063 0.0668 STB 2016 0.0003 0.0040 0.0668 0.8885 0.5989 0.0668 0.2025 19.6207 0.0474 0.0621 STB 2017 0.0029 0.0440 0.0631 0.8874 0.6051 0.0428 0.3346 19.7249 0.0353 0.0681 STB 2018 0.0046 0.0748 0.0607 0.8630 0.6320 0.0220 0.2575 19.8220 0.0354 0.0708 STB 2019 0.0057 0.0956 0.0590 0.8837 0.6439 0.0194 0.2451 19.9327 0.0279 0.0720 STB 2020 0.0057 0.0963 0.0588 0.8690 0.7824 0.0173 0.2399 20.0150 0.0320 0.0291 TCB 2010 0.0171 0.2480 0.0625 0.5360 0.3481 0.0229 0.2983 18.8281 0.1175 0.0642 TCB 2011 0.0191 0.2879 0.0693 0.7026 0.3515 0.0249 0.3167 19.0114 0.1812 0.0624 TCB 2012 0.0042 0.0593 0.0739 0.7024 0.3794 0.0229 0.1542 19.0081 0.0681 0.0524 TCB 2013 0.0039 0.0484 0.0876 0.7838 0.4423 0.0283 0.1249 18.8838 0.0604 0.0542 TCB 2014 0.0065 0.0749 0.0852 0.7933 0.4565 0.0269 0.1209 18.9854 0.0409 0.0599
TCB 2015 0.0083 0.0973 0.0857 0.7829 0.8514 0.0365 0.1009 19.0730 0.0063 0.0668 TCB 2016 0.0147 0.1747 0.0832 0.8012 0.6059 0.0153 0.1342 19.2766 0.0474 0.0621 TCB 2017 0.0255 0.2771 0.1000 0.7136 0.5971 0.0193 0.1208 19.4117 0.0353 0.0681 TCB 2018 0.0287 0.2156 0.1613 0.7177 0.4983 0.0175 0.0891 19.5869 0.0354 0.0708 TCB 2019 0.0295 0.1823 0.1618 0.6028 0.5939 0.0133 0.1242 19.7654 0.0279 0.0720 TCB 2020 0.0299 0.1803 0.1697 0.6312 0.6263 0.0047 0.1312 19.9014 0.0320 0.0291 TPB 2010 0.0102 0.0669 0.1531 0.3618 0.2468 0.0002 0.2448 16.8547 0.1175 0.0642 TPB 2011 -0.0599 -0.5633 0.0672 0.2508 0.1448 0.0067 0.0354 16.3628 0.1812 0.0624 TPB 2012 0.0058 0.0466 0.2195 0.6154 0.4023 0.0366 0.2952 16.5316 0.0681 0.0524 TPB 2013 0.0162 0.1087 0.1153 0.6548 0.3717 0.0197 0.1949 17.2840 0.0604 0.0542 TPB 2014 0.0128 0.1350 0.0823 0.6569 0.3854 0.0101 0.1423 17.7567 0.0409 0.0599 TPB 2015 0.0088 0.1244 0.0630 0.7675 0.3705 0.0066 0.1059 18.1491 0.0063 0.0668 TPB 2016 0.0062 0.1079 0.0537 0.7545 0.4409 0.0071 0.0745 18.4769 0.0474 0.0621 TPB 2017 0.0084 0.1559 0.0538 0.7900 0.5110 0.0110 0.1007 18.6367 0.0353 0.0681 TPB 2018 0.0139 0.2087 0.0780 0.6961 0.5668 0.0112 0.1035 18.7295 0.0354 0.0708 TPB 2019 0.0206 0.2611 0.0795 0.5622 0.5743 0.0129 0.0731 18.9180 0.0279 0.0720 TPB 2020 0.0189 0.2354 0.0812 0.5618 0.5724 0.0114 0.0695 19.1449 0.0320 0.0291 VCB 2010 0.0150 0.2255 0.0672 0.6659 0.5565 0.0283 0.1834 19.5440 0.1175 0.0642
VCB 2011 0.0124 0.1702 0.0781 0.6810 0.5711 0.0201 0.1430 19.7201 0.1812 0.0624 VCB 2012 0.0113 0.1253 0.1002 0.7294 0.5818 0.024 0.1746 19.8426 0.0681 0.0524 VCB 2013 0.0099 0.1038 0.0904 0.7749 0.5849 0.0272 0.2042 19.9661 0.0604 0.0542 VCB 2014 0.0087 0.1065 0.0751 0.7901 0.5604 0.023 0.2905 20.1733 0.0409 0.0599 VCB 2015 0.0085 0.1201 0.0670 0.8189 0.6279 0.0184 0.3199 20.3293 0.0063 0.0668 VCB 2016 0.0093 0.1465 0.0610 0.8170 0.6337 0.0148 0.2454 20.4849 0.0474 0.0621 VCB 2017 0.0100 0.1806 0.0508 0.7383 0.5631 0.0114 0.2152 20.7580 0.0353 0.0681 VCB 2018 0.0138 0.2546 0.0579 0.8167 0.6378 0.0098 0.1642 20.7947 0.0354 0.0708 VCB 2019 0.0161 0.2588 0.0662 0.7593 0.5924 0.0078 0.1851 20.9243 0.0279 0.0720 VCB 2020 0.0145 0.2109 0.0709 0.7782 0.6187 0.0064 0.1164 21.0056 0.0320 0.0291 VIB 2010 0.0105 0.1658 0.0703 0.4795 0.4397 0.0159 0.3521 18.3570 0.1175 0.0642 VIB 2011 0.0067 0.0866 0.0842 0.7310 0.4487 0.0269 0.2935 18.3897 0.1812 0.0624 VIB 2012 0.0065 0.0633 0.1287 0.6563 0.5212 0.0262 0.3921 17.9903 0.0681 0.0524 VIB 2013 0.0007 0.0061 0.1038 0.6529 0.4584 0.0282 0.2308 18.1577 0.0604 0.0542 VIB 2014 0.0066 0.0634 0.1054 0.7147 0.4733 0.0251 0.1760 18.2058 0.0409 0.0599 VIB 2015 0.0063 0.0609 0.1021 0.7109 0.5667 0.02665 0.1618 18.2500 0.0063 0.0668 VIB 2016 0.0059 0.0647 0.0836 0.7336 0.5758 0.0258 0.1014 18.4649 0.0474 0.0621 VIB 2017 0.0099 0.1283 0.0714 0.6954 0.6485 0.0264 0.1556 18.6290 0.0353 0.0681
VIB 2018 0.0167 0.2255 0.0767 0.7335 0.6908 0.0252 0.1653 18.7512 0.0354 0.0708 VIB 2019 0.0202 0.2711 0.0728 0.6631 0.6932 0.0190 0.2034 19.0333 0.0279 0.0720 VIB 2020 0.0216 0.2957 0.0735 0.6145 0.6857 0.0144 0.2159 19.3154 0.0320 0.0291 VAB 2010 0.0134 0.1043 0.1410 0.3901 0.5436 0.0250 0.1250 16.9970 0.1175 0.0642 VAB 2011 0.0106 0.0712 0.1588 0.5502 0.5143 0.0260 0.1354 16.9296 0.1812 0.0624 VAB 2012 0.0070 0.0462 0.1436 0.6649 0.5238 0.0460 0.1562 17.0186 0.0681 0.0524 VAB 2013 0.0023 0.0169 0.3270 0.7208 0.5323 0.0280 0.2900 17.1126 0.0604 0.0542 VAB 2014 0.0015 0.0131 0.1022 0.6421 0.4446 0.0230 0.2654 17.3876 0.0409 0.0599 VAB 2015 0.0021 0.0217 0.0936 0.7032 0.4840 0.0226 0.1468 17.5503 0.0063 0.0668 VAB 2016 0.0019 0.0251 0.0654 0.7290 0.4948 0.0201 0.1179 17.9340 0.0474 0.0621 VAB 2017 0.0016 0.0243 0.0639 0.7458 0.5312 0.0125 0.0897 17.9812 0.0353 0.0681 VAB 2018 0.0022 0.0350 0.0060 0.8056 0.5315 0.0135 0.0811 18.0829 0.0354 0.0708 VAB 2019 0.0028 0.0478 0.0581 0.6204 0.5515 0.0132 0.1853 18.1521 0.0279 0.0720 VAB 2020 0.0041 0.0653 0.0662 0.6850 0.5525 0.0175 0.1038 18.2760 0.0320 0.0291 VPB 2010 0.0115 0.1298 0.0870 0.4008 0.4196 0.012 0.2220 17.9066 0.1175 0.0642 VPB 2011 0.0112 0.1428 0.0724 0.6641 0.3524 0.0182 0.1573 18.2322 0.1812 0.0624 VPB 2012 0.0069 0.1019 0.0647 0.7317 0.3598 0.0272 0.1479 18.4461 0.0681 0.0524 VPB 2013 0.0091 0.1417 0.0637 0.7581 0.4327 0.0281 0.0903 18.6135 0.0604 0.0542
VPB 2014 0.0088 0.1501 0.0550 0.7538 0.4801 0.0254 0.0883 19.9107 0.0409 0.0599 VPB 2015 0.0134 0.2142 0.0691 0.7215 0.6025 0.02675 0.1319 19.0827 0.0063 0.0668 VPB 2016 0.0186 0.2575 0.0751 0.5998 0.6324 0.0279 0.1985 19.2482 0.0474 0.0621 VPB 2017 0.0254 0.2748 0.1069 0.5090 0.6577 0.0339 0.1979 19.4422 0.0353 0.0681 VPB 2018 0.0245 0.2283 0.1075 0.5939 0.6866 0.0351 0.2247 19.5941 0.0354 0.0708 VPB 2019 0.0236 0.2147 0.1119 0.5672 0.6710 0.0342 0.1614 19.7483 0.0279 0.0720 VPB 2020 0.0262 0.2192 0.1260 0.5571 0.6833 0.0237 0.1680 19.8534 0.0320 0.0291