BACHELOR THESIS FACTORS AFFECTING THE FINANCIAL STABILITY OF VIETNAMESE COMMERCIAL BANKS MAJOR: FINANCE AND BANKING CODE: 7340201 NGUYEN HUONG GIANG HO CHI MINH CITY, 2025... BACHEL
INTRODUCTION
REASON FOR RESEARCHING
In Vietnam, commercial banks serve as pivotal institutions in the socio- economic landscape, functioning as key drivers of sustainable economic growth and development across the nation Due to the function of providing capital for the economy and being an intermediate between enterprises and the market, commercial banks not only support the macro-regulation of the national economy but also connect the domestic financial system with the world In the context of global finance facing numerous challenges, along with Vietnam's integration into the global economy, new challenges have emerged regarding the ability to recover and face potential crises in the system This emphasized the importance of maintaining the financial stability of commercial banks, creating a foundation for sustainable development and contributing to the overall prosperity of the economy
Since the 2008 global financial crisis, Vietnam's banking system has exposed weaknesses when neglecting the role of financial stability This issue was further exacerbated by the outbreak of the COVID-19 pandemic, which severely impacted the financial system and economy.Furthermore,during the research period (2011-
2023), the banking sector experienced notable events, including restructuring, mergers and acquisitions, the zero-VND acquisition of banks These events emphasized the need to prioritize the Vietnamese banking system's financial stability and safety Financial instability in banks will lead to hinder customers' access to loans and savings, erode trust among customers and investors, and potentially trigger negative ripple effects across the banking system, ultimately affecting the broader economy
In recent years, ensuring the financial stability of the banking system has become a growing priority for the Government and the State Bank of Vietnam Notably, in the Scheme on Restructuring the System of Credit Institutions in connection with resolving non-performing loan, approved under Decision No 1058/QĐ-TTg issued on July 19, 2017, for the 2016-2020 period and Decision No 689/QĐ-TTg issued on June 8, 2022, for the 2021-2025 period, emphasis is ensured the safe and sound operation of banks while actively addressing non-performing loan to strengthen the system and support financial stability development Additionally, Vietnamese banks have been actively implementing Basel I and II capital accords, with some institutions already moving toward adopting Basel III and the IFRS 9 to enhance financial capacity and stabilize the banking sector
The financial stability of commercial banks brings many practical benefits, including promoting economic growth, protecting the financial system, enhancing public confidence and strengthening the competitive position of banks On the contrary, a banking financial crisis will lead to serious consequences, negatively affecting the entire economy Maintaining financial stability plays a vital role for commercial banks, which helps to minimize systemic risks, protecting the economy from fluctuations Financial stability of commercial banks has been considered in numerous studies Chai et al (2022) investigated the connection between bank- specific risks and financial stability Le Dinh Luan et al (2021) researched the impact of income diversification on financial stability Nguyen Thi Tuyet Lan (2021) examined the effects of bank profitability on financial stability Rashid et al (2017) investigated the impact of funding ratio and bank size on financial stability Inheriting previous studies, the topic "Factors affecting the financial stability of Vietnamese commercial banks" was chosen by the author to conduct the research The author sees the importance and urgency of this topic for the banking system in particular and the Vietnamese economic system in general in the current economic situation.
RESEARCH OBJECTIVES
The general objective of the thesis is to analyze the factors affecting the financial stability of Vietnamese commercial banks during the period 2011 to 2023 Therefore, providing implications to increase the financial stability of the banking system in Vietnam
Based on the general objective, the author establishes the following three specific objectives to achieve the general objective:
First, identify factors affecting the financial stability of Vietnamese commercial banks
Second, identify the direction, measure and evaluate the level of influence of these factors on the financial stability of Vietnamese commercial banks
Third, propose policy implications and solutions to promote financial stability for Vietnamese commercial banks in the future.
RESEARCH QUESTIONS
To achieve the research objectives, the thesis concentrates on answering the following research questions:
Question 1: What factors affect the financial stability of Vietnamese commercial banks?
Question 2: What are the directions and levels of impact of these factors on financial stability of Vietnamese commercial banks?
Question 3: What are policy implications to promote financial stability for
RESEARCH SUBJECT AND RESEARCH SCOPE
The object of the research is the factors affecting the financial stability of Vietnamese commercial banks
Scope of spatial research: The study was conducted based on data collected from 29 Vietnamese commercial banks The selection of 29 Vietnamese commercial banks is based on their financial statements which are publicly disclosed with a relatively high level of transparency, which is facilitated to collect data and ensure the highest possible accuracy of the gathered information
Scope of time research: The study was conducted based on data collected between 2011 and 2023.In this period, Vietnam's economy experienced significant fluctuations In 2011, Vietnam's banking system was still be affected by the 2008 global financial crisis This is characterized by declining profits, rising bad debts, and poor liquidity, posing the risk of system collapse In 2020, the outbreak of the COVID-19 pandemic caused a major influence to the global economy, including numerous challenges and risks to the operations of commercial banks The 2011–
2023 period was chosen to ensure relevance and practical value for the research, contributing recommendations to enhance the resilience and financial stability of Vietnamese commercial banks.
RESEARCH METHODOLOGY
In this thesis, the author applies the method of synthesizing existing documents and previous research Relevant literature is reviewed to establish the theoretical framework for financial stability in banking and the factors influencing the financial stability of commercial banks
The study adopts a quantitative approach, using data collected from 29 Vietnamese commercial banks during the period 2011–2023 Correspondingly, the research model is proposed to examine how independent variables impact the dependent variable To ensure the reliability of the data, banks with incomplete or inconsistent records for the entire 2011–2023 period were excluded during the data collection process Before conducting regression analysis, the author performs preliminary assessments of the dataset using descriptive statistics and correlation matrix analysis to gain an overview of the data For the regression analysis, three estimation methods are applied: Pooled OLS, FEM, and REM To determine the most suitable model, the study employs the F-test, Hausman test, and Breusch-Pagan test Additionally, the model is tested for issues such as multicollinearity, heteroskedasticity, and autocorrelation If any of these issues arise, the GMM is used to address the problems and to test for endogeneity
Finally, after resolving the model’s deficiencies, the thesis proceeds with data analysis and presents the final results and conclusions.
CONTRIBUTION OF THE RESEARCH
In terms of scientific perspective, the thesis provides empirical evidence on the factors influencing the financial stability of Vietnamese commercial banks Therefore, contributing to the academic reference and creating a theoretical foundation for subsequent studies
In terms of practical perspective, the thesis helps commercial banks manage risks more effectively Due to a deep understanding of the factors affecting financial stability, potential risks are identified and assessed more accurately Therefore, banks can develop appropriate risk management strategies to minimize risks, ensure safe and effective operations, which contributes to maintaining the highest level of financial stability for the Vietnamese commercial banks.
RESEARCH STRUCTURE
The thesis is organized into five chapters, with an overview of the content as follows:
This chapter provides an overview of the research paper, including the rationale for topic selection, the research objectives, research questions, research scope, research methodology, research contributions, and the structure of the thesis.
LITERATURE REVIEW
OVERVIEW OF FINANCIAL STABILITY
According to the World Bank (2024), financial stability is defined as the ability of a financial system to efficiently allocate resources, assess and manage financial risks, and maintain employment levels close to the natural rate of the economy It also ensures monetary stability by minimizing fluctuations in the relative prices of real or financial assets A stable financial system is one that can self-adjust and adapt to mitigate negative impacts on the broader economy or other financial systems This stability plays a crucial role in safeguarding and sustaining economic growth, as most transactions are conducted through the financial system
According to the Bank of Korea (2024), financial stability in banking is defined as a state in which financial institutions effectively perform their intermediary functions, ensuring smooth economic activities and the ability to address financial imbalances arising from shocks In addition, financial stability is essential not only for maintaining price stability and implementing central bank policy objectives but also for fostering the sustainable development of the economy
According to the Deutsche Bundesbank (2003), financial stability refers to a financial system's ability to effectively perform its core functions, even during periods of economic volatility and structural adjustments This includes ensuring the ability to meet financial obligations, efficiently allocate resources, manage financial risks, and develop a robust financial infrastructure
According to Nguyen Thi Kieu Nga (2021), the financial stability of commercial banks is defined as a state in which banks operate efficiently and securely, remain unaffected by adverse factors in both the present and future, and effectively adapt to economic shocks
Therefore, based on these perspectives, this thesis defines the financial stability of commercial banks as a state in which they operate safely, efficiently, and sustainably A financially stable bank effectively fulfills its key economic functions as a financial intermediary, including payment processing and capital circulation within the economy, resource allocation, and risk management Moreover, it possesses a robust financial infrastructure, swiftly adapts to economic fluctuations, and does not trigger shocks that could negatively impact the economy It ensures timely debt payments and maintains continuous, effective operations
2.1.2 The measure of financial stability
The Z-Score index was initially developed by Edward Altman in 1968, which is used to measure the financial stability of corporations.Building on this basis, Boyd
& Runkle (1993) proposed the following calculation formula to assess the financial stability of commercial banks:
𝑘: the equity-to-assets ratio, including retained earnings over total assets 𝜇: the average ROA
𝜎: the standard deviation of ROA, representing the volatility of bank profits
Based on the formula proposed by Boyd et al (1993), the Z-Score index was further developed and applied in empirical studies such as studies by Beck et al
(2013) and Fernández et al (2016), using the following formula:
𝑅𝑂𝐴 𝑖𝑡 : The rate of return on total assets of bank i in year t
𝐸/𝐴 𝑖𝑡 : The equity to total assets ratio of bank i in year t
𝜎 𝑅𝑂𝐴 𝑖𝑡 : Standard deviation of ROA for bank i in year t
The Z-Score reflects three key dimensions in assessing the financial stability of commercial banks: operational efficiency, which is measured by the rate of return on total assets, capital adequacy, which is measured by the equity to total assets ratio, and earnings volatility, which is measured by the standard deviation of ROA A high Z-Score implies lower risk and indicates a more stable financial condition of the bank Conversely, a low Z-Score reflects financial distress and suggests a higher probability of bank default
Beck et al (2013) employed a three-year time window, rather than annual data or a longer time frame, to calculate the standard deviation of the ROA in the Z-Score formula The authors highlighted two main advantages of this approach First, it helps reduce the volatility of the Z-Score for banks that experienced significant fluctuations in capital or profitability during the study period, such as in cases of mergers or equitization Second, it contributes to balancing the panel data by minimizing discrepancies arising from differences in the calculation period among banks
The Z-Score index relies on historical data and financial reports from banks, allowing for the identification of trends in key financial indicators over time This model integrates critical components such as capital strength and profitability, offering a comprehensive overview of a bank’s financial condition Additionally, the Z-Score index facilitates risk comparison across financial institutions, regardless of differences in ownership structures or operational goals Its simplicity and transparency in calculation also contribute to its appeal as a practical tool for financial stability assessment
Although the Z-Score offers advantages in terms of flexibility, ease of calculation, and effectiveness in assessing financial stability, it also has certain limitations Specifically, the accuracy of the Z-Score is highly dependent on the quality and reliability of the input financial data Inaccurate or incomplete data may lead to misleading results, potentially distorting the true assessment of a bank’s financial stability.
THEORETICAL FRAMEWORK
Modern portfolio theory was originally formulated by Harry Markowitz in
1952 This theory posits that investors can construct optimal portfolios by carefully selecting a combination of assets that maximize expected return for a given level of risk, or alternatively, minimize risk for a given level of expected return
Expanding on this theoretical foundation, Sharpe (1964) developed the capital asset pricing model, which incorporates a risk-free asset and quantifies the relationship between expected return and systematic risk through the concept of beta According to the capital asset pricing model, investors are compensated only for systematic risk, which cannot be diversified, while unsystematic risk can be eliminated through proper diversification
Hence, the modern portfolio theory suggests that by applying disciplined diversification strategies, investors and financial institutions, including banks, can better manage risk exposures and protect themselves against adverse market movements In the context of banking, adopting portfolio optimization principles may help enhance asset allocation, ensure more stable income streams, and contribute to the overall financial stability of the institution
In the field of finance and banking, the concept of market power is primarily explored through two theoretical approaches: the Structure-Conduct-Performance
(SCP) framework and the Relative Market Power (RMP) theory (Chortareas et al.,
2011) These perspectives offer a foundational basis for analyzing the relationship between market concentration, competitive behavior, and the operational performance of commercial banks
The SCP theory, which originated in the 1930s through the work of Edward Chamberlin and Joan Robinson and was later expanded by Joe S Bain in 1951, posits that market structure, encompassing factors such as concentration levels, the number of firms, and entry barriers, shapes firm behavior, which in turn influences market performance (Bain, 1951) According to this view, highly concentrated markets may encourage coordinated behavior among large banks, such as setting non-competitive interest rates This can reduce competitive pressure, enabling banks to increase profits by offering lower deposit rates and higher lending rates (Chortareas et al., 2011)
In contrast, the RMP theory takes a more favorable stance, suggesting that large banks with strong branding and effective product differentiation can outperform competitors through operational efficiency rather than anti-competitive practices This advantage allows them to achieve sustained profitability without relying on market manipulation (Chortareas et al., 2011) Within the banking sector, the RMP approach argues that larger institutions tend to operate more efficiently due to benefits such as economies of scale, advanced technology, and expansive distribution networks
In summary, the theory of market power suggests that banks with significant market share are not only better positioned to enhance profitability and performance but also more capable of withstanding risks, thereby contributing to greater financial stability within the banking system
Demsetz (1973) was the first to propose an alternative explanation for the relationship between market structure and firm performance through what became known as the efficient structure theory This theory argues that in a competitive environment, banks that operate more efficiently gain a competitive edge, allowing them to expand their market share and earn higher profits In this view, differences in efficiency, not market power, lead to unequal market positions and increased market concentration
Building on this, Berger (1995) further refined the Efficient Structure Theory by introducing two sub-hypotheses: the X-efficiency hypothesis – ESX, and the Scale Efficiency hypothesis - ESS According to the X-efficiency hypothesis, banks with superior cost management, advanced technologies, or more effective production processes can achieve lower operational costs These efficiencies enable them to offer better prices, gain larger market shares, and ultimately earn greater profits
Meanwhile, the Scale Efficiency hypothesis suggests that performance differences also stem from economies of scale Banks operating at an optimal scale can reduce average unit costs and increase per-unit profitability As a result, larger banks are in a better position to outperform their smaller counterparts, reinforcing their profitability and enhancing their overall financial stability
In essence, the efficient structure theory posits that firms with higher internal efficiency, whether through superior management, better technology, or optimal scale, are better equipped to withstand competitive pressure and improve profitability These efficiencies translate into a greater ability to expand market share, lower costs, and strengthen the bank’s resilience against systemic risks, thereby contributing to long-term financial stability
Accordingly, within the scope of this thesis, the author not only considers macroeconomic factors but also incorporates bank-specific variables, such as internal efficiency, as potential determinants of the financial stability of Vietnamese commercial banks.
RESEARCH MODEL AND METHODOLOGY
RESEARCH PROCESS
(Source: Compiled by the author)
Based on analyzing the practical context and identifying the research problem, the research objectives and questions for the thesis are formulated This step is crucial as it ensures the research is conducted accurately and effectively
Step 2: Review theoretical framework and previous studies
The author reviews the existing theoretical framework and previous studies related to the research topic, including domestic studies and international studies, which leads to identifying research gaps and assessing their scientific and practical contributions These insights help in designing an appropriate research model in the next step
Review theoretical framework and previous studies
Propose research model and hypotheses
Run the regression model and test for model defects
Compare results and draw conclusions
Step 3: Propose research model and hypotheses
Based on the theoretical framework and literature review, the author constructs the research model, explains the measurement of variables, and proposes hypotheses for the independent variables
Step 4: Collect and process data
The author determines the study’s subjects, scope, and timeframe to collect data that aligns with the proposed research model
Step 5: Run the regression model and test for model defects
The dataset is run through Stata 17 software Three regression models, including Pooled OLS, FEM, and REM, are used to estimate regression coefficient models and determine the most appropriate model for the study Subsequently, the author checks for potential defects in the selected model by performing tests for heteroskedasticity, multicollinearity, autocorrelation, and other model deficiencies If any defects are detected, the author addresses them using the FGLS method In addition, the GMM model is used to address the endogeneity issues that may exist in the model
Step 6: Compare results and draw conclusions
Once the regression results are obtained, the author interprets the findings, draws conclusions, and proposes relevant policy recommendations to address the research objectives.
RESEARCH MODEL
According to the research conducted by Martin Čihák & Heiko Hesse (2010) and Le Ngoc Quynh Anh et al (2020), the author proposes the following research model:
Z − score it = β 0 + β 1 SIZE it + β 2 ROE it + β 3 ETA it + β 4 NIM it + β 5 LTA it + β 6 INF t + β 7 GDP t + ε it (3.1)
In which, β 0 : Intercept β 1 , β 2 , … , β 10 : Regression coefficients of independent variables ε : Error term
𝑍 − 𝑠𝑐𝑜𝑟𝑒 𝑖𝑡 : The Z-score value of bank i in year t, which serves as a measure of financial stability and bankruptcy risk
SIZE it : Bank i's size in year t
ROE it : Return on equity of bank i in year t
ETA it : Equity to total assets ratio for bank i in year t
NIM it : Net interest margin for bank i in year t
LTA it : Loan to total assets ratio for bank i in year t
INF t : Inflation rate in year t
GDP t : Gross domestic product growth rate in year t
The Z-Score index plays a crucial role in supporting banks to measure financial stability This index was introduced by Edward Altman in 1968 to predict the bankruptcy risk and default probability of operating organizations A higher Z-Score signifies stronger financial performance and greater stability, characterized by higher profitability and lower risk exposure Conversely, a lower Z-Score indicates higher financial risk.Additionally, studies by Le Ngoc Quynh Anh et al (2020) and Martin Čihák & Heiko Hesse (2010) confirm that the Z-Score is widely used to assess financial stability However, because the Z-Score is known to have a strong skewness, the natural logarithm has been used in this thesis since it follows a normal distribution Several studies such as Laeven and Levine (2009), Houston et al (2010), Beck et al (2013), and Fernández et al (2016) have employed this approach to represent bank insolvency risk when assessing financial stability The Z-Score is calculated as follows:
𝑍 − 𝑠𝑐𝑜𝑟𝑒 𝑖𝑡 : The Z-score value of bank i in year t
𝑅𝑂𝐴 𝑖𝑡 : The rate of return on total assets of bank i in year t
𝐸/𝐴 𝑖𝑡 : The equity to total assets ratio of bank i in year t
𝜎 𝑅𝑂𝐴 𝑖𝑡 : Standard deviation of ROA for bank i in year t
Bank size (SIZE) is a concept used to measure the scale of a bank, represented by its total assets Bank size is typically measured using the logarithm of the average total assets According to studies by Pham Thuy Tu & Dao Le Kieu Oanh (2021) and Chai et al (2022), the SIZE index is calculated using the following formula:
Return on equity (ROE) is a key financial ratio used to assess a bank’s operational efficiency at a given point in time ROE measures a bank's profitability after taxes relative to its average equity capital Studies by Nguyen Thi Huong & Nguyen Thi Thu Huyen (2022) and Rashid et al (2017) provide the formula for calculating the ROE index as follows:
Equity to total assets ratio (ETA)
The equityto total assets ratio (ETA) is a key financial indicator used to assess a bank’s financial health at a specific point in time It represents the proportion of capital contributed by shareholders relative to the total value of all assets owned by the bank Studies by Le Ngoc Quynh Anh et al (2020) and Pham Thuy Tu & Dao Le Kieu Oanh (2021) provide the formula for calculating the equity to total assets ratio as follows:
Net interest margin (NIM) is a crucial metric in the banking sector, reflecting a bank’s profitability In other words, NIM measures the difference between the net interest income generated from loans and investments and the interest expenses paid on deposits and other funding sources Studies by Nguyen Thi Tuyet Lan (2021) and
Le Ngoc Quynh Anh et al (2020) provide the formula for calculating the NIM ratio as follows:
Loan to total assets ratio (LTA)
The loan to total assets ratio (LTA) is a key financial indicator used to assess the financial health of credit institutions, including banks This ratio compares the total amount of loans issued by a bank to the total value of its assets, providing insights into the bank’s lending activities and overall asset allocation Studies by Rashid et al (2012) and Pham Thuy Tu & Dao Le Kieu Oanh (2021) define the loan to total assets ratio with the following formula:
The inflation rate represents the rate at which the overall price level of an economy increases over a specific period, typically measured using the Consumer Price Index (CPI) It reflects the declining purchasing power of a currency, meaning that the prices of goods and services in the economy are rising According to studies by Nguyen Thi Tuyet Lan (2021) and Rashid et al (2017), the inflation rate is calculated using the following formula:
The GDP growth rate (GDP)
The GDP growth rate is a key indicator of a nation's economic development over a given period, measured by the percentage increase or decrease in GDP As a macroeconomic variable, GDP significantly impacts the financial stability of commercial banks Studies by Nguyen Thi Tuyet Lan (2021) and Rashid et al (2017) provide the formula for calculating GDP as follows:
RESEARCH HYPOTHESIS
Studies by Le Ngoc Quynh Anh et al (2020) and Nguyen Thi Huong & Nguyen Thi Thu Huyen (2022) claimed that larger banks tend to achieve stronger financial stability Similarly, studies by Le Dinh Luan et al (2021), Samuel Mwangi Kiemo et al (2019) and Martin Čihák & Heiko Hesse (2010) have also identified a positive correlation between bank size and financial stability However, it is undeniable that large bank size also comes with certain inherent risks A notable example is the bankruptcy of Lehman Brothers, one of the largest banks in the world Moreover, studies by Nguyen Thi Tuyet Lan (2021), Chai et al (2022) and Omondi Godfrey Odundo & Maengwe Hames Orwaru (2018) suggest that bank size can introduce significant risks and negatively impact financial stability Additionally, empirical studies have provided a multidimensional perspective on the relationship between bank size and financial stability in banks Based on the above analysis, the author proposes the following hypothesis:
Hypothesis H1: Bank size (SIZE) has a positive impact with the financial stability of banks
In the commercial banking system, if ROE indicates that the bank is not generating business profits from its equity capital, it suggests inefficiency in capital utilization Conversely, if equity capital is effectively managed, business profitability will be higher, reflecting improved operational efficiency and a positive financial outlook for the bank Furthermore, research by Nguyen Thi Huong & Nguyen Thi Thu Huyen (2022), Martin Čihák & Heiko Hesse (2010) and Rashid et al (2017) also highlighted a positive relationship between ROE and financial stability Based on these arguments, the author proposes the following hypothesis:
Hypothesis H2: Return on equity (ROE) has a positive impact on the financial stability of banks
Equity to total assets ratio (ETA)
The equity to total assets ratio represents a bank’s level of financial safety and its attractiveness to investors Study by Le Ngoc Quynh Anh et al (2020) suggests that a higher equity to total assets ratio helps reduce non-performing loans, thereby enhancing bank’s financial stability.Similarly, Pham Thuy Tu & Dao Le Kieu Oanh (2021); Nguyen Thi Huong & Nguyen Thi Thu Huyen (2022) identified a positive relationship between the equity to total assets ratio and banking efficiency A lower equity to total assets ratio, meaning increased financial leverage, can lead to a decline in financial stability Based on these arguments, the author proposes the following hypothesis:
Hypothesis H3: The equity to total assets ratio (ETA) has a positive impact with the financial stability of banks
Nguyen Thi Tuyet Lan (2021) found that the net interest margin has a positive correlation with financial stability However, several other studies suggest that a high NIM may also generate risks that negatively impact financial stability Specifically, study by Le Ngoc Quynh Anh et al (2020) showed that an increase in NIM indicates that banks are focusing on expanding credit and pursuing higher-risk loans with higher interest rates to maximize profit margins As a result, the likelihood of non- performing loans also rises, forcing banks to allocate more provisions to cover these bad debts Consequently, this situation can lead to greater financial instability for banks A negative NIM indicates that the bank’s operations are unprofitable Based on these arguments, the author proposes the following hypothesis:
Hypothesis H4: Net interest margin (NIM) has a negative impact with the financial stability of banks
Loan to total assets ratio (LTA)
According to Pham Thuy Tu & Dao Le Kieu Oanh (2021), credit activities contribute significantly to a Vietnamese commercial bank's overall revenue Maintaining a low non-performing loan ratio enables banks to meet the regulatory standards set by the State Bank of Vietnam, allowing them to expand their credit operations This not only increases revenue but also strengthens their financial position Additionally, studies by Pham Thuy Tu & Dao Le Kieu Oanh (2021), Rashid et al (2017) and Omondi Godfrey Odundo & Maengwe Hames Orwaru (2018) have indicated a positive relationship between the loan to total assets ratio and financial stability Based on these arguments, the author proposes the following hypothesis:
Hypothesis H5: The loan to total assets (LTA) ratio has a positive impact with the financial stability of banks
According to studies by Nguyen Thi Tuyet Lan (2021), Le Dinh Luan et al
(2021) and Rashid et al (2017), rising inflation poses a significant burden on both the economy and the banking system, which have a negative effect on financial stability An increase in inflation creates a double burden, negatively impacting economic stability and financial institutions As the prices of goods and services rise, the purchasing power of money declines, leading to various adverse consequences The most immediate impact is macroeconomic imbalance, which disrupts economic stability and hampers economic growth, ultimately affecting the standard of living For banks, inflation introduces substantial challenges It leads to declining profits, rising operational costs, and an increasing ratio of non-performing loans - all of which threaten the resilience of the financial system Based on these arguments, the author proposes the following hypothesis:
Hypothesis H6: The inflation rate (INF) has a negative impact with the financial stability of banks
The GDP growth rate (GDP)
According to Nguyen Thi Tuyet Lan (2021), the GDP growth rate leads to an improved standard of living As income levels rise, individuals tend to increase their spending, savings, and investment activities, which in turn stimulates financial markets This suggests a positive relationship between the GDP growth rate and a bank’s financial stability Similarly, studies by Le Dinh Luan et al (2021), Nguyen Thi Tuyet Lan (2021) and Rashid et al (2017) also confirm that GDP growth has a direct positive impact on the financial stability of banks Based on these arguments, the author proposes the following hypothesis:
Hypothesis H7: The GDP growth rate (GDP) has a positive impact with the financial stability of banks
Table 3.1 Description of variables and expected sign
No Variable name Symbol Sign expectations Dependent variable
1 The GDP growth rate GDP +
5 Equity to total assets ETA +
7 Loan to total assets LTA +
(Source: Compiled by the author)
RESEARCH DATA
The thesis’s research sample is derived from secondary data of 29 Vietnamese commercial banks between 2011 and 2023 collected from audited financial statements A detailed list of banks is displayed in Appendix 1
The set of secondary data belonging to internal bank factors is taken from financial reports provided by the specialized Data Information System for the Vietnamese market - FIINPRO For the set of secondary data of macroeconomic factors, including GDP and inflation indicators, data is obtained from the statistical report of the World Bank
After collecting sufficient data and calculating relevant indicators using Excel, the author employs STATA 17 software to process the data This approach enables the development of estimation models to support the analysis in the thesis.
RESEARCH RESULTS AND DISCUSSION
DESCRIPTIVE STATISTICS
Descriptive statistics is used to describe various characteristics of the data, including the number of observations (Obs), average value (Mean), standard deviation (Std dev.), minimum value (Min) and maximum value (Max).
Variable Obs Mean St dev Min Max
(Source: Analysis results from STATA software)
Table 4.1 presents data from 29 Vietnamese commercial banks over the period 2011–2023, with a total of 377 observations The analysis of the statistical results is explained as follows:
ZSCORE is a variable that represents the financial stability of Vietnamese commercial banks The analysis results indicate that the average ZSCORE value is
2.9548, with a standard deviation of 0.5803 Particularly, TPB has the lowest value of -1.0594 in 2011, while BaoVietBank has the highest index of 4.5044 in 2012
Bank size (SIZE) has an average value of 32.5929, with a standard deviation of 1.2148 The lowest value is 30.2131 at BaoVietBank in 2011, while the highest is 35.3721 at BID in 2023 Correspondingly, BID continues to maintain the position, which is the largest joint stock commercial bank in Vietnam in terms of total assets
Return on equity (ROE) has an average value of 0.1039, with a standard deviation of 0.0852 The highest ROE was recorded by VIB in 2021 at 0.3033, while the lowest was observed at TPB in 2011 at -0.5633
Equity to total assets ratio (ETA) has an average value of 0.0899 across banks, with a standard deviation of 0.0373 The highest ETA was recorded by SGB in 2013 at 0.2384, while the lowest was observed in BID in 2017 at 0.0406
Net interest margin (NIM) has an average value of 0.0311 with a standard deviation of 0.0140 This stability may indicate a relatively consistent approach among banks in generating profits from their core business activities The highest NIM value (0.0943) was recorded by VPB in 2019, while the lowest (-0.0199) was observed at PVcomBank in 2012
Loan to total assets ratio (LTA) has an average value of 0.5715 with a standard deviation of 0.1234 The lowest value is 0.1448, which was recorded by TPB in 2011, while the highest value is 0.7926, which was observed in AGRB in 2011
Inflation rate (INF): The average inflation rate is 0.0485, with a standard deviation of 0.0448 The highest value is 0.1868 in 2011 This is driven by monetary policy effects and rising input costs, including fuel, electricity, and other essential materials, which negatively impacted various sectors of the economy In contrast, the lowest inflation rate during the analysis period is 0.0063 in 2015
The GDP growth rate (GDP): The average value is 0.0599, with a standard deviation of 0.0163 The lowest value is 0.0255 in 2021, while the highest value is
0.0812 in 2022 Additionally, lockdown measures affected production and key economic regions across the country However, despite focusing on pandemic prevention, Vietnam continued certain business and production activities, allowing GDP to maintain positive growth.
CORRELATION MATRIX
Table 4.2 Correlation matrix between research variables
Variable ZSCORE SIZE ROE ETA NIM LTA INF GDP ZSCORE 1.0000
(Source: Analysis results from STATA software)
Corresponding to the results in Table 4.2, the correlation coefficient matrix indicates that variables in the model have low correlation with each other, with correlation coefficients remaining below 0.8 The variables SIZE, ROE, NIM and GDP demonstrate a negative correlation with financial stability, as measured by the Z-Score index, whereas ETA, LTA and INF show a positive correlation Notably, the strongest correlation is observed between ROE and NIM, with a coefficient of 0.5779
Table 4.3 Results of multicollinearity test
(Source: Analysis results from STATA software)
Based on the results in Table 4.2, the average VIF value is 1.82, and the magnification factor of all variables is below 10 Therefore, the author concludes that the research model does not exhibit severe multicollinearity, ensuring that the study's result does not be affected.
MODEL ESTIMATION RESULTS
4.3.1 Regression results of the models
The author performed three models, including Pooled OLS, FEM and REM, to select the appropriate model and obtained the following results:
Table 4.4 Regression results using Pooled OLS, FEM, REM methods
Independent variable Coef P>t Coef P>t Coef P>z SIZE 0.0485092 0.151 -0.0922263 0.000 -0.0885323 0.000
(Source: Analysis results from STATA software)
Pooled OLS: Four variables, including ETA, NIM, LTA and INF, are statistically significant Among them, NIM has a negative impact on financial stability at a 1% significance level Meanwhile, ETA, LTA, and INF positively affect financial stability, with significance levels of 1% In addition, the other three variables such as SIZE, ROE, and GDP do not have statistical significance
FEM: SIZE, ROE, ETA, and INF are variables which are statistically significant SIZE and INF negatively impacts financial stability at a 1% significance level In contrast, ROE and ETA positively influence financial stability, with significance levels of 1% Moreover, NIM, LTA, and GDP are variables which do not have statistical significance
REM: Variables are statistically significant in this model including SIZE,
ROE, ETA, and INF SIZE and INF negatively affects financial stability at a 1% significance level Contrastly, ROE and ETA positively impact financial stability with significance levels of 1% Furthermore, three variables such as NIM, LTA, and GDP do not have statistical significance
After performing regression model using the Pooled OLS, FEM, and REM methods, the author conducted the F-test and Hausman test to determine the most appropriate regression model among them
Table 4.5 Model testing results Test Pooled OLS and FEM FEM and REM F-test F(28, 341) = 360.79
(Source: Analysis results from STATA software)
According to Table 4.5, the model selection results are as follows:
The results indicate that the p-value of the F-test is 0.0000, which is less than 0.05, leading to the rejection of the null hypothesis (H0), which is Pooled OLS is the appropriate model, and the acceptance of the alternative hypothesis (H1), meaning that FEM is the appropriate model As a result, FEM was superior to Pooled OLS and was selected for further testing
Next, the Hausman test was conducted to compare the effectiveness of FEM and REM The test result showed the p-value (Prob > chi2) of 0.0001, which is below 0.05, demonstrating that the null hypothesis (H0) is rejected, and the alternative hypothesis (H1) is accepted Therefore, FEM is more appropriate than REM
After using the F-test and Hausman test to compare the suitability of three models including Pooled OLS, FEM, and REM, the author concludes that FEM is the most appropriate model for assessing the factors that influence the financial stability of Vietnamese commercial banks
4.3.3 Testing for heteroskedasticity and autocorrelation
The author proceeded with testing for heteroskedasticity and autocorrelation in the model
Table 4.6 Results of Breusch-Pagan test chi2(1) 1210.77
(Source: Analysis results from STATA software)
Based on results displayed in Table 4.6, the Breusch-Pagan test for groupwise heteroskedasticity shows the p-value (Prob > chi2) of 0.0000, which is less than 0.05 This results in the rejection of H0 and the acceptance of H1, confirming the presence of heteroscedasticity in the model.
Table 4.7 Results of Wooldridge test
(Source: Analysis results from STATA software)
As shown in Table 4.7, the results of the Wooldridge test for autocorrelation in panel data yields the p-value of 0.0000, which is less than 0.05 This leads to the rejection of H0 and acceptance of H1 Therefore, it can be concluded that autocorrelation exists in the model
4.3.4 Feasible Generalized Least Squares method
According to model misspecification testing results, the results indicate the presence of autocorrelation and heteroskedasticity among the variables To address these issues, the author employs the FGLS method The results of the FGLS method are presented in the following table:
Table 4.8 Regression results of the FGLS model
(Source: Analysis results from STATA software)
The results presented in Table 4.7 show a p-value of 0.0000, which is below 0.05, indicating that the model is statistically significant Additionally, four variables such as SIZE, ETA, LTA, and INF have a positive effect on financial stability, with a significance level of 1% On the contrary, NIM is the only variable which has a negative impact on financial stability, with a 1% significance level Furthermore, the remaining variables, including ROE and GDP, are not statistically significant in the model
4.3.5 Endogenous variables and GMM regression model method
Although the FGLS method is applied to address deficiencies, the model may still contain endogenous variables that this approach cannot resolve Therefore, the author conducts an endogeneity test using the Durbin-Wu-Hausman test for the independent variables in the research model Thus, the test hypotheses are as follows:
Table 4.9 Results of Durbin Wu-Hausman test
(Source: Analysis results from STATA software)
According to the results in Table 4.9, LTA is the only variable with a p-value of 0.0007, which is below the 5% threshold, while all other variables have p-values exceeding 5% This indicates that LTA is the only endogenous variable in the model
This thesis employs the GMM model to eliminate endogeneity issues, ensuring the model's accuracy and reliability with the results displayed in the table below The results are displayed in Table 4.10 below.
Table 4.10 Results of using GMM model
Arellano-Bond test for AR(2) in first differences: z = -0.02 Pr > z = 0.984
Sargan test of overid Restrictions: chi2(18) = 22.70 Prob > chi2 = 0.203
Hansen test of overid Restrictions: chi2(18) = 18.92 Prob > chi2 = 0.397
Number of instruments = 27 < Number of groups = 29
Note: *,**,***: coefficients are statistically significant at the 10%, 5% and 1% significance levels, respectively
(Source: Analysis results from STATA software)
The results based on Table 4.10 indicate that the GMM is reliable, as it satisfies all four conditions: AR(2) = 0.984 > 5%, Hansen test with Prob > chi2 = 0.397 > 5%, Sargan test with Prob > chi2 = 0.203 > 5%, and the number of instrumental variables is 27, which is smaller than the number of groups is 29 Hence, the model is considered robust and the results are suitable for assessing factors affecting the financial stability of Vietnamese commercial banks
According to Table 4.10, the results reveal that six variables including SIZE, ROE, ETA, NIM, LTA, and GDP are statistically significant In detail, SIZE, ROE, and NIM are statistically significant at the 1% level; ETA, LTA, and GDP are statistically significant at the 5% level Among these, SIZE, ETA, and NIM negatively impact the dependent variable, whereas ROE, LTA, and GDP positively influence the dependent variable In addition, the remaining variable, which is INF, is the only variable that does not have a statistically significant relationship with the dependent variable Based on the research results, the author establishes the regression model which has the following equation:
ZSCORE = 1.460822 - 0.0587564*SIZE + 2.470966*ROE - 1.04846*ETA - 7.754863*NIM + 0.5544679*LTA + 0.0511538*INF + 0.3690245*GDP (4.1)
CONCLUSIONS AND RECOMMENDATIONS
CONCLUSIONS
The thesis aims to analyze the factors affecting the financial stability of Vietnamese commercial banks, using data collected from 29 Vietnamese commercial banks during the period 2011–2023 Through the analysis process, the thesis has enabled the author to achieve three research questions initially posed, contributing to a deeper understanding of the factors influencing the financial stability of Vietnamese commercial banks, specifically:
The first question, what factors affect the financial stability of Vietnamese commercial banks?
The author conducted an analysis, synthesis, and review of previous studies to opt for the dependent variable representing financial stability, which is Z-Score index There are seven independent variables which were presented in Chapter 3 under the proposed research model, reflecting both bank-specific and macroeconomic determinants impacting financial stability
The second question, what are the directions and levels of impact of these factors on financial stability of Vietnamese commercial banks?
The author applied panel data regression techniques and various tests to select an appropriate model and check for model defects Then, the FGLS method was used to address violations, followed by the GMM to resolve endogeneity issues, ensuring more reliable and robust results These steps were implemented using the STATA 17 software The research findings indicate that factors including return on equity (ROE), loan to total assets ratio (LTA), and the GDP growth rate (GDP) positively affect the financial stability of Vietnamese commercial banks Conversely, bank size (SIZE), equity to total assets ratio (ETA), and net interest margin (NIM) negatively impact financial stability Additionally, the study found no significant impact of inflation rate (INF) on the financial stability of Vietnamese commercial banks
The final question, what are policy implications to promote financial stability for Vietnamese commercial banks?
The author will elaborate further in section 5.2.
POLICY IMPLICATIONS
Based on research findings, the author proposes several implications to help enhance the financial stability of Vietnamese commercial banks
The empirical results indicate that bank size has a negative effect on the financial stability, which indicates that as a bank's size increases, its financial stability may decline Therefore, the author proposes the following policies to mitigate the impact of bank size:
First, banks should prioritize the improvement of capital utilization effectiveness rather than pursuing rapid expansion, contemplate restructuring by streamlining low-efficiency activities and intensifying the development of digital financial services to reduce costs and increase flexibility to minimize systemic risks
Second, authorities should consider implementing stricter regulatory frameworks for larger banks due to their systemic importance If large-scale banks collapse, the impact could quickly spread to other banks and even affect the broader economy This could involve more stringent capital requirements, closer monitoring of risk exposure, and incentives for banks to maintain a robust risk management framework
The results of the thesis demonstrate that return on equity has a positive impact on the financial stability of banks Thus, the author suggests the following measures to ensure the return on equity ratio:
First,in order to enhance ROE and thereby contribute to improved financial stability, commercial banks should prioritize strategies aimed at increasing EAT To increase EAT, banks should invest in digital transformation and automating routine processes, which improve customer service and scalability Furthermore, banks should diversify revenue streams by expanding non-interest income sources such as fees from asset management, digital payments, and bancassurance Strengthening credit risk assessment mechanisms, through better use of big data, Artificial Intelligence, and early-warning systems, can also reduce loan loss provisions, thus enhancing net income and overall profitability
Second, reducing equity can directly contribute to an increase in ROE Banks may consider lowering their equity by adopting strategies such as increasing cash dividend payouts, executing share repurchase programs, or limiting retained earnings
In parallel, divesting from underperforming or inefficient investment areas can further enhance financial structure efficiency and prevent the unnecessary accumulation of equity
5.2.3 Equity to total assets ratio
Based on the research results, equity to total assets ratio negatively affects financial stability Therefore, the author proposes the following policies to help reduce this ratio:
First, to lower the ratio of equity to total assets effectively, banks might opt to adjust their capital structure by reducing retained earnings, which are a critical component of shareholders' equity This can be achieved by increasing the cash dividend payout ratio, thereby limiting the portion of profits retained for reinvestment Simultaneously, banks should optimize the investment portfolio through the divestment of underperforming assets and reallocate resources toward high-yield investments, which can enhance capital efficiency and reduce the necessity for equity accumulation
Second, expanding total assets offers another avenue to reduce the equity to total assets ratio Banks can cautiously increase credit exposure or invest in low-risk, highly liquid instruments such as government bonds In addition, issuing long-term debt provides a means of growing the asset base without proportionally increasing equity capital This approach not only avoids share dilution, which would occur through the issuance of additional equity, but also contributes to a more resilient debt structure, thereby supporting long-term financial stability
Finally, it is essential for banks to implement advanced risk management systems capable of effectively monitoring the risks associated with asset expansion
By establishing clear and specific risk thresholds for various asset categories, banks can minimize potential vulnerabilities arising from unsustainable growth
According to the research findings, the net interest margin has a negative effect on financial stability However, a decline in this ratio could raise significant concerns for banks Hence, several recommendations are proposed:
First, to mitigate the adverse effects of a low net interest margin, Vietnamese commercial banks should focus on building an efficient capital utilization portfolio This approach ensures that capital is allocated appropriately and effectively, optimizing cash flow structure and improving investment efficiency
Second, implementing a flexible, efficient, and well-suited interest rate policy is also crucial By adjusting interest rates appropriately, banks can enhance income while mitigating risks A dynamic interest rate strategy also enables banks to better respond to market fluctuations and adapt to changing customer demands
Finally, banks should adopt rigorous credit assessment procedures and establish stringent lending standards Specifically, it is essential to implement advanced and sophisticated credit risk evaluation models to thoroughly screen potential borrowers This ensures that only high-quality loans, particularly those in stable and low-risk sectors, are approved Furthermore, an effective system for monitoring and managing non-performing loans should be established This includes maintaining a watchlist for potentially problematic loans and employing timely, flexible strategies for handling distressed assets, such as debt restructuring or selling bad debts to asset management companies By minimizing losses from defaulted loans, banks not only strengthen financial stability but also preserve a high-quality, liquid asset portfolio
5.2.5 Loan to total assets ratio
The research findings indicate that the loan to total assets ratio has a positive impact on the financial stability of Vietnamese commercial banks Accordingly, the study recommends the following measures:
First, to maintain this ratio effectively, optimizing the lending process remains a key priority By improving and simplifying procedures, leveraging technology to automate loan processing, and providing online services, banks can reduce processing time and facilitate easier access to capital for customers
Second, developing financial products and services plays a vital role By diversifying and enhancing the quality of financial offerings, banks can attract a broader range of customers and create new lending opportunities This strategy results in meeting the diverse financial needs of clients, from small enterprises to large organizations, increasing flexibility and convenience in the borrowing process
Third, improving the credit quality of the loan portfolio is essential This requires strengthening credit appraisal and evaluation processes before loan approval, including thorough assessments of projects and borrowing businesses, as well as identifying potential risks and the borrower’s repayment capacity Therefore, banks could ensure that approved loans are both profitable and feasible, which helps minimize risks and enhance the effectiveness of the investment portfolio
LIMITATIONS AND FURTHER RESEARCH DIRECTIONS
Although the study has achieved its initial objectives and drawn several conclusions, certain limitations remain inevitable, specifically:
Firstly, the scope of spatial research remains limited as it only includes 29
Vietnamese commercial banks and excluding foreign banks and joint venture banks
The reason is that some banks do not fully disclose their financial statements Therefore, the study does not represent the entire banking system of Vietnam
Secondly, in terms of the scope of time research, the study focuses on the period from 2011 to 2023 While this timeframe allows for significant analysis, it does not fully capture the dynamics of commercial banks before and after this period This limitation derives from the fact that several banks do not have complete data, and a few were only established in recent years
Finally, due to time constraints, the thesis lacks other microeconomic and macroeconomic variables In addition to the factors examined in this study, other variables have been used in previous studies, such as income diversification (Le Dinh Luan et al, 2021), capital adequacy ratio (Nguyen Minh Sang, 2021), earning after tax (Le Ngoc Quynh Anh et al, 2020) also have significant effects As a consequence, this thesis only reflects a part of the factors affecting the financial stability of Vietnamese commercial banks
Based on the limitations have mentioned above, the author proposes several directions for future research:
Firstly, future studies could expand the sample size by increasing the number of commercial banks once these banks have fully disclosed their financial statements or including foreign banks and joint venture banks to cover the entire banking system in Vietnam This approach would enhance the results and provide greater significance within the context of Vietnamese studies
Secondly, extending the research period by including a longer timeframe with more comprehensive data could lead to more accurate and meaningful results This also offers a thorough understanding of the fluctuations experienced by banks over different periods
Finally, future studies should expand additional factors to examine their impact on the financial stability of Vietnamese commercial banks This approach would provide a broader perspective and ensure a more accurate result In addition, the author suggests measuring financial stability using indicators beyond the Z-score to capture multiple aspects of financial stability
CONCLUSIONS OF CHAPTER 5 Chapter 5 of the thesis presents the final conclusions regarding the factors influencing the stability of Vietnamese commercial banks This chapter addresses how the research has successfully addressed the research questions and achieved its specific objectives Additionally, drawing from the results of the research model, the thesis proposes implications to enhance the financial stability of Vietnamese commercial banks Moreover, the author also discusses the limitations of this study and provides directions for future research
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