Factors Affecting The Financial Stability Of Vietnamese Commercial Banks.pdf

HO CHI MINH UNIVERSITY OF BANKING UNIVERSITY GRADUATION THESIS FACTORS AFFECTING THE FINANCIAL STABILITY OF VIETNAMESE COMMERCIAL BANKS MAJOR: FINANCE – BANKING CODE: 7340201 HÀ MỸ T

INTRODUCTION

REASONS FOR CHOOSING A RESEARCH TOPIC

For any financial system in the world, commercial banks play an important role in providing the main source of capital for the economy, promoting the development of production forces, and acting as a bridge between businesses and markets Therefore, the stability of commercial banks will greatly affect the "health" of the economy In recent years, the banking industry has often faced many risk factors such as credit risk, market risk, and operational risk This not only causes financial losses, and reduces the market value of bank capital, but can cause the bank's business to suffer losses, even bankruptcy

To face challenges as well as grasp opportunities from trends, Vietnamese commercial banks must always adjust and build business strategies by the constant fluctuations in each period to achieve effective goals, creating a strong financial system, improving competitiveness, and international integration From the review of previous studies, we can see that most of the studies only focus on analyzing the influence of certain factors on a bank's financial stability such as globalization (Yin, 2019), competitiveness of banks (Võ Xuân Vinh & Đặng Bửu Kiếm, 2016), foreign ownership (Lee & Hsieh, 2014), A small number of other studies have simultaneously looked at the impact of many factors affecting the financial stability of the banking system On the side of studies in the world such as the study of Fu et al (2014), and the study of Diaconu and Oanea (2014), only considered and applied in regions and countries outside the territory of Vietnam As for the studies for the data set in Vietnam such as the study of Lê Ngọc Quỳnh Anh et al

(2020), they use an old and newly updated data set to 2018

Based on an overview of the current research situation, the author chose the topic

"Factors affecting the financial stability of Vietnamese commercial banks" as the research topic based on the latest data set at the present to have a more accurate and consistent view of the current situation of the Vietnamese commercial banking system.

RESEARCH OBJECTIVES

The general objective of the thesis is identifying and measuring the impact of factors on financial stability of commercial banks in Vietnam From there, the thesis suggests several appropriate recommendations to improve the financial stability of the Vietnamese commercial banking system in the future

To achieve the above objectives, the thesis gradually completes the following specific objectives:

First, identify factors affecting the financial stability of Vietnamese commercial banks

Second, measure the level of impact and direction of impact on factors affecting the financial stability of Vietnamese commercial banks

Third, proposes relevant policy implications to improve the financial stability of Vietnamese commercial banks.

RESEARCH QUESTIONS

To achieve these research objectives, the author responds to the following research questions:

First, what factors affecting the financial stability of Vietnamese commercial banks?

Second, how do these factors impact the financial stability of Vietnamese commercial banks?

Third, what are the policy implications to improve the financial stability of Vietnamese commercial banks through those factors?

SCOPE AND OBJECT OF THE RESEARCH

The research object of the thesis is the factors affecting the financial stability of Vietnamese commercial banks

Scope of time: The study uses 10-year data from 2013 to 2022 This is the period when the researchers published enough data for the study In addition, the COVID-19 pandemic that appeared at the end of 2019 has greatly affected the economy in general and the operations of banks in particular From there, the thesis has an overview of the factors affecting the financial stability of Vietnamese banks during this period of economic fluctuations

Scope of space: The author based the thesis on data collected from 25 banks because these banks have published data fully and transparently, facilitating the process of data collection and financial reporting Their records have been audited so they can guarantee the accuracy of their data At the time of the study, these 25 commercial banks represent 70% of all commercial banks and 83% of all charter capital Thus, the list of selected banks ensures the representativeness of Vietnamese banks.

RESEARCH METHODOLOGY

The author uses secondary data collected from published annual financial reports of Vietnamese commercial banks in the period 2013–2022 Based on the ability to collect data, the author has selected 25 banks as a research sample From there, the author calculated and classified the independent and dependent variables of individual banks, including 250 observations

To analyze the factors affecting the financial stability of commercial banks, the author makes estimates with three models: (i) Pooled OLS, (ii) FEM and (iii) REM Next, the author provides a comparison between the results of each regression method through tests such as F-test, Hausman, Breusch and Pagan Lagrangian multiplier to select the most suitable model Besides, the author also checks the heteroskedasticity and autocorrelation phenomena through the White test and Wooldridge test Finally, the author uses Generalized Method of Moments (GMM) to overcome the above two phenomena and the endogenous phenomenon in the model.

CONTRIBUTIONS OF THE RESEARCH

Academic contributions: The thesis adds empirical evidence on factors affecting the financial stability of Vietnamese conglomerates Although there have been many studies on this topic, most of the results are not consistent, so further research is necessary to provide a more comprehensive and accurate view

Practical contributions: The analysis results will help researchers understand the factors affecting banking stability, thereby providing policy implications for bank administrators to improve stability finances of Vietnamese banks in the coming time.

STRUCTURE OF THE RESEARCH

The research paper includes 5 chapters with the following contents:

Chapter 1 Introduction Chapter 1 presents the reasons for choosing the topic, objectives, questions, scope, methods, research data, new points of the topic contributions and the structure of the research paper These contents will serve as the basis for subsequent chapters

Chapter 2 Theoretical and research overview Chapter 2 presents the concept of financial stability in banking, analyzing factors affecting the financial stability of Vietnamese commercial banks Besides, review domestic and international research to find research gaps In addition, this chapter also draws on a number of expert opinions and previous studies to propose hypotheses to develop the research model in chapter 3

Chapter 3 Research methodology Chapter 3 specifically presents the research process, model, methodology and describes the research data In addition, the author suggests the research hypotheses about the factors’ impact on the financial stability of Vietnamese commercial banks

Chapter 4 Analyze research results In this chapter, the thesis performs descriptive statistics and analyzes the correlation coefficient matrix Then chapter 4 presents the regression results of quantitative methods Finally, the study conducts analysis of the research results

Chapter 5 Conclusion and some policy implications Chapter 5 presents the conclusions of the research, proposes some policy implications, points out the limitations of the study and some future research directions

The content of chapter 1 presents the reason why the author chose the topic

"Factors affecting the financial stability of Vietnamese commercial banks" as a research topic In this chapter, the author also briefly presents the objectives, methods used for research and contributions of the topic Finally, the structure of the research paper is mentioned and an overview of the content of each research chapter is presented.

LITERATURE REVIEW

THEORETICAL BASIS

2.1.1 Definition of financial stability and financial instability

According to Alawode and Sadek (2008), the definition of financial stability can be approached from two different directions: the direct definition of financial stability and the indirect definition of financial instability

The school of direct definition of financial stability is carried out by banks around the world such as European, British, Swiss, German, and Australian banks (Alawode & Sadek, 2008) British central bank affirmed that financial stability is about identifying risks in the financial system and taking action to minimize them (Kim & Mehrotra,

2017) According to European bank, financial stability is a state in which the building up of systemic risks is prevented, in which they conceive systemic risks can best be described as the risk that the provision of essential financial products and services by the financial system will be impaired to such an extent that economic growth and welfare may be seriously affected

According to the Swiss National Bank, financial system stability means a financial system in which entities - financial intermediaries, financial markets and financial infrastructure - perform their functions well and are able to ability to withstand potential shocks German central bank takes the view that financial stability is the ability to operate the main functions of the financial system well, even in times of economic stress and periods of structural adjustment to effectively allocate financial resources and risks as well as create an effective financial infrastructure In Australia, financial system stability is a state in which financial intermediaries, markets and financial infrastructure allocate capital flows between savings and investment well, thereby promoting economic development

Allen & Wood (2006) argue that financial stability is a characteristic of an economic system that is not prone to periods of financial instability, minimizing disturbances, such as unexpected events, shocks, unforeseen development or unexpected failure According to Goodhart (2006), financial stability is the absence of financial crises, meaning there are no events or risks that weaken the credit intermediation system and affect capital allocation On the other hand, it is possible to define financial stability through an opposite term to “financial stability” as “financial instability” (Alawode & Sadek, 2008) According to Mishkin (1999), financial instability occurs when shocks impact the financial system, causing the flow of information to change, thereby making the financial system unable to perform its function of replenishing resources well for those who have the opportunity to invest in production Allen & Wood (2006) offer three characteristics to identify financial crises including: (i) mass collapse of financial institutions; (ii) inability to perform; (iii) provision of payment services and inability to extend credit to potential investment opportunities

When specifically analyzing instability in the banking system, Ngalawa et al

(2016) believes that there are two causes leading to banking instability Firstly, customers (including individual customers and corporate customers) tend to withdraw deposits when they are concerned about the bank's solvency, when the bank is unable to pay and one of the solutions to their problem is to liquidate assets at a loss Second, in lending activities, the bank's solvency will be affected when there are many bad debts that cannot be recovered In addition, another cause of banking instability is that the demand for short-term payments exceeds reserves of liquid assets If debt assets tend to be short- term while assets tend to be long-term and have low liquidity, it will create an imbalance between assets and capital in the bank's balance sheet (Lai, 2002)

In conclusion, it can be seen that financial stability is defined with different concepts However, based on the definitions just presented by the author, it can be understood that financial stability is the state in which the financial system operates its functions effectively even when adverse impacts occur ability to withstand economic shocks

Swamy (2014) defines that banking stability is a state in which the financial system can simultaneously fulfill conditions including: effective allocation of resources, assessment and management of financial risks, absorbing ongoing shocks, ensuring payments remain smooth, enhancing equilibrium by managing assets and price fluctuations and ultimately steering the economy in a direction that is beneficial to economic well-being Bank stability depends on the effectiveness of a number of parameters of each bank such as asset quality, liquidity, capital adequacy ratio and profitability Accordingly, the study believes that banking stability is a measure to determine whether an economy is strong enough to withstand internal and external shocks, and the study makes the judgment that stability of banking sector are in a special position because they are considered more susceptible to instability than other sectors Banking stability Definition of banking stability

Djebali & Zaghdoudi (2020) define “a bank is considered stable if it meets two basic requirements: improving economic performance and eliminating imbalances due to endogenous factors of unforeseen or undesirable events from various banking risks”, the study makes the point that banking stability is difficult to define and even difficult to measure A banking system can be described as unstable when there is excessive asset volatility or a crisis This study also argues that such a definition of banking stability is simple to construct, but does not represent a positive contribution in understanding the stability of the banking and financial system Another study by Brunnermeier et al

(2009) shows that define that “banking stability is the absence of a banking crisis Banking stability is achieved through the stability of all banks in the banking system or sector”

According to Segoviano & Goodhart (2009), for banks in the system that are interdependent, banking stability can be defined as "the stability of interconnected banks either directly through the interbank deposit market, participation in syndicated loans or indirectly through lending to general sectors and trades self-employment” At the same time, the study also suggests that the determinants of banking stability and its influence on the stability of the financial system will vary across countries Therefore, National bank supervisors are interested in understanding the factors that determine bank stability The empirical literature demonstrates a number of economic factors, financial structures, regulations and institutional factors that influence bank stability

Thus, there are many different definitions of banking stability, but in general, Banking stability is a state in which banks can operate smoothly, perform payment intermediary functions and other functions well At the same time, it is able to withstand external shocks and the bank itself does not cause shocks that negatively affect the economy, thereby contributing to a positive impact in the development of the financial system in particular and the economy of a country in general (Nguyen Thi Nhu Quynh,

2.1.2.2 The role and significance of banking stability

Banking stability plays an important role in maintaining and developing the economy According to Nguyen Thi Hoai Phuong & Le Van Chi (2020), banking stability helps maintain a safe and efficient financial environment, thereby supporting sustainable economic growth When the banking system is stable, banks are able to provide credit and financial services continuously and effectively, helping businesses and individuals access capital for investment and consumption Nguyễn Thị Như Quỳnh (2020) believes that banking stability helps minimize systemic risks, preventing the spread of financial shocks from one bank or one financial sector to the entire financial system This helps maintain public confidence in the banking system and prevent financial crises that can cause major damage to the economy

When the banking system operates stably, public confidence in the financial system will be strengthened (Nguyễn Thị Như Quỳnh, 2020) This not only encourages people to deposit money in banks but also promotes the use of other financial services, thereby creating a more favorable business and investment environment

In summary, through the above analysis, it can be seen that when a bank develops stably and sustainably, not only the bank's operations but also the operations of all other subjects in the economy run smoothly and effectively From there, the government can easily control inflation, maintain economic growth, and ensure liquidity in the financial system This helps the economy avoid major fluctuations and maintain sustainable development

Currently, there are many documents using different indicators to measure banking stability, typically Fernández et al (2016) analyze banking stability using four indicators including the Z-score, the ratio of non-performing loans to total loans, the standard deviation of the ratio of private credit to GDP, and the ratio of loan - loss provisions to total gross loans In his research, Dwumfour (2017) uses Z-score, bank regulatory capital to risk-weighted assets, bank non-performing loans to gross loans to measure banking stability

FACTORS AFFECTING FINANCIAL STABILITY OF BANK

Scale is calculated based on total bank assets High total assets of a bank represent the scale of a large bank, in which the main proportion is the amount of money lent to customers and customer deposits (Lê Ngọc Quỳnh Anh et al, 2021) Large scale not only has strong financial potential but also stronger potential thanks to abundant human resources Therefore, banks have many competitive advantages in the market, increasing financial stability (Laeven & Levine, 2009) The larger the scale, the higher the level of public trust, making it easier to mobilize deposits or borrow from other organizations Scale not only reflects financial capacity and business efficiency but also reflects the bank's influence on the market According to the too big to fail theory, large scale will make the bank more stable because its influence is too great to prevent the bank from collapsing Ozili (2018), Rahim et al (2012), Ramzan et al (2021) argue that scale has a positive impact on banking stability However, there are also cases where large scale has a negative impact on the financial stability of the bank For example, Lehman Brothers bank had to declare bankruptcy even though this bank was the fourth largest bank in the US Shim (2013) argues that it is understandable that large banks are less stable, because these banks often have a high risk-taking attitude because they have easier access to the capital market than other small banks Ghenimi et al (2017) also argue that large banks tend to engage in riskier lending and investment activities than small banks

Demonstrates the bank's profitability, which is also an indicator to evaluate the bank's operational efficiency, demonstrating its ability to survive Commercial banks operate with the main goal of generating profits; the higher this ratio, the more profit they generate According to Ghenimi et al (2017), banks with high profits will have an advantage in reliability in credit activities, which is why banking stability is increasing day by day However, De Jonghe (2010) found a negative correlation between profitability and bank stability because these banks are willing to bet on their safety in exchange for more profits High profits will also bring higher risks, thus increasing banking and financial instability

It is a measure that represents risk management capacity and evaluates the effectiveness of a bank's debt management Besides, it also raises the issue of borrowers' inability to pay fees or loans as agreed, causing bank bad debt to increase The bank makes provisions for these debts, causing costs to increase and the bank's profits to decrease, making the bank's operational efficiency worse High non-performing loans make the bank's ability to recover capital weak, leading to bank collapse (Ghenimi et al.,

CAR is a basic measure of a bank's health against financial risks and fluctuations in the economic environment, and it represents the internal strength of a commercial bank The capital adequacy ratio is measured by the difference between equity capital and total assets of the bank Well-capitalized banks are considered less risky (Li &

Malone, 2016) Agreeing with the above point of view, Shijaku (2017) affirms that equity is an important key when banks fall into crisis, it acts as a safety cushion to help prevent financial losses, protect banks from payment risks, so a high capital adequacy ratio will help banks increase stability

Loan to deposit ratio (LDR)

This ratio reflects the bank's ability to offset losses from bad debts Ali et al

(2018) use loan to deposit ratio to represent credit risk because this ratio clearly reflects the bank's vulnerability to changes in policies and actions of borrowers in paying The author believes that the higher the borrower's probability of default, the higher the bank's probability of default On the other hand, a high loan to deposit ratio means lending more than the deposit the bank receives, easily causing depositors to be afraid that the bank may lose liquidity, thus creating a situation In a state of bank run, people withdraw money in droves, causing the bank to fall into financial instability However, depending on the quality of the loan, the results also tend to be more positive If the quality of the loan is good and the bad debt ratio is low, the more loans you lend, the more profits will increase, thereby leading to financial stability for commercial banks (Rashid et al., 2017; Phan et al., (2019); Pham et al., 2021)

The bank's income source comes from two activities including interest income and non-interest income According to Oniango (2015), non-interest income is income from the following sources: income from service activities, income from investment business activities and other income In the commercial banking industry, non-interest income plays an important role in diversifying revenue sources and stabilizing profits, thereby helping the bank's financial stability increase Pham Quoc Thang & Nguyen Hong Son (2019) believe that non-interest income helps banks reduce their dependence on interest income, thereby minimizing risks when market fluctuations occur and increasing stability of commercial banks The results in the studies of Nguyen Minh Sang et al (2018), Oniango (2015) also pointed out the positive impact of non-interest income on bank stability

The inflation rate is a concern for many people because it is a determining factor in the value of assets held At the same par value, increased inflation means the value of assets held will be lower than at the beginning, this rate has a strong impact on the entire economy The inflation rate is determined based on the consumer price index (CPI) Jokipii and Monnin (2013) argue that inflation promotes bank stability Because when inflation occurs, bank services will be pushed to higher prices From there, the profit margin that the bank enjoys increases higher, increasing profitability, making the bank more stable However, studies by Shahid & Abbas (2012), Rashid et al (2017) showed that inflation has an inverse relationship with banking stability It can be explained that rising inflation motivates depositors to no longer prefer savings but to switch to other forms of accumulation, making capital mobilization difficult, and at the same time, that many people withdrawing money makes the bank susceptible to bank runs, which reduces the stability of the bank

This index is often used to reflect the "health" of the economy Economic growth is an increase in gross national product (GNP - Gross National Products) or gross domestic product (GDP - Gross Domestic Products) or maybe per capita income (PCI - Per Capita Income) that is measured over a certain period of time Increased economic growth shows an increase in people's income, stimulating the need for savings and investment, leading to a greater need for loans from banks For that reason, the bank's business operations become more effective, making the bank more stable The positive relationship of GDP to banking stability is also proven in the studies of R.-I Diaconu & Oanea (2014), Chen et al (2017), Abuzayed et al (2018), Quynh et al (2018), Ngambou Djatche (2019).

LITERATURE REVIEW

Research by Diaconu & Oanea (2014) analyzed and evaluates the main factors determining the financial stability of two important banking groups: commercial banks and cooperative banks in Romania The author selected 14 banks, which are: 1 cooperative bank and 13 commercial banks based on annual reports, for the period from

2008 to 2012 Using the regression analysis method for researching, the authors chose these specific variables to see the impact of three main groups of influences: the general macroeconomic situation (inflation and GDP growth), the financial market situation (BET ratio) and the banking sector situation The research results showed that GDP growth rate and interbank offered interest rate in 3 months were two factors that had the positive impact on financial stability of cooperative banks While the BET ratio, which was a proxy for the financial market situation, had no statistically significant impact on the Z-score for cooperative banks

The study of Ghenimi et al (2017) looked into the primary causes of fragility in the financial system Utilizing a sample of 49 banks active from 2006 to 2013, the author examined the connection between credit risk and liquidity risk as well as how it affected bank stability in the MENA region The findings demonstrated that there was no economically significant reciprocal contemporaneous or time-lagged link between credit risk and liquidity risk Nonetheless, each risk had an impact on bank stability on its own, and the combination of these risks made banks less stable

Ozili (2018) conducted a study on the factors that determine bank stability in Africa through four main measures of bank stability including: loan loss coverage ratio, insolvency risk, asset quality ratio, and level of financial development The author estimated the influence of institutional, bank-level, and financial structure determinants on bank stability using the regression methodology The results shown that banking efficiency, foreign bank presence, banking concentration, size of the banking sector, government effectiveness, political stability, regulatory quality, investor protection, corruption control, and unemployment rates were important factors of banking stability in Africa The impact level of each factor depended on the variable presenting bank stability and the analysis period

The study carried out by Rupeika-Apoga et al (2018) analyzed both bank-specific factors and macroeconomic factors affecting bank stability, using Z-score to measure banking stability The authors used multivariate regression model analysis techniques and collected data from Latvian in the period 2003-2016 The study found evidence that credit risk ratio and efficiency had a significant negative impact on bank stability, while bank size, liquidity ratio, profitability, inflation and GDP growth had a positive impact on banking stability

Kočišová' (2020) study analyzed the stability of important banks in global system which were located in European countries from 2008 to 2017, to find out whether the changing competitive environment had affect to the stability of these banks or not and identify variables that had a significant impact on the stability of banks here The study suggested that bank stability was estimated by two representative indicators, Z-score and loan loss provisions, while competition level was estimated inversely by two indicators (asset market share and Lerner index) represented the market power of a specific bank The results showed that the proportion of fixed assets to total assets, bank liquidity, and economic growth had a positive impact on banking stability Besides, high market share contributed to increased risk-taking behavior and could harm the stability of the banking sector

The study by Chand et al (2021) used a sample of seven financial institutions and banks over the period 2000 - 2018 in Fiji, based on the application of fixed-effects regression to control for banking heterogeneity The dependent variable was bank stability, based on three measures of Z-score, risk-adjusted return on assets, and risk- adjusted equity-to-assets ratio, which this study focused on the determinants of bank stability based on three measures of bank stability while taking into account the structural variables, the bank's internal finances, and the economy's macroeconomics Research results showed that bank size, funding risk, credit risk and Herfindahl-Hirschman index were positively related to banking stability In the extended model, both inflation and economic growth were positively related to banking stability, although only inflation was statistically significant In addition, factors that had a negative relationship with banking stability were liquidity risk, net interest margin and remittance flows

Chai et al (2022) also examined the relationship between bank-specific risks and the financial stability of banks in the period 2009 – 2020 using a panel data set of 15 listed banks in Pakistan The authors found that there was a negative relationship between bank size, liquidity risk, bad debt ratio on bank stability On the contrary, return on assets had a positive impact on stability In addition, banks were encouraged to focus on effectively mobilizing customer deposits to increase financial stability

The study of Vo Xuan Vinh & Dang Buu Kiem (2016) examined the impact of competitiveness on the profitability and stability of Vietnamese banks in the context of integration Research data included annual audited financial statements of 37 Vietnamese commercial banks for the period 2006–2014 The authors used estimations for panel data and the Lerner index to measure the index representing the competitiveness of banks Research results showed that improving competitiveness helped banks generate higher and more stable profits (adjusted by risk) In addition, factors related to bank characteristics such as capital mobilization, lending and listing rates also affected the bank's profits and stability

Le Ngoc Quynh Anh et al (2020) caried out the research on factors affecting banking stability by using data from 19 banks in Vietnam during the period 2014-2018 to calculate the dependent variable Z-score - a measure of the financial stability of banks through panel data regression models The model was estimated by the ordinary least squares method, fixed effects regression model, random effects regression model and generalized least squares model The results showed that the ratio of equity to total assets, bank size and loan-to-deposit ratio positively affected the financial stability of these banks Net interest margin was considered the most important and decisive factor that negatively affected the financial stability of the banks in the study sample

Pham Thuy Tu et al (2021) considered the factors that affect banking stability in an emerging nation The authors gathered data from the commercial banks that were listed on the stock exchanges in Viet Nam between 2010 and 2018 In this study, GMM regression methodology was used to take into account unobserved heterogeneity, simultaneity and dynamic endogeneity The findings showed that the stability of banks was positively impacted by the equity-to-asset ratio, bank size, loans-to-assets ratio, and income diversification Furthermore, the research results indicated the positive impact of macroeconomic issues in the banking industry on banking stability and the typical correlation between banking stability in the previous year and the current year Ultimately, it could be observed that the market structure, loan loss provisions, and market share of mobilized capital have a detrimental impact on the stability of the bank

Nguyen Thi Tuyet Lan (2021) examined the effects of bank profitability on the financial stability of 25 Vietnamese commercial banks between 2008 and 2018 Employing quantitative research techniques, the study used data from 25 commercial banks between 2008 and 2018 The findings from using quantitative research techniques demonstrated the positive impact of the profit component, the variable capital mobilization ratio, and the economic growth variable on financial stability Conversely, other variables that have an negative effect on financial stability include size, the loan ratio variable, and the inflation variable Additionally, the financial health of Vietnamese commercial banks is unaffected by the asset growth variable

Nguyen Thi Huong & Nguyen Thi Thu Huyen (2022) determined the factors affecting Vietnamese commercial banks' financial stability through the Z-score between

2011 and 2020 The research findings from using panel data regression indicated that while the ratio of outstanding loans to total assets had the opposite effect, total assets, return on equity (ROE), and the equity to total asset ratio all had a favorable impact on z-score Furthermore, there was not enough basis to conclude how the growth rates of credit and earnings after taxes affect the z-score

Table 2.1: Summary of previous studies Author name and year of publication

Factors affect the financial stability of two important banking groups: commercial banks and cooperative banks in Romania

Simple regression model using Ljung- Box Q with data collected from 14 banks including 1 cooperative bank and 13 commercial banks from 2008 to

(+): GDP, interbank offered interest rate in 3 months

BET ratio had no impact on Z-score

The effects of liquidity risk and credit risk on bank stability: Evidence from the MENA region

Use GMM with data from 49 banks operating in the MENA region in the period 2006 –

There was no economically significant reciprocal contemporaneous or time-lagged link between credit crisk and liquidity crisk Each risk affected bank stability on its own, and the combination of these 2 risks made banks less stable

Ozili (2018) Banking stability determinants in Africa

Use FEM with data from 48 African countries in the period 1996-2015

Banking efficiency, foreign bank presence, banking concentration, size of the banking sector, government effectiveness, political stability, regulatory quality, investor protection, corruption control, and unemployment rates had impact on banking stability The impact level of each factor depended on the variable presenting bank stability and the analysis period

Bank-specific factors and macroeconomic factors affecting

Use multivariate regression model analysis techniques and data from

(+): bank size, liquidity ratio, profitability, inflation, and GDP (-): credit risk ratio, bank stability Latvian in the period 2003-2016 efficiency

Competition and stability in the European global systemically important banks

Use GMM for panel data

The data used in the research was collected from

(+): fixed assets to total assets ratio, bank liquidity, and GDP (-): high market share

Determinants of bank stability in a small island economy: a study of Fiji

Use FEM with data from 7 financial institutions and banks in Fiji over the period 2000-

(+): bank size, funding risk, credit risk, inflation

(-): liquidity risk, NIM, remittance flows

Bank specific risks and financial stability nexus:

Use FEM with a panel data set of 15 scheduled banks in Parkistan from

(+): bank size, liquidity risk, bad debt ratio (-): ROA

Competitiveness, profitability and stability of Vietnamese banks

FEM model with data from 37 Vietnamese commercial banks in the period 2006-

Increasing bank’s competitiveness made bank’s profits lager and more consistent The profits and stability of the bank are also impacted by bank features (lending, capital mobilization, and listing rates

Factors affecting the financial stability of Vietnamese commercial banks

Use OLS, FEM, REM, and GLS model with data from 19

Vietnamese commercial banks in the period 2014-

(+): equity to total assets ratio, bank size, and LDR

Pham Thuy Tu et al (2021)

The determinants of bank’s stability

Use a system GMM panel analysis with data from the commercial banks that were listed on the stock exchanges in Viet Nam between 2010 and

(+): equity-to-asset ratio, bank size, loans- to-assets ratio, and income diversification (-): market structure, loan loss provisions, and market share of mobilized capital

The impact of bank profits on financial stability in Vietnam

Use OLS, FEM, REM models with data from 25 Vietnamese commercial banks in the period 2008-

(+): profit, capital mobilization, GDP (-): size, loan ratio, inflation

Asset growth had no impact on financial

Factors affecting the financial stability of Vietnamese commercial banks

Use OLS, FEM, REM, and GLS with data from 31 joint stock commercial banks in Vietnam between 2011 and

(+): total assets, ROE, and the equity to total asset ratio

(-): outstanding loans to total assets ratio

2.2.3 Review previous studies and identify research gaps

RESEARCH PROCESS

The research process for this thesis includes the following steps:

Step 1: Identify research objectives and questions

Step 2: Build a theoretical basis and overview of research on factors affecting banking stability

Step 3: Based on theoretical basis and empirical research, the author proposes a research model and conducts data collection

Step 4: Use Stata 17.0 to estimate the research model and perform specific tests as follows:

− Performing descriptive statistics on the compiled data, the author determines the correlation coefficient matrix between variables, then analyzes the correlation of independent variables in the model Use the VIF (Variance Inflation Factor) test to check the level of multicollinearity

− Perform regression using three methods: Pooled OLS, FEM and REM Next, the author compare the results of 3 regression models and choose the most suitable one through F test and Breusch – Pagan Lagrangian multiplier test

− Testing the defects of the regression model: heteroskedasticity phenomenon, testing cross-unit residual correlation phenomenon, testing serial correlation

− If the phenomenon of heteroskedasticity and autocorrelation occurs, the author uses the GMM method to handle and overcome When running the regression model with the GMM estimation method, the author also performed second-order autocorrelation (AR2) tests and the Sargan/Hansen test to test the appropriateness of the instrumental variables If the results of the tests meet the conditions, the model is suitable to determine the research results

Step 5: Analyze research results and compare them with proposed hypotheses Step 6: Proposed policy implications to improve the financial stability of commercial banks

RESEARCH MODEL

Based on the studies of Ghenimi et al (2017), Ozili (2018), Lê Ngọc Quỳnh Anh et al (2020), the author proposes a research model with the dependent variable lnZ-score representing banking stability The reason that the theis uses lnZ-score instead of Z-score because the Z-score coefficient of banks over the years has significant differences (Nguyen Thi Nhu Quynh, 2020)

The research model has the following form:

Stability it = α 0 + α 1 SIZE it + α 2 ROE it + α 3 NPL it + α 4 CAR it + α 5 LDR it + α 6

NII t + α 7 INF t + α 8 GDP it + ε it (3.1)

Stability: Banking stability, measured by lnZ-score

ROE: Ratio of profit after tax on equity

LDR: Loan to deposit ratio

NII: Ratio of non-interest income to total income

GDP: Economic growth rate it: Bank i in year t ε: Residual of the model

3.2.2 Explain variables in the research model and develop research hypotheses

In previous empirical studies, the authors used the Z-score index as an indicator of banking stability, typically the study of Ozili (2018); Kočišová (2020); Chand et al

(2021) A high Z-score indicates a more stable bank because the Z-score is inversely proportional to the bank's probability of insolvency (Fernández et al., 2016) To calculate this indicator, the thesis inherits the Z-score calculation method for banks used in studies by Rupeika-Apoga et al (2018); Ghenimi et al (2017); Chai et al (2022)

The formula to calculate Z-score is as follows:

ROA: Is the rate of return on total assets

E/A: Is the ratio of equity to total assets σ(ROA): Is the standard deviation of ROA

Specifically, the standard deviation of ROA reflects income fluctuations with the bank's risk-taking ability, calculated as the standard deviation of profit on average total assets in a period (usually calculated according to accounting data for 3 years) (Kabir et al., 2015) The larger the Z-score, the lower the risk level, which means the stability level will increase The ratio of average equity to average total assets reflects the bank's level of financial leverage

Large total bank assets mean the bank has abundant sources of customer deposit mobilization and good liquidity, thereby helping the bank's financial stability increase (Nguyen Minh Ha & Nguyen Ba Huong, 2016) Another study conducted by Lozis et al

(2012) point out that scale and high market share allow commercial banks to diversify their credit activities, thereby minimizing the risk of credit concentration Hence, the thesis expects the relationship between size and bank stability to be in the positive direction

Hypothesis H1: Bank size (SIZE) has a positive relationship with banking stability

ROE is an important financial indicator that measures a bank's profitability based on equity A study conducted by Chand et al (2021) point out that higher return on equity indicates that banks have good profitability, thereby increasing their stability and resilience to financial shocks In addition, banks with high profits will have an advantage in terms of reliability in credit operations, which is why banking stability is increasing day by day (Ghenimi et al., 2017) From the above research results, the author expects the relationship between return on equity and banking stability to be in the positive direction

Hypothesis H2: Profitability (ROE) has a positive relationship with banking stability

The ratio of bad debt to total outstanding loans is one of the important indicators showing a bank's credit risk This indicator is always of special interest to operational administrators and policy makers Increased credit risk causes the condition of bank loans to deteriorate, become unrecoverable, and reduce credit quality, thereby affecting the ability to meet payables because the bank has not yet recovered loan capital and will subsequently affect the bank's ability to maintain operations In other words, higher levels of credit risk are associated with greater probability of bank failure Specifically, when credit risk increases, bank stability will decrease (Ghenimi & al., 2017); (Imbierowicz & Rauch, 2014) Therefore, the bad debt ratio has a negative impact on banking stability

Hypothesis H3: Bad debt ratio (NPL) has a negative relationship with banking stability

Minimum capital adequacy ratio (CAR)

Minimum capital adequacy ratio is an indicator that reflects the relationship between equity capital and risk-adjusted assets of a bank This indicator indicates the level of equity capital that can offset risks and reduce risks bank insolvency during operations (Yen & Ngan, 2016) According to de Moraes & de Mendonỗa (2019), banks with capital adequacy ratio high will create a cushion against financial shocks, capable of ensuring operational safety as well as protecting depositors, thereby reducing the risk of bankruptcy for banks The results of the study by Ghenimi et al (2017) also show that CAR has a positive impact on banking stability

Hypothesis H4: Capital adequacy ratio (CAR) has a positive relationship with banking stability

Loan to Deposit Ratio (LDR)

LDR is an important indicator in the banking industry, indicating the relationship between total loans and total deposits of a bank Nguyen Thi Nhu Quynh (2020) explained: "If this ratio is too high, it means commercial banks are using most of their deposits to lend On the one hand, this helps commercial banks increase profits On the other hand, commercial banks also face liquidity risks and lack of capital to fulfill the bank's due obligations such as returning deposits Savings withdrawn before maturity or credits committed to disbursement in the future, leading to banking instability However, this does not mean that banks should maintain the loan-to-deposit ratio at too low a level

A level that is too low means that the bank is operating inefficiently and is not taking full advantage of the bank's resources to carry out credit activities - an activity that accounts for a large proportion of the bank's total income, leading to the bank not being able to achieve profits at the expected level In summary, although a high LDR ratio can increase profitability, it can also increase liquidity and bad debt risks if not managed carefully In this thesis, the author expects the relationship between loan to deposit ratio and banking stability to be negative

Hypothesis H5: Loan-to-deposit ratio (LDR) has a negative relationship with banking stability n Vietnamese commercial goods

The bank's income comes from two specific activities: interest income and non- interest income Interest income accounts for a large proportion of their total income, mainly coming from the bank's lending activities (Pham Thi Kim Anh et al, 2021) The ratio of non-interest income to total income includes three parts: ratio of net income from service activities; ratio of net income from business and investment activities; and the proportion of net income from other non-interest activities (Hoang Ngoc Tien & Vo Thi Hien, 2010) If a bank only focuses on revenue from credit activities, the risk is higher because this activity is very sensitive to market fluctuations Meanwhile, non-interest income is said to be less affected when the economy changes, thus contributing to dispersing risks and improving banking stability Research by Pham Quoc Thang & Nguyen Hong Son (2019) also shows that non-interest income helps banks minimize dependence on interest income, thereby minimizing risks when market fluctuations occur and increasing the stability of commercial banks The results in the studies of Nguyen Minh Sang et al (2018), Oniango (2015) also said that non-interest income has a positive impact on the stability of commercial banks Therefore, in this thesis, the author expects the relationship between non-interest income and bank stability to be in the positive direction

Hypothesis H6: Non-interest income (NII) has a positive relationship with banking stability

Inflation is the phenomenon of increasing the general price level of goods and services in the economy in a certain period of time, meaning that the same amount of money can only buy fewer goods and services than before When inflation increases, consumers reduce their real income, reduce their spending needs, causing business goods to stagnate, leading to lower profits than expected, and even losses can occur affect ability The ability of businesses to repay debt, thereby leading to increased bank bad debt, increased risk of bank bankruptcy, and increased bank instability Previous empirical studies also suggested that the inflation rate has a negative impact on bank stability, such as research by R.-I Diaconu & Oanea, 2014; Chen et al., 2017; Abuzayed et al., 2018; and Quynh et al., 2018

Hypothesis H7: Inflation has a negative relationship with banking stability

According to Abuzayed et al (2018), banks are expected to operate stably in more developed economies There have been many studies that suggest that GDP growth has a positive impact on banking stability, because when GDP increases, it means the economy is growing well At that time, the income of individuals and households increases, on the one hand, it will cause them to consume more, creating conditions for businesses to operate effectively and increase profits so that these businesses can pay off debts easily On the other hand, when individual and household income increases, it will boost the ability of these subjects to repay loans As a result, the bank will enjoy the above benefits so that it can operate stably Some previous empirical studies have shown that GDP economic growth has a positive relationship with banking stability such as the researches of R.-I Diaconu & Oanea, 2014; Chen et al., 2017; Ngambou Djatche, 2019; Abuzayed et al., 2018; Quynh et al., 2018

Hypothesis H8: GDP growth rate has a positive relationship with banking stability

Table 3.1: Describe the variables and expected sign Symbol Variable name

Dependent variable lnZ-score lnZ-score lnZ-score = ln

(2006), Fernández et al (2016), Abuzayed et al (2018)

SIZE Bank size ln(total assets) + Adusei (2015), Chand et al (2021)

(Debt group 3 + debt group 4 + debt group 5) / total loans

(2018), Fernández et al (2016), Pan and

LDR Loan to deposit ratio

Total outstanding loans/total deposits

NII Ratio of non- interest income to total income

Non-interest income/total income

Nguyen Thi Hanh Hoa (2013), Nguyen Thi Canh & Ho Thi Hong Minh (2014)

Oanea (2014), Chen et al (2017), Abuzayed et al

First, arrange and analyze the general situation of financial stability of Vietnamese commercial banks Second, review previous related empirical studies on factors affecting financial stability of banks Third, discuss the design of the research model, explain the assumptions for the variables corresponding to the dependent variable Finally, comment on the research results, draw conclusions and make related recommendations

To analyze the research model, the author applies a quantitative method to quantify the research results using Stata 17.0 software Methods used in the research include: descriptive statistics, correlation matrix, regression models (Pooled OLS, FEM,

REM, and GMM) In addition, accompanying tests are also used to check and fix defects in the model so that the results are most accurate

This method is used to provide general information about the variables in the model including: Observations, standard deviation, mean, minimum, maximum

The correlation matrix is used to consider the relationship between the dependent variable and the dependent variable, even the relationship between independent variables If the correlation matrix coefficient between variables is greater than 0.8, there is a high possibility that multicollinearity will occur (Gujarati, 2009)

The regression method is applied to examine the trend and level of impact of factors affecting the financial stability of Vietnamese commercial banks Traditional estimation methods commonly used with panel data include Pooled OLS, FEM REM

In case the model has defects such as autocorrelation, heteroscedasticity, and endogeneity, the estimation results by these three methods will be biased, so the author estimates using the GMM method to overcome the defects that occur (Pham et al., 2021, Ghenimi et al., 2017; Khouri & Arouri, 2016) In recent empirical studies on banking stability or internal bank factors, the authors often use regression models with the lagged variable of the dependent variable as the independent variable, then the research is in the form of a model with dynamic panel data, in which the lagged variable of the dependent variable is endogenous (Abuzayed et al., 2018; Altunbas et al., 2018)

F-test: Used to choose between two Pooled OLS and FEM models Based on the p-value of the test results, if p-value < 5%, choose the FEM model and vice versa, if p- value > 5%, choose the Pooled OLS model

Breusch-Pagan Lagrange Multiplier (LM) test: Used to choose between two models Pooled OLS and REM Based on the p-value of the test results, if p-value < 5% choose the REM model, otherwise if p-value > 5% choose the Pooled OLS model

Multicollinearity test: Multicollinearity occurs when the variables in the model are significantly correlated with each other The consequences of multicollinearity occurring can change the sign of the regression results, leading to erroneous results To test multicollinearity, the author uses the VIF coefficient (Variance Inflation Factor) If the VIF coefficient of any variable has a value less than 10, serious multicollinearity will not occur and vice versa If multicollinearity occurs, the author will handle it in one of two ways: eliminating variables or increasing the research sample size

Test for heteroscedasticity phenomenon: The phenomenon of heteroscedasticity appears when the residuals of the overall regression function do not have the same variance The reliability of each observation is not the same, the error values can be very small or very large, resulting in less accurate results To check whether this phenomenon exists or not, the author uses the White test, Wald test, Breush Pagan Lagrangian Multiplier tests corresponding to the Pooled OLS, FEM, REM models respectively Based on the p-value of the test results, if p-value < 5% there is heteroscedasticity, conversely if p-value > 5% there is no heteroscedasticity

Test for autocorrelation phenomenon: The autocorrelation phenomenon appears, making the standard error of the model ineffective, causing the p-value to increase Even though the results are not skewed, the accuracy of the results is high The results become worse The author uses the Wooldridge test to test the autocorrelation phenomenon in the model Based on the p-value of the test results, if p-value < 5%, autocorrelation occurs, whereas if p-value > 5%, there is no autocorrelation

Test for endogeneity: If the model appears endogenous, the estimates obtained by conventional regression will be unstable, so the results become less accurate This thesis uses the Durbin-Wu Hausman test to test endogenous variables Based on the p-value of the test results, if p-value < 5%, there is endogeneity, whereas if p-value > 5%, there is no endogeneity

Test for second-order autocorrelation of residuals: Use the Arellano-Bond / AR(2) test to see if the model has this phenomenon or not Based on the p-value of the test results, if p-value < 5%, there is a second-order autocorrelation phenomenon, on the contrary, if the p-value > 5%, there is no second-order autocorrelation

Test the appropriateness of instrumental variables: The study uses the Hansen/Sargan test to check whether the instrumental variables are appropriate or not Based on the p-value of the test results, if p-value < 5% the instrumental variable in the model is not appropriate, conversely if p-value > 5% then the instrumental variable in the model is appropriate.

RESEARCH DATA

The research sample of the project is based on secondary data of 25 Vietnamese commercial banks from 2010 - 2022 collected from audited financial statements The author's list of banks is presented in Appendix 1

For the dependent and independent variables belonging to internal bank factors, the data source is taken from financial reports provided by the specialized Data Information System for the Vietnamese market - FIINPRO For the independent variable belonging to macro factors, the author took it from the statistical report of the World Bank

This research article on factors affecting the financial stability of Vietnamese commercial banks is based on panel data, conducted with the support of Excel and Stata 17.0 software

Chapter 3 proposed a research model based on previous studies, and also presented research hypotheses with the dependent variable lnZ-scoreZ-score and independent variables including: Bank size (SIZE), Return on equity (ROE), Non- performing loan (NPL), Capital adequacy ratio (CAR), Loan to deposit ratio (LDR),

Non-interest income (NII), Inflation (INF) and Economic growth rate (GDP) Besides, in this chapter, the author also details the process and tests that the author will perform during the process of running the regression model Finally, the author makes hypotheses based on previous studies to serve as a basis for continuing chapter 4.

ANALYZE RESEARCH RESULTS

STATISTICAL DESCRIPTION OF VARIABLES IN THE RESEARCH

The data in the research article was collected by the author from audited financial statements of 25 Vietnamese commercial banks in the period 2013-2022 The data obtained is in the form of panel data with 250 observations Descriptive statistical results are presented in the table below:

Table 4.1: Results of descriptive statistics Variable Observation Mean Standard deviation

(Source: Compiled from Stata software)

According to the results from table 4.1, it can be seen that there are a total of 250 observations from 25 years of Vietnamese commercial goods from 2013 to 2022, including:

The variable lnZ-score represents the financial stability of commercial banks The higher the lnZ-score, the higher the stability and vice versa Based on the results in table 4.1, the lowest lnZ-score is 0.83 for TPB bank in 2014, the highest lnZ-score is 6.70 for BAB in 2020 In particular, this index has an average value of 3.93, the standard deviation of 0.96 shows that the difference in financial stability of banks is relatively low

Bank size (SIZE) of the 25 commercial banks in the research sample is 11.83, the largest value is 14.41 belonging to CTG bank in 2022 and the lowest value is 9.59 belonging to SGB bank in 2013, the standard deviation is 1.06

Profitability is also measured through return on equity (ROE) This factor of commercial banks had an average value of 10.77% during the research period In 2022, NVB bank had the lowest return on equity ratio of 0% while VIB bank achieved the highest rate up to 30.33% However, the level of fluctuation in the research sample is not high with a standard deviation of 7.86%

The average non-performing loan (NPL) of banks in the research period is 2.11%, standard deviation is 0.01 According to Circular 22/2019/TT-NHNN, commercial banks need to achieve a bad debt ratio of less than 3%, so the lowest bad debt ratio of 0.47% belongs to NVB bank in 2022, showing that this bank is implementing effective credit risk management policies and processes On the other hand, in 2020 TCB bank accounted for the highest bad debt ratio of up to 17.93%, this ratio is much higher than the ratio prescribed by the State Bank, showing that the total value of loans in debt groups

3, 4, and 5 is too high because the bank has not performed well its role in managing credit quality

Loan to deposit ratio (CAR) has an average value of 12.60% and the standard deviation is 0.04, thereby showing that the average minimum capital adequacy ratio of

25 commercial banks remains relatively equal The lowest CAR is 8.35% for LPB bank in 2019 and the highest CAR is 27.98% for OCB bank in 2014

Loan to deposit ratio (LDR) has an average value of 72.92% and the standard deviation is 0.18 The smallest value is 14% belongs to BVB bank in 2013 and the largest value is 129.2% belongs to VPB bank in 2017, this shows that in 2017 the bank lent a larger amount than the amount they mobilized LDR that is too high will greatly affect liquidity and increase credit risk

The ratio of non-interest income to total income (NII) in the research sample is on average 22.56% with the highest ratio at 232.97% of BVB bank in 2013, the lowest ratio at 2.55% of LPB bank in 2014, the standard deviation is close to 0.18

GDP is 2.60% in 2021 and the highest GDP is 8.02% in 2022, standard deviation is approximately 0.02 In addition, the 10-year average inflation rate from 2013 to 2022 is 3.21%, with the lowest rate being 0.63% in 2015 and the highest rate being 6.60% in

To check for multicollinearity, the thesis considers the degree of correlation between variables through the correlation coefficient matrix presented in the following table:

Variable lnZ-score SIZE ROE NPL CAR LDR NII INF GDP lnZ-score 1.0000

Note: *,**,***: coefficients are statistically significant at the 10%, 5% and 1% significance levels, respectively

Based on the regression results, the correlation coefficient matrix between variables in the model shows that most of the correlation coefficients of pairs of independent variables in the model are less than 0.8, that means the majority of independent variables in the model have low correlation with each other

Besides considering the correlation coefficient matrix, the author checked the multicollinearity phenomenon using the Variance Inflation Factor (VIF) method If the study results in a VIF index < 10, the research model does not have multicollinearity and vice versa (Hair et al, 1995)

Table 4.3: Results of multicollinearity test

Results from table 4.3 show that the average VIF value is 1.22 and the magnification factor of all variables is less than 10 It can be seen that the largest VIF value is 1.59 (ROE) and the smallest VIF value is 1.01 (GDP) Therefore, the multicollinearity phenomenon is not serious, so the variables in the model continue to be regressed.

RESEARCH RESULTS

In this thesis, the author conducts regression using STATA 17.0 software with three estimation methods Pooled OLS, FEM and REM to identify and evaluate the impact of independent variables: SIZE, ROE, NPL, CAR, LDR, NII, INF and GDP to the dependent variable lnZ-score based on the estimated coefficient and statistical significance level of each coefficient The regression results are presented in table 4.4:

Table 4.4: Results of Pooled OLS, FEM, REM regression models

FEM model REM model lnZ-score lnZ-score lnZ-score l.lnZ-score 0.588*** 0.420*** 0.588***

The results of the regression model according to the three estimation methods Pooled OLS, FEM, REM are summarized in the table above:

Based on the regression results in table 4.4, for the Pooled OLS model, only the lagged variable of the dependent variable is statistically significant at the 1% significance level, the variables SIZE and ROE are at the 5% statistical significance level At the same time, the R-squared coefficient value = 0.394 shows that the model explains 39.4% of the variation in the data

The results of FEM model estimation show that there are 3 variables, including l.lnZ-score, SIZE, ROE, which are significant at 1% Meanwhile, the NPL variable is significant at the 10% significance level The coefficient value R-squared = 0.309 shows that the independent variables can explain 30.9% of the variation in the data in the model

Similar to the Pooled OLS model, the l.lnZ-score lagged variable in the REM figure reaches a 1% statistical significance level and two variables are statistically significant at a 5% significance level: SIZE and ROE The regression results show that R-squared = 0.394 represents the independent variables can explain 39.4% of the variation in the data in the model

After performing model regression using three methods Pooled OLS, FEM, and REM, the thesis proceeds to select the appropriate model First, to choose one of the two

Pooled OLS and FEM models, the author performed an F-test with the following hypothesis:

H0: The Pooled OLS model is more suitable than the FEM model

H1: The FEM model is more suitable than the Pooled OLS model

From the test results, we have Prob > F = 0.1411 > 5%, showing that we accept hypothesis H0 and reject hypothesis H1 Therefore, the Pooled OLS is more suitable for estimating research variables than the FEM model

Next, the thesis performs the Hausman test to choose the more suitable model between the two models FEM and REM with the following hypothesis:

H0: The REM model is more suitable than the FEM model

H1: The FEM model is more suitable than the REM model

Table 4.6: Result of Hausman test

From the test results, we have Prob > chibar2 = 0.0003 < 5%, showing that we reject hypothesis H0 Therefore, the fixed effects model (FEM) is more appropriate in the study

Finally, the author relies on the result of Breusch and Pagan Lagrangian multiplier test to choose the more suitable model between the two Pooled OLS and REM models with the following hypothesis:

H0: The Pooled OLS model is more suitable than the REM model

H1: The REM model is more suitable than the Pooled OLS model

Table 4.7: Result of Breusch and Pagan Lagrangian multiplier test

Breusch and Pagan Lagrangian multiplier test Prob > chibar2 = 1.0000

From the test results, we have Prob > F = 1.0000 > 5%, showing that we accept hypothesis H0 and reject hypothesis H1 Therefore, the Pooled OLS model is more suitable for estimating research variables than the REM model

Through the two tests just conducted, the author found that the Pooled OLS model is the most appropriate model for estimation Therefore, the author checked the phenomenon of heteroskedasticity, autocorrelation in the model based on the regression results of the Pooled OLS model First, the author uses White test to test the phenomenon of heteroskedasticity with the following research hypothesis:

H0: There is no heteroscedasticity phenomenon

H1: There is a phenomenon of heteroskedasticity

Table 4.8: Result of heteroskedasticity test

White test results show that Prob > chi2 = 0.0512 > 5% represents acceptance of hypothesis H0 Therefore, it can be concluded that the model has no the phenomenon of heteroskedasticity

Second, to test the autocorrelation phenomenon, the author uses the Wooldridge test and the research hypothesis is:

H0: There is no autocorrelation phenomenon

H1: There is an autocorrelation phenomenon

Table 4.9: Result of autocorrelation test

Wooldridge test results show that Prob > chi2 = 0.0000 < 5% represents rejection of the hypothesis H0 Therefore, it can be concluded that the model has autocorrelation phenomenon

Thus, the author uses the Hausman test to choose between the FEM and REM models and the F test to choose between the Pooled OLS and FEM models The final result shows that the Pooled OLS model is the most suitable model in this thesis The author then checked for defects in the model through the White and Wooldridge test and found that the model had autocorrelation These defects can be handled by the GMM estimation method while also solving the endogeneity problem in the model (Greene,

According to Altunbas et al (2018), an economic model that uses the lag of the dependent variable as the independent variable will have an endogenous phenomenon with the endogenous variable being the lag variable Besides, the author also checks whether the independent variables in the model are internal variables by using the Durbin Wu-Hausman test with the following hypotheses:

Table 4.10: Results of Durbin Wu-Hausman test

Variable Durbin Wu-Hausman l.lnZ-score p = 0.1775 p = 0.1894

Based on the results in the table 4.9 only the INF variable has a p-value = 0.0003

< 5% and the remaining variables have a p-value greater than 5% So the model only has the INF variable as an endogenous variable

Greene (2003) believes that the GMM estimation method can overcome the phenomena of heteroskedasticity, autocorrelation and endogeneity in the research model Therefore, this thesis will use the GMM method to handle those defects, the results are presented in the table below:

Table 4.11: Results of using GMM method

Variables Coefficient Std err lnZ-score

Arellano-Bond test for AR(2) in first differences: z = -1.36 Pr > z = 0.174

Sargan test of overriding Restrictions: chi2(14) = 13.16 Prob > chi2 = 0.514

Hansen test of overriding Restrictions: chi2(14) = 14.79 Prob > chi2 = 0.393

Number of instruments = 24 < Number of groups = 25

The results show that the GMM method is reliable when it meets all 4 conditions, including: AR(2) = 0.174 > 10%, Hansen test with Prob > chi2 = 0.393 > 10%, Sargan test has Prob > chi2 = 0.514 > 10%, and the number of instrumental variables is 24 smaller than the number of groups is 25 Therefore, it can be concluded that the model is robust and the results are highly reliable

The results of the GMM model show that the variables l.lnZ-score, CAR, NII, INF are statistically significant at 1%; the variables SIZE, NPL, GDP are statistically significant at 5%; LDR variable is statistically significant at 10% and only ROE variable is not statistically significant In which, there are 3 variables that have a negative impact on the dependent variable: lnZ-score, SIZE, CAR and the remaining variables NPL, LDR, NII, INF, GDP have a positive impact on banking stability So the regression model has the following equation:

Stability it = 0.987*l.lnZ-score + 0.147*SIZE it – 4.725*NPL it + 3.478*CAR it – 0.705*LDR it – 1.071*NII it – 15.20*INF it – 3.131*GDP it – 0.498 (4.1)

DISCUSSION ABOUT RESEARCH RESULTS

The results of regression analysis show that the independent variable SIZE has a positive impact on the dependent variable lnZ-score with a statistical significance level of 5% and a regression coefficient of 0.147, showing a proportional relationship between size and stability banking regulations This result is consistent with the study conducted by Lê Ngọc Quuỳnh Anh et al (2020), the author believes that as bank size increases, the bank's level of financial stability increases Because larger banks often have more diverse portfolios and customers, this helps to spread risk and reduce the impact of bank- specific risks Another study conducted by Nguyen Thi Nhu Quynh (2020) said that large-scale banks often have prestige and reputation in the market, so they pay great attention to risk management This means that bad debt at these banks is often low, which further increases bank stability Hence, the author accepts the hypothesis "Bank size is positively related to banking stability"

In this finding, the variable ROE is not significantly significant with the stability of banks This result matches the research conducted by Ghosh (2016), who showed that the return on equity is not an important factor determining banking stability The result contradicts the study by Demirgỹỗ-Kunt et al (2010) which indicates a negative relationship between return on equity and banking stability The author argues that banks with high ROE tend to engage in riskier activities, such as subprime lending or investing in high-risk assets Although this may increase short-term profits, it increases the risk of long-term loss, especially during difficult economic periods, thereby affecting the bank's stability Therefore, the hypothesis "ROE has a positive impact on banking stability" is rejected

The results of regression analysis show that the independent variable NPL has a negative impact on the dependent variable lnZ-score with a statistical significance level of 5% and a regression coefficient of -4.725 This is meaningful under the condition that other factors do not change, when the independent variable NPL increases by 1%, the dependent variable lnZ-score will decrease by 4.725% This result is similar to the author's initial expectations and is supported by the research of Ghenimi et al (2017), Chai et al (2022) When bad debt increases, financial stability decreases, which is understandable Banks with increased bad debt lead to increased credit risk, banks are at risk of not being able to recover the initial loan capital, leading to loss of liquidity, thereby making the bank less stable Moreover, increased bad debt causes banks to have to increase the cost of setting up risk provisions for these items, which increases the bank's expenses, causing bank profits to decrease, leading to bank financial instability Therefore, the thesis accepts the hypothesis "NPL has a negative relationship with banking stability"

The results of regression analysis show that the independent variable CAR has a positive impact on the dependent variable lnZ-score with a statistical significance level of 1% and the regression coefficient is 3.478 This is meaningful under the condition that other factors do not change, when the independent variable CAR increases by 1%, the dependent variable lnZ-score will increase by 3.478% This result is consistent with the author's initial expectations and is supported by Li & Malone's (2016) view that well- capitalized banks are considered more stable because they have capital reserves available to absorb losses from bad debts or other financial shocks Another study conducted by Shijaku (2017) confirms that capital adequacy ratio is an important key when banks fall into crisis, it acts as a safety cushion to help prevent financial losses, protects banks from payment risks, so a high capital adequacy ratio will help banks increase stability Therefore, the thesis accepts the hypothesis "CAR has a positive relationship with banking stability"

4.3.5 Loan to deposit ratio (LDR)

The results of regression analysis show that the independent variable LDR has a negative impact on the dependent variable lnZ-score with a statistical significance level of 10% and a regression coefficient of -0.705 This is meaningful under the condition that other factors do not change, when the independent variable LDR increases by 1%, the dependent variable lnZ-score will decrease by 0.705% This result is consistent with the author's initial expectations and is supported by studies by Trung and Chung (2018), Yen and Ngan (2016) When a bank's total loans are too large, they will have less spare cash to deal with unexpected customer withdrawal needs, leading to liquidity risk Besides liquidity risk, high LDR often comes with banks issuing more loans, which means the bank's credit risk level is higher If credit quality decreases, the bad debt ratio will increase, negatively affecting the bank's financial health The negative impact was also verified by Nguyen Thanh et al (2017) Therefore, the thesis accepts the hypothesis

"LDR has a negative relationship with banking stability"

The results of regression analysis show that the independent variable NII has a negative impact on the dependent variable lnZ-score with a statistical significance level of 1% and a regression coefficient of -1.071 This is meaningful under the condition that other factors do not change, when the independent variable NII increases by 1%, the dependent variable lnZ-score will decrease by 1,071% This result is contrary to the author's initial expectations but is consistent with the research results of Brunnermeier et al (2012) who argue that expanding non-interest activities to generate income can increase systemic risk if not managed carefully In addition, activities that generate non- interest income such as stock trading, investment banking services, service fees, etc often have a higher level of risk than traditional activities such as lending and receiving deposits These activities are easily affected by market fluctuations leading to income fluctuations, thereby reducing the bank's stability (Stiroh, 2004) From these results, the hypothesis "NII has a positive relationship with banking stability" is rejected

The results of regression analysis show that the independent variable INF has a negative impact on the dependent variable lnZ-score with a statistical significance level of 1% and a regression coefficient of -15.20 This is meaningful under the condition that other factors do not change, when the independent variable INF increases by 1%, the dependent variable lnZ-score will decrease by 15.20% High inflation reduces the real value of bank deposits and assets This could lead to reduced customer confidence in the banking system, causing them to withdraw money or switch to other forms of investment Besides, High inflation often leads to higher interest rates, increasing borrowing costs for customers This can reduce customers' ability to repay debt, leading to an increase in bad debt ratio This result is consistent with the research of Lan (2021) and Rahim et al (2012) Therefore, the hypothesis “INF is negatively related to banking stability” is accepted in this thesis

The results of regression analysis show that the independent variable GDP has a negative impact on the dependent variable lnZ-score with a statistical significance level of 5% and a regression coefficient of -3.131 This is meaningful under the condition that other factors do not change, when the independent variable GDP increases by 1%, the dependent variable lnZ-score will decrease by 3.131% The research results contradict the author's initial hypothesis and previous studies by Rashid et al (2017), Chen et al

(2017), Abuzayed et al (2018) However, the research of Dell'Ariccia et al (2016) shows that economic growth has a negative impact on banking stability During periods of strong GDP growth, banks tend to expand credit excessively to increase profits, but this also comes with increased credit risk, thereby reducing bank stability Therefore, the hypothesis "GDP has a positive relationship with banking stability" is rejected

4.3.9 Lagged variable of banking stability (l.lnZ-score)

From the results in table 4.10, it shows that bank stability in the previous year has a positive impact on bank stability in the following year with a statistical significance level of 1% and a regression coefficient of 0.987 When the lagged variable of bank stability has a positive impact on current stability, this means that a bank that was stable in the past tends to maintain that stability in the present Laeven & Levine (2009) argue that banks that have been stable in the past often have effective risk management systems and consistent risk management policies These banks are able to maintain good risk management practices over time, helping to maintain financial stability Thus, it can be concluded that bank stability in the previous year has a positive impact on bank stability in the following year

Through the research model, the author will present a summary of the hypothesis and research results of the GMM regression model through the following table:

Table 4.12: Summary of research results Variable Hope Reality Level of significance

Chapter 4 details the data analysis process from descriptive statistics, correlation matrix, model estimates to accompanying tests After choosing the appropriate model, the author checked for heteroskedasticity, autocorrelation, and found endogenous variables in the model Finally, the study uses generalized method of moments (GMM) to overcome autocorrelation and endogeneity in the model The results show that the ROE variable is not statistically significant Besides, the remaining variables such as SIZE and CAR have a positive impact on financial stability; the variables NPL, LDR,

NII, INF, and GDP have a negative impact on the financial stability of Vietnamese commercial banks Thereby, the author also compares the results with expectations and discusses the results the research found.

CONCLUSION

This thesis researches the factors affecting the financial stability of consumer goods in Vietnam using data from 25 commercial banks in the period 2013 - 2022 For the dependent and independent variables belonging to internal bank factors, the data source is taken from financial reports provided by the specialized Data Information System for the Vietnamese market - FIINPRO For the independent variable belonging to macro factors, the author took it from the statistical report of the World Bank The research uses quantitative research methods to analyze regression models using panel data with three methods: Pooled OLS, FEM, REM Then, the author compared the regression results of each method through F test, Hausman test, Breusch and Pagan Lagrangian multiplier test to choose the most suitable model is the Pooled OLS model After choosing the appropriate model, the author checked for heteroskedasticity, autocorrelation, and found endogenous variables in the model Finally, the author uses generalized method of moments (GMM) to overcome defects and endogenous variables in the model

From the above research steps, the thesis has answered the three research questions initially posed In the first and second questions, the research results show that there are 7 factors affecting the financial stability of Vietnamese commercial banks There are two factors: Bank size (SIZE) and Captial adequacy ratio (CAR) that impact in the positive direction on the financial stability of Vietnamese commercial banks and factors such as Non-performing loan (NPL), Loan to deposit ratio (LDR), Non-interest income (NII), Inflation (INF) and Economic growth rate (GDP) have negative effects

In addition, the study also shows that Return on equity (ROE) does not really affect the financial stability of banks and needs to be studied further

In the last research question, the author suggests some policy implications to improve the financial stability of Vietnamese commercial banks through the impact of independent variables on the dependent variable Z-score in section 5.2.

POLICY IMPLICATIONS

First, bank size (SIZE) impact in the positive direction on the financial stability of Vietnamese commercial banks Therefore, Vietnamese commercial banks need to consider expanding their bank scale, improving corresponding operational efficiency, ensuring the expansion of operations is under control, thereby improving banking stability When expanding their scale, banks need to pay attention to focusing on increasing highly liquid assets to prevent risks Focusing on improving asset liquidity is an important part of ensuring banks can face unforeseen situations and maintain stability in their operations

Second, non-performing loan (NPL) has a negative impact on the financial stability of Vietnamese commercial banks This shows that commercial banks need to maintain a reasonable debt ratio to ensure output business activities, but they must combine it with tightening bad loans to avoid increasing debt The higher the bad value, the more difficult it is for commercial banks to operate stably Therefore, to improve credit quality and limit risks in the system, commercial banks can implement the following solutions:

− Credit process: To improve credit quality, commercial banks need to strictly improve credit processes, requiring risk assessment at each stage in the process From the pre-disbursement process until the disbursement is completed, customers need to be monitored and controlled to receive support and timely intervention if the customer shows signs of poor repayment

− Internal credit rating system: The construction of an internal credit rating system already exists at commercial banks, however this credit rating system does not follow any unified standards among banks For commercial banks, the system is only unified internally at each bank depending on that bank's risk appetite This causes difficulties and inadequacies in comparing and contrasting borrower information, leading to inconsistent credit granting decisions

− Bad debt handling and recovery: Commercial banks need to promote bad debt handling and recovery Debt monitoring and collection need to be done quickly right after disbursement to customers, then commercial banks need to allocate resources to evaluate debts awaiting recovery so that they can allocate time and human resources appropriately Commercial banks can sell debts to VAMC to avoid spending a lot of time processing bad debts that require lawsuits, thereby focusing on handling recoverable debts

Third, the capital adequacy ratio (CAR) has a positive impact on the financial stability of Vietnam commercial banks Therefore, commercial banks must always ensure the minimum capital adequacy ratio in accordance with the regulations of the State Bank of Vietnam and international standards according to the Basel Agreement It is necessary to comply with the successful completion of Project No 689/QD-TTg of the Prime Minister, which sets out the goal that by 2023, the CAR should reach at least

10 - 11%, and by 2025, it should reach a minimum of 11 - 12%

The research results also show that, the higher the ratio of equity to total assets, the more financially stable a commercial bank is Therefore, the Bank's Board of Directors should have a roadmap towards the goal of increasing equity capital appropriately to increase financial stability In addition, to increase equity such as issuing shares, equitization, mergers, etc., banks can also retain a portion of profits to reinvest and add to their equity portfolio through adjusting the balance in the process of distributing financial results between dividend payments and profits However, increasing equity capital also depends on the actual situation to avoid excessive capital increase due to banks not using capital effectively, business profits falling, causing financial instability

Each bank needs to have an organizational structure and decentralize tasks to each department and individual in monitoring and managing the capital adequacy ratio This is not only to ensure regulations on the minimum capital safety ratio but also to quickly detect losses and promptly take measures to strengthen this ratio to avoid falling into unnecessary bad situations

Fourth, loan to deposit ratio (LDR) has a negative impact on the financial stability of Vietnam ese commercial banks That is, when this ratio increases, the bank's financial stability will decrease This factor essentially depends on the quality of the loan Therefore, banks are encouraged to instead lend less with quality loans rather than lend more with the risk of capital loss Banks need to have plans so that their loans come with quality to ensure financial stability

Banks need to set specific criteria for the minimum loan amount depending on the borrower's financial capacity or the purpose of using the capital to avoid customers not being able to repay debt, causing financial loss for bank When making a lending decision, it is necessary to go through typical credit checking activities such as checking CIC to check the current bad debt status in order to make the most accurate lending decision It is also possible to apply the loan method with collateral so that when the customer cannot repay the debt, the initial capital can still be recovered thanks to the assets that the customer mortgaged, which will help the bank avoid falling into customer risk The goods are unable to pay their debts At the same time, loan inspection must be strict to increase loan quality, avoid the risk of losing these loans, and help the bank have more financial stability

If the loan input is quality loans, bad debt will be minimized, thereby reducing the cost of risk provisions for loans, making the bank operate more stably Therefore, loan appraisal plays a very important role It is necessary to improve professional qualifications and build a team of qualified staff to undertake this work If credit officers are incompetent, lack professional qualifications or have poor ethics, there will be errors in the appraisal process, analysis and evaluation when making lending decisions, increasing the level of bank risk

Fifth, inflation (INF) negative impact on the financial stability of Vietnamese commercial banks Rising inflation causes banking and financial stability to decrease

On the banks’ side, it is necessary to monitor information on economic developments published on the website of the State Bank of Vietnam, the General Statistics Office, the International Monetary Fund (IMF), and the World Bank When building a business strategy, banks need to analyze the current situation and calculate macroeconomic conditions, especially inflation, development trends of the service market and capital market to bring set reasonable interest rates, build business strategies suitable to the actual situation

On the government's side, it is necessary to have policies to control inflation, manage monetary policy proactively and flexibly, and closely coordinate with fiscal policy and macroeconomic policy

Sixth, economic growth rate (GDP) has a negative impact on the financial stability of Vietnam commercial banks In practical terms, when the economy develops, the income of individuals and households increases, on the one hand, it will cause them to consume more, creating conditions for businesses to operate effectively and increase profits so that these businesses can easily pay off their debts On the other hand, when individual and household income increases, it will boost the ability of these subjects to repay loans (if any) As a result, the bank will enjoy the above benefits so that it can operate stably But the thesis argues that the economic growth factor has a negative impact on the stability of Vietnamese commercial banks, because during the period of economic expansion, banks often loosen lending standards and increase lending, but failure to strictly manage risks could lead to increased bad debt ratios as economic conditions change This accumulation of bad debt can cause major problems for the stability of the banking system and the entire economy The nature of this problem lies in loan quality similar to the LDR factor, so commercial banks can refer to the solutions presented by the author above.

LIMITING FURTHER RESEARCH DIRECTIONS

Although the thesis has made certain contributions both practically and theoretically, there are still some limitations as follows:

The data used in the study only accesses 25 commercial banks, and cannot access all commercial banks in Vietnam Some banks were not included in the study by the author due to insufficient data during the research period Future studies can increase the sample size by increasing the number of commercial banks when these banks have fully published data

Due to limitations in time and resources, in addition to the factors that affect the financial stability of commercial banks mentioned in the thesis, there are many other factors that also have an equally impact such as: foreign investment, loan risk provisions (Pham et al., 2021), competitiveness (Vo Xuan Vinh & Dang Buu Kiem, 2016) Therefore, future studies can consider studying the impact of these factors on the financial stability of Vietnamese commercial banks

Based on results obtained from regression model in the previous chapter, this chapter once again raised the conclusion factors affecting the financial stability of Vietnamese commercial banks Based on the direction of impact, author topic propose some policy implications to increase the financial stability for banks At the same time, the author points out the limitations that the thesis still encounters and provides directions for further researches.

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APPENDIX APPENDIX 1: LIST OF BANKS IN RESEARCH DATA

1 ABB An Binh Commercial Joint Stock Bank

2 ACB Asia Commercial Joint Stock Bank

3 BAB Bac A Commercial Joint Stock Bank

4 BVB Viet Capital Commercial Joint Stock Bank

5 CTG Vietnam Joint Stock Commercial Bank for Industry and

6 EIB Vietnam Commercial Joint Stock Export Import Bank

7 HDB Ho Chi Minh City Housing Development Commercial Joint

8 KLB Kien Long Commercial Joint Stock Bank

9 LPB LienViet Post Joint Stock Commercial Bank

10 MBB Military Commercial Joint Stock Bank

11 MSB Vietnam Maritime Commercial Joint Stock Bank

12 NAB Nam A Commercial Joint Stock Bank

13 NVB National Citizen Commercial Joint Stock Bank

14 OCB Orient Commercial Joint Stock Bank

15 PGB Prosperity and Growth Commercial Joint Stock Bank

16 SGB Saigon Bank for Industry and Trade

17 SHB Saigon-Hanoi Commercial Joint Stock Bank

18 SSB Southeast Asia Commercial Joint Stock Bank

19 STB Saigon Thuong Tin Commercial Joint Stock Bank

20 TCB Vietnam Technological and Commercial Joint Stock Bank

21 TPB Tien Phong Commercial Joint Stock Bank

22 VAB Vietnam - Asia Commercial Joint Stock Bank

23 VCB Bank for Foreign Trade of Vietnam

24 VIB Vietnam International Commercial Joint Stock Bank

25 VPB Vietnam Prosperity Joint Stock Commercial Bank

APPENDIX 2: RESULT OF TESTING MODEL

1 Set up space and time variables

5 Pooled OLS regression results and save the results

6 FEM regression results and save the results

7 REM regression results and save the results

8 Summary of regression results according to Pooles OLS, FEM, REM

10 Breusch and Pagan Lagrangian multiplier test

11 Test the phenomenon of heteroskedasticity

Tiêu đề	Factors Affecting The Financial Stability Of Vietnamese Commercial Banks
Tác giả	HÀ MỸ TÂM
Người hướng dẫn	DR. NGUYỄN THỊ NHƯ QUỲNH
Trường học	HO CHI MINH UNIVERSITY OF BANKING
Chuyên ngành	FINANCE – BANKING
Thể loại	University Graduation Thesis
Năm xuất bản	2024
Thành phố	HO CHI MINH CITY

Định dạng
Số trang	103
Dung lượng	1,61 MB