MINISTRY OF EDUCATION AND TRAINING STATE BANK OF VIETNAM HO CHI MINH UNIVERSITY OF BANKING NGUYEN THI NHU YEN DETERMINANTS OF CREDIT RISK IN VIETNAMESE COMMERCIAL BANKS GRADUATE THESIS MAJOR FINANCE –[.]
INTRODUCTION
Reasons for choosing the study
Commercial banks serve as vital payment intermediaries and sources of capital for economic entities, while also acting as tools for government regulation of the macroeconomy They enable the implementation of fiscal and monetary policies that support domestic businesses and economic growth The activities of commercial banks can significantly impact the economy, with credit risk being a key indicator of their operational health In the current market economy and era of digital transformation, banks are prioritizing the reduction of credit risk, as its increase signals poor performance that can lead to losses for banks and adverse effects on other economic entities Therefore, assessing the factors influencing credit risk is crucial for understanding its microeconomic and macroeconomic implications, ultimately assisting bank managers in developing effective strategies to minimize credit risk in their operations.
The global economic crisis of 2007-2008 significantly impacted the world economy, leading to increased unemployment rates, declining trade activities, and fluctuating capital flows across markets Despite a growth rate of 5.1% in 2010, the following years saw a decline, with rates dropping to 3.9% in 2011 and further to 3.2% in 2012 and 2013 However, signs of recovery emerged in 2014, as capital flows stabilized and financial institutions, particularly banks, began to show positive growth signals This recovery reflected the economic efforts of various countries, including Vietnam, where the credit growth rate in the commercial banking system improved compared to previous periods.
In 2020-2021, the global economy faced significant changes due to the adverse effects of the Covid-19 pandemic, leading to a marked increase in credit risk for commercial banks in Vietnam Many businesses experienced reduced revenues or struggled to meet debt obligations, heightening concerns over debt recovery and slowing capital turnover Despite these challenges, 2020 remained a prosperous year for the Vietnamese economy, with notable growth in gross domestic product (GDP).
In 2020, the economy was projected to grow by 2.91%, attributed to effective management of the Covid-19 pandemic and the implementation of the European-Vietnam Free Trade Agreement (EVFTA) While the industrial production sector experienced significant growth compared to 2019, transportation, particularly international transport, encountered challenges due to global market shutdowns According to the BIDV Training and Research Institute's report on the banking industry's profits in 2020 and forecasts for 2021, interest income remained a crucial factor for banking profits, although its contribution was on the decline The ongoing spread of the fourth Covid-19 wave in 2021 posed further uncertainties for the sector.
The COVID-19 pandemic, coupled with stringent epidemic prevention measures, has led to the withdrawal of 119,800 businesses from the market Among these, nearly 55,000 enterprises have temporarily suspended operations, while 48,100 have ceased operations pending dissolution procedures Additionally, 16,700 businesses have completed their dissolution, including 211 enterprises with a capital scale exceeding 100 billion.
In 2021, the banking industry experienced a significant rise in the non-performing loan ratio, with internal non-performing loans and potential non-performing loans, including restructured debts under Circulars 01, 03, and 14 from the State Bank of Vietnam (SBV), projected to reach between 7.1% and 7.7%.
In 2021, despite the State Bank's supportive policies and proactive measures taken by commercial banks, the sector is still grappling with an unprecedented high ratio of non-performing loans.
In 2022, the prolonged conflict between Russia and Ukraine significantly drove up global commodity prices, resulting in the highest inflation rates in over 40 years In response, central banks worldwide aggressively tightened monetary policies, raising interest rates frequently and substantially This challenging global economic landscape put immense pressure on governments' macroeconomic management Despite these challenges, the Vietnamese economy showed positive recovery, stabilizing its macroeconomic conditions and controlling inflation, largely thanks to the crucial role of bank credit capital.
Stemming from that importance, the author chooses the study with the title
The determinants of credit risk in Vietnamese commercial banks play a crucial role in guiding managers and strategic planners By reviewing and assessing the factors influencing credit risk, these insights aid in making informed investment decisions and shaping the development strategies of commercial banks in Vietnam.
Research objectives
To review and analyze the determinants of credit risk in Vietnamese commercial banks, this study aims at the following specific objectives:
The author analyzes the influence of bank-specific and macroeconomic factors on the credit risk of Vietnamese commercial banks from 2010 to 2022 Based on empirical findings, the study offers strategic recommendations for bank managers to mitigate credit risk in the future.
From the general objectives mentioned above, the author determines three specific objectives as follows:
Firstly, the study determines the bank-specific and macroeconomic variables affecting the credit risk of Vietnamese commercial banks
Secondly, the study measures the level and direction of the impact of these variables on the credit risk of commercial banks in Vietnam
Finally, the author proposes some appropriate solutions to decrease credit risk for commercial banks in particular and the Vietnamese economy in general.
Research questions
After identifying the research issue and research objectives, the research questions are posed to shape the scientific idea Specifically, the study will mainly focus on the following research questions:
- What elements affect the credit risk of Vietnamese commercial banks from 2010 to 2022?
- How are the factors affecting the credit risk of Vietnamese commercial banks?
- What is the optimal solution to decrease the credit risk of Vietnamese commercial banks through these elements?
The research subject and scope of study
Determinants of credit risk in Vietnamese commercial banks
- Scope of time : The data of the research are collected from the annual financial statements of commercial banks over the period of 13 years, from 2010 to
The article focuses on the audited reports published by Vietnamese commercial banks on their official websites for the year 2022 The author selects this period due to significant events affecting the banking system's credit from 2010 to 2013, followed by the impact of the Covid-19 pandemic on bank activities between 2020 and 2022 Consequently, it is evident that the credit risk of commercial banks during this timeframe may have experienced considerable fluctuations.
The study focuses on 27 commercial banks in Vietnam, all of which are listed on major stock exchanges, including the Ho Chi Minh Stock Exchange (HOSE), the Hanoi Stock Exchange (HNX), and the UPCOM stock exchange This selection ensures that the research data is comprehensive and reliable, as it has been censored prior to publication and includes complete financial information over the years, allowing for thorough analysis.
- Joint Stock Commercial Bank for Foreign Trade of Vietnam (VCB)
- Vietnam Joint Stock Commercial Bank of Industry and Trade (CTG)
- Joint Stock Commercial Bank for Investment and Development of Vietnam (BID)
- Vietnam Technological and Commercial Joint Stock Bank (TCB)
- Military Commercial Joint Stock Bank (MBB)
- Saigon Thuong Tin Commercial Joint Stock Bank (STB)
- Vietnam Commercial Joint Stock Bank for Private Enterprise (VPB)
- Ho Chi Minh city Development Joint Stock Commercial Bank (HDB)
- Asia Commercial Joint Stock Bank (ACB)
- The Maritime Commercial Joint Stock Bank (MSB)
- Vietnam Export Import Commercial Joint Stock (EIB)
- LienViet Commercial Joint Stock Bank (LPB)
- TienPhong Commercial Joint Stock Bank (TPB)
- Vietnam International Commercial Joint Stock Bank (VIB)
- Southeast Asia Commercial Joint Stock Bank (SSB)
- Orient Commercial Joint Stock Bank (OCB)
- Saigon Commercial Joint Stock Bank (SCB)
- National Citizen Commercial Bank (NCB)
- An Binh Commercial Joint Stock Bank (ABB)
- Kien Long Commercial Joint Stock Bank (KLB)
- BAC A Commercial Joint Stock Bank (BAB)
- Nam A Commercial Joint Stock Bank (NAB)
- Viet Capital Commercial Joint Stock Bank (BVB)
- Saigon Bank For Industry And Trade (SGB)
- Petrolimex Group Commercial Joint Stock Bank (PGB)
- Bao Viet Joint Stock Commercial Bank (BaoVietBank)
- Vietnam – Asia Commercial Joint Stock Bank (VAB)
Research method
This thesis employs a quantitative approach, utilizing multivariate linear regression analysis to investigate the factors influencing credit risk in Vietnamese commercial banks from 2010 to 2022, using STATA17 software The author applies the Pooled OLS model, fixed effects model (FEM), and random effects model (REM) to analyze these determinants Additionally, F-tests are conducted to determine the suitability of the OLS or FEM models, while the Hausman test is used to compare the FEM and REM models, and the Breusch & Pagan test helps in selecting between the OLS and REM models.
After choosing a suitable model, the author tests for autocorrelation and variable variance If defects are identified in the research model, the Feasible Generalized Least Squares (FGLS) method is employed to address issues of autocorrelation and variable variance Additionally, the study utilizes the System Generalized Method of Moments (S-GMM) to tackle endogeneity, variable variance, and autocorrelation, enhancing the robustness of the methodology.
Research data collected includes microeconomic data and macroeconomic data:
For microeconomic data: Research data is collected from audited financial statements of 27 Vietnamese joint stock commercial banks published year by year at website https://cafef.vn/
For macroeconomic data: Research data is collected from the information on the database of GSO at website https://www.gso.gov.vn/.
Contribution
This study employs a quantitative research method to provide empirical evidence regarding the factors influencing credit risk in commercial banks in Vietnam, thereby enhancing the scientific understanding of this critical issue.
The study aims to identify key factors influencing the credit risk of commercial banks, enabling bank managers to implement effective policies to mitigate credit risk in Vietnamese commercial banks.
Secondly, because the research subjects of the study are 27 Vietnamese commercial banks, and the scope of the research is over the period of 13 years, from
From 2010 to 2022, this study provides updated insights and evaluations for commercial banks, reflecting the latest research aligned with current socio-economic conditions Furthermore, it serves as a valuable reference for future studies in the field.
Research structure
The study includes an introduction, conclusion, list of abbreviations, list of tables and images, list of references, and appendices Besides, the study is also structured into 5 chapters as follows:
This chapter outlines the author's motivation for selecting the topic, defines the research objectives, identifies the research subjects, and delineates the scope of the study in terms of both time and space Additionally, the author will formulate key research questions related to the topic.
Chapter 2: Theoretical foundations and overview of prior experimental studies
This chapter explores key concepts relevant to the research problem, focusing on the theoretical foundations of credit risk and the criteria for assessing credit risk in Vietnamese commercial banks Additionally, it reviews previous domestic and international studies, providing a solid foundation for the research methods discussed in the following chapter.
This chapter outlines the key components of the research, including the research model, hypotheses, and methods for measuring microeconomic and macroeconomic variables It will provide a detailed overview of the research process, highlighting the research methodologies, calculation techniques, and data processing methods employed.
This chapter presents the research findings, including a multicollinearity test, a correlation coefficient matrix among variables, and regression data analysis using OLS, FEM, and REM methods The author employs the generalized least squares regression method (FGLS) to address issues of variable variance and autocorrelation, as well as the system generalized least squares regression method (S-GMM) to tackle endogenous variables The empirical results identify the key factors influencing the credit risk of commercial banks in Vietnam.
To mitigate credit risk in Vietnam's commercial banks, the author presents several recommendations grounded in research findings and personal insights.
In chapter 1, the author presented the reason for choosing the topic
This article explores the determinants of credit risk in Vietnamese commercial banks, outlining key research objectives and questions while defining the study's subject and scope It employs a comprehensive research methodology and highlights the contributions of the study to the field Additionally, the author organizes the research into five structured chapters, providing a clear framework for understanding the complexities of credit risk management in this context.
THEORETICAL FRAMEWORK AND REVIEW OF
Review of commercial banks
According to Ha (2013) and Tien (2015), commercial banks serve as crucial intermediary financial institutions within the economy and financial markets They engage in a variety of activities, primarily attracting capital through demand deposits, term deposits, savings deposits, and other methods like issuing bank promissory notes and bonds This mobilized capital is then utilized to provide production and business loans, as well as consumer loans Additionally, commercial banks offer essential payment services and other functions, including money transfers and guarantees.
According to Clause 3, Article 4 of the Law on Credit Institutions (2010), a bank is defined as a credit institution authorized to perform all banking activities in compliance with the law Banks are categorized based on their operations and objectives, including commercial banks, policy banks, and cooperative banks Notably, commercial banks are permitted to engage in all banking and business activities outlined in the law with the aim of generating profit.
In summary, commercial banks serve as essential financial institutions that offer a wide range of services, primarily focusing on accepting deposits, extending loans, and facilitating payment services Furthermore, they cater to the diverse needs of society by providing additional services to meet the growing demand for various financial products.
The deposit management function of commercial banks involves receiving, holding, and safeguarding customer deposits, as well as processing withdrawal requests and facilitating expenditures This essential service not only ensures the security of funds but also provides significant advantages for various entities.
Firstly, for customers, the deposit management function helps customers not only ensure the safety of their assets but also helps them to make a profit from the temporary excess capital
Deposit management is crucial for commercial banks as it provides the necessary capital for credit functions and serves as the foundation for their role as payment intermediaries.
Finally, for the economy, the deposit management function encourages accumulation in society and concentrates the temporary excess capital to serve economic development
Commercial banks play a crucial role by facilitating transactions on behalf of customers, allowing them to deduct funds from their accounts for purchases of goods and services, as well as receive payments from sales and other transactions This function is essential to the operations of commercial banks.
The payment intermediary function enables customers to make quick, safe, and efficient payments, reducing the risks and costs associated with direct transactions between different economic entities, particularly due to geographical factors and transaction expenses.
Commercial banks enhance their ability to attract deposit capital by offering high-quality non-cash payment services, which serve as a foundation for creating representative money and ultimately contribute to the expansion of the economy's credit scale.
This function enhances the circulation of goods, drives economic growth, and improves the efficiency of social reproduction processes Additionally, it reduces the volume of cash in circulation, resulting in significant savings on cash handling costs.
A commercial bank serves as a financial intermediary, linking individuals with excess capital to those in need of funds By mobilizing and concentrating temporary capital within the economy, commercial banks facilitate the creation and allocation of loans This credit intermediation function provides significant benefits to various entities involved in the financial system.
Depositors enjoy the advantage of earning interest on their temporary capital while ensuring the safety of their funds Conversely, borrowers benefit from access to necessary capital to address short-term financial needs in production, business, and consumption activities.
Secondly, for commercial banks, this function is the foundation for the existence and development of banks through profits from the arbitrage activities between lending and deposit interest rates
The economy benefits from the ability to transfer monetary capital from areas of temporary surplus to those experiencing shortages, facilitating production and business development while promoting overall economic growth.
Review of bank credit
Nowadays, in many different parts of the world, there are some definitions of commercial bank credit
According to Anh (2011), credit is defined as a transaction where a credit grantor, such as a bank or credit institution, provides assets to a credit recipient, which can be either firms or individuals, with the expectation of repayment of both the principal amount and interest.
The Law on Credit Institutions (2010) defines credit as an agreement that allows organizations or individuals to utilize funds, with the obligation of repayment, through various methods such as loans, discounting, leasing, bank guarantees, and other credit operations.
2.2.2.1 Transactional assets in bank credit are diversified
Common transactional assets include currencies and commodities, while bank credit can encompass monetary value, real property, or signatures The banking system serves as both a credit and payment intermediary, with bank credit primarily representing monetary value in the form of representative money.
Granting credit with real assets is a widely utilized method in the economy, particularly through installment sales by retail businesses For credit institutions, this involves leasing assets to customers via financial leasing transactions In Vietnam, the Law on Credit Institutions designates financial leasing as a distinct offering from financial leasing companies, which are categorized as non-banking credit institutions, allowing banks to indirectly provide this type of product.
In recent years, the rise of credit activities has led to the emergence of signature credit, a conditional payment commitment offered by banks to their clients This form of credit encompasses various types, including bank guarantees, letters of credit (LC), and bills of exchange.
2.2.2.2 Risks in bank credit are inevitable and cannot be completely eliminated
Credit transactions, including bank credit, are fundamentally based on trust, with credit risk arising from the uncertainty of a borrower's ability or willingness to repay a debt This goodwill to repay is intangible and difficult to measure, making credit risk an inherent aspect of credit relationships that banks cannot entirely eliminate Various external factors, often beyond the control of both banks and customers, can impact a borrower's repayment capacity, contributing to a high level of potential risk To mitigate this risk, commercial banks implement several measures, such as developing comprehensive credit policies for effective risk management and establishing a multi-stage credit approval process to ensure stringent oversight in credit granting.
2.2.2.3 Refund in bank credit is unconditional
Bank-credit documents, including credit contracts and debt receipts, represent a customer's unconditional commitment to repay loans on time This legal obligation is crucial for maintaining a responsible banking relationship However, banks may offer flexibility in certain situations, such as extending repayment terms or reducing interest rates, based on the customer's specific circumstances.
2.2.3 The role of commercial bank credit
Firstly, credit contributes to promoting the reproduction process of society
Credit serves as a vital mechanism for reallocating excess capital from those who have it to those who require it, transforming temporarily unprofitable funds into valuable resources This process not only enhances profitability but also enables individuals and businesses to secure additional capital, facilitating market expansion and fostering business growth.
Secondly, credit is a channel to convey the influence of the government on macroeconomic objectives
The primary macroeconomic goals of an economy are to stabilize prices, foster economic growth, and generate employment to lower the unemployment rate These objectives are significantly impacted by the volume and structure of market credit By influencing interest rates and loan conditions, the government can implement policies to either expand or restrict credit and tailor the credit structure to meet the needs of specific industries or regions.
Thirdly, credit creates a condition to expand foreign economic relationships
The advancement of economic integration has elevated the significance of credit in enhancing international economic and financial relations By facilitating credits for import and export activities and attracting foreign credit sources, credit plays a crucial role in driving industrialization and modernization.
Finally, credit is a tool to implement the state's social policies
To effectively implement social policies, the government can utilize non-refundable capital from the national budget, although this approach often proves insufficient due to the limited resources available To address these constraints, the state has increasingly turned to credit channels, offering preferential credit policies aimed at supporting remote areas and assisting underprivileged students.
Review of credit risk
Credit risk refers to the potential loss a bank faces when borrowers cannot meet their repayment obligations, as highlighted by Yurdakul (2014) Chen and Pan (2012) further define it as the fluctuation in value of debt instruments and derivatives due to issues related to the fundamental credit quality of both borrowers and lenders.
In Vietnam, Clause 24 of Article 2 in Circular 41/2016 issued by the State Bank defines credit risk as the possibility that customers may not meet their debt repayment obligations, either partially or fully, under a contract or agreement with a bank or foreign bank branch.
In summary, credit risk poses a significant threat to banks, as it can lead to financial losses when customers fail to meet their debt repayment and interest obligations Essentially, credit risk arises when anticipated earnings from profitable assets are not repaid within the loan term, adversely impacting the bank's profits and capital.
2.3.2 The impact of credit risk on commercial banks
Credit risk significantly impacts the operational efficiency of Vietnamese commercial banks, which in turn plays a crucial role in the overall economic development of the country An increase in credit risk within these banks can have serious repercussions on national economic growth, particularly during times of economic integration.
- The effect of credit risk on the liquidity of commercial banks
Non-performing loans hinder the recovery of capital for banks, complicating their ability to meet debt obligations and increasing the risk of insolvency due to customer deposits Research by Imbierowicz and Rauch (2014) highlights the connection between credit risk and liquidity risk in commercial banks.
US from 1998 to 2010 The research results demonstrated the existence of a positive relationship between credit risk and liquidity risk of banks in stable economic periods and crisis periods
- Credit risk can lead to bankruptcy of commercial banks
Non-performing loans significantly impact the assets of commercial banks, leading to detrimental effects such as diminished reputation and customer trust High levels of these loans can reduce a bank's ability to raise capital, as customers are reluctant to deposit money in institutions with poor credit quality Continuous insolvency may trigger a crisis due to mass withdrawals, ultimately resulting in bankruptcy.
- Credit risk will affect the operational performance of commercial banks
Provisioning for credit risks can lead to increased costs associated with managing non-performing loans, ultimately resulting in decreased bank profits A study by Petria et al (2015) analyzed the factors influencing bank profitability in 27 EU countries from 2004 to 2011, utilizing return on equity (ROE) and return on total assets (ROA) as indicators of business performance for 10 banks The findings revealed a negative relationship between credit risk and bank performance, as indicated by the non-performing loans ratio.
- Credit risk affects the economy
Credit risk significantly impacts both commercial banks and the broader economy, as banks serve as crucial intermediaries that supply capital to businesses and individuals The interconnected nature of banking means that the failure of one institution can lead to a domino effect, causing instability across the entire banking system This instability not only undermines the national financial framework but can also precipitate an economic crisis Consequently, the occurrence of credit risk has far-reaching implications, affecting both economic performance and societal well-being (Andrianil and Wiryono, 2015).
In conclusion, bank managers need to take active measures to contribute to the management of banking activities and have appropriate solutions to minimize credit risks
2.3.3 Factors affecting credit risk of commercial banks
The author finds that the credit risk of commercial banks is influenced by many different factors including the group of microeconomic factors and the group of macroeconomic factors
- The size of a bank (SIZE)
Bank size is a measure of the size of a bank's total assets by taking the natural logarithm of total assets (Rabab’ah, 2015) Chernykh and Theodossiou
In 2011, it was highlighted that larger banks tend to be more diversified, allowing them to manage substantial funds and connect with borrowers from major firms that hold significant credit card balances Additionally, these banks have the necessary resources to create sophisticated systems for assessing and managing credit risk, enabling them to provide a more extensive range of credit options.
- The equity-to-asset ratio (CAP)
The equity-to-asset ratio, calculated by dividing a bank's capital by its total assets in a given year, reflects the financial strength of commercial banks and their ability to withstand losses while continuing operations in varying economic conditions (Rabab’ah, 2015) Rabab’ah's empirical research indicates that a stable capital adequacy ratio (CAP) enables banks to effectively manage their assets and minimize losses from credit issuance Following the 2007-2008 global economic crisis, central banks mandated that commercial banks bolster their equity to meet capital adequacy requirements (CAR) A higher CAR allows banks to better navigate credit risk.
Return on Assets (ROA) is determined by dividing net income after taxes by the average total assets of banks, serving as a key indicator of bank profitability (Nabila & Younes, 2011) This metric reflects a bank's ability to generate profits relative to its total capital and allows for performance comparisons among banks with similar risk levels, effectively accounting for variations in tax policies and financial leverage (Kupiec & Lee, 2012; Messai & Jouini, 2013).
Return on equity (ROE) is determined by dividing net income after taxes by total equity, serving as a key indicator of a bank's management efficiency in utilizing shareholders' capital (Anh & Phuong, 2021) According to Hien and Giang (2020), commercial banks often increase their risk levels in pursuit of higher profitability Additionally, research conducted on commercial banks in China further supports these findings.
A study conducted in 2016 revealed that there is no correlation between bank profitability and risk Profitability is assessed using Return on Assets (ROA) and Return on Equity (ROE) ROA indicates a bank's ability to generate profits relative to its assets, while ROE measures the return on shareholders' investments In the strategic development of commercial banks, an increase in equity is linked to both profitability and credit risk management.
Non-performing loans (NPLs) are assessed by calculating the ratio of NPLs to total loans According to Whalen (1988), a higher NPL ratio correlates with an increased loan loss provision (LLP) to gross loans ratio, indicating elevated risk An increase in LLP signifies greater credit risk for commercial banks, leading to a rise in NPLs, a decline in asset quality, and a negative impact on profitability.
Loans are evaluated by calculating the ratio of loans to total assets in commercial banks An increase in loans, which signifies operational control, positively impacts credit risk, as higher lending often leads to inadequate credit limit guarantees, thereby elevating credit risk When commercial banks expand their lending without proper oversight of loan quality, the potential for increased credit risk rises To mitigate this risk, it is essential for banks to implement stringent governance, adhere to strict credit granting procedures, and ensure their safety, as highlighted by Casu (2006).
(2019) showed that commercial banks with a high ratio of loans to total assets do not guarantee credit limits, tend to reduce credit risk
RESEARCH METHODS
Research process
The study was conducted according to the following steps:
Step 1: Identifying the research problem The author determines research issue and the elements affecting the credit risk of Vietnamese commercial banks
Step 2: Based on the theoretical framework and empirical studies, the author evaluates the review of previous studies and builds an appropriate research model
Step 3: Analyzing the effect of elements affecting the credit risk of Vietnamese commercial banks From the proposed research model, applying quantitative methods through OLS, FEM, REM models; FGLS and GMM methods, the author will estimate the impact of independent variables on the credit risk of commercial banks
Step 4: Regression test In order to ensure transparent research results, the author conducts related tests such as tests for multicollinearity, autocorrelation, variable variance, and endogeneity
Step 5: Regression analysis and discussion of research results and presentation of research result on factors affecting credit risk of Vietnamese commercial banks; discussion and comparison of results with other studies related experiments
Step 6: Conclusions and recommendations In order to reduce the credit risk of Vietnamese commercial banks
The process is shown in the figure as follow:
Sample and research data
The study uses data from 27 commercial banks in Vietnam, and such data was collected from the consolidated financial statements of commercial banks over the period of 13 years from 2010 to 2022
The research utilizes secondary data to analyze the dependent and independent variables related to microeconomic factors affecting Vietnamese commercial banks This data is sourced from financial statements and annual business performance reports spanning from 2010 to 2022.
Key macroeconomic indicators, including the economic growth rate (GDP), inflation rate (INF), and unemployment rate (UNEMP), are sourced from the annual statistics provided by the General Statistics Office of Vietnam.
Conclusions and recommendations Regression analysis and discussion about research results
Regression test Analyzing the impact of elements affecting the credit risk Overview of theoretical foundations and empirical studies
Identifying the research problem investing.com website from 2010 to 2022.
Research method
This study employs a quantitative approach, specifically multivariable linear regression analysis, to investigate the factors influencing credit risk in Vietnamese commercial banks from 2010 to 2022, utilizing Stata 17.0 software Additionally, it incorporates a panel data regression model that combines the fixed effects model (FEM) and random effects model (REM), along with the Generalized Method of Moments (GMM) to effectively estimate data impacted by endogenous phenomena.
In addition, the study also uses the method of Ordinary Least Square (OLS), multicollinearity test, Hausman test, regression model, and correlation matrix to bring objective results for the study
Multicollinearity, indicated by the Variance Inflation Factor (VIF), arises when two or more variables in a model are correlated, leading to unreasonable estimated coefficients and inefficient estimations To assess multicollinearity, the VIF is used, with a coefficient greater than 10 signifying the presence of this issue To address multicollinearity, it is essential to remove the correlated variables from the model.
3.3.2 Analysis and selection of effective models
The Pooled OLS model is suitable when there are no distinct factors or time elements present In contrast, the Fixed Effect Model (FEM) and Random Effects Model (REM) account for time and separate factors, making them more appropriate for regression analysis To determine the most suitable regression model among these three options, specific tests are employed.
- F-Test: To choose Pooled OLS or FEM model When the P-value is less than or equal to 5%, the FEM model is selected
- Hausman test: To choose between FEM and REM models When the P- value is less than or equal to 5%, the FEM model is selected, otherwise, the REM model is used
The Breusch & Pagan test is utilized to determine whether to use Ordinary Least Squares (OLS) or Random Effects Model (REM) in statistical analysis If the P-value is 5% or lower, the REM model is preferred; otherwise, the OLS model is employed.
Upon choosing the suitable model, the analysis will proceed with the REM model if selected, while the FEM model will lead to further variance testing and autocorrelation assessment.
3.3.3 Feasible Generalized Least Square - FGLS
A correlation matrix is a table that displays the correlation coefficients among multiple variables in a dataset It is commonly utilized before or after conducting Exploratory Factor Analysis (EFA) to assess the relationships between factors and to identify multicollinearity in multivariable linear regression models The strength of the linear relationship is indicated by the Pearson Correlation coefficient (r), which typically ranges from -1 to +1.
- r = 0: Two variables have no linear correlation;
- r = 1 or = -1: Two variables have absolute linear correlation;
A negative correlation coefficient (r < 0) indicates that variables x and y move in opposite directions; when variable x increases, variable y decreases, and vice versa This means that an increase in the value of one variable corresponds to a decrease in the value of the other.
A positive correlation coefficient (r > 0) indicates that variables x and y move in the same direction; when the value of variable x increases, variable y also rises, and conversely, a decrease in variable y corresponds with a decrease in variable x.
3.3.4 Testing and handling defects of the research model
3.3.4.1 Testing for the phenomenon of Heteroscedasticity
H 0 : the model does not have the phenomenon of Heteroscedasticity
H 1 : the model has the phenomenon of Heteroscedasticity
If the P-value is less than or equal to 5% (P-value ≤ 5), then the hypothesis
H 0 is rejected, meaning that the model has the phenomenon of Heteroscedasticity
H 0 : the model does not have autocorrelation
According to Wooldridge (2010), if the P-value is less than or equal to 5% (P-value ≤ 5%), then the hypothesis H 0 is rejected, which means that the model has autocorrelation phenomenon
In cases where a model exhibits autocorrelation and heteroscedasticity, the Feasible Generalized Least Squares (FGLS) method is employed to address these issues However, FGLS is only reliable when the model lacks lagged and endogenous variables In such scenarios, the Generalized Method of Moments (GMM) provides a more precise solution to these problems.
If the P-value is less than or equal to 5%, the author rejects hypothesis H 0 , it means that the variable is endogenous Conversely, if the author accepts hypothesis
H 0 , it means that the variable is exogenous Using the GMM method considered appropriate for panel data, endogenous variables are overcome by the following conditions:
Number of instruments ≤ number of groups
The Hansen and Sargan tests assess the significance level of instrumental variables in econometric models, with the null hypothesis (H0) positing that the instrumental variable is exogenous and uncorrelated with the model's error term A higher P-value indicates stronger evidence in favor of the null hypothesis, suggesting the validity of the instrumental variable.
RESEARCH RESULTS
Descriptive statistical analysis
The study utilizes the descriptive statistical method within STATA 17.0 software, employing the SUM command to analyze the research model's variables This approach provides a comprehensive overview of the total observations, along with key statistics such as the mean, standard deviation, minimum, and maximum values.
Research data is the secondary data collected from 27 Vietnamese commercial banks and the World Bank over the period of 13 years from 2010 to
2022, which is shown through the variables in the statistical table as follows:
Table 4.1 Statistics of variables used in the research model
Variable Obs Mean Std.Dev Min Max
(Source: Analysis results from STATA17.0 software)
Table 4.1 presents the descriptive statistics for independent research variables, revealing that the average credit risk (CR) percentage for 27 Vietnamese commercial banks has been analyzed over a 13-year period from 2010.
In 2022, the credit risk for Vietnamese commercial banks was maintained at a low average of 1.48%, with a range between 0.13% and 20.83% and a standard deviation of 1.66% This indicates that both individual banks and the overall banking system effectively manage and control credit risk, ensuring it remains at a manageable level.
The average bank size (SIZE) is 18.5258, with a maximum of 21.2895 and a minimum of 15.9277, indicating a trend of increasing total assets among commercial banks.
The average equity-to-asset ratio (CAP) in Vietnamese commercial banks is around 9.57%, with a range from 2.7% to 90.77% and a standard deviation of approximately 5.99% This indicates that the equity-to-asset ratio tends to be low across these banks, and there is significant variability among different institutions.
Return on asset (ROA) has an average statistical result of 0.84%, the highest value of ROA is 6.74%, and the lowest value is -1.13%, and the standard deviation of this variable is 1.08%
Return on equity (ROE) has an average statistical value of 10.21% with the maximum value being 26.82% and the minimum value of ROE being -82%, and the standard deviation is about 8.37%
Loans in Vietnamese commercial banks exhibit a significant concentration in customer deposit-based lending, with an average value of 58.2% The maximum recorded value reaches 82.98%, while the minimum stands at 3.07% This data highlights the reliance of 27 Vietnamese banks on customer deposits for their lending activities.
During the research period, the average non-performing loans (NPL) ratio stood at 2.01%, with a range from 0% to 12.46% and a standard deviation of 1.48% This indicates that the average NPL proportion is relatively low, suggesting that Vietnamese commercial banks should strive to maintain this favorable ratio.
Bank profitability (Prof) has an average statistical result of 2.64% with the maximum value of Prof being 8.13% and the minimum value being -0.64%; the standard deviation is about 1.29%
The average GDP growth rate among Vietnamese commercial banks is approximately 5.82%, with rates varying between 2.58% and 8.02%, and a standard deviation of 1.47% This indicates a level of instability in Vietnam's economic growth rates during the research period.
Between 2010 and 2022, the average inflation rate was approximately 5.4%, with fluctuations ranging from 0.63% to 18.68% and a standard deviation of 4.68% This indicates significant variability in the inflation rate year over year, highlighting a relatively high inflation trend during this period.
The average unemployment rate (UNEMP) in Vietnamese commercial banks during the research period is about 2.33%, and the ratio ranges from 1.96% to 3.22% with a standard deviation of 0.34%.
Check for multicollinearity
Table 4.2 Results of multicollinearity test
(Source: Analysis results from STATA17.0 software)
The Variance Inflation Factor (VIF) is utilized to detect multicollinearity within a research model, with a VIF value exceeding 10 indicating potential multicollinearity issues The experimental findings reveal an average VIF of 1.57, with values ranging from 1.07 to 2.18, all of which are below the threshold of 10 Consequently, it can be concluded that the research model does not exhibit multicollinearity.
Correlation analysis
The study examines the relationship between the dependent variable, CR, and several independent variables, including bank size, equity-to-asset ratio, return on assets, return on equity, loans, non-performing loans, bank profitability, economic growth rate, inflation rate, and unemployment rate The findings from the correlation analysis are detailed in the accompanying table.
Table 4.3 Correlation coefficients between research variables
CR SIZE CAP ROA ROE Loans NPL PROF GDP INF UNEMP
(Source: Analysis results from STATA17.0 software)
The size of commercial banks in Vietnam exhibits a negative correlation with credit risk, indicating that as bank size increases, credit risk decreases by 0.0619 Additionally, key financial metrics such as return on assets, return on equity, non-performing loans, and bank profitability also show negative correlations with credit risk, measuring at 0.1001, 0.4036, 0.0050, and 0.1190, respectively Conversely, the equity-to-asset ratio and loans of commercial banks are positively correlated with credit risk, with increases leading to rises in credit risk of 0.0657 and 0.0235, respectively Macroeconomic factors, including economic growth and unemployment rates, are negatively correlated with credit risk at 0.0207 and 0.0522, while the inflation rate shows a positive correlation of 0.1373 The study's findings reveal that the correlation coefficients among the independent variables range from -0.4036 to 0.1373, suggesting the absence of multicollinearity in the research model.
Table Regression Data Analysis (OLS/FEM/REM Model)
The author will implement Ordinary Least Squares (OLS), Fixed Effects Model (FEM), and Random Effects Model (REM) along with various tests, including the F-test, Hausman test, and Breusch-Pagan test, to determine the most suitable model for the study.
Table 4.4 Results of Pooled-OLS, FEM and REM
Variable Pooled OLS FEM REM
Coef P-value Coef P-value Coef P-value Size 0.0022*** 0.008 -0.0076*** 0.001 -0.0001 0.950
(Source: Analysis results from STATA17.0 software)
Selection of estimation method
Pooled OLS and FEM FEM and REM Pooled OLS and REM F-test F (26, 296) = 6.74
Prob > F = 0.0000 < 0.05 Prob > Chi2 = 0.0000 < 0.05 Prob > Chi2 = 0.0000 < 0.05
Conclusion Choosing FEM Choosing FEM Choosing REM
(Source: Analysis results from STATA17.0 software)
From the regression results of Pooled OLS model, FEM model, and REM model with the credit risk, the author compares and selects models as follows:
Firstly, when comparing and evaluating between Pooled OLS model and the
In the analysis using the FEM model, an F-test was conducted, and the results from Stata 17.0, displayed in tables 4.4 and 4.5, indicate a P-value of 0.0000, which is significantly less than the 5% threshold This finding supports the selection of the FEM model over the Pooled OLS model.
To assess the effectiveness of FEM and REM models, the Hausman test was performed The findings indicate a Prob value of 0.0000, which is below the 5% threshold, confirming the acceptance of the FEM model over the REM model.
To determine the appropriate model between Pooled OLS and the Random Effects Model (REM), the author performs the Breusch and Pagan test The results indicate a Prob value of 0.0000, which is less than the 5% significance level (0.05) Therefore, it can be concluded that the REM model is preferred and more suitable than the Pooled OLS model.
In conclusion, through the above tests, the fixed effects model (FEM) is consistent with the research model compared with the Pooled OLS model and the REM model.
Test for heteroscedasticity and autocorrelation
The comparison of the OLS, FEM, and REM models indicates that the Fixed Effects Model (FEM) is the most appropriate for analyzing credit risk Consequently, it is essential to evaluate the research model and identify its shortcomings to enhance the accuracy of the results.
H 0 : The model has no heteroscedasticity
Chi2(27) = 5160.79 Prob > Chi2 = 0.0000 The research model has heteroscedasticity
(Source: Analysis results from STATA17.0 software)
With the significance level of 5%, the result shows that the value of Prob 0.0000 < 5%, so the author rejects hypothesis H 0 , which means that there is a phenomenon of heteroscedasticity
H 0 : The model does not have autocorrelation
If the test results are obtained with Prob > F > 0.05 (5%), the research model will accept H 0 , meaning that the research model has no phenomenon of autocorrelation
F(1, 26) = 349.791 Prob > F = 0.0000 The research model has autocorrelation
(Source: Analysis results from STATA17.0 software)
With the significance level of 5%, the result shows that the value of Prob 0.0000 < 5%, so the author rejects hypothesis H 0 , meaning that the model has autocorrelation.
Overcoming the research model by FGLS
The empirical results and model testing indicate that the Fixed Effects Model (FEM) is the most suitable choice However, the research model exhibits two issues: autocorrelation and variable variance, as identified through the Wooldridge and Modified Wald tests To address these challenges, the author employs the Feasible Generalized Least Squares (FGLS) method in the research model.
Table 4.8 FGLS model troubleshooting results Cross-sectional time-series FGLS regression
Coef Std.Err z P > |z| [95% Conf Interval ] Size 0.0022*** 0.0008 2.72 0.007 0.0006 0.0039
(Source: Analysis results from STATA17.0 software)
The FGLS research model identifies size, ROA, ROE, and INF as significant variables, revealing that ROE negatively impacts credit risk, while size, ROA, and INF positively correlate with it Conversely, other independent variables, including CAP, loans, NPL, Prof, GDP, and UNEMP, do not show statistical significance in relation to credit risk.
From the estimated results, the research model of the elements affecting the credit risk of commercial banks in Vietnam over the period of 13 years from 2010 to 2022 as follows:
CR = -0.0208 + 0.0022***Size - 0.0046CAP + 0.3983***ROA - 0.1308***ROE + 0.0048LOANS - 0.0728NPL + 0.0651PROF - 0.0108GDP + 0.0836***INF - 0.1924UNEMP +
Test for endogenous variable and GMM regression model method
If the P-value is less than or equal to 5%, then the author rejects hypothesis
H 0 , meaning that the variable is endogenous or vice versa If the P-value is greater than or equal to 5%, the author accepts hypothesis H 0 , which means that the variable is exogenous
Table 4.9 Endogenous and exogenous variables in the research model
(Source: Analysis results from STATA17.0 software)
The analysis conducted using Stata 17.0 software reveals a research model comprising three endogenous variables: the equity-to-asset ratio (CAP), return on assets (ROA), and return on equity (ROE) Additionally, the model includes seven exogenous variables: bank size (SIZE), loans, non-performing loans (NPL), bank profitability (Prof), economic growth rate (GDP), inflation rate (INF), and unemployment rate (UNEMP).
Utilizing the FGLS method, the author will implement endogenous control via the GMM regression technique after addressing the research model, with the experimental results from the GMM regression model detailed in the following table.
Table 4.10 GMM endogenous test results
Arellano-Bond test for AR(2) in first differences Prob > z = 0.337
Sargan test of overid restrictions Prob > chi2 = 0.889
Hansen test of overid restrictions Prob > chi2 = 0.169
CR Coef Std Err z P > |z| [95% Conf Interval ]
(Source: Analysis results from STATA17.0 software)
The GMM model analysis reveals that the number of instruments (20) is less than the number of groups (27), indicating a well-structured model The Arellano-Bond test yields a P-value of 0.337, surpassing the 0.1 threshold, which confirms the absence of autocorrelation Furthermore, the Sargan test shows a P-value of 0.889, indicating the appropriateness of the instrumental variable and the presence of an endogenous phenomenon Additionally, the Hansen test presents a P-value of 0.169, affirming the suitability of the tools utilized in the model In summary, the GMM regression model meets all four essential conditions, demonstrating its suitability, efficiency, and high accuracy.
The analysis reveals that after accounting for endogenous variables, the GMM model yields distinct results compared to the FGLS model, indicating that the GMM regression provides more comprehensive insights Consequently, the GMM method is utilized to conclude the research on the determinants of credit risk in Vietnamese commercial banks over a 13-year period from 2010 to 2022, as demonstrated by the following equation.
CR = - 0.0575** + 0.9662***L1.CR + 0.0023*Size - 0.0348CAP + 0.1983**ROA - 0.1107**ROE + 0.0118**LOANS - 0.0523*NPL + 0.2190PROF + 0.0514***GDP + 0.0712***INF + 0.3276***UNEMP +
CONCLUSIONS AND RECOMMENDATIONS
Conclusions about the study
Understanding the elements that affect credit risk is crucial for commercial banks and the Vietnamese banking system This study investigates ten key factors influencing credit risk in Vietnamese commercial banks, which include both bank-specific variables and macroeconomic indicators The factors analyzed are bank size, return on equity (ROE), equity-to-asset ratio (CAP), return on assets (ROA), total loans, non-performing loans (NPL), bank profitability, economic growth rate (GDP), inflation rate (INF), and unemployment rate (UNEMP).
Based on unbalanced panel data with 333 observations of 27 commercial banks operating in the Vietnamese stock market over the period of 13 years from
From 2010 to 2022, research data was processed and analyzed using STATA17 software, with empirical results presented through descriptive statistical analysis and various estimation methods, including Pooled-OLS, FEM, REM, FGLS, and GMM regression models Additionally, experimental research findings were validated through tests such as the F-test, Breusch-Pagan test, and Hausman test to ensure the reliability of the results This study effectively identifies the determinants of credit risk in Vietnamese commercial banks.
Research on Vietnamese commercial banks reveals that bank-specific factors such as bank size, return on assets, and loans positively influence credit risk Conversely, non-performing loans and return on equity negatively affect credit risk Additionally, macroeconomic variables—including economic growth rate, inflation rate, and unemployment rate—also show a positive relationship with the credit risk faced by these banks.
In summary, the study successfully met its general and specific objectives outlined in chapter 1, while also addressing the research questions posed The empirical findings from the research model have clarified the factors influencing credit risk in commercial banks in Vietnam over the past 13 years.
Some suggested solutions
The thesis presents a research model and methodology based on theoretical and empirical findings, indicating that both microeconomic and macroeconomic factors influence credit risk in Vietnamese commercial banks Key elements such as bank size, return on assets, loan volume, economic growth rate, inflation rate, and unemployment rate positively impact credit risk, while non-performing loans and return on equity exhibit a negative relationship with it To effectively manage and reduce credit risk in these banks, the author proposes several strategic solutions.
Commercial banks must prioritize the management and monitoring of credit risk, focusing on improving risk management capabilities for medium and long-term loans Additionally, they should implement effective strategies to address non-performing loans and encourage timely debt repayment from customers.
To effectively manage credit risk, Vietnamese commercial banks must strictly control their size Empirical research indicates that larger banks tend to face increased credit risks Consequently, banks should assess their current resources, financial capabilities, and staff quality to determine the optimal size for operations that minimizes various risks.
Vietnamese commercial banks must develop a loan growth policy that aligns with their risk management capabilities It is essential for these banks to monitor the purposes for which customers use their loans to ensure proper allocation of funds As they seek to expand their credit policies, strengthening management practices is crucial Additionally, banks should prioritize the monitoring of lending conditions and the underwriting processes To effectively mitigate risks, enhancing the underwriting skills of their staff is also necessary.
Commercial banks can raise capital by issuing stocks, allowing them to sell shares to strategic partners, clients, and shareholders It is crucial for these banks to carefully evaluate effective capital-raising strategies and identify the optimal timing for stock issuance, as this can significantly enhance the benefits and rights of their shareholders.
Vietnamese commercial banks need to establish a lending policy that effectively balances profitability with credit risk It's essential for banks to assess their management capabilities to create suitable lending strategies Additionally, they must identify the types of businesses and customers they can lend to while ensuring effective management of these loans While setting higher lending rates may benefit banks, they must also evaluate their customers' ability to repay the loans.
Vietnamese commercial banks face challenges in managing macroeconomic fluctuations, making it essential for them to conduct regular assessments and forecasts to effectively respond to unexpected economic changes, particularly concerning inflation and unemployment rates Rising inflation and unemployment can adversely impact all sectors and individuals, complicating business operations and potentially increasing banks' bad debt Additionally, a decline in investment demand and enterprise expansion can weaken the operational capacity of these banks, heightening credit risk Therefore, maintaining a proactive stance enables commercial banks to safeguard their assets, sustain growth, and effectively manage credit risk throughout their operations.
The study indicates a positive correlation between GDP and the credit risk of Vietnamese commercial banks Despite stagnant GDP growth during the Covid-19 pandemic, the State Bank implemented effective policies to extend non-performing loans and adjust interest rates, facilitating economic recovery These measures, along with increased credit limits, allowed commercial banks to achieve notable growth despite challenging economic conditions Consequently, it is crucial for Vietnamese commercial banks to focus on addressing bad debt issues and managing loan terms to prevent inflation resulting from an inability to recover the money supply.
Limitations of the graduate thesis and direction for future research
Firstly, the study is conducted over the period of 13 years, between 2010 and
In 2022, research data from 27 commercial banks indicates that comprehensive conclusions regarding the credit risk of banks operating in the Vietnamese stock market cannot be definitively drawn.
The author primarily concentrates on assessing the bank-specific factors and macroeconomic elements influencing the credit risk of commercial banks However, the analysis lacks consideration of both subjective and objective groups that contribute to the rising credit risk in Vietnamese commercial banks, limiting the ability to propose effective risk prevention measures.
Finally, these days, there are 31 Vietnamese commercial banks However, the author only selected 27 commercial banks to serve the research process
The research data utilized in this study is somewhat limited, and certain Vietnamese commercial banks may be excluded during the implementation phase, potentially affecting the overall research outcomes.
Future studies should extend the research duration and categorize banks into distinct groups, such as state-owned commercial banks, fully foreign-owned commercial banks, and joint-venture banks, to enhance the accuracy and effectiveness of the research outcomes.
Future research should incorporate various tests and additional factors influencing the credit risk of commercial banks in Vietnam to enhance the rigor and accuracy of the study model.
To effectively mitigate risks and negative impacts, future research should focus on identifying both subjective and objective factors that contribute to the rising credit risk faced by commercial banks.
In Chapter 5, the author summarizes the research findings and offers recommendations for Vietnamese commercial banks to reduce credit risk These results aim to serve as a valuable reference for future research, as well as for bank managers and strategic planners Additionally, the author addresses the limitations of the study that were not explored.
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APPENDIX APPENDIX 1: COMMERCIAL BANKS IN THE THESIS
Number Stock code Name of commercial bank
1 ABB An Binh Commercial Joint Stock Bank
2 ACB Asia Commercial Joint Stock Bank
3 BAB Bac A Commercial Joint Stock Bank
4 BaoVietBank Bao Viet Joint Stock Commercial Bank
5 BID Joint Stock Commercial Bank for Investment and
6 BVB Viet Capital Commercial Joint Stock Bank
7 CTG Vietnam Joint Stock Commercial Bank of Industry and Trade
8 EIB Vietnam Export Import Commercial Joint Stock
9 HDB Ho Chi Minh city Development Joint Stock
10 KLB Kien Long Commercial Joint Stock Bank
11 LPB LienViet Commercial Joint Stock Bank
12 MBB Military Commercial Joint Stock Bank
13 MSB The Maritime Commercial Joint Stock Bank
14 NAB Nam A Commercial Joint Stock Bank
15 NCB National Citizen Commercial Joint Stock Bank
16 OCB Orient Commercial Joint Stock Bank
17 PGB Petrolimex Group Commercial Joint Stock Bank
18 SGB Saigon Bank For Industry And Trade
19 SCB Saigon Commercial Joint Stock Bank
20 SSB Southeast Asia Commercial Joint Stock Bank
21 STB Saigon Thuong Tin Commercial Joint Stock Bank
22 TCB Vietnam Technological and Commercial Joint Stock
23 TPB Tien Phong Commercial Joint Stock Bank
24 VAB Vietnam - Asia Commercial Joint Stock Bank
25 VPB Vietnam Prosperity Joint Stock Commercial Bank
26 VCB Joint Stock Commercial Bank for Foreign Trade of
27 VIB Vietnam International Commercial Joint Stock Bank
APPENDIX 3: RESULTS OF POOL-OLS, FEM, REM, FGLS,
AND GMM Table 3.1 Results of Pool-OLS model
Table 3.2 Results of FEM model
Table 3.3 Results of REM model
Table 3.4 Results of FGLS model
Table 3.5 Results of GMM model
APPENDIX 4: TESTS ESTIMATING MODELS Table 4.1 VIF test
Code Year CR Size CAP ROA ROE LOANS NPL PROF GDP INF UNEMP