Research results, based on a data set of 22 Vietnamese commercial banks, show that credit risk has a negative impact on not only the stability but also the profitability of banks.. The r
INTRODUCTION
A bank is a financial institution that facilitates customer deposits, provides loans, and offers various monetary services, including currency exchange and credit cards It plays a crucial role in helping central banks achieve national monetary policy objectives and has been instrumental in human, social, and economic development In a well-functioning economy, banks serve as intermediaries, channeling investment funds to high-return opportunities, thereby enhancing specialization and labor division, which drives economic growth In today's era of integration and globalization, the stability of banks is paramount for financial development and is a key factor in GDP growth The preservation of financial system stability has long been central to bank supervision and regulation, gaining increased attention following the 2007/2008 global financial crisis, which highlighted the significant role major banks played in the economic downturn.
The recent global financial crisis highlights the importance of maintaining stability within the banking system, particularly in countries like Vietnam, where banks are vital for funding private enterprises and economic growth Understanding the factors that influence bank stability is essential for effective assessment and management of the banking sector Recent studies have focused on bank risk-taking channels, emphasizing the need for a comprehensive approach to ensure financial stability in the face of economic downturns.
This study enhances understanding of the factors influencing bank stability, particularly in emerging economies like Vietnam By comparing bank behaviors across various country datasets, it identifies key elements affecting bank stability Findings reveal that bank size, capital structure, profitability (measured by return on equity), solvency, credit risk (indicated by non-performing loans), inflation, and GDP positively contribute to bank stability Conversely, credit risk and solvency are found to negatively impact bank stability.
Financial risks faced by banks include liquidity risk from sudden withdrawals, credit risk from borrowers defaulting, interest rate risk due to fluctuations in rates, and operational risk from system failures or disasters (Cecchetti & Schoenholtz, 2014) Banks primarily function by creating deposit and loan accounts, making lending and credit operations their main sources of income (Njanike, 2018) Consequently, credit risk significantly affects both the effectiveness and stability of financial institutions.
Banking crises often arise from macroeconomic shifts, including GDP decline, rising unemployment, and fluctuations in interest rates and inflation, all of which significantly impact credit risk (Festić, Kavkler and Repina, 2011; Nkusu, 2011) Credit risk poses a critical challenge for banks and the broader economy, directly affecting capital resources and increasing the likelihood of bank insolvency Therefore, effective credit risk management is essential, particularly for developing market countries Additionally, banking factors such as total assets, scale, bad debt, and liquidity further influence credit risk alongside macroeconomic conditions.
Since the initiation of political and economic reforms in 1986, Vietnam's banking sector has emerged as a crucial driver for the development of its financial system and has contributed to an impressive economic growth rate of approximately 7% This growth has been influenced by a historically weak governance system and fluctuations in macroeconomic conditions preceding global financial impacts.
Since 2012, the Vietnamese commercial banking system has been restructuring to mitigate credit risk, reduce bad debts, and enhance governance in line with international standards, aiming to improve overall business efficiency Despite these efforts, profits in the banking sector significantly declined from 2012 to 2015, primarily due to high levels of bad debts and an economic downturn, with the bad debt ratio reaching 17.2%, far exceeding the acceptable limit of 2% However, the past year has brought notable improvements in the banking sector's stability and development, with a recent State Bank of Vietnam survey indicating stable loan growth of 8-9% and a 9-10% increase in capital mobilization.
This study explores the critical relationship between credit risk and bank stability within the context of Vietnam's recovering economy post-2008-2009 financial crisis It aims to assess whether credit risk has a positive or negative impact on bank stability, alongside other influencing factors By analyzing these relationships, the research seeks to determine how credit risks and various drivers contribute to bank instability Ultimately, the study will provide recommendations for mitigating credit risk and enhancing operational efficiency in Vietnamese banks.
To approach these research goals, the study raises the following research questions:
What factors influence bank stability?
What is the relation between credit risk and bank stability?
What should be the solutions to restrain credit risk and enhance the Vietnamese bank stability?
Object and scope of the research
The object of this research is the factors that affecting bank stability and specifically the impact of credit risk on the stability of Vietnamese banks
This study utilizes various statistical methods, including pooled ordinary least squares (OLS), fixed effects model (FEM), random effects model (REM), and generalized least squares (GLS), to effectively analyze its objectives The use of GLS is particularly important as it addresses common issues found in conventional models, such as multicollinearity, heteroscedasticity, and autocorrelation.
This research examines the factors influencing credit risk and the stability of Vietnamese banks, utilizing data from the financial statements of 22 banks and macroeconomic indicators from the Asian Development Bank (ADB) spanning eleven years, from 2012 to 2022.
LITERATURE
The definition and theory of credit risk
Banks are inherently exposed to credit risk, which is defined as the potential loss of net revenue and credit capital due to customers' non-payment or late payment, as outlined by Timothy W Koch (1995) The Basel Committee (2006) states that credit risk arises when a loan customer or counterparty fails to fulfill their contractual obligations A bank's risk of loss stems from the obligor's breach of contract, specifically any significant failure to repay debt and interest Effectively managing credit risk is crucial for banks, as their success largely depends on accurately assessing and addressing this risk compared to other challenges they encounter.
According to Circular 02/2013/TT-NHNN issued by the State Bank of Vietnam on January 21, 2013, the classification of assets, along with the levels and methods for establishing risk provisions, is outlined for credit institutions and foreign bank branches It defines credit risk as the potential for a borrower to fail to repay part or all of a loan as agreed upon.
Credit risk refers to the extent to which the value of debt instruments and derivatives changes due to shifts in the credit quality of borrowers and counterparties It constitutes a significant portion of banking risks and is a leading factor in economic downturns, serving as a crucial indicator of financial vulnerability Consequently, empirical research on the determinants of credit risk is vital for ensuring a stable economy.
Credit risk primarily arises from factors such as limited institutional capacity, inappropriate credit policies, volatile interest rates, and poor management Other contributing elements include inadequate laws, low capital and liquidity levels, direct lending practices, excessive bank licensing, poor loan underwriting, lax credit assessments, ineffective lending practices, government interference, and insufficient supervision by central banks (Kithinji, 2010) Additionally, lending to debtors with insufficient knowledge can further elevate credit risk.
Credit risk arises from various internal and external factors, as highlighted by Ghosh (2012) Internal causes within banks often include overly lenient credit decisions, ineffective credit management, and borrower reluctance to repay debts On the other hand, external factors such as macroeconomic instability, deteriorating economic conditions, and unfavorable developments in external markets significantly impact borrowers These adverse macroeconomic conditions can reduce borrowers' income sources, increasing the likelihood of default Additionally, changes in fiscal policy, money supply, and import-export dynamics further contribute to the complexities of credit risk management.
6 policy, trade restriction policy, or changes in the financial market will also affect a company's credit portfolio bank
External factors can trigger an economic recession, characterized by slowed economic activity, decreased product volume, and reduced revenue for enterprises, leading to diminished demand for goods and services This downturn also negatively impacts the value of banks' credit portfolios Conversely, during an economic boom, production increases, demand rises, and businesses enjoy higher profits, making it easier for borrowers to repay loans and lowering the risk of default Consequently, credit risk escalates during a recession and declines during a boom.
Internal factors related to the borrower and their business significantly influence a bank's credit risk Key elements include business risks, financial management practices, limitations in technical processes, management experience, and inadequate inventory management These issues can lead to decreased production efficiency and product quality, ultimately reducing the borrower's income and raising the likelihood of default.
Besides, the borrower's dishonesty and unethical attitude are also one of the main causes of credit risk
Thus, the causes from external or internal factors, from the borrower's side, affect credit risk
In addition, reasons such as the effectiveness of the legal system, economic and political environment affect the granting of credit.
The bank stability and its determinant
Financial stability is essential for global social and economic development, as highlighted by various studies (Adusei & Elliott, 2015; Ali & Puah, 2018; Dao et al., 2020; Shair et al., 2021; Yin, 2019) Achieving this stability involves ensuring that all banks within the banking sector remain stable, which in turn helps prevent banking crises (Brunnermeier et al., 2009).
Banking interdependence is characterized by the stability of banks linked through direct channels like interbank deposits and syndicated loans, or indirectly via shared industry lending and proprietary transactions (Segoviano & Goodhart, 2009) Additionally, banks face scrutiny not only from political, legislative, regulatory, and management bodies but also from customers who are increasingly focused on the long-term viability of bank operations The determinants of banking stability and their effects on financial system stability vary across nations, prompting national bank supervisors to seek a deeper understanding of these factors.
Empirical research highlights various economic, financial, regulatory, and institutional factors that affect banking stability, with studies in Vietnam and globally showing that both internal and external elements play a significant role Jahn and Kick (2012) define financial stability in the banking system as a condition where it effectively fulfills its roles, including resource allocation, risk dispersion, and income distribution Therefore, the effective operation and profitability of banks are crucial to maintaining overall banking stability.
This paper examines the impact of credit risk on bank stability while also considering the influence of various internal and external factors, including bank size, solvency, capital structure, and macroeconomic indicators such as GDP and inflation.
Bank size refers to the scale and scope of a bank's operations, measured through indicators like total assets, revenue, net income, market capitalization, branch count, employee numbers, and market share The size of a bank significantly influences its performance, risk profile, efficiency, and overall impact on the financial market.
The competitiveness and stability of banks are significantly influenced by their size, as larger banks may pose greater risks to the financial system due to their interconnectedness and systemic importance The "too-big-to-fail" theory highlights the dangers associated with large banks, which may benefit from government guarantees that reduce their incentive to manage risks effectively, potentially leading to moral hazard and adverse selection issues Conversely, economies of scale allow larger banks to achieve lower costs and higher profits through production efficiencies and market power, although they may also encounter diseconomies of scale related to complexity and bureaucracy Additionally, larger banks wield considerable market power, impacting competition and efficiency within the banking sector by creating entry barriers and influencing regulatory practices Understanding the implications of bank size is essential for practitioners and researchers, as it affects banking performance and stability amidst various factors, including management, regulation, and macroeconomic conditions.
Turning to the capital structure, this factor refers to the way a company finances its operations and investments by utilizing a combination of equity (shares) and debt (loans, bonds, etc.) It
9 represents the composition of a company's long-term capital, including the proportion of equity and debt used to fund its activities
Solvency refers to a bank's capacity to fulfill its long-term financial obligations, indicating the sufficiency of its capital and assets to cover liabilities A key measure of solvency is the ratio of total equity to total assets; a higher ratio signifies greater solvency, suggesting that the bank possesses more equity than assets, which results in lower leverage This solvency indicator is crucial as it can highlight potential risks, including the likelihood of bank failures.
The relationship between bank credit risk and stability is influenced by systemic theory and firm diversification While some theories posit that credit risk transfer—through methods like securitization or credit derivatives—can bolster bank stability by mitigating credit loss exposure, diversifying assets, enhancing liquidity, and reducing funding costs, others contend that it may jeopardize stability Critics argue that credit risk transfer can complicate the financial system, introduce moral hazard and adverse selection issues, diminish banks' monitoring and screening incentives, and escalate systemic risk.
Gross Domestic Product (GDP) measures the total value of goods and services produced within a country over a specific timeframe, serving as a key indicator of economic growth and development A rising GDP signifies heightened economic activity and increased income levels, reflecting greater production and consumption of goods and services.
Empirical review
Credit risk significantly impacts the stability of banks, as it is a core activity that constitutes a substantial portion of their operations Numerous studies have explored the effects of credit risk on banks from various perspectives Research by Yong Tan and Christos Floros in 2012 indicates that an increase in credit risk correlates with a rise in bad debts, which in turn reflects a decrease in bank stability.
A study by Ogboi et al (2013) examined the impact of credit risk management and capital sufficiency on the profitability ratios of Nigerian commercial banks, analyzing financial data from six banks between 2004 and 2009 Utilizing a panel data model, the research assessed the relationships among loan loss provisions, loans and advances, bad debts, capital sufficiency, and return on assets (ROA) The results indicated that loan loss provisions, bad debts, and capital adequacy positively influence a bank's ROA, while loans and advances negatively affect the profitability ratio during the study period.
Numerous studies indicate that bank-specific factors significantly impact bank stability Adusei (2015) highlights that bank size, measured by the natural logarithm of total assets or deposits, plays a crucial role in enhancing financial market stability Ali and Puah (2018) examined the effects of bank size on five Islamic and nineteen conventional banks in Pakistan, finding that a higher loans-to-assets ratio positively correlates with bank stability Consequently, an increase in credit risk can lead to improved bank stability, as supported by both Adusei (2015) and Ali & Puah (2018).
In 2011, Kargi examined the impact of credit risk on the profitability of Nigerian banks, utilizing financial ratios derived from annual reports spanning 2004 to 2008 The study employed descriptive, correlation, and regression methods to analyze the data The findings revealed that effective credit risk management significantly influences the profitability of these banks.
11 that the profitability of banks was negatively related to the levels of loans and advances, non- performing loans and deposits, which made them more vulnerable to illiquidity and distress
Zou et al (2014) analyzed the impact of credit risk management on the profitability of commercial banks, using return on equity (ROE) and return on assets (ROA) as profitability indicators and the bad debt ratio and capital adequacy ratio as measures of credit risk management Their study, which spanned from 2007 to 2012 and included data from 47 leading European banks, found that a higher bad debt ratio negatively affects profitability, while the capital adequacy ratio does not influence profitability Similarly, Felix and Claudine (2008) revealed an inverse relationship between bank profitability and the ratio of non-performing loans to total loans, indicating that increased non-performing loans lead to reduced profitability Ahmad and Ariff (2007) focused on credit risk factors in emerging market banking systems, highlighting the importance of regulation and management quality in banks that primarily offer loans Their findings suggested that credit risk is more pronounced in emerging markets compared to developed ones, with rising loan loss provisions serving as a strong indicator of potential credit risk.
A study by Bhattarai (2016) analyzed the impact of credit risk on the profitability ratios of Nepalese commercial banks using a regression model with data from 14 banks between 2010 and 2015 The findings indicate that a higher bad debt ratio negatively affects profitability, while the cost per loan asset positively influences it Additionally, the size of the bank is found to have a favorable effect on profitability ratios.
12 there is no statistical evidence that the capital adequacy ratio and cash reserves have an impact on the bank's profitability ratio
A study by Boahene et al (2012) examines the relationship between credit risk and profitability in six commercial banks in Ghana from 2005 to 2009 The researchers evaluated credit risk using three key metrics: bad debt ratio, net charge-off rate, and profit ratio before provisioning relative to total outstanding debt, with return on equity (ROE) as the dependent variable The panel data regression analysis reveals a positive correlation between credit risk and bank performance, suggesting that Ghanaian banks maintain high profitability despite facing considerable credit risk.
In a study by Alshatti (2015), a panel data model was utilized to investigate the relationship between credit risk measurement factors and the performance of commercial banks in Jordan, specifically focusing on return on assets (ROA) and return on equity (ROE) The results revealed that the ratio of bad debt to total outstanding debt positively influences bank profitability.
Nguyen Thanh Dat et al (2021) examined the impact of credit risk on the performance of Vietnamese commercial banks by surveying 30 joint-stock banks from 2007 to 2019 Utilizing a regression model with panel data and the Hausman test, the study assessed the effects of human resources and bad debt ratios on bank performance The results revealed a significant positive correlation between credit risk indicators and bank profitability, alongside a relationship between bank size and performance Based on these findings, the authors propose solutions to mitigate the effects of credit risk on the profitability of commercial banks in Vietnam.
Between 1998 and 2008, Al-Khouri (2011) analyzed the influence of specific risk characteristics and the overall banking environment on the performance of 43 commercial banks in six Gulf Cooperation Council (GCC) countries The study employed fixed effect regression analysis, revealing significant findings related to credit risk, liquidity risk, and capital risk.
Key factors influencing bank performance include return on assets and liquidity risk, which notably affects profitability as measured by return on equity Research by Ben-Naceur and Omran (2008) highlights that bank capitalization and credit risk significantly enhance net interest margin, cost efficiency, and profitability in MENA countries from 1989 to 2005, emphasizing the role of bank regulations and financial development Additionally, Ahmed, Takeda, and Shawn (1998) found that loan loss provisions positively impact non-performing loans; however, an increase in these provisions indicates higher credit risk and deteriorating loan quality, ultimately harming bank performance.
Bank stability is significantly influenced by various factors, with bank size being a key determinant Research indicates a two-way correlation between bank size and stability, revealing that larger banks benefit from increased market share, dominance, and higher revenues, which in turn enhances their stability (Martin Cihák & Heiko Hesse, 2008; Luc Laeven et al., 2014; Boyd et al., 2004) However, other studies suggest that large banks often engage in high-risk ventures that can jeopardize their stability (Mirzaei et al., 2013; Fu et al., 2014; Pak & Nurmakhanova, 2013) Larger institutions can diversify their lending and investment portfolios, invest in technology, and cultivate a strong reputation, leading to high consumer and investor confidence (Tarek Al-Kayed et al., 2014) Consequently, the scale of a bank positively impacts its profitability, while specialization helps major banks reduce operational costs (Gupta & Mahakud, 2020; Nguyen & Nguyen, 2016; Sufian, 2011).
Research indicates that better capitalized banks are often assumed to face lower bankruptcy costs, resulting in decreased capital costs and enhanced profitability However, studies focusing on banks in the Sub-Saharan region from 2000 to 2006 reveal that capital structure does not significantly affect bank effectiveness Instead, a notable negative relationship exists between bank profitability and capital structure, as highlighted by Adesina et al (2015) and Sufian & Habibullah (2009), with further confirmation from Amidu (2007).
Wassim Rajhi and Slim A Hassairi (2013) emphasize that a bank's solvency is directly linked to its safety, indicating that higher solvency leads to reduced asset loss This highlights a positive correlation between solvency and bank stability.
Most research on banking stability has been conducted within specific macroeconomic contexts, focusing on the impact of various economic factors such as GDP, inflation, exchange rates, and government policies These factors have been assessed for their dual effects, highlighting both positive and negative influences on banking stability.
THE OVERVIEW OF VIETNAMESE BANK STABILITY IN THE PERIOD FROM 2012
A study by Nguyen et al examined the factors influencing bank stability in Vietnam, focusing on 22 commercial banks from 2012 to 2022 Utilizing Bayesian linear regression, the research identified key factors such as bank size, credit growth, liquidity ratios, income diversification, loan to total assets ratio, inflation rate, and GDP growth The findings revealed that bank size, credit growth, and liquidity ratios significantly contribute to enhancing bank stability in the region.
16 while income diversification and loan to total assets ratio had a negative impact Inflation rate also had a negative effect on bank stability, while GDP growth had a positive effect
A study by Le et al revealed that the COVID-19 pandemic significantly affected financial stability in Vietnam, particularly during the outbreak's early stages Analyzing daily data from January 23, 2020, to June 30, 2022, and utilizing VECM and NARDL models, the research explored the pandemic's impact on the interbank and stock markets Findings showed that the pandemic led to decreased interbank lending and borrowing rates, along with a decline in the stock market index, both in the short and long term However, the adverse effects of the pandemic diminished over time, and an asymmetric relationship between the financial market and the pandemic was identified in both time frames.
A dataset by Tran et al reveals significant changes in the Vietnamese banking system from 2002 to 2021, covering key statistics from 45 banks It includes vital information on deposits, loans, assets, and labor productivity, alongside macroeconomic indicators like GDP growth, inflation, exchange rates, and interest rates This dataset serves multiple purposes, enabling analysis of bank performance, efficiency, profitability, and stability, while also assessing the influence of macroeconomic factors on the banking sector.
In 2020, the Vietnamese banking sector encountered significant challenges due to the COVID-19 pandemic, including liquidity pressures, decreased credit growth, increased provisioning, and diminished profitability Despite these obstacles, the sector demonstrated resilience and adaptability, bolstered by the prompt and effective actions of the government and central bank, along with the strong fundamentals and promising business outlook of the banks.
Vietcombank, one of the largest and most reputable banks in Vietnam, achieved impressive results in 2020, despite the adverse impacts of the pandemic The bank reported a total
In 2022, Vietcombank reported a revenue of VND 132.8 trillion, reflecting a 10.3% increase from 2019, alongside a pre-tax profit of VND 23.1 trillion, which rose by 12.3% compared to the same year The bank showcased strong asset quality, achieving a low non-performing loan ratio of just 0.62% and maintaining a robust capital adequacy ratio of 11.3% Additionally, Vietcombank improved its brand value, climbing to 207th place in the Brand Finance Banking 500 list, up from 325th in 2019.
The Vietnamese banking sector is poised for recovery and growth in 2021, driven by an economic rebound from the pandemic and rising credit demand The World Bank forecasts a credit growth rate of 12% for Vietnam in 2021, surpassing the estimated 10.1% in 2020 Additionally, the sector stands to gain from ongoing digital transformation, advancements in fintech and e-commerce, and increased integration into regional and global markets.
METHODOLOGY
Data
This study analyzes financial data sourced from the Finance.vietstock.vn database, along with the annual reports and audited financial statements of 22 Vietnamese commercial banks The selected banks are those that comply with legislation by providing comprehensive public reporting, ensuring credible income statements, balance sheets, and other relevant financial and non-financial data The observation period for this analysis is specified.
Panel data analysis enhances estimator efficiency by utilizing a larger number of observations, allowing for more accurate estimations This approach effectively manages unobserved time-invariant heterogeneity, such as cultural traits and organizational differences, while also enabling the examination of individual behavior dynamics that cannot be captured through cross-sectional data.
Methodology
This research employs panel data analysis to identify the factors influencing the financial stability of commercial banks and to assess the impact of credit risk on the banks' return on assets (ROA) and return on equity (ROE) The study utilizes the Pooled Ordinary Least Squares (Pooled OLS), Fixed Effect Model (FEM), and Random Effect Model (REM) for its analysis The F test is conducted to differentiate between the FEM and Pooled OLS models, while the Hausman test is used to select between the FEM and REM models Following this, the Modified Wald test for heteroskedasticity and the Wooldridge test for autocorrelation are applied to the chosen models If issues of autocorrelation or heteroskedasticity are detected, the Feasible Generalized Least Squares (FGLS) model is employed to address these concerns The analysis is performed using Stata 13.0 software.
Pooled Ordinary Least Squares (OLS) requires stationary and balanced panel data, assuming constant coefficients for slopes and intercepts across time and individuals, which can obscure true relationships among variables by neglecting individual effects in the error term (Gil-Garcia & Puron-Cid, 2015) This method pools data for analysis using OLS, but Wooldridge (2010) cautions that it should only be applied when the sample for each time period differs However, pooled OLS has significant drawbacks, including biases due to serial correlation and heteroskedasticity in error terms (Podesta, 2000) Given the similarity of samples across individuals in this study, it is advisable to utilize Fixed Effect Model (FEM) and Random Effect Model (REM) for more accurate results.
The Fixed Effects Model (FEM) addresses the limitations of Pooled Ordinary Least Squares (OLS) by emphasizing individual differences, allowing for a clearer examination of the relationship between outcome and explanatory variables within specific entities, such as countries or companies This approach enables researchers to analyze the influence of time-varying variables while excluding time-invariant factors, like gender, due to collinearity issues (M Wooldridge, 2010) Consequently, FEM facilitates the assessment of the net impact of independent variables on dependent variables.
Unlike Fixed Effects Models (FEM), Random Effects Models (REM) account for random fluctuations among entities, treating them as uncorrelated with the variables REM also permits the inclusion of time-invariant variables, which FEM absorbs into intercepts, allowing these variables to be fully represented in the analysis.
This study employs three regression analysis methods and conducts essential robustness tests, including the Hausman Test for selecting between Random Effects Model (REM) and Fixed Effects Model (FEM), the Variance Inflation Factor (VIF) test for multicollinearity, the Lagrange Multiplier (LM) test for heteroskedasticity, and the Wooldridge Test for autocorrelation Ultimately, only the most effective results will be presented.
FGLS modeling involves two key stages: initially, the model is estimated using OLS or another consistent but inefficient estimator, from which residuals are obtained to create a consistent estimator of the error covariance matrix This process often requires additional constraints, particularly if the errors exhibit a time series process, necessitating theoretical assumptions for a reliable estimator Subsequently, the consistent error covariance matrix allows for the application of GLS concepts.
Generalized Least Squares (GLS) is more efficient than Ordinary Least Squares (OLS) when dealing with heteroscedasticity or autocorrelation; however, Feasible Generalized Least Squares (FGLS) may not always be superior (Baltagi, B H., 2008) While FGLS offers asymptotic efficiency when the error covariance matrix is accurately estimated, it can underperform compared to OLS in small to medium-sized samples Consequently, some researchers opt for OLS and adjust their variance estimates to account for heteroscedasticity or serial correlation For larger samples, FGLS is generally favored over OLS when faced with these issues (Hansen, Christian B., 2007).
FGLS is a versatile method applicable to various data formats, such as cross-sectional, time series, panel, and spatial data, where error correlations may vary It effectively addresses heteroskedasticity, indicating differences in error variances Commonly used in econometrics and applied statistics, FGLS enhances the accuracy of statistical analyses.
Variables
Research by Nicolae Petria (2013), Hasan Ayaydin (2014), and Aremu Mukaila Ayanda (2013) highlights that bad debt significantly affects the stability and profitability of commercial banks Building on their findings, this study develops a model to examine the influence of credit risk on bank stability, utilizing return on equity (ROE) as a measure of profitability and return on assets (ROA) to assess stability The model incorporates the bad debt ratio (NPL) as the primary variable for credit risk, along with several controllable variables, and employs a multivariate regression analysis to derive its conclusions.
Model 1: ROA = β0 + β1* CSTRUCTURE + β2* SIZE + β3* NPL + β4*
Model 2: ROE = β0 + β1* CSTRUCTURE + β2* SIZE + β3* NPL + β4*
ROA: return on total assets of the bank;
CSTRUCTURE: Capital structure, measured by total debt to total asset;
SIZE: Bank size, calculated by the natural logarithm of the bank's total assets; NPL: represented credit risk, which is calculated by total loans to total assets;
Solvency: measured by debt-to-equity (D/E) ratio;
GDP: Gross Domestic Product Growth Rate INF: Inflation index β0, β1, β2, …B6: are coefficients of regression u: is error term a Dependent variables
This paper utilizes Return on Assets (ROA) as the dependent variable to assess bank stability while exploring the impact of various determinants, particularly credit risk, on bank profitability To represent bank profitability, Return on Equity (ROE) is selected as the independent variable.
Banks possess a unique capital structure compared to non-financial firms, primarily relying on customer deposits for their capital (Gropp & Heider, 2010) This structure can be analyzed using various proxies, such as the total debt ratio, which measures total debt against total assets, and the short-term debt ratio, which assesses short-term debt in relation to total assets (Ayalew &).
McMillan, 2021), customer deposits-total deposits to total assets and non-deposit
22 liabilities to total assets (Gropp & Heider, 2010; Al-Qudah., 2014) This researchuse the total debt to total asset as capital structure measures
Total assets Return on equity
= Non- performing loans / Total loans
Table 1: Model specification (Author’s construction)
Bank size, a key independent variable, is determined by the natural logarithm of a bank's total assets (Amidu & Wolfe, 2013) Additionally, the natural logarithm of customer deposits serves as a supplementary measure of bank size For the purposes of this study, total assets are utilized to assess bank size.
This research utilizes the total loans to total assets ratio, also known as the non-performing loan ratio, to evaluate credit risk This ratio indicates the bank's exposure to shifts in borrowers' repayment behaviors, with a higher ratio signifying increased vulnerability.
A 25% loans-to-assets ratio signifies that a significant portion of a bank's assets is tied up in loans, increasing the risk of collapse if borrower defaults rise This ratio has long been recognized as a critical indicator of credit risk, as highlighted by researchers like Curak et al (2012).
Solvency is assessed using the debt-to-equity (D/E) ratio, which measures the proportion of debt and equity financing a bank's assets This ratio is calculated by dividing the total debt of a bank by its total equity A lower D/E ratio indicates higher solvency, reflecting a bank's stronger equity position relative to its debt Conversely, a higher D/E ratio signifies lower solvency, as it reveals a bank's reliance on debt over equity.
The table below outlines the dependent and independent variables utilized in this study, detailing their measurements and the expected signs based on the hypotheses presented in the Literature section.
RESEARCH RESULTS AND DISCUSSION
Descriptive statistics
The Descriptive Statistics table provides a concise overview of the research data's fundamental parameters, highlighting the variability among sample observations through key metrics such as mean, maximum, minimum, and standard deviation This indicates an uneven distribution of variable values, as reflected in the mean and standard deviation Additionally, the collected data is characterized as unbalanced.
Table 2 : Descriptive statistics (Author's calculation on Stata)
The analysis reveals that the average Return on Assets (ROA) and Return on Equity (ROE) for the banks are 0.02 and 0.14, respectively, suggesting moderate profitability However, the standard deviations of ROA and ROE, at 0.01 and 0.08, indicate significant variations in profitability levels among the banks.
The average Non-Performing Loan Ratio (NPLR) stands at 0.04, suggesting that banks generally maintain a low credit risk However, the highest recorded NPLR is 0.12, highlighting that certain banks face significant non-performing loans, potentially jeopardizing their asset quality and overall stability.
The average solvency ratio among banks is 0.15, suggesting they maintain a healthy level of solvency However, with a minimum solvency ratio of 0.08, some banks face potential insolvency risks due to their lower solvency levels.
Variables Obs Mean Std.Dev Min Max
The average GDP stands at 6.32, reflecting robust economic growth, while the average inflation rate is 4.21, indicating significant inflationary pressures within the economy.
Correlation analysis
ROA ROE CSTRUCTURE SIZE NPLR SOLVENCY GDP INF
Table 3: Correlation matrix (Author's calculation on Stata)
The correlation matrix illustrates the relationships between variables, serving two key purposes Firstly, analyzing the correlations among independent variables helps predict potential collinearity issues within the model.
The correlation coefficient quantifies the strength of the relationship between two variables, revealing that the correlation between the two dependent variables related to bank stability, Return on Assets (ROA) and Return on Equity (ROE), is notably high at 0.8293 This strong correlation is expected, as these measures are alternative indicators and should not be included in the same model specification Consequently, the author introduces two new models to further analyze these relationships.
Model 1: ROA = β0 + β1* Capital Structure + β2* Bank Size + β3* Credit Risk + β4* Solvency + β5*GDP + β6*Inflation+ u
Model 2: ROE = β0 + β1* Capital Structure + β2* Bank Size + β3* Credit Risk + β4* Solvency + β5*GDP + β6*Inflation+ u
The correlation coefficient of -0.2413 between non-performing loan ratios (NPLR) and solvency suggests a weak negative relationship, indicating that banks with higher NPLR often experience lower solvency ratios This relationship highlights the impact of credit risk, which diminishes asset quality and value, thereby increasing the probability of insolvency.
The correlation coefficient of -0.3279 indicates a moderate negative relationship between CSTRUCTURE and INF, highlighting that in the context of Vietnamese banks from 2012 to 2022, a higher proportion of debt in funding sources correlates with lower inflation rates This suggests that banks with elevated debt ratios experience reduced inflation, while those with lower debt ratios face higher inflation This relationship can be attributed to the influence of monetary policy on interest and inflation rates, which impacts the borrowing and lending behaviors of banks For instance, when the central bank lowers interest rates to stimulate economic growth and raise inflation, banks tend to increase borrowing and lending, thereby raising their debt ratios Conversely, higher interest rates aimed at curbing inflation lead banks to reduce their borrowing and lending activities, resulting in lower debt ratios.
VIF Test for Multicollinearity
Multicollinearity arises when explanatory variables exhibit strong correlations with one another, potentially skewing the results of a regression model The Variance Inflation Factor (VIF) is a statistical tool used to assess the presence of collinearity issues within the model It is calculated by creating auxiliary regression models, where each independent variable from the original model is treated as the dependent variable in these new models.
A Variance Inflation Factor (VIF) of 1 indicates the absence of multicollinearity among variables When the mean VIF falls between 1 and 10, multicollinearity is present but at an acceptable level However, a VIF of 10 or higher signifies a very high correlation among the variables, which can lead to significant issues in regression analysis.
The result for VIF Test is as follows:
Table 4: VIF Test (Author's calculation on Stata)
The mean Variance Inflation Factor (VIF) is 1.46, suggesting a moderate level of multicollinearity among the independent variables A VIF exceeding 10 or a 1/VIF value below 0.1 signals a significant multicollinearity issue that could compromise the reliability and validity of regression results.
The highest variance inflation factor (VIF) recorded is 2.06, indicating that GDP is the most correlated independent variable, exhibiting the greatest variance inflation This strong correlation suggests that GDP is influenced by numerous factors.
30 macroeconomic factors, such as inflation, interest rate, exchange rate, etc., which may also influence the performance and stability of banks
The variable INF has the lowest variance inflation factor (VIF) at 1.01, indicating it is the least correlated with other independent variables This low VIF suggests that INF's coefficient exhibits minimal variance inflation, likely due to its relative stability and predictability compared to other economic indicators.
Dignosis test
Heteroskedasticity in a model indicates that the variance of the error term is not constant In the Fixed Effects Model (FEM), the author employs the Wald Test to assess the presence of Heteroskedasticity, where the null hypothesis posits constant variance (Homokedasticity) and the alternative hypothesis suggests Heteroskedasticity The results of this test are presented in the table below.
Table 5: Wald test results (Author's calculation on Stata)
The result shows that for both ROA and ROE models, the probability is 0.000, which is less than 0.05 This means that we can reject the null hypothesis and there is
Heteroskedasticity in models indicates that standard errors and test statistics derived from ordinary least squares (OLS) estimation are biased and inconsistent when serial correlation is present Therefore, it is crucial to avoid using these estimates to ensure accurate analysis.
4.2 Wooldridge Test for Serial correlation
The Wooldridge test for serial correlation is designed to identify first-order autocorrelation in panel data models The null hypothesis posits the absence of serial correlation in the error term, while the alternative suggests its presence The test statistic is derived from regressing the first-differenced residuals against their lagged values The p-value associated with the test statistic indicates the likelihood of observing the data if the null hypothesis is true.
Table 6: Woodridge Test (Author's calculation on Stata)
The analysis indicates that both the ROA and ROE models yield a probability of 0.000, which is below the 0.05 threshold This allows us to reject the null hypothesis, confirming the presence of serial correlation in the error term Consequently, it is inadvisable to rely on the standard errors and test statistics derived from ordinary least squares (OLS) estimation, as they become biased and inconsistent when serial correlation is present.
The Breusch-Pagan test reveals significant heteroskedasticity in the model, with a Prob>chi2 value of 0.0000, while the Wooldridge test indicates the presence of serial correlation, shown by a Prob>F value of 0.0000 To address these issues, Generalized Least Squares (GLS) is employed as a solution.
Table 7: Regression result (Author's calculation on Stata)
The analysis of the ROA model indicates that the variables Cstructure, Size, and Inflation exhibit a positive correlation with ROA Each of these variables has a statistically significant coefficient, suggesting that state-owned banks and larger institutions are positively influenced by these factors.
A study by Mirzaei, Moore, and Liu (2013) reveals a complex relationship between bank size and stability, indicating that larger banks may actually experience higher instability While higher inflation and the presence of 33 banks are linked to increased profitability and stability, the research highlights that GDP does not significantly affect Return on Assets (ROA) This suggests that a rise in GDP does not guarantee enhanced stability for banks.
The analysis reveals a negative and statistically significant relationship between solvency and return on assets (ROA), indicating that an increase in solvency corresponds to a decrease in profitability Specifically, a one-unit rise in solvency results in a 0.0783-unit decline in ROA, assuming other variables remain constant This finding contradicts the theoretical expectation that higher solvency should enhance ROA by reducing financial leverage and risk Additionally, it suggests that banks in Vietnam may be overcapitalized, with excess equity not being effectively utilized in profitable investments, ultimately diminishing their ROA.
(2013) in previous investigation stated that, the higher the solvency of the bank is, the more stable of bank will be, which is different from the results of this paper
The analysis reveals a significant negative correlation between the Non-Performing Loan Ratio (NPLR) and Return on Assets (ROA), indicating that higher credit risk diminishes financial stability Specifically, a one-unit rise in NPLR results in a 0.0995 unit decline in ROA, assuming other factors remain unchanged This finding aligns with the theoretical expectation that increased credit risk adversely affects bank income and raises provision expenses, ultimately leading to a reduced ROA.
The ROE model reveals that six factors influence bank profitability, with SIZE, NPLR, GDP, INF, and the constant term being statistically significant at the 10% level or lower These factors moderately affect ROE, with SIZE, GDP, INF, and the constant term positively impacting profitability Specifically, a one-unit increase in SIZE results in a 7.07e-09-unit increase in ROE, while NPLR negatively affects bank profitability, aligning with previous research by Nicolae Petria.
Research conducted in 2013 examined the factors influencing bank profits across 27 EU countries from 2004 to 2011 Similarly, Trinh Quoc Trung's study investigated the performance determinants of 39 Vietnamese commercial banks from 2005 to 2013 The findings revealed that a higher bad debt ratio negatively impacts bank performance, while an increased loan-to-total-asset ratio enhances operational efficiency Additionally, there is an inverse relationship between total operating costs to revenue and return on equity (ROE); as the self-financing ratio increases, ROE tends to decrease.
The analysis indicates that the capital structure and solvency of the bank do not significantly impact its profitability, as evidenced by p-values exceeding 10% Consequently, there is no statistically significant relationship between these factors and bank profitability, which is measured by Return on Equity (ROE).
After examining and analyzing, some solutions are proposed belows:
To lower the non-performing loan ratio (NPLR), it is essential to implement stricter credit policies, enhance credit risk assessment and monitoring, diversify the loan portfolio, and effectively resolve bad debts These measures will significantly improve overall asset quality.
35 profitability, and solvency of the banks, as well as reduce the provision expenses and the credit risk exposure
To optimize capital structure (CSTRUCTURE), it is essential to balance the advantages and disadvantages of debt and equity financing, considering factors such as tax shields, cost of capital, financial risk, and regulatory requirements This strategic approach will improve banks' solvency, profitability, and stability while enhancing their leverage and borrowing capacity.
To enhance bank size (SIZE), focus on expanding market share, customer base, product range, branch network, and geographic coverage This strategy will boost profitability, stability, and competitiveness while leveraging economies of scale and scope.
CONCLUSION
Credit risk poses significant challenges for banks, representing the potential for borrowers or counterparties to default on payments This risk can adversely affect a bank's profitability, asset quality, liquidity, and solvency, ultimately impacting its overall stability Bank stability is essential for maintaining public confidence in the financial system and plays a vital role in fostering economic growth and development within the country.
This study aimed to analyze the effects of credit risk on bank stability in Vietnam, utilizing a panel dataset of 22 commercial banks from 2012 to 2022 The research applied the feasible generalized least squares (FGLS) method to estimate two models: one focusing on return on assets (ROA) and the other on return on equity (ROE) as dependent variables Key independent variables included capital structure (CSTRUCTURE), bank size (SIZE), non-performing loan ratio (NPLR), solvency ratio (SOLVENCY), gross domestic product growth rate (GDP), and inflation rate (INF).
The study found that credit risk, as indicated by the Non-Performing Loan Ratio (NPLR), negatively affected both Return on Assets (ROA) and Return on Equity (ROE), highlighting that increased credit risk diminishes bank profitability and stability This aligns with theoretical expectations, as higher credit risk leads to reduced income and increased provisioning expenses Additionally, various factors influenced ROA and ROE differently; for ROA, capital structure, bank size, solvency ratio, and inflation rate positively impacted profitability and stability, while GDP had a positive but non-significant effect Conversely, for ROE, bank size, GDP, and inflation rate significantly enhanced profitability, whereas capital structure and solvency ratio showed positive but non-significant effects.
This study enhances existing literature by offering empirical evidence on the correlation between credit risk and bank stability within the Vietnamese context, utilizing a comprehensive dataset and robust estimation methods.
Policymakers and bank managers in Vietnam must prioritize reducing credit risk to enhance bank stability amid the economic uncertainties brought on by the COVID-19 pandemic Additionally, improving capital adequacy, asset quality, liquidity management, and risk management practices is essential for sustaining this stability Furthermore, monitoring Return on Assets (ROA) and Return on Equity (ROE) is crucial, as these metrics provide valuable insights into various aspects of bank performance.
The study identified several limitations that future research could address, including the exclusion of factors such as market share, competition, regulation, and governance that may influence bank stability Additionally, it did not establish a causal relationship between credit risk and bank stability, leaving unanswered whether changes in credit risk lead to changes in bank stability or the other way around Furthermore, the research overlooked alternative measures of credit risk and bank stability that could provide a more comprehensive understanding of these concepts Future studies should aim to broaden the scope and depth of this investigation by integrating additional variables, methodologies, and metrics to better analyze the relationship between credit risk and bank stability in the context of Vietnam.
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List of banks in observation
1 An Binh Commercial Joint Stock Bank ABB
2 Asia Commercial Joint Stock Bank ACB
3 Vietnam Bank for Agriculture and Rural Development AGRB
4 Joint Stock Commercial Bank for Investment and
5 Vietnam Joint Stock Commercial Bank of Industry and
6 Vietnam Joint Stock Commercial Bank of Industry and
7 Vietnam Export Import Commercial Joint Stock Bank EIB
8 Kienlong Commercial Joint Stock Bank KLB
9 Lien Viet Post Joint Stock Commercial Bank LPB
10 Military Commercial Joint Stock Bank MBB
11 Vietnam Maritime Commercial Joint Stock Bank MSB
12 Nam A Commercial Joint Stock Bank NAB
13 Orient Commercial Joint Stock Bank OCB
14 Orient Commercial Joint Stock Bank PGB
16 Saigon Bank for Industry & Trade SGB
17 Saigon – Hanoi Commercial Joint Stock Bank SHB
18 Saigon Thuong Tin Commercial Joint Stock Bank STB
19 Viet Nam Technological and Commercial Joint Stock Bank TCB
20 Joint Stock Commercial Bank for Foreign Trade of Vietnam VCB
21 Vietnam International Commercial Joint Stock Bank VIB
22 Vietnam Commercial Joint Stock Bank for Private Enterprise VPB
Appendix 1 List of banks in observation
Appendix 7: Hausman test of ROA