Impact of credit risk on the financial performance of vietnamese banks

This study therefore investigates the impact of credit risk with focus on non-performing loans on the financial performance of commercial banks in Vietnam.. The analysis focuses on key c

INTRODUCTION

Banks act as profit-seeking intermediaries between borrowers and lenders in economies (Dietrich,

Commercial banks serve as vital financial intermediaries, channeling funds into productive projects and providing essential capital for industrial expansion (Hadad, Hall, & Santoso, 2021) Their role is critical for the overall performance of economies, as a strong financial sector fosters economic growth (Le & Thuy, 2020) Primarily, commercial banks accept deposits and issue loans, which are fundamental to their operations and a significant source of income (Nkem & Akujima, 2017; Chipeta & Muthinja, 2018) However, the dynamic and complex economic environment exposes these banks to various risks, including credit, liquidity, market, operational, and legal risks (Sriyana et al.).

In 2016, various risks significantly impact the profitability, market value, liabilities, and equity of financial institutions, with credit risk being paramount due to its direct correlation with the banking sector's primary income source—loans from commercial banks Defined by The Basel Committee on Banking Supervision, credit risk represents the likelihood of losing part or all of an outstanding loan due to nonpayment As credit risk increases, so does the marginal cost of debt and equity, consequently raising the bank's funding costs (Wachter, 2018) Additionally, risks associated with a trading partner's failure to meet contractual obligations can severely undermine the operational efficiency of banking organizations.

Banks with high credit risk face significant bankruptcy threats, jeopardizing depositors' safety A substantial level of non-performing loans negatively impacts profitability and operational efficiency Consequently, effective credit risk management is crucial for the survival and growth of financial institutions By implementing robust credit risk strategies, banks can enhance operational sustainability and profitability while fostering economic stability and ensuring efficient capital allocation According to Gadzo et al (2019), credit risk remains the primary challenge for banks and financial institutions.

Financial distress often stems from inadequate credit risk management, as noted by Forson (2017) Poor credit risk practices can hinder capital acquisition, complicate the development of credit products, and strain customer relationships, ultimately resulting in operational instability (Ozili).

2017) However, Tan argues that banks accept higher credit risk because they expect to be compensated by higher profits, and it is a trade-off situation (Poudel, 2012)

Credit risk is the most significant and costly risk in commercial banking, as it directly affects the bank's stability (Kaaya, 2013) In Vietnam, following a phase of rapid credit expansion, numerous risks have emerged.

Since 2011, the State Bank of Vietnam (SBV) has implemented strict controls on credit growth within the banking sector, using it as a key tool for monetary policy management This has led to annual assignments of specific credit growth targets for commercial banks (CBs), whose revenue structures have diversified, increasing the share of non-interest income Despite this, lending remains the core business of banks, with credit risk significantly influencing their health and operations Effective control of credit risk is essential for supporting economic growth and ensuring profitability for banks Following the global economic crisis of 2008-2009, Vietnam's banking sector saw a sharp rise in bad debt ratios, which were reported to be significantly higher than recommended levels The recent challenges posed by the COVID-19 pandemic and the Russia-Ukraine conflict have further exacerbated the situation, leading to an increase in bad debt from late 2022 By June 2023, the bad debt ratio in Vietnam's credit institutions reached 3.36%, up from 1.69% at the end of 2020, underscoring the potential for a capital crisis and a loss of depositor confidence, which could trigger a systemic crisis.

As of the end of 2023, Vietnam's total outstanding credit reached approximately VND 12,749 trillion, reflecting a growth of 6.92% However, the quality of corporate governance among Vietnamese banks varies significantly, with effective governance being crucial for establishing a stable and sustainable banking system Banks that excel in corporate governance can enhance operational efficiency, improve access to capital markets, lower capital costs, increase asset values, and bolster their corporate reputation A critical aspect of bank governance is risk management; while some banks have adopted international risk management standards such as Basel II and Basel III, there remains a lack of uniformity in their implementation across the Vietnamese banking system Achieving consistent adherence to these international standards is essential for mitigating the adverse effects of financial crises and facilitating better recovery measures.

Numerous studies have explored the impact of credit risk on the profitability of commercial banks, yielding inconsistent results While some research indicates that credit risk negatively affects financial performance (Jacob, 2022), other studies suggest a positive correlation (Alshatti, 2017) Additionally, the relationship between monetary policy, credit risk, and bank performance has been examined (Nguyen & Nguyen, 2021), yet there remains a significant research gap regarding the direct connection between credit risk and bank performance before and after the financial crisis This study aims to fill this gap, particularly in the context of the Vietnamese economy, and is expected to provide valuable theoretical and practical insights for emerging markets Furthermore, it contributes to existing literature on credit risk and bank performance and is conducted in light of the implementation of Basel II by Vietnamese commercial banks, which emphasizes credit, market, and operational risks.

The application of IFRS 9 in Vietnam's banking sector is crucial for addressing credit loss impairments, significantly benefiting policymakers, bank personnel, executives, board members, and financial investors by enabling proactive credit risk management The study's findings will assist in crafting effective policies to enhance the financial stability of the banking industry, ensuring its vital role in promoting economic growth Notably, this research incorporates economic value added as a key financial performance metric, an aspect overlooked in prior analyses of the credit risk and financial performance relationship The paper is organized as follows: Section 2 presents a thorough literature review, Section 3 details the methodological framework, Section 4 summarizes the key findings, and Sections 5 and 6 provide conclusions and recommendations based on the research.

LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT

Credit risk

The credit intermediation role of banks is fraught with the prime challenge of a credit default, borrowers not able to repay the loans obtained from their banks (Musyoki, 2022) The Merton

In 1977, the default model established a connection between a firm's credit risk and its capital structure, highlighting the impact of borrower defaults on banks' capital Central banks play a crucial role in ensuring that banks implement effective processes to manage delinquent loans, issuing guidelines and enforcing sanctions to maintain financial stability and uphold mutual respect in financial covenants As a result, banks often impose higher interest rates on loans with elevated default risks Balancing financial performance with effective credit risk management is essential, and bank management teams are expected to adopt suitable methodologies while adhering to their central banks' prudential guidelines and corporate governance standards.

Credit risk, as defined by Donald et al (1996), refers to the possibility that a borrower will fail to fulfill their obligations Effective credit risk management aims to optimize the risk-adjusted return while keeping credit exposure within acceptable limits It is essential for banks to manage credit risk across their entire portfolio, as well as for individual transactions, to ensure long-term success According to Tan et al (2019), the banking sector has improved its credit risk management practices In the past, credit risk analysis focused primarily on individual loans, with banks holding loans until maturity However, modern credit risk management now includes both loan assessments and portfolio analysis, aided by new technologies that enable banks to adopt more proactive strategies rather than simply retaining loans.

In today's dynamic financial landscape, banks strategically seek the optimal asset mix, considering the current credit environment and market conditions Enhanced management capabilities allow banks to effectively control obligor concentrations, maturities, and loan sizes, enabling them to proactively address potential problem assets before they result in losses (Baidoo, 2019) Additionally, many banks conduct stress tests on their portfolios by business line to enhance overall management decisions The credit process consists of three key stages, beginning with effective risk control to prevent over-concentration in any single sector, while also estimating default probabilities and assessing potential recovery outcomes.

Bank performance

A company's performance serves as a crucial indicator of its operational health, encompassing various factors and metrics that determine its effectiveness This is particularly relevant in the banking sector, where performance assessments help ascertain a bank's stability over specific periods Evaluating financial performance allows management to meet obligations to stakeholders and achieve organizational goals In banking, financial performance reflects the institution's financial condition during a given timeframe, focusing on aspects such as capital adequacy, liquidity, and profitability.

Bank profitability is influenced by the operational environment and is essential for stability in the banking sector (Wireko, 2017) While healthy profitability is crucial, excessively high profits can indicate reduced competition and hinder efficiency in savings intermediation Conversely, low profitability can deter private banking activities and weaken a bank's ability to withstand negative shocks (Fama, 1985) Bank efficiency assesses how well inputs are minimized to achieve specific outputs, reflecting management's capability in resource allocation Additionally, bank productivity serves as a key performance indicator and an alternative measure of efficiency.

Bank productivity, which measures total output per unit of input, has gained significant attention from economists and policymakers, especially in European and transition economies (Kaimu, 2021) Unlike bank efficiency, bank productivity highlights the influence of technology on production Marshal (2016) identifies two key indicators of bank performance: quality and quantity, with profitability and risk as its dimensions Profitability is typically assessed using Return on Assets (RoA) and Return on Equity (RoE), while risk is measured through Non-Performing Loans (NPL) and Loan Loss Provision (LLP) (Jalal) Gitman and Zutter define profitability as a company's ability to generate profits, which is crucial for attracting investors RoA reflects how effectively a bank manages its assets to generate profits, indicating strong financial performance and alignment with shareholder goals (Noman, 2015) RoE, on the other hand, measures the return on equity capital, showcasing a company's ability to generate net income from shareholder investments (Gitman and Zutter; Taswan).

Relation between Credit risk and Bank performance

This section reviews the theoretical and empirical literature relevant to the study, focusing on key theories such as information asymmetry, moral hazard, adverse selection, loan pricing, and agency theory, as highlighted by Stiglitz.

Information asymmetry occurs when one party in an economic transaction has more information than the other, as defined by 2002 Spence (1973) highlights that often, one party possesses better knowledge about a deal, such as borrowers knowing more about their repayment abilities than lenders, or sellers being more aware of product quality than buyers Additionally, company directors typically have more insight into a company's performance than shareholders, and policyholders are usually more informed about their risks than insurance providers Kane (1965) and Fama (1985) suggest that banks can only accurately assess a borrower's characteristics by extending more loans rather than relying solely on provided information This reliance on historical data helps banks estimate default risks However, information asymmetry can lead to banks issuing poor loans, ultimately affecting their financial performance due to increased credit risk.

Information asymmetry is a critical factor in understanding moral hazard and adverse selection, which describe situations where one party in a transaction faces disadvantages due to unequal information Moral hazard occurs when one party alters their behavior after a contract is signed, while adverse selection arises when one party possesses information that the other does not This imbalance complicates decision-making regarding the risks associated with contracts, particularly in loan agreements where borrowers typically have more insight into their repayment capabilities but may only disclose favorable information during the application process Akerlof highlights that moral hazard affects both lenders and borrowers, potentially leading to competitive bias and diminished quality in products and services Krugman further explains that moral hazard involves one party taking risks with the expectation that the other will absorb the consequences if things go awry Ultimately, information asymmetry underpins both moral hazard and adverse selection, significantly impacting economic interactions.

The banking sector faces significant challenges, including potential declines in profits, liquidity, and increased loan pricing due to moral hazard and adverse selection, which can lead to higher credit risk (Gladwell, 2019) Loan pricing is influenced by the actual cost, profit margins, and risk premiums, with banks often setting high lending rates To maximize interest income, it is crucial to consider issues like moral hazard, adverse selection, and asymmetric information when determining loan rates (Sakyi, 2021) Stiglitz (2012) notes that borrowers frequently engage in riskier activities after securing loans, which can distort the pricing of loans and affect overall loan disbursement volumes Without thorough due diligence to evaluate moral hazard behaviors, loans may be underpriced, increasing credit risk in the event of defaults Additionally, high lending rates may deter risk-averse borrowers, leading to reduced loan portfolio diversification and heightened credit risk concentration within the customer base.

The agency theory highlights the conflicts of interest that arise between bank shareholders and management, leading to agency costs within the principal-agent relationship between debtors and shareholders of commercial banks.

In 2022, the involvement of shareholders in financing investments increases the credit risk faced by commercial banks and their debtors While banks can achieve significant profits from successful high-risk financial investments, the potential losses from credit risk are solely absorbed by the banks, impacting their overall financial performance.

The financial performance of commercial banks is crucial for effective performance assessment amidst rising competition in the financial market To meet stakeholder expectations and achieve broader objectives, bank management must prioritize high financial performance Additionally, the stability and survival of banks, along with the overall financial system, largely depend on their ability to manage credit risk effectively.

In challenging economic conditions, banks face significant trade-offs regarding credit loss provisioning According to Herald et al (2019), the extent of these provisions is directly influenced by prior credit risk levels.

Increased operating costs significantly impact the financial performance of banks by reducing profits (Herath, 2021) Bad debts and provisions for credit losses are often viewed as indicators of credit risk (Dietrich, 2017) Tan (2019) highlights a trade-off between banks' risk-taking ability and their financial performance, noting that while higher credit risk can lead to the expectation of greater profits, it may also diminish overall financial performance due to potential loan defaults Research presents mixed findings on the relationship between credit risk and bank performance, with some studies indicating a positive correlation (Embaye, 2018) and others showing negative effects (Oketch, 2018) Effective management of internal factors can enhance profitability, whereas mismanagement can harm a firm's financial statements (Nguyen Q K., 2022) Various studies have explored different bank-specific variables, such as cost-efficiency ratio (CER), average lending rate (ALR), and liquidity ratio (LR), in relation to firm performance Aspal et al (2019) examined both macroeconomic and bank-specific factors, using GDP and inflation as proxies for the macroeconomic variables impacting the financial performance of commercial banks in India (Ajao, 2019).

A bank-specific variables’ proxy includes capital adequacy ratio, asset quality, management efficiency, liquidity and earnings quality Data of 20 private banks have been used from 2008 to

In 2014, panel data analysis revealed that GDP is a significant macroeconomic factor influencing financial performance, while inflation is not Among bank-specific factors, earning quality, asset quality, management efficiency, and liquidity all significantly impact financial performance, except for capital adequacy ratio (CAR), which is deemed insignificant Haile et al (2018) explored the relationship between bank-specific and macroeconomic determinants affecting the banking performance in Azerbaijan, an oil-dependent economy (Haile, 2022).

On the empirical front, some studies have also been conducted For instance, (Nguyen & Nguyen,

A study conducted in 2021 explored the relationship between banks' profitability and credit risk in Vietnam, revealing a significant positive correlation This relationship was measured through various metrics, including the net charge-off rate, pre-provision profit relative to new loans and advances, and the non-performing loan rate Additionally, the research indicated that profitability is positively associated with factors such as bank growth, capital depth, and bank size Similarly, a study in Ghana utilized return on assets (ROA) and return on equity (ROE) to assess financial performance, alongside capital adequacy ratios and non-performing loans.

Recent studies highlight the complex relationship between credit risk and bank performance Khai (2022) establishes a significant positive correlation between banks’ profitability and credit risk, while Marshal (2016) finds that bank performance is positively related to non-performing loans in Nigeria Conversely, Suela (2019) demonstrates a significant negative effect of credit risk on commercial banks’ performance, indicating that loans and advances to total deposits adversely impact performance This negative trend is further supported by Ebenezer and Omar (2016) in Nigeria and Noman et al (2015) in Bangladesh, where credit risk negatively affects financial performance as measured by return on assets, return on equity, and net income margin.

2.3.2 Relation between Non-performing loan and financial performance

Credit risk is assessed through various metrics, including the capital adequacy ratio, loan loss ratio relative to gross loans, the proportion of non-performing loans to gross loans, and the loan loss ratio concerning non-performing loans This risk arises when a commercial bank fails to recover the anticipated interest and principal payments from borrowers.

Non-performing loans (NPLs), defined as loans that remain unpaid for 90 days or more, are also referred to as bad or impaired loans Commercial banks view NPLs as risky assets that can adversely impact their overall performance Researchers often utilize NPLs as a key indicator to evaluate management's approach to credit risk and its effects on bank performance To mitigate these risks, bank management must adhere to the guidelines set forth by the State Bank of Pakistan and the Basel Accord Previous studies have indicated a negative relationship between NPLs and bank performance, highlighting the importance of effective risk management strategies.

Kolapo (2022) explores the connection between credit risk and bank profitability, finding a positive relationship between non-performing loans to total loans and profitability, as indicated by return on assets Additionally, the study reveals a positive correlation between bank size and profitability Similarly, Sheikhi et al (2022) examine the impact of credit risk on the profitability of commercial banks in the United Kingdom, using return on assets and return on equity as profitability measures, while credit risk is represented by impairments and non-performing loans.

DATA AND EMPIRICAL METHODOLOGY

Data

This research analyzes annual data from 27 listed commercial banks in Vietnam from 2011 to 2023, utilizing publicly accessible data sources By the end of 2023, these banks had a total charter capital of 671,571 billion VND (approximately 27.51 billion USD), representing a significant portion of the 705,043.32 billion VND total charter capital across 37 commercial banks in the country (State Bank of Vietnam, 2024) Vietnam's banking sector comprises 49 banks, including state-owned, joint-stock, joint-venture, foreign branches, and 100% foreign-owned banks, with non-listed banks being primarily joint-venture and foreign branches with minor capital contributions The selected sample is highly representative of the banking industry The study fills a research gap identified in previous studies by Trang (2020) and Vo (2020) by using comprehensive financial reports from 2011 to 2023, including balance sheets, income statements, and cash flow statements Data was sourced from various financial statements, Fiin Pro, the State Bank of Vietnam, bank websites, the General Statistics Office of Vietnam, and the Ministry of Finance The data was meticulously imported into Excel for review and editing, followed by a rigorous cleaning process to ensure accuracy and completeness before analysis using Stata 17 software.

Model specification

This research focuses on financial performance as the primary variable, with credit risk and bank-specific factors serving as regressors Building on the model established by Ameni Ghenimi (2017), it examines the influence of credit risk on the financial stability of Vietnamese commercial banks Financial performance is assessed using Return on Assets (ROA) and Return on Equity (ROE), while credit risk is evaluated through the Non-Performing Loan (NPL) ratio and Loan Loss Provision (LLP) ratio, alongside five specific variables: Capital Expenditure Ratio (CER), Loan Ratio (LR), Asset-Liability Ratio (ALR), Capital Adequacy Ratio (CAR), Size (SIZE), and Age (AGE) Additionally, previous studies (Hamza, 2017; Belas, 2018) highlight the importance of controlling for macro and micro variables that significantly impact financial performance To address this, the model incorporates two control variables: Gross Domestic Product (GDP) growth rate and inflation (INF).

The panel estimation technique is suitable for this research due to the nature of the data, as it accounts for the heterogeneity among individual banks (Kwashie et al., 2021) The specified equations are as follows:

The author incorporates a dummy variable, CRISIS, into the model to analyze the impact of financial crisis conditions This variable is assigned a value of 1 for the years 2020 to 2023, indicating crisis conditions, while it takes a value of 0 for the normal conditions observed from 2011 to 2019.

In the context of banking performance and risk assessment, econometric models are utilized to analyze the relationship between essential financial metrics and bank outcomes The performance and fund diversification model includes key indicators such as the return on assets (ROA) and return on equity (ROE), with various coefficients (β0 to β10) representing the independent variables to be estimated, while ε denotes the error term.

The indices i and t denote the position of bank i in year t, while I,t signifies the individual-specific effect that remains stable over time These models provide a robust framework for assessing credit risk and the financial performance of banks in Vietnam by analyzing various financial indicators and their corresponding profitability coefficients.

Variable measurements

In this study, financial performance is assessed through return on assets (ROA) and return on equity (ROE), chosen for their advantages over net interest margin (NIM), which has been criticized for failing to adequately reflect management's ability to maximize shareholder wealth (Kadar & Rikumahu, 2018) Sinkey (1992) argues that ROA and ROE are superior metrics for evaluating commercial bank performance, as they account for financial leverage and minimize distortions Furthermore, ROA provides valuable insights for bank management, aiding decisions that enhance shareholder wealth The Stern Stewart Corporation (Stern & Shew, 1995) supports the notion that ROE is the most effective measure of performance, accurately reflecting an organization's economic profit.

Return on Assets (ROA) is a key financial metric that measures a bank's efficiency in generating earnings from its assets over a specific period Alongside Return on Equity (ROE), it indicates how effectively a bank utilizes its assets and equity to generate income ROA represents the ratio of a bank's net income to its total assets, highlighting the management's effectiveness in profit generation with limited resources.

Non-performing loans serve as a crucial indicator of credit risk in commercial banks, measured by the percentage of loan defaults relative to the total loan volume This study anticipates a negative impact of non-performing loans on banks' financial performance, as a higher percentage of unrecovered loans adversely affects profitability Research by Herath et al (2021) and Yeasin (2022) supports the negative correlation between non-performing loans and the financial performance of commercial banks In Vietnam, bad debt is classified as debts in groups 3, 4, and 5 according to Circular No 08/2017/TT-NHNN from the State Bank of Vietnam Additionally, the credit loss provision ratio indicates the amount set aside in operating expenses to mitigate potential debts; a higher ratio reflects poorer loan portfolio quality and correlates positively with bad debt and increased credit risk (Alshatti, 2015).

Loan loss provision (LLP) is an essential accounting practice for banks, enabling them to account for potential losses from non-performing loans (NPLs) by setting aside a portion of their income This practice helps ensure that balance sheets accurately reflect credit risk, which is vital for financial health, risk management, and regulatory compliance, especially during economic downturns when loan defaults are more likely Rooted in prudential regulation and accounting standards, loan loss provisioning allows banks to maintain adequate reserves to cover anticipated credit losses, thus preparing for future defaults and minimizing negative impacts on profitability and capital adequacy.

LLPR = Provision for credit losses/ Total loans and advances (4)

The capital adequacy ratio (CAR) is a critical measure of a bank's financial health, representing the proportion of available resources to risk-weighted credit exposures It serves as a buffer against volatile assets, with the Basel Committee on Banking Supervision (BCBS) mandating an international minimum CAR of 8% In Vietnam, the State Bank of Vietnam (SBV) has set a higher minimum CAR requirement of 10-11% for 2023 and 11-12% for 2025 Regulators continuously monitor this ratio, as a strong CAR indicates a bank's ability to absorb losses and maintain solvency Research, including studies by Embaye et al (2017), Herath et al (2021), and Kaimu and Muba (2021), suggests a positive correlation between CAR and bank performance.

The Cost-Efficiency Ratio (CER) is a vital metric for assessing the operational efficiency of banking institutions, reflecting the relationship between operating costs and income A lower CER indicates higher efficiency, while a higher ratio suggests inefficiency or increased costs Grounded in efficiency theories, particularly the neoclassical theory of the firm, the CER highlights the optimal use of resources to minimize costs and maximize output Additionally, financial intermediation theory underscores the importance of banks in effectively allocating capital between savers and borrowers By striving for operational efficiency, banks can gain a competitive advantage, enhance profitability, and meet regulatory standards, with the CER serving as a key indicator of their performance Operational efficiency encompasses both cost efficiency and profit efficiency, as noted by Berger and Mester.

CER = Total operating cost/ Total revenue (6)

The average lending rate (ALR) is the weighted average interest rate that banks charge borrowers, significantly impacting borrowing costs, profitability, and economic activity It reflects the overall cost of credit influenced by central bank policies, market conditions, and risk premiums Understanding the ALR is crucial for assessing the financial health of banks, the effectiveness of monetary policy, and the accessibility of credit for consumers and businesses The determination of the ALR is based on economic theories like the supply and demand for loanable funds and the risk-based pricing model Central banks utilize monetary policy tools to influence the money supply, prompting commercial banks to adjust their lending rates accordingly The risk-based pricing model indicates that lending rates account for both the bank's cost of funds and the borrower's risk profile, leading to higher rates for riskier borrowers While higher lending rates can enhance bank profitability, excessive increases may deter credit demand, ultimately reducing loan volumes and income Studies indicate that banks strategically adjust their lending rates to maximize return on equity while managing credit risk.

ALR= Net interest income/ Total assets (7)

The liquidity ratio is an essential financial metric for banking organizations, assessing their ability to meet short-term obligations with liquid assets This ratio indicates a bank's capacity to handle deposit withdrawals, cover short-term debts, and manage cash flow needs, playing a crucial role in ensuring financial stability and mitigating liquidity risk Liquidity risk occurs when a bank struggles to convert assets into cash without incurring significant losses In the context of financial intermediation, banks serve as intermediaries between savers and borrowers, maintaining liquid assets to satisfy depositor demands while investing in less liquid assets for returns.

Achieving an optimal balance between liquid assets and investments is essential for the stability and profitability of banks According to liquidity preference theory (Keynes, 1936), banks face a trade-off between maintaining liquid, low-yield assets and pursuing less liquid, higher-yield investments While a higher liquidity ratio enhances financial stability, it can lead to reduced returns, as liquid assets like government bonds and central bank reserves typically generate lower yields compared to loans or investments in financial markets Thus, banks must carefully navigate the need for sufficient liquidity to meet obligations while also investing in higher-yielding, less liquid assets to maximize profitability (Dietrich et al., 2014).

LR = Total loans/ Total deposits (8)

Bank size, quantified as the natural logarithm of total assets, is a crucial metric in the financial sector, reflecting potential economies or diseconomies of scale This study posits that larger banks positively impact financial performance due to their ability to leverage economies of scale and diversify effectively, ultimately enhancing their overall performance.

The age of a bank, reflecting its duration of existence, is positively correlated with its financial performance, as supported by studies from Boahene et al (2012), Bhattarai (2016), and Kutum (2015) Older banks tend to possess greater expertise in credit risk management, which mitigates negative impacts on their financial health This relationship is reinforced by moral hazard and adverse selection theories, suggesting that a bank's historical data on borrowers helps reduce adverse selection and lower credit defaults Consequently, older banks have more comprehensive historical data to evaluate potential borrowers compared to their newer counterparts.

AGE = Age of commercial banks from established to the year calculated (10)

Gross domestic product (GDP) represents the total monetary value of all goods and services produced within an economy over a specific timeframe Research indicates a positive correlation between GDP and the financial performance of banks, as supported by findings from Rwayitare et al (2016) Typically, banks experience improved performance during periods of economic expansion, highlighting the significance of GDP growth on their profitability.

An increase of 25% in GDP is expected to drive higher demand for loans and deposits, ultimately improving the financial performance of banks Additionally, inflation refers to the ongoing rise in the general price levels of goods and services.

GDP = Growth rate of gross domestic products

The inflation rate (INF) measures the increase in the price level of a consistent basket of goods and services over time, which is captured by the consumer price index (CPI) This topic has been extensively explored in academic literature, including studies by Claessens et al (2001), Drakos (2002), Alexiou & Sofoklis (2009), Kasman et al (2010), and Tarusa et al.

Inflation = Annual inflation rate declared by world bank yearly

The COVID-19 pandemic has profoundly affected the relationship between credit risk and profitability in banking organizations, leading to an increase in non-performing loans (NPLs) as businesses and consumers faced financial difficulties (Demirgỹỗ-Kunt, Pedraza, & Ruiz-Ortega, 2021) Research indicates that banks that adapted their risk management strategies and embraced digital transformation were better positioned to mitigate these negative impacts (Bian et al., 2021) Additionally, government measures such as loan forbearance and stimulus programs temporarily stabilized credit markets (Auer et al., 2021) Despite the heightened credit risk during the pandemic, many banks exhibited resilience and maintained profitability through strategic responses, underscoring the complex effects of external shocks like COVID-19 on the credit risk-profitability dynamic in the banking sector.

Estimation strategy

This paper utilizes the Hausman specification test to compare the fixed effect model (FEM) and the random effect model (REM) in econometric analysis The null hypothesis of the test suggests that the random effect is suitable, while the alternative indicates the fixed effect is more appropriate If the Hausman test is significant at the 5% level, the fixed effect estimator is favored; otherwise, the random effect estimator is deemed appropriate In the context of banking and financial economics, employing advanced econometric methods like FEM and REM is essential for enhancing analytical rigor and understanding complex relationships in financial data These models effectively address unobserved heterogeneity and time-invariant characteristics, reflecting a commitment to methodological refinement in empirical research.

The FEM, rooted in its premise of subtracting individual-specific means from the data, represents a tailored approach to examining the evolution of variables within entities over time By effectively

The Fixed Effects Model (FEM) enhances our understanding of financial phenomena by accounting for unobservable entity-specific effects and their temporal dynamics In contrast, the Random Effects Model (REM) addresses unobserved individual-specific effects as random variables, providing insights into average effects across multiple entities The REM is especially valuable when the research aims to capture a broader perspective that goes beyond individual entity variations.

The reliability of research findings hinges on fundamental econometric assumptions, particularly the absence of heteroscedasticity and autocorrelation This study employs the Modified Wald test to check for heteroscedasticity, where the null hypothesis suggests equal variances among errors, confirming homoscedasticity Accepting this null hypothesis validates the unbiasedness of estimated regression coefficients in relation to the true coefficients across the population In time series data, autocorrelation occurs when a variable correlates with its lagged version, potentially distorting regression coefficient variances and hypothesis testing The Wooldridge test addresses this by positing the null hypothesis of no autocorrelation, which, when accepted, mitigates bias and enhances the integrity of model inferences This focus on autocorrelation aligns with the scholarly work of Wooldridge (2010), underscoring its importance in econometric analysis.

EMPIRICAL RESULTS AND DISCUSSIONS

Descriptive statistic

This research paper presents empirical evidence on the relationship between credit risk and internal factors affecting the financial performance of commercial banks in Vietnam The study employs descriptive analysis to provide an overview of the data, followed by a correlation analysis, and concludes with a discussion of the regression results.

Table 2: The descriptive statsitcs of of the variables

Variable Obs Mean Std dev Min Max

The dataset, consisting of 351 observations, offers valuable insights into the financial and risk factors affecting the performance of Vietnamese commercial banks Key performance indicators, Return on Assets (ROA) and Return on Equity (ROE), highlight the banks' financial health, with ROA serving as a measure of profitability relative to total assets The standard deviation for ROA is 0.012, indicating a modest average profitability of 1.9%, yet revealing significant variability among banks, with some achieving returns as high as 7.4%, while others struggle to generate substantial returns.

Achieving a break-even point as low as 0.2% highlights the profitability levels typical of banks in emerging markets, where growth potential is often coupled with substantial risks The variation in Return on Assets (ROA) indicates that while some banks excel in asset utilization for profit generation, others may face challenges stemming from increased risk exposure or operational inefficiencies.

The Return on Equity (ROE) for Vietnamese banks shows a significant standard deviation of 0.128, indicating notable differences in the effectiveness of banks in utilizing shareholders’ equity for profit generation With an average ROE of 22.1%, these banks generally yield strong returns, yet the range from 1.7% to 64.6% underscores the performance disparity among them Factors such as varying capitalization levels, lending practices, and risk exposure contribute to this gap, where larger banks often leverage economies of scale and superior financial management, while smaller or riskier banks may face profitability challenges Additionally, the high ROE reflects the aggressive expansion strategies of many commercial banks in Vietnam, driven by robust GDP growth and favorable macroeconomic conditions.

The Non-Performing Loan (NPL) ratio, averaging 2.2%, is a vital indicator of credit risk, representing the percentage of loans in default or nearing default In Vietnam, significant progress has been made in reducing NPLs due to reforms and stricter regulations by the State Bank of Vietnam (SBV) While an average of 2.2% is manageable, it still signals potential risks within banks' loan portfolios, with a wide range from 0.5% to 29.8% reflecting varying risk exposures among banks The standard deviation of 0.021 in NPL ratios reveals considerable differences in credit risk management practices Smaller or less capitalized commercial banks often face higher NPLs, jeopardizing their stability and operational efficiency This issue is particularly evident in state-owned and smaller banks, which struggle with credit risk, especially in sectors like real estate and small and medium-sized enterprises (SMEs) In contrast, larger banks such as Vietcombank and Vietinbank exhibit lower NPL ratios, indicating more effective credit risk management strategies.

The Crisis Indicator (CRISIS) variable demonstrates significant variability, with a mean of 0.308 and a standard deviation of 0.462 Its range spans from 0.000 to 1.000, indicating the coexistence of non-crisis and crisis periods within the dataset This extensive range and elevated standard deviation highlight the diverse economic conditions represented in the sample.

The analysis reveals that economic crises were present in about 30.8% of the observations from 2011 to 2023, indicating a moderate prevalence of crisis conditions during this period This variable effectively captures significant fluctuations in both the presence and severity of these economic crises.

The Capital Adequacy Ratio (CAR) is a crucial risk metric that indicates a bank's capacity to absorb potential losses, with a mean of 10.5% and a standard deviation of 0.037 While most banks exceed the regulatory minimum of 8%, the range of CAR from 5.5% to 26.6% highlights significant undercapitalization in some institutions, increasing their vulnerability to financial shocks A higher CAR signifies a bank's ability to protect depositors and absorb losses In Vietnam, commercial banks must adhere to Basel II regulations, with an average CAR of 10.5% reflecting healthy capital reserves essential for financial stability However, concerns arise as certain banks report a minimum CAR of 5.5%, indicating potential struggles to withstand economic downturns The variability in CAR among banks suggests that while larger institutions generally meet or exceed requirements, smaller banks may encounter difficulties in raising adequate capital, particularly during the ongoing restructuring efforts aimed at aligning with international standards in Vietnam's financial sector.

The cost-to-income ratio (CER) in the banking sector shows a standard deviation of 0.066, indicating significant efficiency disparities among banks With a mean CER of 23.2%, some banks experience costs relative to income as high as 56.4%, revealing inefficiencies in cost management and revenue generation Lower CER values signify greater efficiency, as banks can produce more income for each unit of cost Although the Vietnamese banking sector is enhancing its efficiency, the variation in CER highlights that certain banks face higher operational costs, often due to inefficient processes or the challenges of smaller scale operations These performance discrepancies can be linked to differences in scale, technology adoption, and management practices.

The Asset-Liability Ratio (ALR) and Liquidity Ratio (LR) further reflect the risk management practices of banks, with ALR averaging 0.029 and LR showing a mean of 0.891, suggesting that

Most banks maintain high liquidity levels compared to their liabilities, with an average Liquidity Ratio (LR) of 89.1%, indicating strong liquidity positions However, the LR's standard deviation of 0.186 highlights significant variability, leaving some banks vulnerable to liquidity risks during economic downturns This emphasizes the necessity for effective liquidity management, especially in the rapidly expanding Vietnamese market Additionally, Vietnamese banks adopt a conservative approach to provisioning, with an average Loan Loss Provision (LLP) of 0.8%, which helps protect against potential defaults Nonetheless, due to fluctuations in Non-Performing Loan (NPL) ratios, certain banks may need to bolster their provisions to better mitigate rising credit risks.

The average age of Vietnamese commercial banks is 24 years, ranging from newly established institutions to those that are 66 years old, indicating a predominance of younger banks that may struggle with market stability and internal processes With an average inflation rate of 4.85%, Vietnam's economic environment presents challenges for banks in managing loan pricing and interest rates, as high inflation can increase borrowing costs and impact profitability The average bank size, measured by total assets, is 5.130, with significant variation, suggesting that the sector is mainly composed of mid-sized banks Larger banks benefit from greater resources and risk management capabilities, allowing them to leverage economies of scale, while smaller banks may encounter difficulties in balancing growth and risk controls Overall, GDP growth plays a crucial role in shaping the economic landscape for banks in Vietnam.

Vietnam's economy is experiencing rapid growth, with a mean GDP growth rate of 8.7% and fluctuations between 2.5% and 17.3% This robust expansion presents significant opportunities for banks to increase their loan portfolios and enhance profitability, especially during peak economic periods However, it also poses challenges, as banks need to ensure they maintain adequate risk controls to prevent excessive exposure to bad loans amid potential economic overheating or downturns.

Table 3: Correlation analysis - the pairwise correlation matrix for variables

CAR CER ALR LR LLP AGE INF BS GDP

The correlation matrix reveals essential insights into the relationships between financial performance indicators and risk management metrics in Vietnamese commercial banks, highlighting their approach to balancing profitability and risk in a changing economic landscape Notably, there is a moderate positive correlation of 0.315 between Return on Assets (ROA) and the Capital Adequacy Ratio (CAR), suggesting that banks with stronger capital buffers tend to utilize their assets more efficiently, leading to improved profitability A higher CAR indicates that banks have increased capital to absorb potential losses, which enhances their capacity to engage in calculated risks in asset management This relationship indicates that well-capitalized banks in Vietnam demonstrate greater profitability and risk management capabilities.

34 resilient to economic fluctuations but are also more efficient in generating income from their assets, likely due to their ability to withstand financial shocks and continue lending during downturns

The positive correlation of 0.493 between Return on Assets (ROA) and the Liquidity Ratio (LR) highlights the importance of effective liquidity management for profitability Banks with higher liquidity can meet short-term obligations without liquidating assets or incurring costly borrowing, protecting their profits This underscores that liquidity management is not only a risk mitigation strategy but also a key factor in enhancing financial performance In Vietnam's fluctuating economic landscape, maintaining adequate liquidity helps banks avoid crises and seize profitable opportunities without financial strain Ultimately, effective liquidity management enables banks to sustain operations during market volatility, leading to improved asset returns.

The weak negative correlation of -0.086 between Return on Assets (ROA) and Non-Performing Loans (NPLs) suggests that Vietnamese banks may effectively mitigate risks through strategies like loan restructuring and maintaining high loan loss provisions This indicates that, despite the higher credit risk associated with NPLs, banks can manage and absorb these risks without significantly impacting profitability However, the slight negative correlation highlights the necessity of minimizing credit risk, as unchecked NPL growth could ultimately erode long-term profitability.

Impact on the profitability

ROA ROE Breusch–Pagan/Cook–Weisberg test for heteroskedasticity

• Variable: Fitted values of roa

The Breusch–Pagan/Cook–Weisberg test is essential for determining if the variance of error terms in a regression model remains constant across observations This test identifies heteroskedasticity by analyzing the correlation between the residuals and the fitted values of the dependent variable.

The analysis of the Return on Assets (ROA) reveals a Chi-Square statistic of 114.82 and a p-value of 0.0221 Given that the p-value is below the significance threshold of 0.05, we reject the null hypothesis (H0: Constant variance), indicating the presence of heteroskedasticity in the data.

The regression model for Return on Assets (ROA) indicates the presence of heteroskedasticity, as the variance of the error terms varies across the fitted values of ROA This variability in residuals suggests that the reliability of the regression estimates and standard errors may be compromised.

The Ordinary Least Squares (OLS) method may be unsuitable for models with variable residuals due to its assumption of homoscedastic error terms For Return on Assets (ROA), the Fixed Effects Model (FEM) is preferred as it controls for time-invariant bank-specific effects, helping to mitigate issues related to heteroskedasticity by accounting for unobserved heterogeneity Conversely, the Random Effects Model (REM) is more appropriate for Return on Equity (ROE) since it assumes that individual-specific effects are uncorrelated with the regressors, which is beneficial in the presence of heteroskedasticity Although FEM and REM can better address model specification and heteroskedasticity than OLS, using robust standard errors or alternative estimation techniques is essential for obtaining accurate and reliable coefficient estimates and inferences in heteroskedastic conditions.

Modifed Wald test and Wooldrigd test for ROA

Table 8: Modified Wald test for groupwise heteroskedasticity in fixed effect regression model

Table 9: Wooldridge test for autocorrelation in panel data

The results from the heteroskedasticity and autocorrelation tests indicate some important characteristics of the panel data regression model The Modified Wald test for groupwise

The analysis indicates a strong presence of heteroskedasticity, evidenced by a chi-square statistic of 358.69 (27 degrees of freedom) and a p-value of 0.0000, suggesting that error variance varies across groups Furthermore, the Wooldridge test reveals significant first-order autocorrelation in the panel data, with an F-statistic of 7.278 (26 degrees of freedom) and a p-value of 0.0121 To address these issues, employing a Random Effects Model (REM) is advisable, as it can effectively manage both heteroskedasticity and autocorrelation, leading to more reliable regression results Additionally, the Generalized Least Squares (GLS) approach in Fixed Effects Models (FEM) enhances the efficiency of coefficient estimates by accounting for within-group heteroskedasticity and autocorrelation, provided that the assumption of time-invariant individual effects holds true and the model adequately addresses these issues.

Table 10: Regression results with fixed effects model of banking organizations

ROA Coefficient Std err z P>z [95% conf interval]

The fixed effects regression analysis examining the determinants of Return on Assets (ROA) in commercial banks reveals several important insights, particularly in the context of the COVID-19

The analysis indicates that Non-Performing Loans (NPL) do not significantly impact Return on Assets (ROA), with a coefficient of 0.004 (p = 0.772), aligning with prior research on credit risk assessment In contrast, the COVID-19 Crisis Indicator (CRISIS) shows a significant positive effect on ROA (coefficient = 0.003, p < 0.001), suggesting that banks may have benefited from increased digital banking demand during the pandemic This resilience during economic shocks is supported by Demirgỹỗ-Kunt and Huizinga (2020), highlighting the advantages of adaptive risk management Furthermore, the Capital Adequacy Ratio (CAR) positively correlates with ROA (coefficient = 0.093, p < 0.001), reinforcing the importance of strong capital buffers for financial performance Conversely, the Capital Efficiency Ratio (CER) presents a negative but statistically insignificant effect on ROA (coefficient = -0.006, p = 0.214), indicating limited direct influence Lastly, the Asset Liability Ratio (ALR) significantly enhances ROA (coefficient = 0.478, p < 0.001), underscoring the critical role of effective asset-liability management in profitability.

The Loan-to-Deposit Ratio (LR) has a positive effect on Return on Assets (ROA), with a coefficient of 0.006, indicating that increased lending relative to deposits can boost profitability In contrast, Loan Loss Provisions (LLP) negatively influence ROA, with a coefficient of -0.248, highlighting that while these provisions are essential for risk management, they can hinder short-term profitability Additionally, the factors of Age, Inflation (INF), and GDP do not show a statistically significant impact on ROA in the current model.

The regression analysis demonstrates that the CRISIS variable, representing the impact of the COVID-19 pandemic, is a significant determinant of Return on Assets (ROA) for banking

A study of 43 institutions reveals a statistically significant positive relationship between the crisis and bank profitability, indicated by a coefficient of 0.003 and a p-value of 0.000 at the 1% level The narrow 95% confidence interval of [0.002, 0.004] reinforces the reliability of this finding, suggesting that some banking organizations may have benefited from regulatory responses or market dynamics during the pandemic This highlights the impact of external financial shocks, such as COVID-19, on the credit risk landscape, potentially influencing other banking metrics like capital adequacy (CAR), liquidity (ALR), and loan loss provisions (LLP).

Breusch and Pagan Lagrangian multiplier test and Wooldridge test

Table 11: Breusch and Pagan Lagrangian multiplier test and Wooldridge test

Var SD = sqrt(Var) Breusch and

Pagan Lagrangian multiplier test for random effects roe[sec_code1,t]

= Xb + u[sec_code1] + e[sec_code1,t]

Test: Var(u) = 0 data chibar2(01) 263.92 Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation

The Breusch and Pagan Lagrangian Multiplier Test evaluates the necessity of random effects in a model by determining if the variance of the random effects component is zero With a test statistic of chibar2(01) = 263.92 and a p-value of 0.0000, the null hypothesis that Var(u) = 0 is strongly rejected This result indicates that random effects are significant, suggesting that a Random Effects Model (REM) is more suitable than a Simple Model.

The Ordinary Least Squares (OLS) model fails to consider random effects, highlighting the necessity of incorporating these elements in panel data analysis This aligns with previous research, including Greene (2012), which underscores the significance of addressing unobserved heterogeneity among entities for more accurate results.

The Wooldridge test for first-order autocorrelation assesses serial correlation in panel data, revealing significant autocorrelation with an F-statistic of 7.278 and a p-value of 0.0121 This finding indicates that the model's residuals are not independent over time, potentially biasing the estimation of standard errors and coefficients Wooldridge (2002) emphasizes that neglecting autocorrelation can result in inefficient estimates and flawed inference, highlighting the necessity for suitable model adjustments.

The findings emphasize the significance of employing a Random Effects Model (REM) to address unobserved heterogeneity and adjust for autocorrelation, ensuring the precision of regression analysis Consequently, these results validate the application of REM and highlight the critical need to manage autocorrelation for achieving reliable and valid estimates of Return on Equity (ROE) effects.

Table 12: Regression results with random effects model of banking organizations

ROE Coefficient Std err z P>z [95% conf interval]

The Fixed Effects Model (FEM) with Generalized Least Squares (GLS) analysis reveals that the Capital Adequacy Ratio (CAR) has a significant negative impact on Return on Equity (ROE) (β = -0.393, p < 0.001), suggesting that higher capital ratios correlate with lower profitability, which contrasts with Berger and Bouwman (2013) who posit that increased capital adequacy fosters financial stability and performance during stress Additionally, the Cost-to-Income Ratio (CER) demonstrates a significant negative effect on ROE (β = -0.206, p < 0.001), aligning with Athanasoglou et al (2008) that higher operational costs relative to income hinder profitability, thus underscoring the necessity for operational efficiency in banking.

The random effects regression analysis of Return on Equity (ROE) reveals that Non-Performing Loans (NPL) have a coefficient of -0.125 (p = 0.394), indicating no significant impact on ROE This suggests that while NPL is important for credit risk assessment, it does not directly affect banks' equity returns in this timeframe Additionally, the COVID-19 Crisis Indicator (CRISIS) shows a coefficient of 0.007 (p = 0.225), which is also statistically insignificant, highlighting that the pandemic's direct influence on ROE is minimal and that the crisis likely did not substantially alter banks' equity returns in this context.

In contrast, the Asset Liability Ratio (ALR) demonstrates a substantial positive effect on ROE (β

Effective asset-liability management is essential for maximizing returns, as supported by Demirgỹỗ-Kunt and Huizinga (2000) Additionally, the Leverage Ratio (LR) positively impacts Return on Equity (ROE) with a coefficient of β = 0.109 (p < 0.001), aligning with DeYoung and Roland's (2001) assertion that prudent management of higher leverage can boost bank profitability by enhancing return on equity.

Results of the effect of credit risk on the financial performance and hypothesis results

Hypothesis 01: There is a negative and significant relationship between NPLs and commercial banks' financial performance

The hypothesis suggests a negative correlation between Non-Performing Loans (NPLs) and the financial performance of commercial banks, specifically through Return on Assets (ROA) and Return on Equity (ROE) However, findings from the Fixed Effects Model (FEM) with Generalized Least Squares (GLS) regression indicate otherwise The coefficient for NPLs related to ROA is positive (0.004) but statistically insignificant (p-value of 0.772), suggesting that NPLs do not significantly impact ROA and contradicting the initial hypothesis This indicates that a higher NPL ratio may not adversely affect the profitability of Vietnamese commercial banks, which is unexpected given that previous studies in developing markets typically report a negative relationship between NPLs and profitability, as noted by Berger & DeYoung (1997).

The analysis indicates that increasing provisioning needs and decreasing interest income are associated with higher non-performing loans (NPLs), which negatively affect return on equity (ROE) with a coefficient of -0.125 However, this relationship is statistically insignificant (p = 0.394), suggesting that while higher NPLs may reduce equity returns, the effect is weak This finding aligns with Ghosh (2015), who reported a similar negative but statistically insignificant impact of NPLs on profitability in Indian commercial banks, particularly in less mature banking markets.

This study's findings partially support existing literature on the negative impact of non-performing loans (NPLs) on banking performance Klein (2013) noted that rising NPLs indicate poor asset quality and increased default risk, often resulting in lower profitability due to higher provisioning and operational inefficiencies However, the insignificant results for return on assets (ROA) and return on equity (ROE) suggest that Vietnamese commercial banks have implemented effective strategies to mitigate NPL impacts, such as enhanced risk management and diversified income sources While Foos et al (2010) indicated that increased credit risk typically leads to reduced profitability, Vietnamese banks appear less affected by NPLs in the short term, potentially due to robust regulatory oversight from the State Bank of Vietnam (SBV), which has enforced stricter capital and loan quality standards Additionally, the SBV's proactive measures, including loan restructuring during the COVID-19 pandemic, may have further alleviated the negative effects of NPLs on bank performance.

In recent years, Vietnam's banking sector has intensified its efforts in non-performing loan (NPL) management, maintaining NPL ratios below critical thresholds despite ongoing challenges Strong government initiatives, particularly through the Vietnam Asset Management Company (VAMC), have played a crucial role in addressing distressed assets Coupled with stricter provisioning requirements from the State Bank of Vietnam (SBV), these measures have helped banks mitigate the adverse effects of NPLs on their financial performance Furthermore, Vietnamese banks are increasingly diversifying their income streams, focusing on non-interest income sources like digital banking fees and wealth management services, thereby reducing reliance on traditional lending.

A diversified business model may account for the insignificant relationship between non-performing loans (NPLs) and bank performance in this study By adopting such a strategy, banks can maintain profitability despite temporary declines in credit quality This approach aligns with practices observed in other developing economies, like India and China, where financial innovation and digital platform adoption have effectively reduced the adverse effects of increasing credit risk.

Hypothesis 02: There is a positive and significant relationship between capital adequacy ratio and commercial banks' financial performance

The hypothesis suggests a significant positive relationship between the Capital Adequacy Ratio (CAR) and the financial performance of commercial banks, as measured by Return on Assets (ROA) and Return on Equity (ROE) The findings from the Fixed Effects Model (FEM) with Generalized Least Squares (GLS) regression support this hypothesis, revealing a positive coefficient for CAR and ROA (0.093) with a highly significant p-value of 0.000 This indicates that a higher CAR, which signifies the bank's financial stability, correlates with improved asset efficiency and profitability Consistent with previous research, such as Kosmidou (2008), banks with larger capital buffers demonstrate better profitability due to their enhanced capacity to absorb shocks during financial downturns Similarly, the positive coefficient for CAR with respect to ROE (0.127) and a significant p-value (0.000) further validates the hypothesis, indicating that higher CAR improves the bank's ability to generate shareholder returns This aligns with findings from Pasiouras and Kosmidou (2007), which established a significant positive relationship between CAR and financial performance in European banks, allowing banks to engage in riskier yet potentially more profitable activities while maintaining a stable capital base.

The existing literature strongly supports the positive correlation between Capital Adequacy Ratio (CAR) and financial performance Vong and Chan (2009) highlight that a higher CAR signifies improved capital adequacy, enabling banks to better withstand financial stress, which in turn boosts profitability and sustainability Well-capitalized banks can pursue riskier yet more lucrative investment opportunities while maintaining a safety buffer This study corroborates these findings, demonstrating that CAR significantly enhances both Return on Assets (ROA) and Return on Equity (ROE), indicating that banks with robust capital are generally more profitable Additionally, Demirgỹỗ-Kunt and Huizinga (2000) underscore the critical role of CAR in financial stability.

In 49 developing economies, banking systems often experience instability, making a high Capital Adequacy Ratio (CAR) crucial for safeguarding banks against insolvency A robust CAR not only enhances financial health but also fosters trust among regulators, investors, and depositors, ultimately boosting profitability The Vietnamese banking sector, similar to other emerging markets, has faced challenges with non-performing loans (NPLs) and loan quality A higher CAR serves as a protective measure, enabling banks to maintain operations during financial stress, exemplified by the COVID-19 pandemic This study's findings align with those observed in other developing markets, such as the research by Al-Tamimi and Obeidat.

A study conducted in 2013 highlighted the positive impact of a strong capital base on bank profitability in the UAE This relationship is especially pronounced in Vietnam, where the banking sector is adapting to international regulations such as Basel III The State Bank of Vietnam (SBV) has implemented stricter capital adequacy regulations, which are contributing to improved bank performance in the country.

In Vietnam, the State Bank of Vietnam (SBV) has highlighted the significance of capital adequacy in alignment with global Basel III regulations Vietnamese commercial banks are enhancing their Capital Adequacy Ratios (CAR) to meet international standards, positively impacting their financial performance, as demonstrated by the strong correlation between CAR and both Return on Assets (ROA) and Return on Equity (ROE) A higher CAR enables banks to absorb potential loan defaults and pursue aggressive growth strategies, such as market expansion and technological investments, while maintaining financial stability Despite the rapid growth of the banking sector, risks from rising non-performing loans (NPLs) and external economic shocks persist The favorable link between CAR and profitability indicates that banks' efforts to strengthen their capital base are paying off, positioning those with higher CARs to attract foreign investment and enhance their competitiveness in an increasingly integrated global financial market.

Hypothesis 03: There is a negative and significant relationship between the cost effiency ratio and commercial banks' financial performance

The hypothesis posits that there is a negative and significant relationship between the Cost Efficiency Ratio (CER) and the financial performance of commercial banks, measured through

The analysis of Return on Assets (ROA) and Return on Equity (ROE) reveals mixed results from the FEM with GLS regression For ROA, the coefficient for Cost Efficiency Ratio (CER) is negative (-0.007) but statistically insignificant (p = 0.214), suggesting that while higher cost inefficiency may reduce asset profitability, this effect is not strong in the short term for Vietnamese commercial banks, which may depend more on factors like asset growth and interest margins This contrasts with Sufian and Chong (2008), who found a positive impact of cost efficiency on profitability in developing economies, indicating unique operational conditions in Vietnam Conversely, for ROE, the CER coefficient is negative (-0.207) and statistically significant (p = 0.002), confirming that cost inefficiency adversely affects banks' ability to generate shareholder returns This aligns with the findings of Athanasoglou, Brissimis, and Delis (2008), highlighting that higher operational costs diminish profitability, particularly in competitive environments where effective cost management is crucial.

The Cost Efficiency Ratio (CER) is a key indicator of a bank's operational efficiency, reflecting the relationship between expenses and income; a higher CER suggests poorer cost control, which can negatively impact profitability Research by Dietrich and Wanzenried (2011) highlights that operational inefficiency, indicated by a higher cost-to-income ratio, significantly hinders profitability in European banks, emphasizing the need for effective cost management in competitive markets Similarly, Pasiouras and Kosmidou (2007) found that increased operational costs, linked to a rising CER, restrict a bank's return generation, particularly in less developed banking sectors In the context of Vietnamese commercial banks, the notable negative correlation between CER and ROE reinforces the necessity of operational efficiency for achieving sustainable returns for shareholders, as banks that struggle to manage operating expenses face diminished profitability, especially where return on equity is a vital performance metric.

The insignificant relationship between Cost Efficiency Ratio (CER) and Return on Assets (ROA) in Vietnam may stem from unique operating conditions, where banks can sustain asset profitability despite operational inefficiencies, thanks to stable interest margins and lower reliance on high-cost operations This contrasts with findings in other emerging markets, such as ASEAN countries, where cost inefficiency significantly negatively impacts both ROA and Return on Equity (ROE) In Vietnam, factors like government support, an expanding customer base, and favorable interest rate spreads may mitigate the short-term effects of cost inefficiency on asset returns.

Vietnamese commercial banks are navigating a rapidly growing economy while facing significant challenges in cost management The sector's expansion, fueled by rising credit demand, financial service digitization, and foreign investments, has led to increasing operational costs, particularly in technology, workforce, and regulatory compliance Consequently, many banks are experiencing high cost-to-income ratios, negatively impacting their return on equity (ROE) In response, the State Bank of Vietnam has emphasized the need for greater cost efficiency, urging banks to enhance operational processes and adopt new technologies Many institutions are investing in digital banking platforms to streamline operations and reduce costs, yet the transition to a more efficient model is still in progress, potentially affecting profitability in the short term The study highlights a significant negative correlation between cost efficiency ratio (CER) and ROE, indicating that banks must prioritize cost reduction to remain competitive, especially as foreign banks enter the market Failure to manage costs may hinder banks' ability to deliver adequate returns to shareholders amidst increasing competition and regulatory pressures.

Hypothesis 04: There is a positive and significant relationship between the average lending rate and commercial banks' financial performance

Hypothesis 04 posits that there is a positive and significant relationship between the average lending rate (ALR) and the financial performance of commercial banks This hypothesis is substantiated by the results from the Fixed Effects Model (FEM) with Generalized Least Squares (GLS), which show a robust and significant positive relationship between ALR and both Return

RECOMMENDATION

Recommendation for commercial bank

Vietnamese commercial banks (NHTM) must implement comprehensive strategies to mitigate credit risk and enhance financial performance, particularly regarding non-performing loans (NPLs), loan loss provisions, and capital adequacy Learning from best practices in developed banking systems can strengthen their resilience and ensure long-term profitability Recommendations based on research and international standards like Basel II and III emphasize the importance of robust provisioning policies that balance safeguarding against bad debts with capital preservation Accurate NPL classification is crucial to avoid unnecessary provisioning that could erode profitability, aligning with regulatory requirements from the State Bank of Vietnam and Basel III Studies indicate that banks employing early warning systems for loan defaults can minimize losses effectively Research shows that sophisticated internal risk monitoring can significantly reduce the time to detect loan impairments, lessening the financial burden of provisioning for NPLs Additionally, a well-structured provision system enables banks to maintain profitability during high NPL periods, as demonstrated by UK banks during the global financial crisis Similarly, Chinese banks have successfully managed bad loans through stringent provisioning rules, suggesting that Vietnamese banks could benefit from data analytics and AI-driven predictive models to proactively manage credit risks In the context of Vietnam's economic fluctuations, the Basel III framework provides a structured approach to mitigating credit risk by reinforcing capital adequacy and ensuring banks maintain sufficient reserves against potential losses, thereby enhancing overall financial performance.

Economic downturns often lead to an increase in non-performing loans (NPLs), highlighting the need for strengthened loan loss provisions and enhanced capital buffers, as seen in developed markets like the US and UK These regions have implemented rigorous capital standards to mitigate credit risk and systemic failure Basel III introduces crucial measures such as the Leverage Ratio and Liquidity Coverage Ratio (LCR), which curb excessive borrowing and ensure banks hold sufficient high-quality liquid assets for short-term obligations For Vietnamese banks, which face liquidity risks, adopting Basel III liquidity measures would bolster their resilience against liquidity crunches, enabling them to fulfill obligations even in stressed conditions This aligns with the recommendation for efficient liquidity management, a strategy successfully utilized in China and Japan to maintain operational stability during economic shocks.

Vietnamese banks must adopt a cautious approach to credit growth to avoid significant increases in non-performing loans (NPLs), as evidenced by past banking crises in Japan and South Korea Rapid credit expansion without proper risk assessment can lead to financial instability, as highlighted by Minsky’s Financial Instability Hypothesis, which shows that banks often take excessive risks during economic booms Historical examples, such as Japan's asset bubble in the late 1980s and the Asian Financial Crisis of 1997 in South Korea, illustrate the dangers of reckless lending practices To mitigate these risks, Vietnamese banks should implement rigorous credit appraisal systems that consider borrowers' financial health and sector-specific risks Learning from the UK, banks like Barclays and HSBC have successfully adopted risk-adjusted lending policies, focusing on sustainable credit growth and maintaining high credit quality through effective credit scoring models and sectoral risk assessments.

To enhance Return on Equity (ROE) and ensure sustainable profitability, Vietnamese banks must focus on long-term business strategies that adhere to strict credit controls It is crucial for banks to resist the allure of quick credit expansion that compromises credit quality, as this can lead to detrimental consequences.

An increase in non-performing loans (NPLs) adversely affects return on equity (ROE), as evidenced by research findings In developed markets like the US and UK, banks have effectively balanced profitability and risk management by implementing robust credit risk frameworks Studies indicate that banks in Malaysia and Singapore, which adopted stricter credit controls after 2008, achieved notable ROE improvements while maintaining loan quality Furthermore, banks pursuing higher profitability from high-risk loans must implement strong risk mitigation strategies to safeguard financial performance Vietnamese banks should consider diversifying their revenue streams by investing in non-interest income sources, such as wealth management services, insurance products, and digital financial platforms, which can lessen reliance on traditional lending susceptible to default risk Research from McKinsey (2020) highlights that Chinese banks have enhanced profitability and resilience by embracing digital banking innovations and expanding into non-lending sectors, presenting a valuable model for revenue diversification for Vietnamese banks.

Vietnamese banks are advised to strategically restructure their assets and scale to ensure financial stability While growth can provide competitive advantages, it also carries risks, especially when fueled by excessive lending to low-quality borrowers Historical examples, such as the rapid expansion of Chinese banks in the early 2000s, highlight the dangers of insufficient risk controls, which resulted in rising non-performing loans (NPLs) To address these challenges, Chinese banks restructured and adopted Basel III guidelines to enhance capital adequacy and risk management Vietnamese banks should similarly prioritize rigorous risk controls and high-quality lending alongside growth Research indicates that effective asset management and growth strategies can minimize bad loans, fostering sustainable financial performance Furthermore, Basel III's focus on capital adequacy ratios and Tier 1 capital requirements offers a framework for ensuring that asset growth is supported by a strong capital buffer, enabling banks to withstand financial shocks and maintain profitability during economic downturns Additionally, during periods of high inflation, a conservative lending approach is recommended, as evidenced by tighter monetary policies in developed markets like the UK and US.

During inflationary periods, indiscriminate lending can lead to increased credit risk, as borrowers face higher costs and a greater likelihood of default Following the 2008 financial crisis, South Korea implemented stricter credit controls to align credit growth with borrowers' repayment capabilities Vietnamese banks can adopt similar strategies by prioritizing high-quality borrowers and limiting credit expansion during inflation to prevent bad debt accumulation Research by Nguyen and Phan (2018) indicates a correlation between rising inflation and higher non-performing loan (NPL) ratios in Vietnam, highlighting the necessity for cautious lending practices in such economic conditions.

To remain competitive and minimize credit risk, Vietnamese banks must adopt advanced credit risk management techniques, leveraging international best practices from the US and UK The integration of big data analytics, artificial intelligence (AI), and machine learning can enhance the assessment of borrower risk and predict loan defaults Research by Chijoriga (1997) indicates that banks utilizing modern risk management technologies are more successful in preventing defaults and maintaining profitability Leading US banks like Wells Fargo and Bank of America have implemented AI-driven credit assessment tools to optimize lending while reducing risk Vietnamese banks should invest in similar technologies, adopting credit scoring models and predictive analytics to improve credit assessments and lower default rates Implementing effective loan pricing models that account for credit risk, akin to those used in the US and South Korea, can further boost financial performance by ensuring appropriate compensation for risks taken Additionally, strengthening corporate governance, as seen in successful financial institutions in Japan and Korea, is vital for managing credit risk By establishing independent risk management committees and promoting transparency in credit practices, Vietnamese banks can navigate credit risk complexities more effectively, enhancing their financial performance and competitiveness in regional and global markets.

Ensuring compliance with Basel III is critical for Vietnamese banks to strengthen their capital positions and manage credit risk effectively Basel III introduces several key measures, including

Higher capital adequacy ratios (CAR), leverage ratios, and liquidity coverage ratios (LCR) are essential for enhancing the resilience of banks during financial crises Research by Pham (2021) shows that Vietnamese banks with elevated CARs and robust liquidity buffers can better absorb bad debt impacts and sustain profitability Additionally, the countercyclical buffer mandated by Basel III requires banks to maintain extra capital during periods of high credit growth, thereby mitigating the risk of non-performing loan (NPL) accumulation By adopting these measures, Vietnamese banks can align with global standards, enhance financial stability, and lower credit risk exposure.

To enhance their risk management strategies, Vietnamese banks need to establish early warning systems that can identify potential loan defaults before they turn into non-performing loans (NPLs) The successful implementation of such systems in Japan has proven effective in helping banks detect distressed loans promptly.

Recommendation for State bank of Vietnam

The State Bank of Vietnam (SBV) is essential in maintaining the financial stability of the Vietnamese banking system Research highlights the negative impacts of credit risk, particularly from non-performing loans (NPLs) and insufficient provisioning, alongside the effects of rapid credit growth on financial performance To enhance the soundness and sustainability of the commercial banking sector, the SBV must implement strategic interventions informed by domestic insights and international best practices from highly regulated markets such as the US, UK, China, Japan, and South Korea.

The State Bank of Vietnam (SBV) should strengthen its regulatory framework for loan loss provisioning to help Vietnamese commercial banks effectively manage increasing non-performing loans (NPLs) Research indicates that insufficient or inaccurate credit loss provisioning adversely affects bank profitability, impacting both Return on Assets (ROA) and Return on Equity (ROE) To address this, the SBV must establish stricter guidelines that encourage banks to adopt conservative, forward-looking provisioning practices By implementing Basel III guidelines, the SBV should require banks to perform regular stress tests to assess their ability to withstand potential losses in varying economic conditions, thereby enhancing their resilience to credit risk Furthermore, the SBV could enforce regulations mandating the use of internal credit risk models, which are already commonplace in more advanced banking markets.

To enhance the accuracy of potential loan default estimates, it is essential to tailor models to the distinctive features of the Vietnamese economy This includes considering sector-specific risks, particularly in areas like real estate, as well as addressing the dynamic nature of the SME sector By doing so, a more refined risk assessment framework can be established.

The State Bank of Vietnam (SBV) should establish a credit growth oversight mechanism to manage the unchecked expansion of credit, a significant contributor to non-performing loans (NPLs) in the nation's commercial banks By promoting sustainable lending practices, the SBV can encourage banks to prioritize credit quality alongside growth Implementing sectoral credit limits will help restrict lending to high-risk industries, such as construction and speculative real estate, which are more vulnerable during economic downturns This approach mirrors China's successful post-2008 strategy of capping loans to speculative sectors to avert banking crises Additionally, the SBV should incentivize banks to invest in sectors with strong growth potential and lower default risks, like technology and manufacturing, aligning credit expansion with national economic objectives Furthermore, adopting the countercyclical capital buffer mechanism from Basel III will require banks to hold extra capital during periods of excessive credit growth, safeguarding them against future economic challenges and minimizing the risk of significant NPL accumulation.

The State Bank of Vietnam (SBV) must enhance its role in managing and resolving non-performing loans (NPLs) to mitigate systemic risks and protect banks' financial performance Establishing a centralized asset management company (AMC) could effectively manage distressed assets, as demonstrated by successful models in South Korea and Japan Additionally, the SBV should streamline legal processes for collateral sales and foreclosures to accelerate NPL recovery, thereby improving banks' capital recovery efforts Furthermore, enhancing supervision and enforcement of risk management practices is essential, as inadequate risk management significantly impacts financial performance The SBV should mandate the integration of Basel III frameworks, focusing on capital adequacy, liquidity coverage ratios, and net stable funding ratios, to ensure banks maintain adequate liquid assets.

The State Bank of Vietnam (SBV) should prioritize covering short-term obligations while ensuring stable, long-term funding sources By adopting rigorous stress-testing procedures and tighter liquidity regulations, similar to those implemented by South Korean regulators after the 1997 financial crisis, the SBV can enhance the stability of its banking sector Additionally, it is crucial for the SBV to strengthen its supervisory departments by equipping them with the necessary tools and training to effectively monitor compliance with these regulations.

The State Bank of Vietnam (SBV) should focus on promoting digital and technological innovation in the banking sector by fostering the adoption of advanced risk management tools like big data analytics, artificial intelligence (AI), and machine learning By creating a supportive regulatory environment, similar to the UK's Financial Conduct Authority (FCA) sandbox model, the SBV can enable banks to integrate these technologies into their risk management systems Establishing a regulatory innovation hub would allow banks to experiment with digital tools for credit risk assessment while ensuring compliance, ultimately improving risk management and enhancing the global competitiveness of the Vietnamese banking sector.

The State Bank of Vietnam (SBV) should actively enhance financial literacy and risk awareness within the banking sector Research highlights the necessity for banks to implement advanced credit assessment techniques to mitigate the increase of non-performing loans (NPLs) To address this, the SBV must require banks to organize regular training for credit officers, emphasizing sophisticated credit appraisal methods and risk assessment tools Additionally, the SBV can partner with international financial institutions like the IMF and World Bank to facilitate capacity-building initiatives, enabling Vietnamese banks to adopt global best practices in credit risk management.

The State Bank of Vietnam (SBV) must proactively address macroeconomic risks, especially inflation and its effects on the banking sector High inflation often results in tighter monetary policies, elevated interest rates, and increased reserve requirements, subsequently raising borrowing costs and default risks To counter these challenges, the SBV should implement a flexible monetary policy that effectively balances inflation control with the necessity of maintaining adequate liquidity in the banking system.

To enhance liquidity in the banking system, the State Bank of Vietnam (SBV) can learn from Japan and South Korea's use of innovative monetary tools to control inflation while fostering credit growth Implementing targeted interest rate policies or quantitative easing during high inflation periods may be beneficial Additionally, the SBV should closely coordinate with fiscal authorities to align monetary and fiscal policies, ensuring robust support for financial stability.

To improve credit management practices, banks need to adopt a robust credit management process that encompasses the entire credit lifecycle, including loan appraisal, disbursement, and ongoing monitoring This stringent framework helps mitigate risks associated with lending, significantly reducing the likelihood of non-performing loans (NPLs) and enabling timely identification of operational deficiencies By implementing effective credit management, banks can sustain a healthy loan portfolio and minimize risks stemming from inadequate credit decisions and operational errors.

Banks should utilize insurance tools and collateral protection as essential risk management strategies It is crucial for borrowers to obtain relevant insurance products, including personal credit insurance, export credit insurance, construction insurance, and goods insurance Given that credit risk can arise from various unpredictable factors, banks must adopt these measures to protect loan repayments and minimize potential losses This approach is especially vital in high-risk sectors like construction and export businesses, where risk profiles can fluctuate significantly.

Banks should consider adopting credit risk insurance instruments, such as Credit Default Swaps (CDS), Credit Default Options, Total Return Swaps (TRS), and Credit-Linked Notes (CLN), which are widely used in more developed financial markets These tools enable banks to hedge against credit risk, transfer risk exposure, and enhance the management of their loan portfolios By incorporating these instruments into their risk management strategies, banks can minimize exposure to high-risk loans while diversifying and optimizing their portfolios, similar to practices in advanced markets like the US and UK Furthermore, developing a robust debt trading market is essential for effectively managing non-performing loans, as it allows banks to restructure or offload bad debts, thereby improving liquidity and reducing overall credit risk.

The establishment of a strong securitization framework is essential for developing a market where banks can transform debt into tradable securities This process enhances market liquidity and draws in financially robust investors, allowing banks greater flexibility in managing their debt portfolios By securitizing debt, similar to practices in advanced financial markets like the US, a dedicated debt trading market emerges, which can alleviate the pressure of non-performing loans (NPLs) on banks, thereby promoting more efficient debt restructuring and risk-sharing throughout the financial ecosystem.

CONCLUSION AND IMPLICATIONS

Conclusion

This study examines how credit risk affects the financial performance of commercial banks in Vietnam, utilizing panel data from 27 banks over a specified period The findings reveal significant insights into the relationship between credit risk management and the overall financial health of these institutions.

From 2011 to 2023, this study analyzes the financial performance of selected banks through key metrics such as return on assets and return on equity It delves into the complex relationships between various financial indicators and examines how credit risk impacts the performance of commercial banks.

The investigation into the relationship between non-performing loans (NPLs) and financial performance revealed unexpected findings, as NPLs did not show a statistically significant correlation with return on assets (ROA) This suggests that Vietnamese banks may have implemented effective strategies to counteract the negative impacts of NPLs, diverging from existing literature that typically highlights a detrimental effect on bank profitability (Oketch, 2018) The lack of a significant relationship could reflect enhanced risk management practices and regulatory interventions by the State Bank of Vietnam (SBV), which may mitigate the adverse effects commonly seen in other developing markets.

The hypothesis supporting a positive relationship between Capital Adequacy Ratio (CAR) and financial performance was strongly validated, with both Return on Assets (ROA) and Return on Equity (ROE) demonstrating significant positive correlations with CAR This highlights the essential role of capital adequacy in boosting profitability, consistent with prior research emphasizing the necessity of a robust capital foundation for financial stability and profitable operations The findings also reflect the effectiveness of the State Bank of Vietnam's regulatory framework, aligning Vietnamese banks with international standards like Basel III However, the financial performance of commercial banks is adversely affected by high bad debt and credit provision ratios While credit risk provisions are crucial for bank stability, their high levels can lead to increased operating costs, negatively impacting financial performance Additionally, bad debt poses varying challenges to banks' financial performance during and outside financial crises, leading to capital losses and liquidity issues, ultimately complicating the pursuit of high profits.

To maximize profits while managing risks during a crisis, banks must balance between accepting higher bad debts and increased operating costs Empirical evidence indicates that bad debts negatively impact bank profits, making it essential for banks to prioritize the control and recovery of bad debts Establishing clear policies regarding responsibilities, benefits, rewards, and sanctions for credit activities is crucial Additionally, since the credit risk provision ratio is influenced by outstanding debts, debt classifications, and collateral, banks should emphasize effective mortgage valuation and management when extending credit.

The analysis of the Cost Efficiency Ratio (CER) presents a mixed perspective, indicating a negative relationship with Return on Assets (ROA) that lacks statistical significance, implying that cost inefficiency may not significantly affect short-term asset returns This finding contrasts with previous studies that highlight the adverse effects of cost inefficiency on profitability However, the notable negative effect of CER on Return on Equity (ROE) underscores the critical role of operational efficiency in enhancing shareholder returns This aligns with existing literature that stresses the importance of effective cost management in sustaining profitability within competitive banking sectors.

The study confirms a strong positive relationship between Average Lending Rate (ALR) and financial performance, with both Return on Assets (ROA) and Return on Equity (ROE) showing significant positive correlations with ALR This aligns with theoretical expectations that increased lending rates enhance profitability through improved interest margins The findings highlight the critical role of strategically managing lending rates to optimize financial performance, especially in Vietnam's dynamic economic and regulatory environment.

The investigation into Loan Loss Provisions (LLP) demonstrated a significant negative correlation with Return on Equity (ROE), while its effect on Return on Assets (ROA) was not statistically significant This finding supports the theory that increased provisions for potential loan losses can hinder profitability and equity returns, as noted by Laeven & Levine (2009) and Berger & DeYoung (1997) Consequently, banks must find a balance between effective provisioning and sustaining financial performance, highlighting the broader challenges encountered by the Vietnamese banking sector.

67 managing credit risk The size of the bank often impacts profitability due to economies of scale, while bank age may influence operational efficiency and market experience

To enhance the robustness of the study's findings on bank performance, control variables such as GDP growth rate, crisis, and inflation were included in the model These macroeconomic factors significantly influence financial performance by affecting economic conditions and interest rate environments, offering a more comprehensive understanding of the dynamics at play in the banking sector.

The study reveals a negative correlation between credit risk and the financial performance of Vietnamese commercial banks, indicating that high levels of bad debt and credit provisions significantly diminish profitability While credit risk provisions are essential for bank stability, they elevate operating costs, adversely impacting financial outcomes Bad debt particularly affects liquidity and poses challenges for banks during economic crises, forcing them to balance profit maximization with risk management To enhance profitability, banks must prioritize controlling bad debts through effective recovery strategies and robust credit policies that outline responsibilities and rewards Additionally, careful assessment of collateral and outstanding debts is crucial for managing credit risk provisions Banks should exercise caution in lending amid rising inflation and falling interest rates, focusing on improving loan quality rather than indiscriminately expanding credit to minimize bad debt ratios.

The study highlights the critical role of equity capital in establishing a balanced capital use plan that integrates mobilization and lending, while also implementing mechanisms to monitor banks' investment activities It reveals that non-performing loans adversely impact the financial performance of selected commercial banks, and the capital adequacy ratio positively influences the return on assets and economic value-added measures Additionally, the loans and advances ratio shows a positive effect on financial performance, though this effect is insignificant Control variables such as bank size, age, gross domestic product, and inflation positively affect financial performance, while the monetary policy rate has a negative impact Diagnostic tests confirm the reliability of these findings, concluding that non-performing loans are a significant determinant of commercial banks' financial performance.

Contribute and limitation

This study analyzes the effect of credit risk management on the profitability of 27 commercial banks in Vietnam from 2016 to 2021, utilizing Fixed Effects Model (FEM) and Random Effects Model (REM) with Generalized Least Squares (GLS) Key metrics assessed include Loan Loss Provision (LLP), Non-Performing Loan Ratio (NPLs), Cost Efficiency Ratio (CER), Liquidity Ratio (LR), Loan-to-Deposit Ratio (LDR), Capital Adequacy Ratio (CAR), bank size (BS), and bank age (AGE), with Return on Assets (ROA) and Return on Equity (ROE) serving as profitability indicators This research enhances the understanding of banking performance by providing a detailed analysis of how various financial metrics influence the profitability of commercial banks in Vietnam, thereby contributing significantly to the literature on the banking sector.

This study examines the Vietnamese banking sector, highlighting its distinct economic and regulatory landscape, which offers valuable insights for emerging markets The research reveals a significant relationship between Capital Adequacy Ratio (CAR) and financial performance, emphasizing the importance of robust capital buffers in improving Return on Assets (ROA) and Return on Equity (ROE) These findings are consistent with established theoretical frameworks and empirical evidence, providing specific insights into the impact of regulatory practices and capital management strategies on bank profitability in a developing economy.

This study introduces a novel variable—CRISIS—to assess the impact of the COVID-19 pandemic on banking institutions, enhancing the understanding of how external shocks affect banks' financial performance By analyzing the State Bank of Vietnam's monetary easing policies during the pandemic, including interest rate cuts and debt restructuring, the research examines the influence of regulatory interventions on banks' credit risk management This unique context of crisis-induced financial stress and monetary easing allows for a comprehensive analysis of the relationship between credit risk, systemic financial crises, and government policy, addressing a significant gap in existing literature.

The study reveals mixed evidence regarding Non-Performing Loans (NPLs) in Vietnam, indicating no significant relationship with Return on Assets (ROA), which may suggest that local banks have effective risk mitigation strategies or successful regulatory interventions This emphasizes the need for context-specific analysis to understand the impact of NPLs on bank performance in various economic environments Furthermore, the positive correlation between Average Lending Rate (ALR) and both ROA and Return on Equity (ROE) highlights the importance of higher lending rates in enhancing bank profitability, offering practical insights for financial management in a competitive market amid ongoing economic reforms in Vietnam Additionally, the findings on Cost Efficiency Ratio (CER) and Loan Loss Provisions (LLP) provide valuable insights into operational efficiency, revealing that CER negatively impacts ROE and emphasizing the intricate relationship between cost management and financial performance, which is crucial for bank management and regulators.

This study has notable limitations, primarily its focus on the Vietnamese banking sector, which restricts the generalizability of its findings to other regions or countries with different economic and regulatory environments Future research should consider comparative analyses across various emerging markets to enhance the applicability of the results Additionally, while the Fixed Effects Model (FEM) with Generalized Least Squares (GLS) offers robust estimates, it is subject to inherent assumptions and potential omitted variable bias, as it presumes constant unobserved heterogeneity over time Although control variables like bank size, age, GDP growth rate, and inflation were included, other significant factors such as technological advancements, competitive dynamics, and regulatory changes were overlooked Future studies should integrate these additional variables and utilize alternative econometric techniques to overcome these limitations.

The study offers important insights into the connection between financial metrics and bank performance; however, caution is necessary in interpreting the results due to potential measurement errors and data limitations Differences in data quality and reporting standards among banks may impact the accuracy of the findings To enhance future research, it is essential to use more detailed data and investigate alternative performance and risk measures.

The author's research is limited by the collection of data from only 27 commercial banks in Vietnam, which may not represent the entire banking sector Beyond credit risk, banks encounter various other risks, including liquidity, market, operational, and reputation risks Additionally, key factors influencing bank profitability, such as bank size, capital structure, economic growth, and inflation, were not examined Profitability represents just one dimension of bank performance, highlighting the need for further exploration of other performance aspects Furthermore, the study's reliance on historical financial data may not adequately reflect the rapidly changing dynamics of the banking industry, as economic conditions, regulatory frameworks, and technological advancements continuously evolve Longitudinal studies are recommended to better understand these changes over time.

71 and account for evolving market conditions could provide a more comprehensive understanding of the factors affecting bank performance

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Raw data and run statistic data in Excel

Sec_code Time ROA ROE NPL Crisis CAR