This increase in non-performing loans has significantly strained banks' profitability, highlighting the urgent need for effective management of NPLs to ensure the resilience and growth o
Research motivation
Credit operations are essential in the banking system, especially in bank-based economies like Vietnam, where most financial intermediation is conducted through banks rather than capital markets In this environment, credit risk, particularly non-performing loans (NPLs), presents major challenges for banks Elevated NPL levels can severely impact profitability, liquidity, and the capacity to lend, ultimately threatening the stability of the financial system.
Numerous studies have examined the factors influencing credit risk and non-performing loans (NPLs) globally and in Vietnam Louzis et al (2012) identified key macroeconomic factors such as GDP, unemployment, interest rates, and management quality as primary contributors to NPLs in the banking sector Similarly, Q Anh and N D Hung (2013) analyzed data from 10 major Vietnamese banks, revealing that GDP growth rate, inflation, previous NPLs, cost inefficiency, bank size, and rapid credit growth significantly impact bad loans Despite the extensive research, inconsistencies in findings highlight the necessity for further investigation into the unique economic factors affecting NPLs in Vietnam.
The current economic climate in Vietnam highlights the urgency of understanding the factors contributing to non-performing loans (NPLs) due to rising inflation and slow recovery from disruptions like the COVID-19 pandemic This research aims to provide insights into credit risk within the Vietnamese banking sector, helping banks develop effective risk management strategies These strategies are vital for enhancing competitive advantage and ensuring the long-term stability and resilience of the national financial system.
Research objectives
This study aims to identify the key macroeconomic, bank-specific, and regulatory factors influencing non-performing loan (NPL) levels in Vietnamese commercial banks High NPL ratios have recently challenged the stability of the banking sector, necessitating an understanding of their underlying causes By analyzing macroeconomic conditions such as inflation rates, GDP growth, and external economic shocks, the research seeks to clarify their effects on NPL levels Additionally, it will explore bank-specific characteristics, including credit risk management, loan portfolio diversification, and operational efficiency, to determine their impact on non-performing loans.
This study aims to evaluate the significance of various factors influencing non-performing loan (NPL) ratios across banks Utilizing quantitative analysis and statistical methods, it will assess the strength and direction of the relationships between these factors and NPL levels The research intends to offer a deeper understanding of the underlying dynamics, facilitating the prioritization of areas needing intervention.
The study aims to provide actionable policy recommendations for Vietnamese policymakers and bank managers to effectively address the issue of high non-performing loans (NPLs) in the banking sector Grounded in empirical findings, these recommendations will be relevant to the current context of Vietnamese banking By targeting the root causes of NPL accumulation, the study seeks to formulate strategies that enhance credit risk assessment frameworks, improve regulatory compliance, and promote prudent lending practices among banks.
This study aims to enhance the understanding of non-performing loan (NPL) determinants in emerging markets, specifically within the Vietnamese banking sector, addressing a significant gap in existing literature that has largely focused on developed economies By providing insights tailored to Vietnam, the research seeks to contribute practical recommendations for strengthening the stability and resilience of the country's financial system Ultimately, the findings will be valuable for both academic discussions and for practitioners and policymakers working to create a more robust banking environment in Vietnam.
Research scopes
The levels of non-performing loans (NPLs) in Vietnamese commercial banks are influenced by a complex interplay of macroeconomic, regulatory, and bank-specific factors Key macroeconomic conditions, such as slow economic growth, high inflation, and increasing unemployment, can severely impact borrowers' ability to repay loans, leading to a rise in NPLs During periods of economic uncertainty, both businesses and individuals often find it challenging to fulfill their financial commitments, which contributes to higher default rates.
Regulatory measures significantly impact non-performing loan (NPL) levels, as seen during the COVID-19 pandemic when loan restructuring programs and relaxed provisioning requirements supported borrowers and banks However, these interventions may have temporarily concealed the actual extent of NPLs, masking deeper issues within the banking sector While essential for alleviating immediate financial distress, these measures underscore the necessity for a more transparent assessment of asset quality in the long run.
At the bank level, several key factors influence non-performing loan (NPL) levels, with effective credit risk management practices being essential Banks that implement rigorous assessment criteria and maintain continuous monitoring of borrowers are better equipped to mitigate default risks Furthermore, the composition of a bank's loan portfolio, particularly the concentration of loans in vulnerable sectors such as real estate, can worsen NPL challenges Lastly, a bank's strategies for loan recovery and its capability to efficiently manage and recover defaulted loans play a crucial role in determining overall asset quality.
Understanding and addressing this multifaceted array of factors is essential for Vietnamese commercial banks to effectively manage asset quality and ensure financial stability
To effectively navigate economic fluctuations and minimize non-performing loans (NPLs), banks in Vietnam should focus on enhancing risk management practices, strengthening regulatory frameworks, and maintaining a diversified loan portfolio A holistic strategy that integrates macroeconomic trends, regulatory dynamics, and internal banking practices is essential for building a resilient banking sector.
Research methodology
This study utilizes a multi-faceted research approach to thoroughly examine the factors affecting non-performing loans (NPL) in Vietnam's banking sector It employs a panel data regression framework to investigate the relationship between NPL and various bank-specific, industry-specific, and macroeconomic variables, using a dataset sourced from multiple origins The analysis includes both fixed effects and random effects models, complemented by rigorous diagnostic tests and robustness checks to validate the findings.
Assumptions POLS model: Same as Multiple regression model
Assumptions for Fixed Effect model: The fixed effect assumption is that the individual- specific effects are correlated with the independent variables
Assumptions for Random Effect model: The random effects assumption is that the individual specific effects are uncorrelated with the independent variables
Research structure
The remainder of this article is structured as follows: Chapter 1 provides the theorical basis; Chapter 2 presents the research design and the result; and Chapter 3 provides conclusions and recommendation
THEORETICAL BASIS ON THE DETERMINANTS OF NON-
Literature review
1.1.1 The concept of non-performing loans
Non-performing loans (NPLs) are defined as loans that have defaulted, rendering them unprofitable for banks (Patterson and Isac, 2004) The International Monetary Fund (IMF,
A non-performing loan (NPL) is defined as any loan where interest and principal payments are overdue by more than 90 days According to the National Bank of Ethiopia (NBE, 2008), NPLs are loans whose credit quality has declined significantly, making full repayment of principal and interest unlikely These loans generally do not produce income for an extended period, as principal and/or interest payments remain unpaid for at least 90 days.
A significant number or percentage of bad loans is often linked to bank failures and financial crises in both developing and developed countries (IMF, 2009)
A loan is deemed non-performing when the interest and principal payments are overdue by 90 days or more, including instances where interest payments have been capitalized, refinanced, or postponed by mutual agreement Furthermore, loans can also be classified as non-performing if there are legitimate concerns regarding the borrower's capacity to make full payments, even if the payments are less than 90 days overdue This classification is especially relevant for mortgages, as these loans are provided by lenders but do not generate expected returns.
When borrowers are unable to fully repay their loans or make adequate payments for the bank's profitability, banks face a challenging situation In these instances, they can either negotiate a revised payment plan with the borrower or initiate foreclosure on the collateral that was pledged.
Bad loans, often synonymous with non-performing loans, refer to loans that have overdue principal and interest payments for an extended duration, breaching the loan contract terms (Fofack, 2005) In contrast, performing loans are those that are current in their repayment, meaning they meet their payment obligations for both principal and interest.
According to Bloem and Gorter (2001), bad loans are characterized by overdue scheduled payments, while various countries also employ qualitative assessments, including evaluations of the borrower's financial health and management's predictions about future payment capabilities.
Non-performing loans (NPLs) are defined as loans that have not generated income for an extended period, specifically when the principal or interest has been unpaid for at least ninety days These loans are characterized by a lack of income generation, an expectation that full repayment of principal and interest is unlikely, or instances where the principal or interest is overdue by ninety days or more, or the loan has matured without full payment.
Non-performing loans (NPL) are assessed by calculating the ratio of non-performing loans to the total loans issued by banks A higher NPL ratio suggests that the bank may lack professionalism in credit management and indicates a significant level of risk associated with its lending practices The formula for calculating non-performing loans is essential for understanding a bank's financial health.
In Vietnam, non-performing loans (NPLs) indicate financial distress among borrowers, leading to missed payments and risks for both borrowers and financial institutions High NPL levels diminish bank profitability and require increased resource allocation to cover potential losses, which can erode capital bases and restrict new credit issuance, ultimately hindering economic growth As banks tighten lending standards in response to rising NPLs, creditworthy borrowers may struggle to obtain loans, creating a cycle that exacerbates economic challenges Additionally, elevated NPL ratios draw regulatory scrutiny, resulting in stricter oversight that limits banks' operational flexibility Therefore, effective NPL management is essential for maintaining banking sector stability and promoting sustainable economic development in Vietnam.
1.1.2 Theory of non-performing loans
This section explores key theories that elucidate the factors affecting non-performing loans, focusing on the principal-agent problem, adverse selection, moral hazard, and information asymmetry These concepts highlight how the interactions between banks and borrowers impact loan performance and lead to the emergence of non-performing loans.
Agency theory, as outlined by Viswanadham and Nahid (2015), examines the principal-agent relationship between shareholders and managers, where shareholders expect managers to prioritize their interests and maximize wealth However, conflicts can arise when managers pursue personal goals over organizational success, adversely affecting financial performance To mitigate these agency conflicts, shareholders can implement performance-based incentives and oversight mechanisms, such as audits and performance reviews, which help align management’s interests with those of shareholders This alignment fosters accountability and transparency, ultimately enhancing financial stability and supporting the long-term strategic objectives of the organization (Swamy, 2012).
The theory suggests that management can be motivated to act in the best interests of the company through various incentives and rewards Owners may also use threats, like the risk of a hostile takeover, to ensure management meets their responsibilities Additionally, principals might incur agency costs, such as audit fees, to effectively monitor management's performance Ultimately, this theory supports the study's findings, highlighting that managers, as agents, may prioritize their personal interests over the owners' objectives.
Asymmetric information poses significant challenges in financial markets, especially in borrowing and lending scenarios where borrowers often have a clearer understanding of their financial status than lenders This discrepancy can result in adverse selection and moral hazard, enabling borrowers to secure more favorable terms than lenders can offer, as highlighted by Asari et al (2011) Such dynamics contribute to the rise of non-performing loans within banks, as loan applicants may withhold critical financial information, leading to increased risks associated with information asymmetry and moral hazard.
This study highlights the relevance of information asymmetry theory in contexts where one party has different information than another, particularly in the banking sector It emphasizes the significance of credit information sharing, which benefits banks, borrowers, and the economy as a whole By providing lenders with access to comprehensive credit data, banks can more effectively evaluate the risk associated with borrowers, thereby minimizing bad debt portfolios Ultimately, this theory sheds light on the difficulties in distinguishing between good and bad borrowers, addressing issues such as adverse selection and moral hazard, and underscores the need to understand the factors leading to non-performing loans and their effects on overall financial performance.
Martha (2017) highlights that sharing information can reduce adverse selection by improving banks' understanding of credit applicants Asymmetric information theory suggests that distinguishing between reliable borrowers and those likely to default is difficult, resulting in adverse selection and moral hazard challenges This theory indicates that borrowers, possessing more information, can secure better terms than lenders, who may find it hard to make informed choices Consequently, this imbalance can lead to unfavorable outcomes, significantly contributing to the rise of non-performing loans in banks.
This theory is crucial to the study as it emphasizes the need for accurate information regarding borrowers, their loan purposes, and the risks associated with misrepresentation Borrowers often do not fully disclose necessary information, and even when they do, it may lack accuracy Typically, borrowers hold private information about their projects that tends to be more reliable than what lenders have access to Consequently, lenders face uncertainty regarding the default risk of loans, complicating their ability to assess and manage borrower behavior effectively Adverse selection issues emerge when lenders attempt to mitigate default risk by setting contractual terms based on the average quality of loan applicants, which may not truly represent the individual risks of borrowers.
Research Gap and Research Questions
Research on non-performing loans (NPL) in Vietnam has been limited, particularly regarding the determinants of NPL using panel data analysis from 2013 to 2023 Previous studies primarily focused on pre-crisis or crisis periods, neglecting the important post-crisis and post-pandemic phases essential for understanding current trends Utilizing panel data analysis is advantageous for capturing the dynamic nature of NPLs amid evolving macroeconomic and banking conditions in Vietnam, especially following the significant impacts of the COVID-19 pandemic Including the COVID-19 variable in the panel data model can yield insights into how this external shock has affected NPL levels in the Vietnamese commercial banking sector The pandemic has posed unique challenges that may have intensified existing vulnerabilities, highlighting the need for targeted research Additionally, a panel approach allows for better control of unobserved bank-specific characteristics, enhancing the robustness of findings compared to traditional methods.
This study utilizes advanced panel data techniques to analyze the factors influencing non-performing loans (NPL) in Vietnam from 2013 to 2023, with a particular focus on the impact of the COVID-19 pandemic By addressing a significant gap in existing literature, it aims to equip policymakers with a thorough understanding of the determinants affecting NPL levels in the Vietnamese commercial banking sector during this crucial timeframe The research findings are intended to guide effective regulatory measures and risk management strategies, ultimately enhancing the stability and resilience of Vietnam's financial system amid ongoing economic challenges.
This research focuses on researching factors affecting the Non-performance loans on Vietnamese commercial banks, so the main research question of the study are:
- How do difference factors affect NPL?
- What policy recommendations can be provided to Vietnamese policymakers and bank regulators to effectively address the issue of high NPL in the banking industry?
DATA AND METHODOLOGY
Research methodology
2.1.1 Sample selection and data source
This study employs a quantitative research approach to examine the factors influencing non-performing loans (NPLs) in Vietnam's commercial banking sector, aiming to identify the determinants that affect NPL levels critical for assessing banking system health and financial stability Secondary data was collected from various sources, including annual reports of commercial banks, which provide insights into their financial performance and risk profiles, as well as financial statements like balance sheets and income statements to evaluate the banks' financial conditions Additionally, reputable databases and platforms such as DataStream and the World Bank website were utilized for reliable economic and financial data.
To address the challenges of representing the entire banking population in Vietnam, a carefully selected sample of twenty-six commercial banks was utilized, reflecting the sector's diversity The study spans from 2013 to 2023, a crucial timeframe that includes significant economic events such as the recovery from the global financial crisis and the impacts of the COVID-19 pandemic This period enables an in-depth analysis of how macroeconomic and bank-specific factors affect non-performing loan (NPL) levels during economic turmoil and recovery phases.
This research will utilize quantitative methods, which involve empirical investigations of observable phenomena through mathematics, engineering, or statistics Essentially, quantitative research focuses on collecting numerical data and analyzing it from a deductive perspective to explore theoretical relationships.
Eviews 10 software is applied in this dissertation The data in this dissertation is panel data because which is collected from 26 Vietnamese commercial banks from 2013 to 2023 For regression analysis, three models were proposed: Testing mediation using Fixed Effect Model (FEM) and Random Effect Model (REM) Furthermore, the Hausman test is also applied to check the suitability of the models Finally, the model with the best test results will be selected Errors such as Heteroskedasticity, Auto-correlation and Multicollinearity are also countered to make the model most complete Results of estimation are illustrated in the next chapter
Variables Symbol Definition Caculation Unit
Non-performing loans (NPL) are loans that have not been paid back in both principal and interest for an extended period, violating the terms of the loan agreement Any loan facility that is overdue in payments, as outlined in the contract, is classified as an NPL Consequently, the total amount of non-performing loans serves as an indicator of the quality of a bank's assets (Tseganesh, 2012).
Each bank has an optimal capital-to-asset ratio, all else being equal The capital adequacy ratios are beneficial for profit enhancement
Capital adequacy ratio = Total Capital/ Total Assets
The loan-to-deposit ratio is the ratio of loans to deposits in commercial banks
Deposits generate interest payments, whereas loans are issued to earn interest income from asset operations Utilizing additional funds for lending, or even excessive lending, can enhance profits despite a constant deposit size However, a high loan-to-deposit ratio elevates risk, which is why it was selected as a control variable.
= Total Loan and Advances/ Total Deposits
The interest income ratio reflects the profit structure of commercial banks and is expected to impact their profits
Net interest income/total income
Net Interest Margin is a financial metric that measures the difference between the interest income generated by a bank or financial institution and the interest paid
= Net Interest Income /Average Earning Assets
% out to its lenders, relative to the amount of their (interest-earning) assets
The size of a bank serves as a crucial metric for assessing its assets, with total assets being a key measure of company size (Barus and Erick, 2016) Larger banks possess significant asset volumes, enabling them to offer substantial credit and consequently lower interest rates.
The natural logarithm of total assets
Basel II is an international banking regulatory framework developed to promote financial stability It establishes minimum capital requirements, a supervisory review process, and market discipline to make the banking system more resilient
The bank has qualified for Basel II:
The COVID-19 pandemic has had a significant impact on the global economy and the banking sector
The ownership structure of banks can vary significantly across different countries and banking systems
NPL it = β 0 + β 1 *CAP it + β 2 *LDR it + β 3 *NII it + β 4 *NIM it + β 5 * SIZE it + β 6 * Basel2 it + β 7 *OWN it + β 8 *COVID it + ℇ it
NPL: Non-performing loans ratio (%)
SIZE: Asset size, the natural logarithm of total assets (%) β1, β2, β3, β4, β5, β6, β7 and β8 = coefficients μ = error term / residuals i: index of banks (i = 1, 2,…,N) t: time (t = 1, 2,…,T)
Research hypothesis
H1: The higher the capital adequacy ratio, the higher the non-performing loans
A study by Lemma-Lalisho (2022) examined the factors affecting non-performing loans (NPLs) in Ethiopian commercial banks, finding that a higher capital adequacy ratio (CAR) significantly reduces loan defaults This indicates that banks with stronger capital reserves can better absorb losses and manage credit risk, resulting in lower levels of NPLs.
In Vietnam, ensuring a strong capital adequacy ratio is crucial for reducing the negative impact of increasing non-performing loans (NPLs) on banks' profitability and financial stability This perspective is supported by Vu et al (2024), who highlight the importance of implementing effective risk management strategies and regulatory oversight to tackle the challenges associated with NPLs.
H2: The higher the loan-deposit ratio, the higher the non-performing loans
Gambo et al (2017) found a significant positive correlation between the loan-to-deposit ratio (LDR) and non-performing loans (NPLs), indicating that as banks increase lending based on customer deposits, the risk of defaults tends to rise, leading to higher NPL ratios.
A 2018 study examined the internal and macroeconomic factors affecting non-performing loans (NPLs) in Nepalese commercial banks, finding a positive correlation between the loan-to-deposit ratio (LTD) and NPLs This suggests that an increased loan-to-deposit ratio can heighten the risk of defaults, underscoring the critical need for effective liquidity management and risk assessment in lending practices.
Both studies highlight the importance of the loan-to-deposit ratio in determining non-performing loans (NPL), stressing that banks need to effectively manage their lending practices to reduce the risks linked to increasing NPL This information is especially pertinent for banking sectors like Vietnam, where achieving a balanced loan-to-deposit ratio is vital for financial stability and reducing credit risk.
H3: The higher the non-interest income rate, the lower the non-performing loans
Gabeshi (2017) identified that a rising non-performing loan (NPL) ratio adversely affects banks' net interest income (NII) by necessitating increased allocations for loan loss provisions This financial strain from high NPL levels forces banks to reserve a portion of their income for potential loan defaults, impacting both their immediate profitability and overall financial stability Consequently, a higher NPL ratio underscores the critical need for effective risk management and strategies to mitigate non-performing loans, which are essential for maintaining banks' lending capabilities and financial health.
H4: The higher the net interest margin, the lower the non-performing loans
Research has consistently shown a link between net interest margin (NIM) and non-performing loans (NPLs) in banking Ghosh (2015) focused on Indian banks and discovered that those with higher NIMs effectively managed credit risk and maintained lower NPL levels The study highlighted that increased interest income enables banks to invest in stronger risk assessment and monitoring systems, which improves their capacity to detect potential defaults early.
H5: The higher the bank size, the lower the non-performing loans
Barus and Erick (2016) discovered a negative correlation between bank size and non-performing loans (NPL) in their analysis of the Bank of the General Company of Indonesia, employing multiple regression estimates with the Fixed Effects Model (FEM) Their research indicates that larger banks possess greater resources and enhanced risk management practices, leading to lower NPL levels This suggests that as banks expand, they benefit from economies of scale, enabling more effective loan portfolio monitoring and a greater ability to absorb potential losses Consequently, the findings highlight the significance of bank size in managing credit risk and ensuring financial stability within the banking sector.
H6: The implementation of the Basel II leads to an increase in non-performing loans (NPL) for banks
The Basel II framework mandated enhanced transparency in banks' reporting of their risk profiles, particularly focusing on asset quality and non-performing loan (NPL) levels This initiative aimed to improve market discipline, encouraging banks to proactively manage NPL issues By offering clearer insights into their risk exposure, banks were motivated to strengthen their risk management practices and respond effectively to potential defaults Increased transparency fosters greater accountability among banks and enhances the overall stability of the financial system, promoting informed decision-making among investors and regulators.
H7: The COVID-19 pandemic has positive impact on NPL
The economic disruption caused by the pandemic has resulted in a significant increase in non-performing loans (NPLs) across many countries, as noted by Acharya and Steffen
In 2020, businesses and individuals encountered significant financial challenges, resulting in increased loan default rates and straining global banking systems As borrowers faced reduced incomes and economic uncertainty, banks saw a rise in non-performing loans (NPLs), threatening their profitability and stability This scenario highlights the urgent necessity for robust risk management strategies and regulatory measures to tackle the escalating issues within the banking sector.
H8: State-owned banks tend to have higher levels of NPLs compared to private banks
State-owned banks often engage in directed lending to meet specific policy objectives, which can lead to less thorough credit assessments and increased risk-taking, as highlighted by Micco and Panizza (2004) By prioritizing government mandates over strict financial criteria, these banks may overlook potential loan risks, resulting in a higher likelihood of defaults and an increase in non-performing loans (NPLs) This situation underscores the challenges that state-owned banks face in balancing policy goals with effective risk management practices, essential for ensuring the stability of the banking sector.
Pham (2013) highlights the influence of state-owned banks on non-performing loan (NPL) ratios in Vietnam, revealing that these banks frequently focus on government-directed lending This approach results in less rigorous credit assessments and a corresponding increase in NPLs The study underscores the necessity for enhanced risk management frameworks in state-owned institutions to address and reduce these financial risks effectively.
EMPIRICAL ANALYSIS
The Vietnamese banking sector
3.1.1 Overview of Vietnamese commercial bank industry
The State Bank of Vietnam serves as the central authority in the country's banking system, overseeing currency issuance and monetary policy management while providing strategic advice to the Vietnamese government on financial matters.
It drafts laws related to banking operations and credit institutions, and oversees s tate-owned commercial banks
Vietnam's banking sector comprises 4 state-owned commercial banks, 31 joint-stock banks, 9 wholly foreign-owned banks, 2 joint venture banks, 2 policy banks, and 1 cooperative bank, along with 48 branches of foreign banks As a banking-based economy, the Vietnamese government prioritizes this sector, with Prime Minister Nguyen Xuan Phuc highlighting its crucial role in the nation's success during a 2021 conference The banking sector has actively responded to challenges such as the Covid-19 pandemic and natural disasters, implementing a proactive monetary policy to stabilize the macro economy, promote growth, and control inflation With a credit growth rate of nearly 11%, the significance of both the State Bank of Vietnam and commercial banks is evident in supporting the country's economic resilience.
The Vietnamese commercial banking industry is a rapidly growing and increasingly competitive sector that plays a vital role in the country's economic development (Nguyen,
The Vietnamese banking industry is largely concentrated, with the top five banks holding over 70% of total assets, as reported by the State Bank of Vietnam in 2022 Historically dominated by state-owned institutions, the sector is witnessing a rise in private and foreign ownership To strengthen the banking sector, the State Bank of Vietnam has introduced regulatory measures, including stricter capital requirements and policies to tackle high non-performing loan ratios As the industry evolves, commercial banks must enhance corporate governance and risk management while meeting the increasing demand for digital banking and innovative financial products Despite these challenges, the sector is expected to grow, supported by the country's robust economic performance and a burgeoning middle class, according to the World Bank in 2023.
The Vietnamese banking system categorizes loans into five primary groups, which assists banks in determining suitable risk provisions and implementing effective strategies for managing non-performing loans As outlined in Circular No 11/2021/TT-NHNN, bad debts are identified within groups 3, 4, and 5 Therefore, this study focused on analyzing data related to bad debts from these designated groups.
Standard Loans (Group 1) consist of borrowers who fully comply with the credit contract's terms and conditions These loans are not overdue and indicate no signs of financial weakness in the borrower.
Special Mention Loans (Group 2) are classified as loans where borrowers have not completely fulfilled the terms of their credit agreements, yet they have not escalated to the status of non-performing loans Typically, these loans are overdue for a period ranging from 10 to 90 days.
Substandard Loans, classified as Group 3, refer to loans where borrowers fail to meet the credit contract's terms, indicating a significant risk of partial principal recovery These loans are generally overdue between 91 and 180 days.
• The following group is Doubtful Loans (Group 4), which comprises loans where the probability of recovering the principal and interest is uncertain These are typically loans overdue from 181 to 360 days
• The final group is Loss Loans (Group 5), which are loans that the bank has determined are unrecoverable These are typically loans overdue for more than 360 days
3.1.2 Overview of non-performing loans in Vietnamese commercial bank
Vietnam's banking sector is embarking on a challenging path to recovery, facing new obstacles arising from external upheavals and internal adjustments
In 2023, Vietnam saw a notable decline in credit growth, largely attributed to a slowdown in labor-intensive industries, particularly export-driven manufacturing, which struggled with decreased global demand and rising costs For the first 11 months, cautious lending by financial institutions reflected the challenges faced by distressed sectors However, as the year progressed, credit growth rebounded, primarily fueled by the real estate and construction sectors, supported by government incentives for infrastructure and housing development While this recovery provided a vital boost to the economy, it raised concerns regarding sustainability and potential financial system imbalances, highlighting the importance of careful monitoring and strategic interventions.
Secondly, NPL formation rates remained elevated, indicating a continued decline in the quality of banks' assets
Figure 2: NPL ratios of credit institutions
The gross non-performing loan (NPL) ratio in Vietnam rose from 6.16% in July 2023 to 7.91% by year-end, indicating a significant decline in banking sector asset quality This deterioration is largely due to unfavorable economic conditions and challenges faced by businesses, particularly in labor-intensive industries reliant on import and export activities Many companies struggled with cash flow and debt obligations, resulting in increased defaults that negatively impacted overall economic growth and stability Additionally, banks faced difficulties in liquidating mortgaged assets to recover debts, as the market for these assets was sluggish and often yielded prices far below the original loan values This situation has placed banks in a vulnerable position, with rising NPLs and heightened financial risks, underscoring the need for effective measures to restore confidence and stability in the financial system.
The Vietnamese commercial banking sector is facing significant challenges due to a rise in non-performing loans (NPLs) that have surpassed provisions for potential losses, which has led to a decrease in the industry's reserve buffer Although the officially reported NPL ratio fluctuates between 2-3%, there are growing concerns that the actual level of bad debts may be underestimated, as many loans have been restructured or transferred to asset management companies.
In 2023, elevated non-performing loan (NPL) levels can be attributed to inadequate credit risk management practices among certain banks, swift credit expansion without thorough evaluation of borrowers' creditworthiness, and the adverse effects of economic slowdowns, especially within the real estate and construction industries.
Figure 3: Bad debt coverage ratios of listed bank
The rising levels of non-performing loans (NPLs) in Vietnamese banks are driven by several factors, including economic instability marked by high inflation and slow growth, which diminish borrowers' ability to repay (Trung, 2022) The COVID-19 pandemic has further intensified these challenges, leading to an increase in NPLs Ineffective credit risk management practices, such as excessive reliance on collateral without proper credit assessments, have also contributed to higher default rates Although the State Bank of Vietnam has introduced measures to tackle NPL issues, ongoing compliance and enforcement challenges highlight the need for continuous updates to regulatory frameworks (C H A Le & Nguyen, 2018) Additionally, sectors like real estate and construction are particularly susceptible to economic fluctuations, resulting in increased loan defaults in these industries.
The State Bank of Vietnam has intensified regulations on loan classification and provisioning to tackle the non-performing loan (NPL) issue, while also establishing the Vietnam Asset Management Company (VAMC) to manage bad debts and promote debt restructuring Commercial banks are enhancing their credit risk management and improving loan recovery processes, yet the NPL challenge remains significant, particularly for smaller banks The economic repercussions of the COVID-19 pandemic have further strained loan portfolios, highlighting the need for ongoing efforts to bolster risk management and recovery strategies for the industry's stability and health.
Empirical results
Table 2: Descriptive statistics Variable Mean Median Maximum Minimum Std Dev Skewness Kurtosis NPL 1.6851 1.3000 29.800 0.2000 2.0720 10.5303 132.7060
This analysis presents a comprehensive overview of descriptive statistics for key financial variables associated with non-performing loans (NPLs) in the banking sector It evaluates each variable's mean, median, maximum, minimum, standard deviation, skewness, and kurtosis to gain insights into their distribution and characteristics.
The non-performing loan (NPL) ratio exhibits a mean of 1.6851 and a median of 1.3000, highlighting a pronounced right skew in its distribution With a maximum of 29.800 and a minimum of 0.2000, there is considerable variability in NPL ratios among banks The standard deviation of 2.0720 indicates a high level of variability, while a skewness of 10.5303 and a kurtosis of 132.7060 reveal the presence of extreme outliers This indicates that although most banks have relatively low NPL ratios, a small number of banks with significantly high ratios considerably impact the overall average.
The capital adequacy ratio (CAR) shows a mean of 9.8295 and a median of 9.4798, which are relatively close, indicating a balanced distribution among banks The maximum
The Capital Adequacy Ratio (CAR) averages at 20.653, with a minimum of 3.3569, indicating that while the majority of banks uphold sufficient capital levels, some operate with lower ratios A slight positive skew of 0.3212 reveals that a few banks possess higher capital ratios, and a kurtosis of 2.7191 suggests a distribution that approximates normality.
The loan-to-deposit ratio (LDR) averages 60.387, with a median of 62.779, and ranges from a minimum of 22.0051 to a maximum of 80.100 A standard deviation of 10.342 reflects the variability among banks, while a negative skewness of -1.0511 indicates that most banks tend to have higher ratios, with only a few exhibiting significantly lower values Additionally, a kurtosis of 4.2373 suggests a distribution with heavier tails, highlighting the presence of more extreme values than typically found in a normal distribution.
Net interest income (NII) has a mean of 22.762 and a median of 21.094, with a maximum value of 86.155 and a minimum of -45.4276, indicating the presence of outliers The positive skewness of 1.1754 suggests that most banks report positive NII, while a few report significantly higher incomes Additionally, the kurtosis of 7.3908 indicates a distribution with heavy tails, highlighting the impact of extreme values on the average.
The net interest margin (NIM) averages 3.3003, with a median of 3.1000, while ranging from a minimum of 0.2000 to a maximum of 38.0000 A standard deviation of 2.4260 indicates significant variability in NIM The data shows a highly positive skewness of 10.4603 and a kurtosis of 147.9310, suggesting that a small number of banks report exceptionally high margins, which may reflect riskier lending practices or distinctive business models.
The analysis of bank size (SIZE) reveals a mean of 32.734 and a median of 32.708, indicating a nearly normal distribution with a slight positive skewness of 0.1705 With a maximum size of 35.372 and a minimum of 30.392, the data suggests that most banks are of similar size, lacking significant outliers This consistency in bank size can enhance effective risk management practices.
The ownership variable (OWN) reveals a mean of 0.1538 and a median of 0.0000, highlighting that many banks lack state ownership while a small number are fully state-owned The positive skewness of 1.9188 indicates a predominance of privately owned banks, with state-owned institutions being in the minority Additionally, the kurtosis of 4.6818 suggests a distribution characterized by a higher likelihood of extreme ownership values.
The COVID impact variable (COVID) reveals that approximately 27.27% of banks faced challenges due to the pandemic, with a mean of 0.2727 and a median of 0.0000 The positive skewness of 1.0206 indicates that while most banks were minimally affected, a small number experienced significant difficulties Additionally, the kurtosis value of 2.0416 suggests a distribution that is relatively normal, characterized by fewer extreme values.
The Basel II compliance analysis reveals that only 25.17% of banks are fully compliant with the regulations, as indicated by a mean of 0.2517 and a median of 0.0000 The positive skewness of 1.1439 highlights that a significant number of banks fall short of full compliance, with only a few meeting all Basel II requirements Additionally, the kurtosis of 2.3086 suggests a distribution that is slightly above normal, demonstrating some variation in compliance levels among banks.
The correlation coefficient is a key statistical measure used to assess the degree of linear relationship between two variables Analysis of the correlation matrix reveals moderate correlations, especially between SIZE and BASEL2, as well as between CAR and BASEL2 Notably, none of the correlations surpass the 0.7 threshold, indicating that multicollinearity is not a significant issue within this model.
NPL CAR LDR NII NIM SIZE OWN BASEL2 COVID
Regression analysis is a statistical method used by data analysts to estimate the relationship between a dependent variable and independent variables
Variable Coefefficient Std.Err t-Statistic Prob
The equation of model is:
NPL it = 3.1786 - 0.0059*CAR it - 0.0083*LDR it + 0.0052*NII it - 0.0073*NIM it - 0.0791* SIZE it + 0.2179* NPL_BASEL2 it + 0.2003* NPL_OWN it + 0.1418* NPL_COVID it + ℇ it
This table, based on the pool model, demonstrates the evaluation of relationships between the dependent variable and various independent variables With a P-value less than 0.05 (Prob > F = 0.0000) and an R-squared value of 0.3616, we can conclude that there is a statistically significant relationship present.
• The relationship between the LDR and NPL is negative and statistically significant at 5% (because Prob = 0.0030