BANKING ACADEMY OF VIETNAM BANKING FACULTY------ GRADUATION THESIS Topic: DETERMINANTS OF NON-PERFORMING LOANS IN VIETNAMESE COMMERCIAL BANKS... BANKING ACADEMY OF VIETNAM BANKI
INTRODUCTION
Urgency of the topic
Financial intermediaries, especially commercial banks, are essential to both national and global economies as they facilitate the flow of funds from surplus to deficit Lending is the primary revenue-generating activity for these banks, but the presence of non-performing loans (NPLs) poses significant challenges NPLs can diminish bank earnings, lead to financial losses, and negatively impact economic and social development, as well as harm banks' reputations and the overall national financial system Consequently, effective management of NPLs is crucial for banks to identify underlying causes, assess potential losses, and implement strategies to mitigate the impact of NPLs, ultimately aiming to reduce associated losses.
Since the onset of the global financial crisis in 2008, non-performing loans (NPLs) have become a critical concern for financial institutions worldwide, with Vietnam experiencing significant challenges before 2017 due to economic downturns and a stagnant real estate market The banking sector made notable progress in restructuring, successfully reducing the NPL ratio from 10.1% in 2016 to 4.4% in 2019, as reported by the State Bank of Vietnam (SBV) However, by the end of 2021, the on-balance sheet NPL ratio increased to 1.9%, reflecting a rise from the previous year, while the total NPL ratio surged to 7.4%, similar to levels seen in late 2017 This increase was largely attributed to the COVID-19 pandemic's impact, which led to ongoing economic complexities.
2 the Delta mutation in 2021 had profoundly affected the entire economy, causing heavy losses to production and business activities of enterprises, and people's lives
The Covid-19 pandemic significantly affected the solvency of businesses and borrowers, leading to challenges in managing non-performing loans (NPLs) While the restructuring of NPLs during this period yielded positive outcomes, persistent difficulties and obstacles remain in effectively addressing these loans.
In the current economic landscape, effectively managing non-performing loans (NPLs) is crucial for the stability of the banking system Identifying and analyzing the factors that lead to NPLs is an urgent priority, as it plays a vital role in establishing a solid financial foundation for banks.
For my graduation thesis, I have chosen the topic "Determinants of Non-Performing Loans in Vietnamese Commercial Banks," reflecting the significant relationship between theory and practice My research aims to identify key factors influencing the non-performing loan (NPL) ratio in commercial banks from 2008 to 2021, with the goal of proposing actionable strategies to enhance NPL management in the future.
Literature Review
The concept of non-performing bank loans (NPLs) originated in Europe in the late 18th and early 19th centuries, coinciding with the rise of traditional commercial banks, known as intermediary banks for their roles in credit and payment facilitation As the global banking industry evolved, NPLs became a well-defined academic term, recognized in the Cambridge dictionary as "bad debt." Extensive research worldwide has explored the causes and implications of NPLs.
Umar & Sun (2018) utilize the one-step system GMM estimation method to analyze the macroeconomic and banking industry-specific factors influencing non-performing loans (NPLs) across 197 Chinese banks from 2005 to 2014 Their research employs three models, with the first two focusing solely on macro or micro variables, while the third incorporates both types The results indicate that GDP growth, bank risk-taking behavior, bank leverage, and loan loss reserves significantly affect NPLs.
Higher effective interest rates, inflation rates, foreign exchange rates, lower ownership concentration, and bank type negatively influence non-performing loans (NPLs) Factors such as bank spread, public debt to GDP ratio, cost efficiency, diversification, profitability, and credit growth do not significantly explain NPL variation Moreover, unlisted banks have a more pronounced effect on the overall findings compared to listed banks.
Khan et al (2020) conducted a study on the causes of non-performing loans (NPLs) in Pakistani commercial banks from 2005 to 2017, focusing on key banking factors such as profitability (measured by return on assets or ROA), bank capital, income diversification, and operating efficiency, utilizing fixed effects and random effects models The findings indicate that both ROA and operating efficiency significantly reduce NPLs, while the impact of other variables was found to be insignificant, contrasting with the results of Umar & Sun (2018).
Kartikasary et al (2020) analyze the impact of microeconomic and macroeconomic factors on non-performing loans (NPLs) in 43 companies listed on the Indonesia Stock Exchange from 2014 to 2017 Key bank-specific factors include Bank Capital, Loans to Deposits, Return on Assets (ROA), Return on Equity (ROE), and previous year's NPLs, while macroeconomic factors encompass GDP, inflation rate, unemployment rate, and government budget surplus or deficit The study employs OLS regression, revealing that prior NPLs and the loan-to-deposit ratio significantly increase NPLs in Indonesian banks, whereas ROE has a negative effect Notably, macroeconomic variables do not individually influence NPLs.
Mohamed et al (2021) examine the macroeconomic factors influencing non-performing loans (NPLs) in the Malaysian banking sector from 2015 to 2019 Their analysis, based on secondary monthly data and the OLS method, reveals a significant negative relationship between unemployment rates and NPLs Conversely, inflation is found to have a significant positive impact on NPLs Additionally, the study indicates that interest rates do not have a statistically significant positive correlation with NPLs in Malaysia.
In recent years, in Vietnam, there are also a lot of investigations on contributing factors of NPLs as below:
In a 2015 study, Hue investigates the factors influencing non-performing loans (NPLs) within Vietnamese commercial banks Utilizing OLS estimation and panel data from 20 banks between 2009 and 2012, the research reveals that four key bank-level factors—previous year's NPLs, total assets, loan growth rate, and a dummy variable—are positively correlated with NPLs.
A study by Vinh (2017) analyzed data from 34 Vietnamese commercial banks between 2005 and 2015, revealing that non-performing loans (NPLs) negatively impact bank profitability and lending behavior Utilizing dynamic panel data and System GMM estimation, the research indicates that a decline in asset quality leads to reduced profitability and lending operations As the volume of unprofitable loans increases, banks become less inclined to offer competitive rates and terms Additionally, the study found a positive correlation between larger capitalization and enhanced profitability and loan growth in banks.
Duong & Huong (2017) investigate the factors influencing credit risks in Vietnamese commercial banks using one-step GMM on unbalanced panel data from 20 banks between 2006 and 2014, focusing on non-performing loans (NPLs) as the dependent variable Their findings indicate that credit risks are significantly influenced by bank-specific factors, with larger bank size and market share negatively impacting credit risks Additionally, rapid credit growth, inefficient capital use, and poor credit management contribute to potential future credit risks The study also identifies a correlation between economic cycles, specifically GDP growth, and credit risk, while noting no significant relationship between general management efficiency, actual lending interest rates, and credit risks.
Indicator Positive impact on NPLs Negative impact on NPLs Economic growth
Rajan & Dhal (2003), Rossi et al (2009), Duong &
Salas & Saurina (2002), Louzis et al (2012), Beck et al (2013), Vinh (2017), Umar & Sun (2018)
Interest rate Louzis et al (2012), Nkusu
Klein (2013), Hue (2015), Vinh (2017) and Duong &
Huong (2017) Profitability Hu et al (2004), Jimenez &
Achou & Tenguch (2008), Banker et al (2010), Trujillo- Ponce (2013), Le (2016) Louzis et al (2012), Karim et al (2010), Vinh (2017)
Bank size Le (2016), Das & Ghosh
Bank capital Constant & Ngosmi (2012) Makri et al (2014), Kumar &
(2018) Credit growth Clair (1992), Salas & Sarina
Le (2016), Vinh (2017), Jimenez et al (2006)
Recent research on non-performing loans (NPLs) and their determinants has significantly enhanced the effectiveness of NPL management in credit institutions, particularly in assessment, prevention, and handling However, these studies reveal several limitations that need to be addressed.
While extensive research exists on non-performing loans (NPLs) and their contributing factors, most studies have predominantly concentrated on internal elements within the lending sector It is crucial to also explore external influences, including natural disasters, economic downturns, and pandemics, that may affect NPL rates The Covid-19 pandemic, in particular, has profoundly impacted global economies; however, there is a noticeable lack of studies addressing its role in the increase of NPLs.
A significant challenge in understanding non-performing loans (NPLs) in Vietnam is the absence of comprehensive and reliable data Although some research utilizes data from official sources like the State Bank of Vietnam, critical gaps remain, particularly concerning borrower characteristics and collateral quality This lack of data hinders the accurate identification of the factors contributing to NPLs in the country.
Finally, some studies have only looked at the relationship between a few variables and NPLs, without considering the complex interactions between
7 different factors This limited scope of analysis makes it difficult to develop effective risk management strategies to address NPLs in Vietnam
Despite extensive research on the factors influencing non-performing loans (NPLs) in Vietnam, significant research gaps remain Addressing these gaps is crucial, which underscores the importance of this thesis.
Research questions raised by this thesis are:
- What are the determinants of NPLs in commercial banks?
Research Objectives
Identify factors affecting NPLs of commercial banks in Vietnam
Evaluate of the influence of factors on NPLs of commercial banks in Vietnam
Propose recommendations to limit NPLs in commercial banks in Vietnam.
Subjects and Scope of the Research
Determinants of NPLs in Vietnamese commercial banks
- Content: Research on factors affecting NPL ratios of 15 commercial banks in Vietnam namely Vietcombank, BIDV, Vietinbank, Agribank, VPBank, Techcombank, MBBank, VIB, ACB, HDBank, Sacombank, SHB, Maritime Bank, SeABank, OCB
- Space: commercial banks operating in Vietnam
- Time: data taken from financial reports and annual reports of 15 commercial banks in Vietnam between 2008 and 2021.
Research methodology
This thesis aims to study and analyze the impact of various factors on the Non-Performing Loan (NPL) ratios of Vietnamese commercial banks using a quantitative approach The research will utilize the two-system Generalized Method of Moments (GMM) method, chosen for its effectiveness in managing unobserved heterogeneity among banks and addressing potential endogeneity issues between the explanatory variables and NPL ratios in a dynamic panel data model.
The structure of the study
Apart from Introduction, Conclusion, and Appendices, the structure of the thesis consists of three chapters as follows:
Chapter 1: Overview of determinants of commercial bank’s non- performing loans
Chapter 2: Research model and results
Chapter 3: Recommendation to limit and handle non-performing loans in the Vietnamese banking system
OVERVIEW OF DETERMINANTS OF COMMERCIAL BANK’S NON-PERFORMING LOANS
Overview of Non-Performing Loans
Non-performing loans (NPLs) are complex and multifaceted, with their definition varying based on research objectives and methodologies Previous studies highlight the diverse approaches to determining NPLs, reflecting the intricacies involved in understanding their implications for individuals and organizations.
Nonperforming loans (NPLs) are classified as such when scheduled repayments are over 90 days overdue, indicating that they no longer generate interest income or have been restructured due to the borrower's changed circumstances Once a loan is deemed nonperforming, any accrued interest that hasn't been received must be deducted from the bank's loan revenues, and the bank cannot record further interest income until a cash payment is made (Rose & Hudgins, 2013).
The term “non-performing loan” is also referred to as “bad debt” or
“doubtful debt” Each central bank and international organization define NPL differently
According to the World Bank, Non-Performing Loans (NPLs) are defined by their delinquency status and the borrower's repayment capacity NPLs are categorized as subprime loans that may be overdue, raising concerns about the debtor's ability to repay and the creditor's chance of capital recovery This situation often arises when borrowers face insolvency or liquidate assets Subprime loans typically include those that are 90 to 180 days past due or have been renegotiated Additionally, debts are deemed doubtful when there is uncertainty about full recovery, indicating a risk of loss, particularly when they are 180 to 360 days delinquent.
The International Monetary Fund’s Compilation Guide on Financial Soundness Indicators (2004) defines nonperforming loans (NPLs) as loans for which payments of interest and/or principal are overdue by 90 days or more, or when interest payments have been capitalized, refinanced, or delayed for a duration of 90 days or longer.
A loan is classified as nonperforming (NPL) if payments are overdue by 90 days or more, although other factors, such as a debtor filing for bankruptcy, may also raise concerns about the likelihood of full repayment Once a loan is designated as nonperforming, it must remain in that classification until it is either written off or payments of interest and/or principal are received on the original or replacement loans The International Monetary Fund (IMF) emphasizes the significance of the 90-day overdue period as the standard for determining a loan's NPL status.
A loan is classified as non-performing when the borrower is 90 days or more overdue on payments or when the borrower is deemed unlikely to fulfill their credit obligations, as defined by the Basel Committee on Banking Supervision (2006) However, the interpretation of "unlikely to pay" varies significantly across different jurisdictions, leading to diverse market practices.
Non-Performing Loans (NPL) are a complex financial concept with no universally agreed-upon definition However, international organizations generally identify NPLs based on two key criteria: a payment overdue by more than 90 days and the borrower's questionable ability to repay This definition aligns with the approach taken by the State Bank of Vietnam in classifying NPLs.
As defined by Circular No 11/2021/TT-NHNN issued by the State Bank of Vietnam on July 30, 2021, non-performing loans (NPLs) are classified as bad debts on the balance sheet that fall into groups 3, 4, or 5, as outlined in Articles 10 and 11 of the circular.
1.1.2.1 Classification of NPLs according to classification basis a Classification of NPLs according to the quantitative method
The quantitative method considers NPLs based on the status of the loans Accordingly, NPL is determined as follows:
(i) Debts/loans which are from 91 days to 180 days overdue;
(ii) Debts/loans with first-time extended repayment terms that are unmatured;
(iii) Debts/loans on which interest is exempted or reduced due to the borrower's inability to pay in full as agreed upon;
Disbursed loans that breach existing laws or internal regulations regarding credit extension, loan management, and risk provisions are categorized as substandard debts by established quantitative criteria.
(i) Debts/loans which are from 181 days to 360 days overdue;
Debts or loans that have undergone first-time rescheduling are considered past due if they exceed 90 days from their new maturity dates Additionally, debts or loans with second-time rescheduled repayment terms are classified as unmatured.
(iv) Debts/loans are classified into doubtful debts/loans by prescribed quantitative criteria
- Debts/loans likely giving rise to loss:
(i) Debts/loans that are more than 360 days past due;
Debts or loans that have first-time rescheduled repayment terms and are at least 91 days overdue from their initial rescheduled maturity dates, as well as those with second-time rescheduled repayment terms that are overdue from their subsequent rescheduled maturity dates, indicate significant financial distress and require immediate attention.
Debts or loans that have undergone rescheduling of repayment terms three or more times are classified as non-performing loans (NPLs) Additionally, loans categorized as likely to result in a loss based on established quantitative criteria also fall under this classification Furthermore, NPLs can be assessed using qualitative methods to determine their risk and potential impact.
Qualitative method considers NPLs based on the status of the loans Accordingly, NPL is determined as follows:
- Sub-standard debts/loans, including:
Loans and debts, including principal and interest, that are deemed unlikely to be recovered by their maturity dates are assessed by credit institutions and foreign bank branches These financial obligations are classified as high-risk, as they are expected to result in losses for the lenders.
Off-BS commitments with borrowers that are unable to fulfill their agreed obligations according to the assessment of credit institutions, foreign bank branches
Loans/debts that are rated by credit institutions and foreign bank branches as those posing high risk of causing loss
Off-BS commitments with high possibility that borrowers fail to fulfill their commitments
- Debts/loans likely giving rise to loss, including:
Loans/debts rated by credit institutions, foreign bank branches as those unlikely to be recovered and posing risk of causing loss
Off-BS commitments with possibility that borrowers are unable to fulfill their agreed obligations
1.1.2.2 Classification of NPLs according to loan guarantee
Non-Performing Loans (NPLs) can be categorized into two main types: secured NPLs and unsecured NPLs Secured NPLs are those that credit institutions or foreign bank branches have issued with the requirement of collateral, pledges, or third-party guarantees from borrowers In contrast, unsecured NPLs are loans provided by these institutions without any collateral or third-party guarantees, relying solely on the borrower's creditworthiness and the regulations set by the government.
1.1.2.3 Classification of NPLs according to accounting principles
Non-Performing Loans (NPLs) are categorized into two types: on-balance sheet NPLs and off-balance sheet NPLs On-balance sheet NPLs are those that remain under the scrutiny of credit institutions' balance sheets, directly influencing their production outcomes and overall financial performance.
Determinants of Non-Performing Loans
Research on credit risk and macroeconomic factors, initiated by King & Plosser (1984) and expanded by Bernanke & Gertler (1989) and Bernanke et al (1999), reveals a significant negative correlation between non-performing loans (NPLs) and overall economic health During economic growth, borrowers experience increased earnings, enhancing their ability to meet debt obligations Conversely, economic downturns lead to higher NPLs as unemployment rises and repayment difficulties ensue (Salas & Saurina, 2002; Jimenez & Saurina, 2006; Pesaran et al., 2006; Quagliariello, 2007; Beck et al., 2013) Notably, the studies by Rajan & Dhal (2003) and Rossi et al (2009) highlight the positive influence of economic growth on loan quality in Indian and Austrian commercial banks, respectively.
Higher prices can reduce the real value of unpaid debt, making it easier to repay However, inflation may also lower real incomes if prices are rigid, and could lead monetary authorities to increase interest rates The interplay between inflation, interest rates, and non-performing loans (NPLs) varies depending on a country's monetary policy and the readiness of its commercial banks Research by Le (2016), Nkusu (2011), and Pestova & Mamonov (2013) indicates that inflation positively affects NPLs, while other studies suggest differing outcomes.
(2014) on Kenyan commercial banks indicates that this relationship is in the opposite direction
Numerous studies have established a significant relationship between exchange rates and non-performing loans (NPLs), with research by Castro (2012), Beck et al (2013), and Beck (2013) highlighting a positive correlation Conversely, findings from Pestova & Mamonov (2013) and Washington (2014) indicate a negative correlation between these variables As exchange rates fluctuate, the impact on NPLs becomes a critical area of analysis in understanding financial stability.
As the exchange rate rises, the cost of imported goods and services increases, forcing businesses that depend on foreign materials to borrow substantial amounts of local currency to manage these extra expenses This devaluation of the domestic currency can create financial pressure on companies with debts in foreign currencies.
Recent studies highlight the importance of assessing banks' cost effectiveness and profitability through the lens of asset quality, particularly focusing on non-performing loans (NPLs) Research by Karim et al (2010) indicates that banks that declare bankruptcy tend to have a high NPL ratio and poor performance Similarly, Kwan & Eisenbeis (1996) found a negative relationship between bank profitability and NPLs in stable operating conditions.
Numerous studies have investigated the impact of non-performing loans (NPLs) on the operational efficiency of commercial banks worldwide, revealing a consistent and statistically significant negative correlation Research by Achou & Tenguch (2008), Banker et al (2010), Trujillo-Ponce (2013), and Le (2016) demonstrates that banks exhibiting a higher return on assets (ROA) ratio tend to have a lower NPL ratio.
Guan et al (2017) argue that minimizing resources dedicated to credit risk management can lead to a more cost-effective strategy A bank with high operational productivity can reduce non-performing loans (NPLs) through efficient management However, operational efficiency without strong management skills may lead to an increase in NPLs, as credit ratings and borrower monitoring can drive up costs, resulting in negative loans (Pradhan & Parajuli, 2017) Additionally, Trung (2019) found that a high cost-to-income ratio negatively impacts NPL levels.
Improving credit quality in banks can be effectively achieved by diversifying their investment portfolios, as noted by Louzis et al (2012) When banks identify viable investment opportunities, they can enhance their overall credit quality.
Investing in companies with growth potential is crucial for fostering economic development During periods of lower capital availability for lending, banks may struggle to extend credit to individuals with limited repayment capacity A study by Le (2016) explores the interplay between macro finance and banking operations in East Asia, highlighting the challenges faced by commercial banks in navigating these financial dynamics.
(2007) on Indian commercial banks both find a positive correlation between bank size and NPLs This is due to large banks accepting risks during expansion, resulting in a rise in NPLs
Research on credit growth factors has produced varied results Clair (1992) explored the relationship between non-performing loans (NPLs) and credit growth for Texas commercial banks from 1980 to 1990 The study categorized explanatory variables into three groups: credit growth, financial characteristics, and business conditions, while examining three types of credit growth: internal growth, mergers, and acquisitions The analysis revealed that credit growth can have time lags of up to three years Using the OLS method, the results showed that improved credit quality at lags 0 and 1 is linked to internal growth and acquisitions, while credit expansion from mergers only reduces NPLs without affecting the rate of uncollectible debt.
Salas and Sarina (2002) distinguish between two types of credit expansion: private sector growth and branch network growth Their study utilizes a dynamic econometric model along with a differential GMM regression technique, incorporating lagged variables over intervals of 2, 3, and 4 years The results reveal a positive correlation between credit expansion and an increase in non-performing loans (NPLs).
Foos et al (2010) analyze bank risk by utilizing credit risk provisions and the ratio of uncollected debt to net interest income Their study employs Ordinary Least Squares (OLS) and Generalized Method of Moments (GMM) models for estimation The results reveal a positive correlation between prior credit transactions and associated expenses.
20 in managing credit risks and the proportion of net interest income that cannot be recovered in the future
The relationship between bank capital and non-performing loans (NPLs) is inversely correlated, as low-capitalized banks often take on high-risk investments and issue loans without sufficient credit assessments, leading to increased loan defaults (Keeton, 1999) In contrast, banks with substantial capital are more willing to extend credit, knowing that their loans are less likely to result in bankruptcy, which fosters a favorable correlation between capital and NPLs (Rajan, 1994).
The Capital Adequacy Ratio (CAR) is crucial for assessing a bank's ability to absorb unexpected losses Research by Hu et al (2004) indicates that larger banks may struggle with Non-Performing Loans (NPLs) when offering risky loans Additionally, studies by Makri et al (2014) and Kumar & Kishore (2019) reveal a negative correlation between NPLs and CAR within the banking sector This trend was also confirmed in a study of Nepal's banking sector by Koju et al (2018) Conversely, Constant & Ngomsi (2012) found a positive correlation between NPLs and CAR In Ghana, Amuakwa & Boakye (2015) discovered that higher bank capital positively influences NPLs.
Research indicates that the relationship between non-performing loans (NPL) and capital ratios, particularly equity, lacks consensus, with studies showing no statistically significant correlation in countries like India and Greece (Das & Ghosh, 2007; Louzis et al., 2012).
Banks receive two distinct forms of revenue: interest income generated from lending activities and noninterest income derived from trading and derivative
RESEARCH MODEL AND RESULTS
Research Model
This study aims to investigate the underlying reasons for non-performing loans (NPLs) in Vietnamese commercial banks, focusing on macroeconomic and specific banking factors as explanatory variables Building on previous experimental models, a research model has been developed to analyze the determinants of NPLs in this context Utilizing a dynamic panel methodology, the study examines time latency in NPL structures, referencing prior research by Louzis et al (2012), Salas & Sarina (2002), Klein (2013), Le (2016), and Vinh (2017).
In this analysis, we examine the non-performing loan (NPL) ratio of bank i in year t, represented as 𝑁𝑃𝐿 𝑖,𝑡 The model incorporates unobservable effects specific to each bank, denoted as 𝑢 𝑖,𝑡, alongside error terms represented by ℰ 𝑖,𝑡 Additionally, we consider the influence of macroeconomic factors, indicated by 𝑀 𝑡, and specific factors unique to each bank, represented as 𝑆 𝑖,𝑡 This framework allows for a comprehensive understanding of the dynamics affecting banks' NPL ratios over time.
2.1.2 Description of variables and hypotheses
Non-performing loans (NPLs) are a key micro variable, quantified by the ratio of NPLs to the total outstanding loans of individual banks In Vietnam, NPLs are classified according to the country's Decision on Debt Classification, encompassing debts categorized as groups 3 to 5 on banks' balance sheets The formula used to calculate NPLs is essential for assessing a bank's financial health.
The loan items classified under groups 3, 4, and 5 are derived from the financial statements and annual reports, while the total outstanding balance is sourced from the banks' balance sheets The NPL ratio for each bank is calculated annually using Formula (2.2).
Previous non-performing loans (NPLs) are expected to positively impact the current NPL ratio, supporting Hypothesis H1 Research indicates that a history of high NPLs reflects poor risk management in banks' lending practices, leading to an increased occurrence of NPLs in the present period.
The asset size variable (SIZE) is a crucial micro variable that indicates a bank's overall size In data analysis, regression techniques often use the logarithm of total assets as a proxy for this variable, given that total assets usually represent a significant absolute value The formula for calculating SIZE is as follows:
In which, the item of total assets is displayed on the annual BS of banks
The SIZE of a bank is expected to positively influence the NPL ratio, in line with the "too big to fail" theory, which suggests larger banks take on greater risks by utilizing more loan capital As banks grow in size, their Non-Performing Loans (NPLs) also tend to increase In Vietnam, state-owned commercial banks, which are larger than their joint-stock counterparts, often engage in riskier lending practices, operating under the belief that government support will be available in the event of insolvency Research by Salas and Saurina (2002) and Boyd and Gertler (1994) indicates that this trend is likely to continue.
The third micro variable is capital adequacy (CAP) which is proxied by the following formula:
Both items of equity and total assets are taken from banks’ BS
The Non-Performing Loan (NPL) ratio is expected to be negatively affected by Capital Adequacy Provisions (CAP), aligning with the "moral hazard" theory This theory suggests that banks with high leverage and risk often reduce their capital, increasing their vulnerability to risk A low capital level reflects the inadequate financial strength of commercial banks, as equity acts as a protective buffer against potential losses.
26 enables the bank to deal with the devaluation of its assets and averts the possibility of the bank becoming insolvent Louzis (2012), Klein (2013), and Duong & Huong
(2017) researched the determinants of NPLs in Europe, Greece, and Vietnam Their findings indicate that equity negatively affects NPLs
Return on assets (ROA) is a key financial metric used to assess the profitability of a financial institution, calculated through a specific formula to measure how effectively a company utilizes its assets to generate earnings.
Profit after tax, also known as net income, is obtained from the income statement (IS), while total assets are sourced from the balance sheet (BS) This research utilizes formula (2.5) to calculate profitability in accordance with the established standards in Vietnam.
Based on aforementioned chapter, ROA is expected to have negative correlation with NPLs (Hypothesis H4)
The fifth micro variable is income diversification (ID) The calculation of this variable is as:
The components of net non-interest income are taken from bank’s IS and then calculated by Excel software
It is expected that there exists a negative correlation between ID and NPLs (Hypothesis H5)
The sixth micro variable is cost efficiency (CIR) which is measured by the ratio of operating cost to operating income
The items in operating expenses and operating income are collected from
IS of banks and processed by Excel software
This article posits an inverse correlation between Cost-to-Income Ratio (CIR) and Non-Performing Loans (NPLs), aligning with Hypothesis H6 Based on Berger and Young's (1997) "bad management" hypothesis, higher operational costs suggest inferior management quality within banks To address these elevated costs, bank managers might engage in riskier strategies.
Reducing credit standards to enhance profitability can lead to an increase in delinquent loans According to the "skimping" hypothesis by Berger & Young (1997), cost-cutting measures in banking operations often result in diminished resources for risk management and borrower oversight, which ultimately contributes to a rise in non-performing loans (NPLs).
The seventh micro variable is the loan-to-deposit ratio (LDR) The mathematical expression used to determine this proportion is as follows:
LDR is anticipated to positively impact NPLs (Hypothesis H7), as proven by previous research conducted by Louzis et al (2012), Misra & Dhal (2010), Makri et al (2014), Kartikasarya et al (2020)
The eighth micro variable is loan loss reserve (LLR) This variable represents the risk premium, calculated based on the following formula:
LLR is expected to affect NPLs positively (Hypothesis H8) If a bank decides to increase the provision for loan balances, it will have an immediate effect on the bank's operating expenses
The ninth micro variable, the credit growth rate (CGR), is defined as the percentage increase in loan balances during the current period compared to the previous period CGR can be calculated using a specific formula that measures this growth effectively.
The outstanding balance items of this year and the previous year are collected in the balance sheet and calculated according to formula (2.9)
CGR is supposed to have a positive effect on NPLs (Hypothesis H9) The publicly empirical evidence indicates a positive correlation, as demonstrated by Keeton (1999), Salas & Saurina (2002), Diep & Kieu (2005), and Duong & Huong
(2017) In contrast, the findings of Le (2016), Jimenez & Saurina (2006), and Vinh
(2017) went into the opposite direction
The economic environment is a crucial macro variable, with GDP growth rate serving as a key indicator of economic growth To calculate the rate of economic growth, a specific equation is utilized.
Vietnam's economy is significantly reliant on bank credit expansion, and a positive GDP outlook is anticipated to decrease non-performing loans (NPLs) High economic growth rates are expected to improve borrowers' ability to repay debts, thereby reducing the occurrence of NPLs.
Results
Table 2.2: Descriptive statistics of variables in the research
Variable Obs Mean Std Dev Min Max
The mean bad debt ratio for a sample of 15 Vietnamese commercial banks stands at 2.0179% of total outstanding loans, highlighting the banks' reliance on credit operations Additionally, the average loan-to-capital ratio is 65.6126%, indicating a strong dependence on credit activities Profitability, measured as the range of profits on total assets, varies significantly from 0.03% to 3.58%, showcasing differences in operational effectiveness among these banks.
2.2.2 Testing of variable selection in regression models
2.2.2.1 Correlation between independent variables in the model
NPL_1 SIZE CAP ROA ID CIR LDR LLR CGR IR INF EXR GDP COVID
Table 2.3 indicates that the independent variables show correlations below 0.8, except for the correlation between GDP growth and Covid-19, which exceeds this threshold To avoid multicollinearity, the GDP variable will be excluded from the model.
2.2.2.2 Testing multicollinearity for Pooled OLS model
Table 2.4 displays the variance inflation factor (VIF) coefficients for each independent variable, which helps detect multicollinearity in the model The average VIF across all variables is 2.44, with none exceeding a VIF of 10, indicating that multicollinearity is not present in the model.
2.2.3 Regression analysis of panel data according to GMM method
To address issues of Heteroscedasticity and Autocorrelation, I employed the 2-step GMM regression method, which not only mitigates regression problems but also enhances the understanding of the variables in the model This approach is deemed reliable for elucidating the impact of both micro and macro factors on Non-Performing Loans (NPLs) in Vietnamese commercial banks The effectiveness of the instrumental variable in the GMM framework is assessed using the Hansen and Arellano-Bond tests The Hansen test evaluates the appropriateness of the instrumental variables, with a higher p-value indicating better suitability, while the Arellano-Bond test for autocorrelation, which assumes no autocorrelation, is particularly significant as it considers all levels of autocorrelation through the AR(2) test.
Table 2.5: GMM estimation results on determinants of NPLs in VNese banks
Source: Extracted from Stata 14.0 2.2.6.1 Analysis of bank-specific factors
The results of Table 2.5 show that previous NPL has a positive effect on current NPL, consistent with hypothesis H1 and the study of Salas & Saurina
Research by Klein (2013), Vinh (2017), and Duong & Huong (2017) indicates a significant positive correlation between NPL_1 and current NPL levels, with a 1% increase in NPL_1 leading to a 0.6039 unit rise in NPLs, holding other factors constant High levels of past non-performing loans (NPLs) highlight a bank's challenges in managing lending risks, contributing to elevated NPL ratios in subsequent years This persistence is due to the delayed resolution of prior NPLs, which not only reflects the bank's risk management capabilities but also serves as an indicator of the broader economic and business environment affecting the bank's operations.
The results of Table 2.5 show that bank size has a negative effect on current NPL, inconsistent with hypothesis H2 but in line with the finding of Hu et al
Research by Louzis et al (2012) and Vinh (2017) indicates that when all factors are constant, a 1-unit increase in bank size (SIZE) correlates with a decrease of 0.8189 units in non-performing loans (NPL) at a 5% significance level Larger banks often implement advanced risk management systems, enabling them to better identify and mitigate potential risks, thereby reducing the likelihood of issuing loans that may become non-performing In Vietnam, banks are significant market capitalization entities closely linked to the national economy, resulting in stringent supervision of the relationship between bank size and NPL.
37 by the SBV SBV sets guidelines and regulations to manage NPLs and maintain the stability of the banking system
The variable Capital Adequacy Ratio (CAP) shows a positive correlation with Non-Performing Loans (NPL) at a 5% significance level, contradicting hypothesis H3 but aligning with findings from Constant & Ngomsi (2012) and Amuakwa & Boakye (2015) Specifically, for every 1 unit increase in CAP, NPL increases by 0.1684 units, suggesting that banks may adopt aggressive lending strategies to boost profits, potentially leading to elevated NPL levels Higher capital levels may embolden banks to pursue riskier loans, resulting in increased NPL ratios Notably, VPB, the largest bank by equity in Vietnam, also reports the highest NPL ratio in the industry In response, commercial banks in Vietnam are enhancing their capital stock to mitigate credit risk and protect overall bank performance.
This study indicates that while Return on Assets (ROA) has a negative influence on Non-Performing Loans (NPL), this effect is not statistically significant There may be a time lag between fluctuations in ROA and subsequent changes in NPL Specifically, when a bank's ROA declines, it could take several quarters for NPLs to rise In the context of Vietnamese commercial banks, a decrease in ROA suggests a tendency for NPLs to increase, highlighting the importance of monitoring profitability to mitigate potential loan defaults.
Table 2.5 indicates that an increase in ID negatively affects NPL, aligning with the expectations set by Rachman et al (2018) Specifically, when all other factors remain constant, a 1-unit rise in ID results in a 1.1648-unit decrease in NPL at a 10% significance level Additionally, diversifying revenue streams allows banks to reduce reliance on traditional lending by incorporating fee-based services, wealth management, and other non-interest income sources.
Increased profitability enables banks to invest more in credit risk management, focusing on proactive strategies to mitigate non-performing loans (NPLs) Recently, there has been a growing emphasis on non-interest income activities within the banking sector.
The variable CIR exhibits a negative correlation with NPL, aligning with hypothesis H6 and the "bad management" and "skimping" theories proposed by Berger & Young (1997) Specifically, when all other factors remain constant, a 1-unit increase in ID results in a 0.0585-unit decrease in NPL at a 5% significance level Banks that experience a high NPL ratio tend to demonstrate low-cost efficiency, often due to inadequate management of both costs and borrower oversight Additionally, the emergence of NPLs can be influenced by external factors, leading to increased costs associated with managing these non-performing loans.
- Loan-to-deposit ratio (LDR)
The result in this study showed that the impact of LDR on NPL is positive but not statistically significant This is parallel with the findings of Makri et al
Research supports the hypothesis that the loan loss reserve ratio (LLR) positively influences non-performing loans (NPL) at a 1% significance level, indicating that a unit increase in LLR corresponds to a 0.8727 unit increase in NPL This implies that a decrease in the LLR may enhance bank efficiency, leading to lower NPLs, and vice versa In Vietnam, banks must adhere to prudential standards set by regulatory authorities like the State Bank of Vietnam (SBV), which include mandatory loan loss reserve requirements These regulations ensure that banks maintain adequate reserves to cover potential losses from NPLs As NPLs rise, banks are likely to boost their loan loss provisions to comply with these requirements, establishing a positive correlation between LLR and NPL.
Contrary to initial expectations, credit growth (CGR) exhibits a negative effect on non-performing loans (NPLs) at a 10% significance level, with a unit increase in CGR linked to a 0.0351 unit decrease in NPL This finding aligns with previous studies by Le (2016), Vinh (2017), and Jimenez et al (2006) The inverse relationship can be understood through Keeton's (1999) theory on shifts in credit demand; during economic expansions, increased credit growth occurs as borrowers experience higher incomes and improved repayment abilities, leading to reduced default rates Additionally, banks often respond to rapid credit growth by tightening lending standards and enhancing risk management practices, further lowering the likelihood of extending credit to higher-risk debtors.
- Inflation rate (INF) and real interest rate (IR)
The study reveals a significant correlation between inflation (INF) and non-performing loans (NPL), indicating that a 1% increase in INF results in a 0.3275 rise in NPL, supporting hypothesis H11 that higher inflation leads to increased NPLs Historical data shows that Vietnam experienced an average inflation rate of 6.84% during the research period, peaking at 23.11% in 2008, which has been a contributing factor to the surge in NPLs within the commercial banking sector However, the research did not find any evidence linking interest rates (IR) to NPL fluctuations.
The regression analysis indicates that, holding other factors constant, a one-unit increase in the exchange rate (EXR) leads to a 1.6206 increase in non-performing loans (NPL) This finding supports Hypothesis H13, as it shows that an appreciation of foreign currency heightens the risk associated with foreign currency loans Consequently, this increased risk diminishes customers' repayment capabilities, ultimately resulting in a rise in NPLs.
RECOMMENDATION TO LIMIT AND HANDLE NON-
Some recommendation to limit and address non-performing loans of
The first recommendation is to enhance banks’ operating efficiency
The study on the determinants of non-performing loans (NPLs) indicates that cost efficiency and historical NPL levels are significant factors affecting current NPL rates Based on these findings, it is recommended that Vietnamese commercial banks focus on enhancing cost efficiency and addressing past NPL issues to improve their financial stability.
To enhance cost efficiency, Vietnamese commercial banks should leverage information technology by automating and digitizing processes to reduce manual errors and boost operational efficiency Implementing data analytics and artificial intelligence can refine credit risk assessment models, enabling more accurate evaluations of borrowers' creditworthiness and better management of loan portfolios Additionally, technology-driven platforms like digital and mobile banking enhance customer engagement, leading to improved loan performance and lower non-performing loan (NPL) ratios Furthermore, utilizing IT infrastructure for data security and fraud prevention strengthens risk management, safeguarding bank assets and minimizing potential losses.
44 credit risks, and potentially reduce NPLs in their loan portfolios through the effective use of information technology
To mitigate the influence of existing non-performing loans (NPLs) on future ones, Vietnamese commercial banks should focus on strengthening their risk management capabilities in line with their expanding total asset size This can be achieved by enhancing risk management and control mechanisms, adopting credit analysis practices from foreign banks, and closely monitoring borrowers' repayment abilities Furthermore, banks can proactively pursue debt collection, reschedule and reassess debts, transfer debts to trading companies, or establish risk provisions to manage potential losses effectively.
The second recommendation is to improve financial capacity and maintain reasonable operating size
To effectively reduce non-performing loans (NPLs), banks must enhance their financial capacity and maintain an optimal operating size This involves improving capital adequacy to better absorb potential credit losses, ensuring that banks possess sufficient capital to manage unexpected financial challenges A robust financial foundation enables banks to navigate tough economic conditions and manage NPLs more efficiently.
Banks must align their operational size with their risk appetite and management capabilities to mitigate the likelihood of increased non-performing loans (NPLs) During rapid growth, inadequate risk management can exacerbate financial vulnerabilities Therefore, it is essential for banks to assess their growth strategies and confirm they possess the necessary resources, infrastructure, and expertise to manage their loan portfolios effectively.
The third recommendation is to promote non-interest income activities
To effectively reduce non-performing loans (NPLs), banks should diversify their income streams by offering fee-based services and value-added offerings, which can generate additional revenue and lessen reliance on interest income tied to loan performance Furthermore, engaging in non-interest income activities can provide a safety net during economic downturns while enhancing customer engagement It is crucial, however, for banks to manage associated risks carefully and maintain regulatory compliance.
The forth recommendation is planning a suitable credit growth
Research indicates a negative correlation between nonperforming loans (NPLs) and credit growth, suggesting that Vietnamese commercial banks can adopt targeted strategies to mitigate NPLs To achieve this, banks should prioritize a cautious lending approach, ensuring that any credit expansion is supported by thorough risk assessments and vigilant credit monitoring By implementing strict lending standards and exercising care in loan approvals, banks can lower the risk of defaults, thereby reducing NPL levels Additionally, fostering a conducive credit environment and minimizing NPLs necessitates close collaboration with regulatory bodies and adherence to sound banking practices.
The fifth recommendation is to continuously monitor macro indicators that impact NPLs
By monitoring macroeconomic indicators, banks can gain insights into the overall economic landscape and identify risks that may lead to a rise in non-performing loans (NPLs) During economic downturns or industry-specific crises, the likelihood of business failures increases, resulting in higher loan defaults To mitigate these risks, banks should adapt their risk management strategies, which may include tightening credit standards, reallocating resources to safer sectors, and proactively supporting borrowers facing financial challenges.
Limitations of Study
Despite the attainment of the established research goals, certain constraints were inevitable due to limitations in research time, data collection, and methodology The following limitations were identified as a result:
The current study utilized secondary data from a sample of 15 out of 46 commercial banks in Vietnam It exclusively relied on non-performing loan (NPL) data provided by Vietnamese commercial banks, without sourcing information from external entities.
46 the Supervisory Authority or international organizations to more precisely evaluate of the NPL status of VNese banking system
The thesis lacks an analysis of how technological innovation, legal frameworks, and institutional factors influence non-performing loans (NPLs) across different industries, primarily due to the absence of pertinent data sources.
Future research should expand the scope and focus of inquiry, taking into account additional factors that could influence the occurrence of bad debt, addressing the limitations identified in this study.
Chapter 3 presents research-based recommendations for reforming the legal system and banking supervision model, alongside establishing a more effective non-performing loan (NPL) management system These proposals are designed to reduce NPLs and promote sustainable growth within the banking sector.
CONCLUSION
The thesis "Determinants of Non-Performing Loans in Vietnamese Commercial Banks" offers a comprehensive theoretical framework for understanding non-performing loans (NPLs) and examines the key factors affecting the NPL ratio in Vietnam's commercial banking sector.
This thesis presents a research model analyzing the factors influencing the non-performing loan (NPL) ratio of commercial banks in Vietnam, focusing on 15 major banks over a 14-year period from 2008 to 2021 The findings reveal that the previous year's NPL ratio, capital adequacy, loan loss reserves, inflation rate, and exchange rate positively affect NPLs Conversely, factors such as bank size, income diversification, cost efficiency, credit growth, and the impact of the pandemic negatively influence NPL ratios.
Based on the above research results, the author proposes some recommendations to limit and handle NPLs for the Vietnamese banking system
The study does not provide a complete representation of all commercial banks in Vietnam and fails to include those in neighboring countries Additionally, it does not utilize all relevant variables that could influence non-performing loans (NPLs) Expanding research in these areas would be advantageous for a more comprehensive understanding of the topic.
APPENDIX Appendix 1 Banks in the research sample
No Sign Bank’s full name
1 ACB Asia Commercial Joint Stock Bank
2 AGRIBANK Vietnam Bank for Agriculture and Rural Development
3 BID Joint Stock Commercial Bank for Investment and
4 CTG Vietnam Joint Stock Commercial Bank for Industry and
5 HDB Ho Chi Minh City Development Joint Stock
6 MBB Military Commercial Joint Stock Bank
7 MSB Vietnam Maritime Commercial Join Stock Bank
8 OCB Orient Commercial Joint Stock Bank
9 SHB Saigon Hanoi Commercial Joint Stock Bank
10 SSB Southeast Asia Commercial Joint Stock Bank
11 STB Sai Gon Thuong Tin Commercial Joint Stock Bank
12 TCB Vietnam Technological and Commercial Joint Stock
13 VCB Bank for Foreign Trade of Vietnam
14 VIB Vietnam International Commercial Joint Stock Bank
15 VPB Vietnam Prosperity Joint Stock Commercial Bank
Appendix 2 Data sources of variables
NPL Annual report, Note to FS of each bank
ROA Profit after tax is taken from the IS
Total assets are taken from the BS CIR Calculation from the data of each bank's IS
SIZE Calculation from the data of each bank's BS
CAP Calculation from the data of each bank's BS
ID Net non-interest income is calculated from the IS
Total assets are taken from the BS CGR Calculation from the data of each bank's BS
LDR Calculation from the data of each bank's BS
LLR Calculation from the data of each bank's BS
GDP International Financial Statistics of International Monetary Fund INF International Financial Statistics of International Monetary Fund
IR International Financial Statistics of International Monetary Fund EXR International Financial Statistics of International Monetary Fund
Appendix 3 Descriptive statistics of variables in the research
Appendix 4 Correlation between independent variables in the model
No BANK YEAR NPL NPL_1 SIZE CAP ROA ID CIR LDR LLR CGR IR INF EXR GDP COVID
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