BANKING ACADEMY --- GRADUATION THESIS ANALYSIS OF THE IMPACT OF MACRO AND MICRO ECONOMIC FLUCTUATIONS ON THE NON-PERFORMING LOAN RATIOS AT COMMERCIAL BANKS IN VIETNAM DURING THE COVID-1
INTRODUCTION
Interest of research
In recent years, Vietnam's economy has faced significant challenges due to global events, particularly the COVID-19 pandemic, which has severely impacted public health and all sectors of economic and social life The financial sector, especially commercial banking, has been notably affected, with Vietnamese banks experiencing a rise in non-performing loans that raises concerns about financial stability and the resilience of the banking system to economic shocks.
A 2020 finance ministry report highlighted a significant rise in non-performing loans during the first quarter, with Kienlongbank's bad debts soaring by 5.7 times to over 2.293 trillion VND TPbank also faced a 53% increase in bad debts, totaling 1.884 trillion VND, particularly in severely delinquent loans, which surged by 61% to 771 billion VND Other banks, including SeABank and VIB, reported notable increases in their bad loan ratios as well.
The COVID-19 pandemic significantly triggered bad debts, compounded by various overlapping factors that led to the collapse of numerous loans and the emergence of hard-to-recover debt groups Additionally, external economic impacts have adversely affected the credit quality within the banking sector The urgency of the study titled "Analysis of the Impact of Macro and Micro Economic Fluctuations on the Non-Performing Loan Ratios at Commercial Banks in Vietnam During the COVID-19 Era" highlights the critical need to thoroughly assess the pandemic's effects alongside other macroeconomic influences on credit quality This research aims to provide valuable insights into the correlation between macroeconomic dynamics and credit quality through non-performing loan ratios, assisting commercial banks and regulatory bodies in evaluating the effectiveness of existing credit risk management strategies and proposing enhancements.
Nguyễn Huy Hoàng 2 2024 appropriate risk management strategies and credit quality improvement tactics for the current and future context.
Research objectives
This study examines the impact of macroeconomic and microeconomic fluctuations on non-performing loan rates across the COVID-19 timeline Its findings are crucial for developing future macroeconomic, financial, and monetary policies aimed at reducing negative effects on the banking system and the overall economy Consequently, the research holds both theoretical and practical significance, providing essential insights for decision-making and policy development for banks and financial authorities.
● How has the COVID-19 pandemic affected the non-performing loans of commercial banks in Vietnam ?
● Does the effect on non-performing loans comes from banks specific factors or macroeconomic factors ?
● Does the COVID-19 pandemic affect the non-performing loans of Vietnamese banks ?
● How have macroeconomic fluctuations influenced the non-performing loan ratios in Vietnamese commercial banks before and after the COVID-19 pandemic?
● What microeconomic factors have contributed to changes in the non- performing loan ratios at Vietnamese commercial banks during the COVID-
Objects and scope of the research
This research examines non-performing loans in Vietnam's banking sector, concentrating on 24 publicly listed commercial banks By focusing on these banks, the study offers a thorough analysis of the sector's performance and associated risks, leveraging their public disclosure obligations and their crucial contribution to the economy.
This research covers the period from 2014 to 2023, enabling a comprehensive analysis of banks' financial health and stability across various economic cycles, including the significant impacts of the COVID-19 pandemic on the financial sector By examining this timeframe, the study aims to gather extensive data on how both macroeconomic and microeconomic factors affect non-performing loan ratios in these essential financial institutions.
Research methodology
To meet the research objectives, a range of analytical methods is utilized, including descriptive and comparative statistics to summarize key data characteristics and track changes over time The research methodology employs advanced econometric models, such as Generalized Least Squares (GLS), to effectively analyze panel data while addressing issues like endogeneity, heteroscedasticity, and autocorrelation Furthermore, Fixed Effects (FEM) and Random Effects (REM) models are applied to determine which best represents the dynamics of non-performing loans (NPLs) across various banks and timeframes These models are essential for handling data that may not meet ordinary least squares regression assumptions Diagnostic tests, including the Breusch-Pagan/Cook-Weisberg test for heteroskedasticity and the Wooldridge test for autocorrelation, validate the model specifications used in the analysis.
Nguyễn Huy Hoàng 4 2024 conducted using Stata 17, which ensures rigorous data processing and computation efficiency.
Structure of the research
Based on the objective of the Research, the thesis is organized as follows:
Chapter I: Literature review & Theoretical framework for the impact of macroeconomic and microeconomic on commercial bank non-performing loans ratio
Chapter II: The current status of non-performing loán in vietnam banking system
Chapter III: Database and Research methods and result
Chapter IV: Solution and recommendations
LITERATURE REVIEW & THEORETICAL FRAMEWORK
LITERATURE REVIEW
In their 2022 study "Determinants of Non-Performing Loans Amidst Unexpected Crises in Malaysia's Commercial Banks," NE Zulkifli and Z Ahmad employed the Random Effect Model to analyze data from 26 commercial banks between 2007 and 2020 This model was preferred over the Pooled Ordinary Least Square Model due to its superior handling of unobserved heterogeneity, as confirmed by the Breusch Pagan Lagrangian multiplier test The research identified that microeconomic factors, including Return on Assets (ROA), Loan to Asset ratio (LTA), and Loan Growth (LG), significantly affect non-performing loan ratios, whereas macroeconomic factors like GDP and interest rates exert minimal influence on loan performance fluctuations Furthermore, the study emphasized that major economic disruptions, such as the Global Financial Crisis and the COVID-19 pandemic, severely impacted the quality of commercial banks' loan portfolios Consequently, it is recommended that commercial banks enhance their internal management and devise strong strategies to mitigate the effects of such crises.
In their 2023 study, OOI KOK LOANG et al examined the relationship between non-performing loans (NPLs), macroeconomic factors, and bank-specific variables in Southeast Asia during the COVID-19 pandemic using panel data regression and distributed lagged regression methods The research revealed a strong correlation between bank-specific variables and NPL rates prior to the pandemic, while the impact of macroeconomic variables on NPLs was minimal during that period However, as the pandemic unfolded, macroeconomic factors became significantly linked to NPL rates, indicating a shift in influence as the effect of bank-specific variables lessened.
Nguyễn Huy Hoàng 6 2024 suggests that macroeconomic factors have a pronounced impact during periods of economic turbulence, affecting most businesses, particularly during the pandemic
In 2008, Podpiera and Weill highlighted that inefficiencies in Czech Republic banks could lead to an increase in non-performing loans, stressing the importance of effective management for financial system stability Similarly, Garcia-Marco and Robles-Fernandez analyzed panel data from 129 Spanish banks between 1993 and 2000, revealing that a higher return on equity (ROE) correlates with increased future risk Their findings suggest that profit-maximizing policies are intrinsically associated with elevated risk levels.
In 2010, Louzis et al conducted a study titled “Macroeconomic and Bank-Specific Determinants of Non-Performing Loans in Greece: A Comparative Study of Mortgage, Business and Consumer Loan Portfolios,” utilizing dynamic panel data methods to analyze the factors influencing non-performing loans (NPLs) in the Greek banking sector The research concluded that macroeconomic variables, such as GDP, unemployment rate, and interest rates, significantly affect the level of non-performing loans.
Research by Hu et al (2004) highlights the relationship between bank characteristics and non-performing loans (NPLs) in Taiwan, revealing that larger banks and those with state ownership tend to have lower NPLs Additionally, studies indicate that supervisory factors and governance play crucial roles in loan quality, with ineffective management contributing to higher NPLs Credit growth is another significant factor influencing loan quality, as demonstrated by Bercoff et al in their analysis of Argentine banks, which found that increased credit growth adversely affects loan quality Similar findings have been reported in other research, underscoring the importance of these elements in managing non-performing loans.
In 2007, using both static and dynamic models, Mario Quagliariello did a research on “Banks' Riskiness Over the Business Cycle: A Panel Analysis on Italian
Research indicates that economic growth, alongside factors such as previous non-performing loans, credit growth, and cost management efficiency, significantly influences banks' non-performing loans A study by Pestova & Mamonov (2012) further supports this, revealing that elements like inflation rates, exchange rates, real estate prices, stock market indices, operational efficiency, bank size, and prior non-performing loan ratios also affect the non-performing loans in the banking system.
Numerous studies worldwide, including research by Nguyen Tuan Kiet & Đinh Hung Phu (2016), have examined non-performing loans (NPLs) in Vietnam Their analysis, which utilized time series data from 2007 to 2013 across 32 commercial banks, employed Random Effects Model (REM), Fixed Effects Model (FEM), and Generalized Method of Moments (GMM) to assess the micro and macro impacts on NPLs The findings indicated that macroeconomic factors, such as GDP growth, generally reduce NPLs, while public debt tends to increase them Furthermore, microeconomic factors, including past NPLs, credit growth, and operational efficiency, typically help lower NPLs, whereas a larger credit size is associated with an increase in NPLs.
The macroeconomic environment significantly influences non-performing loans (NPLs) in Vietnam's commercial banks A study by Đặng Thị Ngọc Lan (2019) examined the impact of macroeconomic factors on NPLs across 13 Asia-Pacific countries over a decade, from 2008 to 2017 The research identified seven key macroeconomic factors, including economic growth rate (GDP), interest rate (IR), and inflation rate (INF), that affect the performance of banks in this region.
In 2024, Nguyễn Huy Hoàng analyzed the impact of the industrial production index (IPI), unemployment rate (UN), money supply (M2), and consumer price index (CPI) on the non-performing loan (NPL) ratio The findings revealed that GDP and IPI typically exhibited an inverse relationship with NPLs, while two other factors showed a positive correlation affecting NPLs.
Contributing to researching the impacts of macroeconomic factor including COVID-
In their 2020 study, Le Dinh Hac et al investigated the enhancement of risk management culture for sustainable growth at Asia Commercial Bank (ACB) in Vietnam, focusing on the mixed effects of macroeconomic factors Utilizing analytical and synthetic statistical methods, along with a dialectical materialism approach and an econometric model that included nine macro variables, the researchers found that the Consumer Price Index (CPI) positively correlates with ACB's Beta CAPM, while both the risk-free rate (Rf) and the lending rate exhibit a negative correlation with the bank's Beta CAPM.
In a study conducted by Nguyen Tho Nho Quynh et al (2018), the factors influencing non-performing loans (NPLs) of 25 commercial banks from 2006 to 2016 were analyzed using the Feasible Generalized Least Squares (FGLS) method The findings revealed that economic growth, bank credit growth, and unemployment rates had an inverse relationship with NPL rates at a 1% significance level Additionally, the inflation rate and the previous year's NPL rates were positively correlated with current NPL rates However, the research found no significant relationship between bank size and profitability with NPL rates.
Pham Duong Phuong Thao and Nguyen Linh Dan (2018) conducted research on the factors influencing the non-performing loan ratio in Vietnamese joint stock commercial banks Utilizing the Generalized Method of Moments (GMM) differencing method, the study effectively addresses endogeneity, variable variance, and autocorrelation issues Key findings indicate that credit risk provision costs, operating costs, market exposure, and bank size significantly impact the non-performing loan ratio.
Inflation and exchange rates positively affect the non-performing loan (NPL) rates of commercial banks, while non-interest income, profitability on equity, interest rates, and unemployment rates negatively influence these rates The research indicates an inverse relationship between after-tax profits on equity and NPL rates; as banks' profits increase, their ability to reduce non-performing loans improves Additionally, economic growth is shown to have a negative impact on NPL rates in commercial banks.
THEORETICAL FRAMEWORK
1.2.1 An overview of Non-performing loans (NPL)
1.2.1.1 Definition of Non-performing loans
Non-performing loans (NPLs) are defined as loans that are at risk of delayed payments, making recovery uncertain for lenders, often due to borrower bankruptcy or asset transfer The financial sector lacks a universally accepted definition of NPLs, with characteristics varying by legal jurisdiction NPLs encompass loans that do not generate interest income or have been restructured due to the borrower's financial changes A loan is classified as non-performing if it is overdue by more than 90 days, and banks must refrain from recognizing accrued interest as income until payments are resumed (Rose & Hudgins, 2013).
Concept of NPLs by the World Bank (WB):
The World Bank defines non-performing loans (NPLs) by assessing delayed repayments and the probability of recovery, categorizing loans that are overdue by 90 to 180 days as subprime or renegotiated Loans are classified as doubtful when there is uncertainty regarding their recovery.
Nguyễn Huy Hoàng 10 2024 contractual terms becomes uncertain, especially when they are overdue between 180 to
Concept of NPLs by the International Monetary Fund (IMF):
The IMF defines NPLs as loans that are past due in principal or interest payments for
A loan is classified as a Non-Performing Loan (NPL) when interest payments are overdue for 90 days or more, or when payments due under 90 days show clear signs of the borrower's inability to repay, such as bankruptcy Once classified as an NPL, the loan, or any replacement loan, must retain this classification until the debt is either written off or the principal and interest are fully recovered.
Concept of NPLs by the Basel Committee on Banking Supervision (BCBS):
The Basel Committee on Banking Supervision does not provide a specific definition for non-performing loans (NPLs), but it suggests that a loan may be classified as non-performing if the borrower is unable to make full payments without bank intervention or if payments are overdue by more than 90 days Additionally, the Committee highlights that loan impairment indicates low recoverability, leading to losses that are recorded through provisions in the bank's income statement, with interest accruing only on actual payments received.
Concept of NPLs by the The European Central Bank:
The European Central Bank (ECB) emphasizes the importance of uniform asset definition and valuation to effectively evaluate risk exposures among central banks in the euro area During stress tests for banks under its authority, the ECB specifies various conditions that classify a loan as non-performing According to the ECB's thorough assessment, loans are considered non-performing if they satisfy any of the established criteria.
● 90 days overdue, regardless of whether the loans have been officially defaulted or impaired
● Classified as impaired in accordance with the accounting requirements of U.S GAAP and International Financial Reporting Standards (IFRS)
● Considered in default as per the Capital Requirements Regulation
International perspectives on non-performing loans (NPLs) reveal a shared understanding among financial institutions regarding the challenges posed by overdue debts A debt is deemed ineffective when it is overdue or when the borrower is unable to repay it NPLs refer to borrowed funds that lenders consider unrecoverable, typically due to the borrower's financial struggles, such as losses or bankruptcy Companies must evaluate potential NPLs by analyzing historical debt data throughout their business operations.
Non-performing loans (NPLs) serve as a critical indicator of the credit quality of financial institutions, highlighting their financial health and risk management effectiveness A rise in NPLs can result in significant losses for these institutions, eroding depositor confidence and damaging their credibility If unresolved, this issue may lead to bankruptcy, posing risks to the broader economy Consequently, the identification and management of NPLs are essential for restructuring the financial system.
1.2.1.2 Classification of Non-performing loan
Article 10 of Circular 02/2013/TT-NHNN, as amended by Article 1 of Circular 09/2014/TT-NHNN, classifies debts of credit institutions into five quality groups.
Group 1 - Standard debt All credit granted by the bank must meet the standards to be classified in this group These are debts with principal and interest within the due period and simultaneously, there are no difficulties in debt payment, and it is forecasted that the debt can be fully repaid as committed
Group 2 - Debt needing special attention These are debts that might face the risk of not being fully paid, with reduced repayment capacity detected during loan monitoring
Although not as severe yet, moving from group 1 to group 2 indicates a worsening condition, thus the bank needs to pay attention and take timely measures
Group 3 - Below-standard debt These are debts that might incur partial principal and interest losses, with principal and/or interest overdue for over 90 days, or the collateral of the debt has depreciated, leading to losses if not handled promptly
Group 4 - Doubtful debt These are debts with a high loss potential identified as unrecoverable, with principal and/or interest overdue for more than 180 days
Group 5 - Potentially irrecoverable debt These debts are considered capital lost when principal and/or interest is overdue for over a year including 08 different types, of which the most common and typical are debts that are overdue for more than 360 days and debt that has been rescheduled for repayment for the third time or more
1.2.1.3 Causes of Non-performing Loan a) Subjective causes
Bank guidelines and risk appetite are fundamental principles that steer credit activities and the management of non-performing loans (NPLs) Established by senior management, these guidelines reflect the bank's willingness to accept risk; a higher risk appetite can lead to increased NPL potential, while a lower appetite promotes a more cautious strategy Additionally, these principles impact the bank's decisions on customer segments, credit sectors, and industries, ultimately shaping its policies and actions related to NPL management.
Effective risk management is essential for banks in managing non-performing loans (NPLs) This effectiveness is highlighted by the implementation of strong risk quantification measures that enable accurate assessment of lending practices.
Nguyễn Huy Hoàng 13 2024 highlights the critical importance of accurate internal credit rating systems in identifying credit risks and implementing effective risk mitigation measures Inadequate risk management practices can lead to an increase in non-performing loans (NPLs) within banks, underscoring the need for thorough internal audits and robust control activities to enhance overall financial stability.
Financial stress among borrowers is a major factor contributing to non-performing loans, often triggered by job loss, reduced income, or unexpected expenses Economic downturns can intensify these challenges, making it difficult for borrowers to meet their loan obligations and increasing the likelihood of defaults Additionally, personal financial issues, such as high debt levels and inadequate money management skills, can further complicate their ability to maintain timely payments.
An overview of Vietnam Economic
Figure 2.1: GDP growth rate of Vietnam
Source: Data report of state bank of VietNam
In the past decade, Vietnam's economy has shown significant fluctuations, with notable periods of strong growth and moderate slowdowns, influenced by various external and internal factors Between 2014 and 2022, the country achieved an average real GDP growth rate of approximately 6.1%, surpassing the average for the Asia-Pacific region and highlighting Vietnam's robust economic performance amid regional trends.
In 2022, the growth trajectory reached its highest point at 8.0%, driven by a robust post-pandemic economic recovery Nevertheless, this momentum faces challenges, including global economic uncertainties and domestic issues such as a slowdown in public investment and anti-corruption measures.
Vietnam's GDP growth for 2023 is projected to be around 6.3%, falling short of the government's target of 6.5% This slowdown in growth expectations is attributed to the recent crackdown and ongoing global challenges, alongside the lingering effects of the pandemic, leading to a cautious optimism regarding the country's economic outlook.
GDP growth rates have varied over the years due to factors such as global demand changes, government policy adjustments, and shifts in key sectors like services, industry, and agriculture For 2024, forecasts indicate a potential growth rate of 6 to 6.5%, driven by a recovery in industrial production and exports, as well as a revitalization of tourism and related services.
Figure 2.2: Inflation rate of Vietnam
Source: Data report of state bank of VietNam
The notably low inflation rate of 0.60% in 2015 suggests a period of significant monetary control, possibly coupled with global commodity price downturns, particularly
Nguyễn Huy Hoàng, in 2024, highlights that low inflation can enhance consumer purchasing power, although it may also indicate weak economic demand For banks, while lower inflation might decrease nominal loan revenue, it can simultaneously improve borrowers' real repayment capacity, potentially lowering the risk of non-performing loans.
Since 2016, inflation has stabilized at approximately 3%, creating an environment favorable for economic growth while minimally impacting purchasing power This stability encourages effective business planning and investment, essential for fostering economic expansion Additionally, a consistent inflation rate benefits the banking sector by enhancing predictions of economic conditions, which improves risk assessment and loan pricing strategies.
In 2020, inflation slightly decreased to 2.80%, reflecting the global economic slowdown caused by the COVID-19 pandemic, which led to reduced consumer demand During economic downturns, the banking sector must address the risk of deflationary pressures, as these can elevate the real burden of debt on borrowers and potentially increase non-performing loans (NPLs) unless mitigated by effective policy measures.
The rise in inflation to 3.30% in 2023 signals an economic recovery, often characterized by increased consumer spending and business investments following the pandemic This resurgence in economic activity can lead to higher prices.
Figure 2.3: The unemployment rate of Vietnam
Source: Data report of state bank of VietNam
From 2014 to 2019, Vietnam experienced a stable unemployment rate of approximately 2%, indicating a robust economy capable of integrating new labor market entrants This consistent employment level highlights inclusive economic growth, primarily fueled by thriving sectors such as manufacturing, services, and a rapidly expanding tech industry that collectively absorbed a substantial share of the workforce.
In 2020, Vietnam demonstrated resilience amid the global disruption of the COVID-19 pandemic, with a slight economic decline to 1.99% This stability can be attributed to swift and effective containment measures that allowed the industrial and agricultural sectors to remain operational, both of which are crucial for employment in the country.
The period after the pandemic saw a subsequent increase in unemployment rates in
2021 and 2022, peaking at 3.22%, reflects the delayed economic consequences of the
The pandemic significantly impacted global supply chains, leading to a downturn in tourism and export-oriented industries, which in turn caused rising unemployment rates However, by 2023, the unemployment rate showed signs of stabilization at 3.25%, indicating a potential economic recovery and adaptation to the new normal This improvement may be attributed to the revival of international trade and tourism, along with government initiatives aimed at boosting domestic consumption and investment.
An overview of Vietnam commercial banks
2.2.1 The number of commercial banks
In recent decades, Vietnam's banking sector has experienced significant growth and diversification, particularly with the emergence of foreign banks that have intensified competition and spurred innovation The sector is divided into four segments: state-owned banks, joint-stock banks, foreign-owned banks, and joint venture banks, all regulated by the State Bank of Vietnam Analysis of data from the State Bank's annual report reveals fluctuations in the number of commercial banks from 2014 to 2016, followed by a period of stability from 2016 to 2023, as illustrated in Table 2.1.
Table 2.1: Number of commercial banks in Vietnam over the decade
Source: Data report of state bank of VietNam
Vietnam's economic growth has slowed ever since 2008, it raised concerns about the associated risks within the banking sector and its impact on macroeconomic stability
The banking landscape underwent significant strategic restructuring through mergers and acquisitions (M&A) of smaller, vulnerable banks, notably highlighted by the 2012 acquisition of Habubank by Saigon Bank, which strengthened Saigon Bank’s market position By 2013, key mergers included DaiA Bank with HD Bank and MHB with BIDV in 2015 Major banks such as Vietcombank and VietinBank supported additional mergers aimed at stabilizing the banking sector, including the 2012 amalgamation of SCB, Ficombank, and TinNghiaBank, as well as the restructuring of CBBank, OceanBank, and GPBank.
2.2.2 Total assets of commercial banks
Figure 2.4: Total Asset of Vietnam Banking System
Source: Data report of state bank of VietNam
The bar chart illustrates the total assets of various credit institutions and foreign bank branches, including state-owned commercial banks, Social Policy Banks, joint-stock banks, foreign-owned banks, and joint venture banks, over a research period starting from 2014.
In 2023, the banking sector and the broader economy experienced a consistent upward trend in asset size, indicating expansion and enhanced operational scope This growth reflects banks' increased lending and investment activities, as well as their success in attracting a larger customer base By year-end, the banking system accumulated nearly 1.002 quadrillion dong, as reported by the State Bank of Vietnam (SBV) A rise in total assets typically signifies greater financial stability and the potential for increased profitability within banks The asset growth from 2014 to 2023 highlights shifts in economic conditions and underscores the effectiveness of government policies aimed at strengthening financial stability and reducing risks in the banking system.
TOTAL ASSET OF VIETNAM BANKING SYSTEM
2.2.3 The total loan growth of commercial bank
Figure 2.5: Total Loan Growth Vietnam Banking System
Source: Data report of state bank of VietNam
The line graph depicts the loan growth rate of Vietnamese commercial banks from 2014 to 2023, showing a generally downward trend influenced by economic policy changes and external factors In 2014, loan growth was modest at 14.38%, reflecting government efforts to tighten credit to control inflation and stabilize the economy A notable surge occurred in 2015, with loan growth peaking at 24%, before slightly declining to 21.18% in 2016, likely due to accommodative monetary policies aimed at boosting economic growth through increased lending.
Although the upward trend lasted for 3 year, there was a noticeable down trend from
2017 to 2021 After the peak, loan growth rates showed a declining trend, with 19.48% in
2017, 13.68% in 2018, and stabilizing somewhat at 13.65% in 2019 The reduction could be attributed to external factors such as global economic uncertainties, including impacts
Nguyễn Huy Hoàng 35 2024 highlights the ongoing effects of the US-China trade war, which contributed to a decline in loan rates that fell to 12.17% in 2020 and further to 12% in 2021 The significant drop in 2021 marked the lowest rate of the decade, primarily driven by the economic repercussions of the COVID-19 pandemic This disruption led to diminished consumer and business demand for loans and increased credit risk concerns.
Following the challenges posed by the COVID pandemic, loan growth experienced a notable recovery in 2022 and 2023 In 2022, the loan growth rate rose to 14.50%, indicating a revival in economic activities and enhanced consumer confidence This upward trend continued into 2023, with the growth rate reaching 13.71%, reflecting sustained economic recovery and an increase in lending by banks.
The graph demonstrates that loan growth rates are highly influenced by various economic conditions and policy adjustments It highlights the significant impact of the Vietnamese banking sector's adaptability to economic changes on the variability of loan growth rates.
An overview of Non-performing loan
2.3.1 Non-performing loan of Vietnam Banking system
Table 2.2: Ratio of NPLs Over Total Loan Outstanding by quarter in Vietnam banking system
Source: Data report of state bank of VietNam
From 2015 to 2023, the Vietnamese banking system saw a significant decline in the ratio of non-performing loans (NPLs) to total loans, dropping from 3.07% in 2015 to a low of 1.65% in 2021 This improvement reflects enhanced credit management and economic stability, supported by robust regulatory frameworks and effective risk assessment practices The NPL ratio decreased to 2.08% in 2018 and further to 1.87% in 2019, indicating ongoing stability in the banking sector's risk management During this period, Vietnam's steady economic growth, driven by strong exports and domestic consumption, likely contributed to borrowers' improved repayment capabilities.
By 2020, Vietnam's Non-Performing Loan (NPL) ratio significantly dropped to 1.85% from 2015, while remaining stable compared to 2019 This stability can be linked to timely government and monetary interventions that alleviated the economic fallout, including support packages for businesses and individuals The downward trend in NPLs persisted, reaching a low of 1.65%, indicating the success of ongoing fiscal and monetary support as the economy began to rebound from the pandemic's impact.
From 2022 to 2023, the data indicates a concerning reversal in this trend starting in
In 2023, the rise in non-performing loans (NPLs) peaked at 3.92%, particularly in the latter quarters, indicating potential economic distress or inefficiencies in debt servicing among borrowers This escalation may be attributed to external economic shocks or internal market adjustments, highlighting the urgent need for banks to implement vigilant risk management practices Consequently, stricter lending criteria may be necessary to mitigate these risks.
Nguyễn Huy Hoàng 37 2024 proactive bad debt resolution strategies to safeguard the financial health of Vietnam's banking sector and maintain systemic stability
The fluctuations in non-performing loan (NPL) ratios over the years underscore the banking sector's vulnerability to domestic economic policies and global conditions Each annual NPL ratio reflects the complex interaction between external and internal factors affecting the financial stability of Vietnam's banking system Therefore, continuous vigilance and adaptability in banking practices and economic governance are essential for effectively managing these uncertainties.
2.3.2 The Current status of Non-performing loans in Commercial banking sector
Table 2.3: Overview of Non-performing loans of 28 commercial banks in 2023
31/12/2022 Billion Dong Change in % NPLs Over
Source: Analysis results of the author
Despite the challenges posed by the Covid-19 pandemic and a struggling global economy, the banking sector has achieved record net profits in recent years However, a concerning trend is the rise in non-performing loans, particularly among banks heavily invested in the real estate and infrastructure sectors.
In 2023, banks experienced a rise in non-performing loans and a decline in loan quality across various categories, highlighting the economic challenges faced during the year The increasing bad loan ratio served as a clear indicator of these financial difficulties.
In 2023, the total non-performing loans in Vietnam's banking sector reached 194.968 trillion VND, marking a significant 40.5% increase since the beginning of the year Alarmingly, 27 out of 28 commercial banks reported a rise in bad debts, with many experiencing substantial growth Among state-owned banks, BIDV reported the highest bad debt balance at 22.229 trillion VND, up 22.9%, ranking second in the industry Vietinbank followed with 16.608 trillion VND, reflecting a 5% increase and ranking third, while Vietcombank's bad debts surged by 59.3% to 12.455 trillion VND, placing sixth Agribank has not disclosed its data.
In 2023, the private banks with the highest non-performing loans include VPBank at 28.344 trillion VND (up 12.8%), ranking first, followed by NCB at 16.469 trillion VND (up 92.5%) in fourth place, SHB at 12.483 trillion VND (up 15%) in fifth, Sacombank at 10.984 trillion VND (up 155.5%) in seventh, and MB at 9.805 trillion VND (up 94.9%) in eighth VIB and HDBank follow in ninth and tenth positions with non-performing loans of 8.375 trillion VND (up 47.3%) and 6.160 trillion VND (up 39.9%), respectively Conversely, the banks with the lowest non-performing loans are Saigonbank at 405 billion VND (up 1.8%), PGBank at 905 billion VND (up 21.5%), and BacABank at 916 billion VND (up 78.2%) Notably, Vietbank is the only bank to report a decrease in non-performing loans, with a 10.9% decline to 2.071 trillion VND by year-end.
A recent report by the State Bank of Vietnam (SBV) to the National Assembly highlights a growing pressure from non-performing loans on the financial system This trend is exacerbated by unfavorable domestic and international macroeconomic conditions, which negatively impact production, business operations, and customers' ability to repay debts.
This chapter provides a comprehensive overview of the Vietnamese banking system, detailing all credit institutions recognized by the State Bank of Vietnam It analyzes the growth of commercial banks, focusing on total asset evolution and loan growth dynamics This analysis is set against the backdrop of the Vietnamese economy, considering key economic indicators such as GDP growth, inflation, and unemployment rates that impact the health of the banking sector.
A comprehensive analysis of non-performing loans (NPLs) in the banking sector reveals trends from recent years and highlights key factors driving the increase in NPLs Despite the strong growth and profitability of banks, the persistent rise in NPLs, especially among institutions heavily invested in real estate and infrastructure loans, remains a significant concern.
In conclusion, this chapter's main purpose was to show the dual impact of macroeconomic fluctuations and microeconomic banking practices on NPL ratios in the current state of Vietnam
DATABASE AND RESEARCH METHOD
Research Model
This research aims to investigate the factors influencing non-performing loans (NPLs) in Vietnamese commercial banks, focusing on the periods before, during, and after the COVID-19 pandemic It adheres to established principles by examining both macroeconomic and microeconomic factors as explanatory variables Building on previous models, this thesis develops a research framework to identify the determinants of NPLs in the context of the pandemic's impact on the banking sector in Vietnam.
The model includes variables where "t" denotes the observed year and "i" represents the specific bank, with 𝜇 𝑖 indicating random effects unique to each bank The error term 𝜖 𝑖,𝑡 accounts for idiosyncratic shocks to non-performing loans (NPL) for bank "i" in year "t." The NPL for bank "i" at year "t" is represented as 𝑁𝑃𝐿 𝑖,𝑡, while 𝑆𝐼𝑍𝐸 𝑖,𝑡, 𝐿𝐺 𝑖,𝑡, 𝐷𝑅 𝑖,𝑡, 𝑅𝑂𝐸 𝑖,𝑡, 𝐺𝐷𝑃 𝑖,𝑡, 𝐼𝑁𝐹 𝑖,𝑡, and 𝑈𝑅 𝑖,𝑡 refer to bank size, loan growth, debt ratio, return on equity, GDP growth, inflation, and unemployment rate, respectively Additionally, 𝐶𝑂𝑉𝐼𝐷_𝐷 𝑖,𝑡 represents dummy variables for the COVID-19 pandemic periods, with coefficients 𝛽 8 reflecting the impact of these periods on NPL.
3.1.2 Description of variables and hypotheses
3.1.2.1 Measurement of Non-performing loan
Microeconomic variables play a crucial role in banking, with the non-performing loan (NPL) ratio being a key indicator This ratio is calculated by dividing the total non-performing loans by the total outstanding loans of each bank According to Vietnam's debt classification guidelines, NPLs encompass loans categorized within groups 3 to 5 on banks' balance sheets.
Loans categorized as groups 3, 4, and 5 are determined using data from banks' financial statements and annual reports, with the total outstanding loan balance derived from their balance sheets This methodology is utilized to compute the annual Non-Performing Loan (NPL) ratio for each bank, drawing on research conducted by Louzis et al (2012), Salas & Saurina (2002), and Curak et al (2013).
The authors identify ten variables influencing the impact of non-performing loans, comprising seven micro variables and three macro variables, detailed in Tables 3.1 and 3.2 This section will elucidate these variables and propose hypotheses on their anticipated effects on the non-performing loans of commercial banks in Vietnam, starting with the bank's size (SIZE).
Microeconomic variables play a crucial role in banking, with bank size (SIZE) being a significant factor that reflects market capacity Research indicates a positive correlation between bank size and non-performing loans, supported by studies from Rajan & Dhal (2003), Ghosh (2015), V.T.H Nguyen (2015), and K.T Nguyen & Dinh (2015) However, contrasting findings by Salas and Saurina (2002) and Jin Li Hu et al (2004) suggest a negative correlation Larger banks typically possess stronger risk management systems, which may provide them with better opportunities to manage non-performing loans effectively.
Nguyễn Huy Hoàng 44 2024 portfolios with lower risk loans compared to smaller banks Thus, the research proposes the following hypothesis:
H1: The size of the bank has a positive impact on non-performing loans in the commercial banking system in Vietnam b) Loan Growth (LG)
Loan growth, or credit growth, is influenced by the credit cycle hypothesis, which highlights its connection to non-performing loans This hypothesis indicates that during economic growth, commercial banks tend to implement expansive credit policies and ease lending standards, potentially resulting in a rise in non-performing loans Loan growth can be quantified using a specific formula.
During economic downturns, banks often tighten their credit growth policies, although empirical research reveals mixed results regarding this relationship Studies by Louzis et al (2010) and Jimenez & Saurina (2006) indicate a negative correlation between loan growth and non-performing loans Conversely, Foos, Norden, and Weber (2010) and V.T.H Nguyen (2015) suggest that excessive loan growth can deteriorate a bank's loan portfolio quality due to weakened credit standards Therefore, the research proposes the following hypothesis:
H2: There is a positive relationship between high loan growth and the increase in non-performing loans c) Debt coverage ratio (DR)
The debt coverage ratio for non-performing loans plays a crucial role in influencing the non-performing loan ratio as it reflects how banks manage their bad debts
A higher coverage ratio indicates that a bank has set aside more provisions to cover potential losses from non-performing loans, thereby potentially reducing the actual impact
Nguyễn Huy Hoàng 45 2024 of such loans on the bank's financial health The calculation for debt coverage ratio follows a formula:
According to the research by Nkusu (2011), a higher debt coverage ratio indicates better financial health of the borrowers and thus lower likelihood of default Thus, the hypothesis proposed is:
H3: There is a negative relationship between the debt coverage ratio and the level of non-performing loans d) Return on Equity (ROE)
Return on equity (ROE) is a key profitability metric that indicates how much profit a company generates for each dollar of shareholders' equity, reflecting the effectiveness of its equity financing in funding operations and driving profit growth The formula for calculating ROE is straightforward and essential for assessing a company's financial performance.
Research indicates that a higher Return on Equity (ROE) reflects efficient management and a reduced risk of non-performing loans, attributed to improved capital allocation and profitability Additionally, findings show that Greek banks with strong profitability metrics, including ROE, were more capable of managing rising non-performing loans during economic downturns.
H4: An increase in return on equity correlates with a decrease in non-performing loans
3.1.2.3 Macroeconomics Variables a) Gross Domestic Products growth (GDP)
The research utilized GDP growth as an indicator of economic growth, measuring the rise in economic activity and output, which reflects the overall health of an economy This growth influences both consumer and business confidence, subsequently affecting borrowing and repayment behaviors The essential equation for calculating GDP growth is as follows:
Research on the relationship between GDP growth and non-performing loans (NPLs) presents mixed findings Salas and Saurina (2002) suggest that increased GDP growth correlates with reduced NPLs, as improved business performance and higher employment rates contribute to better loan repayment Conversely, Dell'Ariccia and Marquez (2006) contend that during periods of robust GDP growth, banks might relax lending standards, which could lead to a subsequent rise in NPLs Thus, the hypothesis remains under investigation.
H5: GDP growth has a mixed effect on non-performing loan b) Inflation rate (INF)
The inflation rate indicates the yearly percentage rise in the overall price level of goods and services within an economy, impacting the purchasing power of both consumers and businesses This annual inflation rate is determined by the average yearly change in the Consumer Price Index (CPI), and it is calculated using a specific formula.
Research indicates that while higher inflation can diminish real incomes and raise borrowing costs, potentially increasing non-performing loans (NPLs), moderate inflation may actually boost revenues and asset prices, facilitating debt servicing.
H6: Inflation rate has a positive effect on non-performing loan c) Unemployment rate (UR)
The unemployment rate significantly impacts the lending activities of commercial banks, as rising unemployment leads to increased joblessness and fewer employment opportunities, which in turn diminishes disposable income for workers This income decline hampers their ability to repay debts, heightening credit risks and the likelihood of non-performing loans, especially in consumer financing, as highlighted by Louzis et al (2011) Conversely, N.T.H.V (2019) notes that elevated unemployment rates can change individuals' perceptions, prompting those without income to avoid loans they cannot repay, thereby lowering the levels of bad debts in banks.
H7: The unemployment rate has a negative effect on non-performing loan d) COVID-19 (COVID_D)
Results
Table 3.2: Descriptive statistics of variables in the research
Variables obs Mean Std Dev Min Max
Source: Analysis results of the author
The average non-performing loan (NPL) ratio stands at 2.06%, with a standard deviation of 2.4%, indicating stability among banks without significant fluctuations that could affect the banking sector This consistent performance aligns with the common objective of banks to maintain low levels of non-performing loans, enabling them to effectively manage risks and improve their competitive edge.
The "SIZE" variable reveals an average value of 3.45e+08 and a high standard deviation of 4.26e+08, indicating significant diversity in the financial scales of banks within the dataset This variation suggests a mix of institutions, from small banks to large multinational entities, with a range spanning from 1.58e+07 to 2.30e+09 Such disparities in bank size may notably impact various financial ratios and operational metrics.
Larger banks tend to benefit from economies of scale, resulting in improved financial health and stability, characterized by lower non-performing loan ratios and enhanced debt coverage Their diversified risk and greater resources enable them to better manage loan defaults In contrast, smaller banks often face higher volatility in these metrics, making them more vulnerable to market fluctuations and economic disruptions, such as those experienced during the COVID-19 pandemic.
Growth Rate of Bank Credit (LG):
The growth rate of bank credit exhibits significant fluctuations, with a standard deviation of 14.18% and an average of 18.49% from 2014 to 2023 This volatility is largely attributed to the economic impact of the COVID-19 pandemic, which severely affected the banking sector As banks faced challenges during this crisis, a contraction in credit size became unavoidable Additionally, banks lacking effective business strategies and market share experienced a decline in clientele, resulting in decreased loan issuances and a slowdown in credit growth.
Bad Debt Coverage Ratio (DR):
The average bad debt coverage ratio stands at 0.899, with a range of 0.77 to 4.89, reflecting a robust capacity among banks to manage potential non-performing loan risks A standard deviation of 0.599 indicates significant variability in this ratio from 2014 to 2023, influenced by each bank's size and the level of their non-performing loans Generally, larger banks exhibit higher provisions for bad debt risks due to their greater financial resources, while smaller banks show lower coverage levels.
The average GDP growth rate in Vietnam stands at 6%, with a standard deviation of 1.68%, reflecting minimal annual fluctuations This stability demonstrates the Vietnamese government's effective management of the economy However, the COVID-19 pandemic has impacted these growth figures in recent years.
In 2021, the GDP plummeted to 2.6% due to the COVID-19 pandemic, leading to significant economic losses from halted production and disrupted exports In response, the government and the State Bank introduced timely support measures to stimulate economic revival By 2022, the GDP growth surged to 8%, marking the highest increase in a decade and showcasing a strong economic recovery attributed to effective governmental leadership.
The average inflation rate stands at 5.8%, with a standard deviation of 9.79%, reflecting moderate fluctuations throughout the study period This aligns with government efforts to stabilize the economy Despite a rise in inflation during the COVID-19 pandemic caused by commodity shortages and subsequent price increases, the rate remains within a manageable and safe range.
Vietnam's average unemployment rate stands at a low 2.4%, with a standard deviation of 0.4%, reflecting a stable economic environment This rate has remained between 1.99% and 3.25%, showcasing the country's robust economic conditions The temporary spike in unemployment during the COVID-19 pandemic was primarily due to businesses facing challenges in exporting and distributing goods, resulting in reduced profits and workforce cuts However, from 2014 to 2023, despite fluctuations, Vietnam has consistently maintained a low unemployment rate, indicating a healthy labor market.
The dataset delineates the temporal context of the financial metrics relative to the COVID-
19 pandemic, categorizing the observations into 'Before', 'During', and 'After' COVID periods With means of 0.6, 0.3, and 0.1 respectively, the data suggests a predominant pre-
Nguyễn Huy Hoàng's analysis highlights the significant impact of the pandemic on the banking sector, with a focus on the 'During COVID' and 'After COVID' phases The pandemic period saw banks grappling with increased non-performing loans and constrained credit growth due to economic lockdowns In contrast, the post-pandemic phase suggests a potential recovery, as banks adapt to evolving economic conditions This data is crucial for understanding the resilience and long-term effects of the pandemic on financial stability in the banking industry.
3.2.2 Evaluation of variable selection in regression analyses
3.2.2.1 The correlation of independent variables in the model
Table 3.3: Correlation matrix table between independent variables
NPL SIZE LG DR ROE GDP INF UR COV
Source: Analysis results of the author
The correlation matrix illustrates the relationships among the variables in the model, with values ranging from -1 to 1, indicating no extreme correlations The highest correlation coefficient recorded is 0.6376, while the lowest is -0.6715 Following the criteria established by Farrar and Glauber (1967), which defines a coefficient of 0.8 as significant, it is clear that the variables in the model are correlated without the presence of multicollinearity.
A strong positive correlation of 0.6376 exists between the COVID-19 dummy variable and the non-performing loans (NPL) ratio, indicating that the pandemic significantly impacts banks' NPL ratios This aligns with the reality that COVID-19 has adversely affected the economy, particularly increasing non-performing loans Additionally, the unemployment rate (UR) has a substantial negative correlation of -0.6715 with economic growth, suggesting that robust economic conditions foster business expansion and job creation, thereby reducing unemployment In contrast, economic recessions hinder business performance, resulting in higher unemployment rates.
3.2.2.2 Checking for the severity of multicollinearity in the ordinary least square
Source: Analysis results of the author
Table 2.4 displays the Variance Inflation Factor (VIF) results from a multicollinearity diagnostic test conducted for an Ordinary Least Squares (OLS) regression model Multicollinearity occurs when predictor variables in a multiple regression model exhibit high correlations, which can result in unreliable and unstable estimates of the regression coefficients.
The "SIZE" variable shows the highest Variance Inflation Factor (VIF) at 5.44, nearing the cautionary threshold of 5, which may indicate potential multicollinearity issues that require further examination Despite this, the average VIF for all variables is 2.44, well below the typical levels of concern, suggesting that the model generally does not display significant multicollinearity among the predictor variables However, it is crucial to recognize that a satisfactory average VIF does not eliminate the possibility of multicollinearity problems with specific predictors.
Table 3.5: OLS, FEM, REM regression results
Source: Analysis results of the author
Table 3.6: Breusch–Pagan/Cook–Weisberg test
Variable: Fitted values of NPL
Source: Analysis results of the author
The analyzed regression models exhibit a strong fit, with R-squared values exceeding 70%: Pooled OLS at 77.5%, FEM at 72.83%, and REM at 76.89% This consistent relationship between independent and dependent variables across all models reinforces the validity of the research framework These results highlight that over 70% of the variation in the dependent variable can be attributed to the independent variables, reflecting the intricate nature of economic phenomena often affected by numerous unmeasurable factors.
Analysis of the result
3.3.1 Microeconomic Factors a) Bank’s size (SIZE)
The findings presented in Table 3.9 indicate a deviation from the initial hypothesis, "H1: The size of the bank has a positive impact on non-performing loans in the commercial banking system in Vietnam." The coefficient for SIZE was recorded at “-2.85e,” revealing a negative correlation with non-performing loan rates This outcome aligns with the research conducted by Salas and Saurina (2002) and Jin Li Hu et al (2004), which suggests that larger banks, equipped with advanced risk management frameworks, are more adept at maintaining lower-risk loan portfolios compared to smaller banks Their capacity to invest in sophisticated risk assessment technologies and effectively diversify loan portfolios further enhances their advantage in managing non-performing loans.
Larger banks possess more resources to manage bad debts and diversify credit risk across various sectors and regions, which helps mitigate the effects of localized economic downturns on their portfolios This aligns with Demsetz and Strahan's (1997) findings that larger banks have lower exposure per unit of credit due to their enhanced risk diversification capabilities In contrast, smaller banks often lack these resources, resulting in higher non-performing loan (NPL) ratios.
The findings indicate that a 1-unit increase in bank size correlates with a decrease in non-performing loans (NPL) by 2.85 units, achieving a significance level of 1% This suggests an inverse relationship between bank size and loan default risk, reinforcing the idea that larger banks can effectively utilize their scale to reduce risks associated with their loan portfolios.
Despite my research indicating a negative correlation, it is essential to consider the positive findings from Rajan & Dhal (2003) and Ghosh (2015) The discrepancy between my results and theirs can be attributed to the fact that their studies focused on banks that actively sought to increase market share, including lending to riskier segments, which may clarify their outcomes In contrast, my study presents different results.
Nguyễn Huy Hoàng 63 2024 highlights that banks with conservative approaches or advanced risk management systems tend to experience lower non-performing loans (NPLs) Additionally, the research relied on earlier data, which lacked the influence of modern technologies, resulting in a different correlation between bank size and NPLs Furthermore, the study emphasizes the importance of loan growth (LG) in this context.
The hypothesis posited for LG indicates a positive relationship between high loan growth (LG) and an increase in non-performing loans (NPL) Table 2.9 confirms this correlation, showing a positive value of 0.0120025, statistically significant at 1% Specifically, a 1-unit increase in loan growth corresponds to a 0.012-unit decrease in NPL, highlighting the necessity for prudent lending practices and a balanced approach to loan portfolio growth This finding aligns with the research of Foos, Norden, and Weber (2010) and V.T.H Nguyen (2015), which suggests that higher loan growth rates are linked to elevated NPL levels Aggressive expansion of loan portfolios often leads to diminished loan quality, as banks may lower lending standards to achieve rapid growth, potentially extending credit to less creditworthy borrowers This correlation reflects a broader economic principle where rapid loan growth, driven by booming economies or competitive pressures, compels banks to take greater risks, suggesting that similar pressures across various studies and economic contexts may result in comparable outcomes regarding loan quality deterioration.
My research findings diverge from those of Louzis et al (2010) and Jimenez & Saurina (2006), who identified a negative correlation between loan growth and non-performing loans This discrepancy can be attributed to several factors that warrant further exploration.
The economic environment during the studied periods significantly impacts the relationship between loan growth and non-performing loans (NPLs) Research by Louzis et al (2010) and Jimenez & Saurina (2006) indicates that during economic recessions, banks may adopt more conservative lending practices, resulting in lower NPLs despite slower loan growth Additionally, the time frame and sample of banks analyzed can greatly influence research outcomes, as variations in periods or bank types can yield different results My research, which encompasses a diverse range of banks across three distinct periods, demonstrates a positive impact of loan growth on NPLs.
Table 3.9 supports the hypothesis DR: "H3: There is a negative relationship between the debt coverage ratio and the level of non-performing loans," indicating a value of "-0.0044." This suggests that, holding all other factors constant, an increase of 1 unit in the debt coverage ratio results in a decrease of 0.0044 units in non-performing loans, with a significance level of 1%, consistent with the other two microeconomic variables discussed.
Research indicates that the debt coverage ratio negatively impacts the non-performing loan (NPL) ratio, aligning with Nkusu (2011), who found that a higher debt coverage ratio reflects improved financial stability among borrowers and reduces default probability Both studies underscore the direct relationship between borrowers' financial health and loan default likelihood, with a higher debt coverage ratio often resulting from stricter lending standards Furthermore, both Nkusu's research and mine observe that banks implement stringent risk management practices, such as evaluating borrowers' debt servicing capabilities prior to loan approval, which leads to elevated debt coverage ratios and subsequently lower NPLs.
While my research has not identified any studies demonstrating a weaker or contradictory relationship between the two variables, it remains crucial to examine the discrepancies between my findings and those that present opposing results These differences may stem from variations in economic conditions or the specific types of loans analyzed For instance, in economies facing downturns or in sectors characterized by greater financial volatility, borrowers with initially strong debt coverage ratios may encounter challenges, resulting in a rise in non-performing loans (NPLs).
The analysis of Return on Equity (ROE) indicates a negative coefficient of -0.0105989, suggesting that higher ROE correlates with a decrease in non-performing loans (NPLs) among the sampled financial institutions This supports the hypothesis that an increase in ROE is associated with a lower likelihood of loan defaults, highlighting the relationship between profitability and credit risk management.
Research by Berger and DeYoung (1997) highlights that a higher Return on Equity (ROE) signifies effective management and a reduced risk of Non-Performing Loans (NPLs) through strategic capital allocation and improved profitability This aligns with the broader banking literature, which suggests that well-managed banks with strong financial performance tend to maintain healthier loan portfolios Additionally, Kosmidou (2008) found that banks exhibiting high profitability, particularly those with elevated ROE, are better positioned to withstand economic downturns, as their financial robustness acts as a buffer against rising NPLs Our findings further support this, demonstrating that banks with higher ROE can sustain loan performance even in challenging economic conditions.
Much similar to Debt coverage ratio (DR), there is yet to find any research which supports the result of ROE and NPL having a positive correlation Although if there were
Nguyễn Huy Hoàng 66 2024 highlights that the relationship between Return on Equity (ROE) and Non-Performing Loans (NPLs) can vary significantly due to differing market conditions and economic environments In areas with high economic volatility, banks may experience fluctuations in NPL rates despite having high ROE, as external economic shocks impact borrowers' repayment abilities Additionally, banks that adopt high-risk, high-return investment strategies may demonstrate a complex relationship between ROE and NPLs, where a high ROE does not necessarily indicate low NPLs, since the risks associated with their lending practices can result in increased default rates.
3.3.2 Macroeconomic Factors a) Gross Domestic Products growth (GDP)
Table 3.9 indicates a negative coefficient of -0.0146441 for GDP growth, aligning with hypothesis H5, which posits that GDP growth has a mixed effect on non-performing loans (NPLs) This suggests that an increase in GDP growth is associated with a reduction in the rate of NPLs among the financial institutions studied, supporting the findings of Salas and Saurina.