Trang 1 BANKING ACADEMY OF VIETNAM FACULTY OF FOREIGN LANGUAGES ------ GRADUATION THESIS DETERMINANTS OF NON-PERFORMING LOANS: EVIDENCE FROM VIETNAMESE JOINT-STOCK COMMERCIAL BANKS
INTRODUCTION
Background of the study
The banking sector, especially in developing countries, is facing a significant challenge from non-performing loans (NPLs) Following the global financial crisis in
Since 2008, many countries have experienced a rise in non-performing loans (NPLs), but Vietnam has seen a significant improvement in its economic situation, leading to a continuous decrease in the NPL ratio However, the stability of the banking sector remains a concern, as NPLs can signal potential financial crises if not effectively monitored and managed The COVID-19 pandemic has worsened this issue, making it difficult for businesses to meet their debt obligations amid an economic downturn, thereby threatening the performance and stability of Vietnam's banking system.
The increase in non-performing loans (NPLs) poses significant risks to investors, commercial banks, state banks, and the broader economy in Vietnam Understanding the determinants of NPLs is essential for addressing this issue effectively This study seeks to identify these factors and offer recommendations to lower NPL levels, thereby enhancing overall banking system stability By examining these determinants, policymakers and bank managers can formulate strategies to reduce NPLs and strengthen the resilience of the banking sector.
This study addresses the gap in existing research regarding non-performing loans (NPLs) in Vietnam, an area that has been underexplored compared to other countries By examining the specific factors contributing to elevated NPL levels in Vietnam's banking sector, this research aims to offer valuable insights that enhance understanding and inform future strategies.
The study aims to achieve the following specific objectives:
1 To identify the bank-specific and macro determinants of NPLs of Vietnamese joint stock commercial banks
2 To examine the impact of bank-specific factors (profitability, loan loss reserve ratio and size of bank) on NPLs of Vietnamese joint stock commercial banks
3 To explore the impact of macroeconomic factors (GDP growth and inflation rate) on NPLs of Vietnamese joint stock commercial banks
4 To provide recommendations for Government, the State Bank and commercial banks in Vietnam to manage and reduce NPLs, enhance the stability of the banking system, and promote sustainable economic growth
Identifying the factors that influence non-performing loans (NPLs) is crucial for both commercial banks and the State Bank of Vietnam (SBV), as it helps them understand the relationship between these factors and NPLs This understanding enables the implementation of timely and effective policies to manage the NPL ratio Furthermore, this research lays the groundwork for future studies, offering researchers valuable insights to develop informed hypotheses and achieve comprehensive, objective results.
1 5 Scope and Limitations of the study
This study focuses on the determinants of non-performing loans (NPLs) in Vietnam's joint-stock commercial banks, specifically analyzing 20 banks from 2014 to 2022 Key factors examined include return on assets, loan loss reserve ratio, bank size, economic growth, and inflation.
The study, however, exits limitations as follows:
+ The study is limited to Vietnam's joint-stock commercial banks' non- performing loans The results might not be applied to other nations or different kinds of financial organizations
+ The study employs secondary data sources, including as financial records and statements, which could not give a full view of the factors that influence non- performing loans.
Definition of terms
100% foreign-owned banks are entirely owned by foreign entities, lacking any local ownership These banks are set up by international financial institutions to broaden their services in a host country or to tap into new market opportunities.
5 are subject to the same regulations and rules as domestic banks and may also face additional regulations and restrictions imposed by the host country (vbpl.vn)
Acquisitions involve the purchase or takeover of a smaller business by a larger company, resulting in the acquiring company obtaining legal ownership rights over the acquired entity.
Autocorrelation is the correlation observed among elements in a sequence of data organized by time (in time series analysis) or by spatial arrangement (in cross-sectional studies).
Bank-specific factors encompass the unique internal characteristics and attributes that influence a bank's performance, risk profile, and operations These distinct elements are subject to change and vary from one bank to another, playing a crucial role in shaping their overall effectiveness and stability.
Capital adequacy is an economic indicator that reflects the relationship between equity and risk-adjusted assets of commercial banks (vietnambiz.vn)
A commercial bank is an institution authorized to engage in a wide range of banking and related business activities aimed at generating profit, in accordance with the Law on Credit Institutions and relevant legal provisions.
Correlation coefficient is an indicator of some kind of correlation, that is, the statistical relationship between two variables (wikipedia.org)
Credit growth is the rate (%) increase in the amount of money loaned to individuals or organizations, in this year compared to the previous year (cfoviet.com)
Credit rating is an assessment of a potential debtor's credit risk, a prediction of their ability to repay, and an implicit prediction of the debtor's likelihood of default (wikipedia.org)
Credit risk relates to the possibility that a borrower will not be able to repay a loan with a lender when the payment is due (vietnambiz.vn)
Creditworthiness is eligibility for a loan, primarily based on a borrower's credit history, in the opinion of the lender, or as determined by a credit scoring system if
6 scoring is used (luatminhkhue.vn)
In an economic model, the dependent variable is influenced by another variable, known as the independent variable Economists typically utilize the inverse function to illustrate the relationship between these variables, with the independent variable commonly represented on the vertical axis.
Exchange rateis understood as the rate at which the value of one country's currency is converted to another country's currency (luatminhkhue.vn)
Gross Domestic Product (GDP) is the market value of all final goods and services produced within a given territory during a given period (imf.org)
Heteroskedasticityis a situation in statistics that occurs when the standard error of a variable remains constant for a particular time period in a linear regression model (vietnambiz.vn)
Independent variable is a variable that is influenced by another variable in the model (vietnamfinance.vn)
Inflation is the continuous increase in the general price level of goods and services over time and is the macroeconomic devaluation of a currency (thuvienphapluat.vn)
A joint venture bank emerges from economic collaboration between governments and central banks, fostering opportunities for cooperation between two distinct economies and financial systems.
Joint-stock banks are a commercial bank established and organized in the form of a joint-stock company (Clause 3 Article 2 Consolidation Document No 20/VBHN- NHNN)
Linear multivariable regression extends simple linear regression by enabling the prediction of a response variable using two or more independent variables This statistical method is essential for analyzing complex relationships in data and enhancing predictive accuracy.
Liquidity risk is a risk in the financial sector This risk occurs when a bank lacks funds or viable short-term assets to meet the needs of depositors and borrowers
A loan loss reserve is a crucial financial concept in the banking sector, representing the funds that credit institutions, like banks, allocate from their income or profits to safeguard against potential risks associated with lending.
Loan portfolio refer to the collection of loans held by a bank, organized according to various criteria and structured based on a specific ratio, serving the bank's management purposes (vietnamfinance.vn)
The loan-to-deposit ratio (LDR) measures the relationship between a bank's total loans and its total deposits A higher LDR signifies increased risk for the bank, whereas a lower LDR reflects a more stable financial position.
Macroeconomic factors are economic factors such as fiscal, natural or geopolitical conditions that have a broad influence on the economy of a region or a country (vietnamfinance.vn)
A merger involves the consolidation of one or more companies (the merged company) into another company (the merging company), where all assets, rights, and obligations of the merged company are transferred to the merging company.
Monetary policy involves utilizing credit and foreign exchange tools to stabilize the currency, which in turn supports economic stability and fosters growth and development.
Multicollinearity is a phenomenon in which a predictor variable in a multiple regression model can be linearly predicted from other variables with considerable accuracy (analyticsvidhya.com)
Panel data or longitudinal data is multidimensional data that involves measurements over time Panel data is a subset of vertical data (mosl.vn)
P-value a statistical measure that represents the likelihood of getting the observed data if the null hypothesis is true It is often used to determine if the results of a study
8 are statistically significant If the p-value is less than a predetermined level of significance (typically 0.05), the result is considered statistically significant and the null hypothesis is rejected (scribbr.com)
Rating agencies are specialized firms that evaluate an issuer's capability to repay principal and interest as per the agreed terms for specific securities They provide a confidence coefficient that reflects this assessment.
Regression model is a statistical technique used to estimate the equation that best fits the set of observations of the dependent and independent variables (vietnambiz.vn)
Return on Assets (ROA) is a key indicator of a company's profitability relative to its total assets This metric informs investors about the efficiency with which a company generates profits from its capital investments.
Risk management involves identifying, assessing, and prioritizing risks, followed by the strategic allocation of resources to mitigate, monitor, and control the likelihood or impact of adverse events while also maximizing potential opportunities.
Size of a bank is understood as the total assets of commercial banks, including all sources of financing such as loans, investments, and deposits (igi-global.com)
State-owned banks are banks owned by the government of a country They were established to provide financial services to citizens and to support government economic policy (imf.org)
Structure of the thesis
The thesis is divided into five chapters as follow:
Chapter 1: Introduction This chapter generally introduces the research and the purpose to conduct it
Chapter 2: Literature Review This chapter reviews related previous empirical research, then proposes a theoretical framework applied in the study
Chapter 3: Research Methodology This chapter shows more details about the method applied and builds hypotheses of the study
Chapter 4: Results and Discussion This chapter presents results associated with the research objectives Then, discuss experimental results based on research theory and compare with previous studies
Chapter 5: Summary of finding, conclusion and recommendations This chapter summarises the main findings and gives a conclusion From there, several recommendations are suggested to handle problems discussed before
LITERATURE REVIEW
The current situation of non-performing loans of Vietnamese joint
2.2.2.1 Overview of operations of Vietnamese commercial banks
The number of commercial banks
Over the last few decades, the banking industry in Vietnam has experienced noteworthy progress and expansion, with the market seeing the entry of several
The banking sector in Vietnam has witnessed increased competition due to the presence of 13 foreign banks, leading to significant improvements in banking services and products The system is categorized into four groups: State-owned banks, Joint-stock banks, 100% foreign-owned banks, and Joint venture banks, all regulated by the State Bank of Vietnam, the country's central bank The number of commercial banks fluctuated between 2014 and 2016, remained relatively stable from 2016 to 2022, as summarized in Table 2.1 below.
Table 2.1: The number of commercial banks over the years Year 2014 2015 2016 2017 2018 2019 2020 2021 2022 State-owned banks
Source: annual report of State bank
Since 2008, the Vietnamese economy has encountered significant challenges, with slowing growth and accumulated risks within the banking system negatively affecting macroeconomic stability To address these issues, it is crucial to restructure and strengthen the banking sector, primarily through mergers and acquisitions (M&A) of weaker commercial banks, resulting in a reduced number of banks A landmark M&A event occurred in 2012 when Saigon Bank acquired Habubank, significantly consolidating Vietnam's banking industry and enhancing Saigon Bank's market position This trend continued with several notable M&A transactions, including DaiA Bank's acquisition by HDBank in 2013 and MHB's merger with BIDV in 2015.
In recent years, the Vietnamese banking sector has witnessed significant mergers and restructurings, including the mergers of Mekongbank with MaritimeBank and SouthernBank with Sacombank in 2015, as well as the consolidation of SCB, Ficombank, and TinNghiaBank in 2012 Additionally, banks such as CBBank, OceanBank, and GPBank have undergone restructuring with the support of major players like Vietcombank, VietinBank, and BIDV This trend is primarily driven by the need for banks to enhance their financial stability and expand their market share amid intense competition within the industry.
Figure 2.1: Scale of total assets in the period 2014-2022
Source: Synthesis of reports from SBV
The bar chart depicts the growth of total assets within Vietnam's commercial banking system from 2014 to 2022, highlighting a consistent upward trend This increase reflects the broader expansion of both the banking sector and the overall economy during this period Since 2014, the total assets of commercial banks have surged, resulting in a declining capital adequacy ratio as a percentage of total assets This trend suggests that banks are enhancing their operations, along with boosting their lending and investment activities.
As of the end of 2022, banks have successfully mobilized nearly 877 quadrillion dong, reflecting their ability to attract more customers and strengthen their capital base, according to the SBV This significant rise in total assets is a clear indicator of a commercial bank's growing financial stability and its potential for higher profit generation.
Between 2014 and 2022, the total assets of Vietnamese banks demonstrated significant growth, reflecting the evolving economic landscape and government initiatives aimed at enhancing financial stability while managing potential banking system risks This upward trend in total assets serves as a crucial indicator of the banking sector's size and strength in Vietnam.
Figure 2.2: Credit growth rate in the period 2014-2022
Source: Synthesis of reports from SBV
The line graph illustrates the credit growth rates of Vietnamese commercial banks from 2014 to 2022, revealing a general downward trend with notable fluctuations influenced by economic policy changes and external factors In 2014, the credit growth rate was notably high, but over the years, it experienced a decline, reflecting the evolving economic landscape in Vietnam.
Credit growth in the economy saw a significant decline to 14.38% due to government policies aimed at controlling inflation and stabilizing the macroeconomy However, this trend reversed in subsequent years, with growth rates soaring to 24% in 2015 and 21.18% in 2016 as the government implemented accommodative measures to encourage lending and foster economic growth From 2017 to 2020, credit growth gradually decreased, fluctuating between 19.48% and 12.17%, likely influenced by the US-China trade war and the COVID-19 pandemic In 2021, credit growth hit a decade-low of 12%, primarily due to the lingering effects of the pandemic, which disrupted industry operations and limited access to credit Furthermore, restrictions on goods transportation and rising unemployment contributed to a decreased demand for loans.
In 2022, signs of robust recovery emerged as the loan-to-deposit ratio rose by 2.5% to 14.50%, largely driven by the resurgence of the Vietnamese economy post-COVID-19 As businesses resumed operations and economic activity intensified, the demand for loans surged to support expansion and manage working capital This heightened loan demand contributed to an increased credit growth rate among banks Additionally, commercial banks adopted various strategies to boost lending, including enhanced marketing efforts and competitive deposit interest rates, further fueling credit growth as they expanded their lending portfolios.
From 2014 to 2022, the credit growth trend of Vietnamese banks showcased the government's commitment to fostering economic growth while ensuring macroeconomic stability and managing risks within the banking system Despite fluctuations in credit growth rates throughout these years, the overall trajectory remained consistent.
17 important indicator of economic activity and the availability of funds for investment and consumption
Figure 2.3: Loan-to-deposit ratio in the period 2014-2022
Source: Synthesis of reports from SBV
The bar chart illustrates the loan-to-deposit (LTD) ratios of various surveyed banks, highlighting a key indicator of their liquidity and lending capacity This ratio represents the relationship between a bank's outstanding loans and its total deposits In Vietnam, the LTD ratios of banks exhibited notable fluctuations from 2014 onwards.
From 2014 to 2022, the Loan-to-Deposit (LTD) ratio of Vietnamese banks exhibited significant fluctuations Initially, in 2014, the LTD ratio was high at 92.94%, reflecting robust lending activity driven by previous years' credit growth However, between 2015 and 2019, the ratio stabilized and decreased due to reduced demand for credit and increased deposit growth In 2020, the LTD ratio rose slightly to 92.40%, but it fell to its lowest points of 73.99% in 2021 and 74.35% in 2022, indicating a positive trend as lower ratios suggest reduced risk for banks.
The State Bank of Vietnam (SBV) implemented monetary policies aimed at controlling inflation and stabilizing the economy As interest rates rise, consumers are more likely to save rather than borrow from banks, resulting in a reduced loan-to-deposit (LTD) ratio.
Between 2004 and 2022, excessive lending and investment led to a dangerously high capital utilization ratio among Vietnamese commercial banks The rapid credit growth outpaced capital mobilization, forcing smaller banks to depend on the central bank and interbank markets for credit expansion and liquidity support Notably, the loan-to-deposit (LTD) ratio peaked in 2014 and 2020, indicating a low safety level within the banking system due to sustained high liquidity risks, as the LTD ratio consistently hovered around 100%.
2.2.2.2 The current situation of non-performing
Figure 2.4: Non-performing loan ratio of banking system from 2014 – 2022
Source: Synthesis of reports from SBV
The line chart illustrates the percentage of non-performing loans (NPLs) among 20 commercial banks in Vietnam from 2014 to 2022, highlighting a persistent challenge within the banking system The ongoing issue of NPLs has serious implications for the financial sector's health and stability, impacting the broader economy significantly.
Researches on factors determining non-performing loans at commercial
2.2 Researches on factors determining non-performing loans at commercial banks
There were many researches on the issue of factors impacting NPLs
The research paper "Macroeconomic and Bank-Specific Determinants of Non-Performing Loans: Evidence from the Nepalese Banking System" by Koju et al (2018) analyzes the factors influencing non-performing loans (NPLs) in Nepal's banking sector using panel data from 30 commercial banks over the period 2003-2015 Utilizing a fixed-effects regression model, the study reveals that both macroeconomic factors, such as GDP growth and inflation, and bank-specific factors, including capital adequacy and loan portfolio quality, significantly impact NPL levels Specifically, GDP growth and inflation are positively correlated with NPLs, while exchange rates and remittance inflows show a negative correlation Furthermore, higher capital adequacy and profitability are linked to lower NPLs, whereas larger bank size and poorer loan portfolio quality contribute to increased NPLs The study recommends that policymakers and bank managers address both economic and bank-specific factors to effectively manage bad debt, emphasizing the importance of stable economic growth, inflation control, and enhancements in loan portfolio quality and capital adequacy to mitigate NPLs.
The research paper "Determinants of Non-Performing Loans: A Comparative Study of Pakistan, India, and Bangladesh" by Waquas et al (2017) investigates the factors affecting non-performing loans (NPLs) in the banking sectors of Pakistan, India, and Bangladesh The study analyzes a sample of commercial banks from these three countries over the period from 2006 onwards.
2015 Secondary data from the central bank's annual report was analyzed, including
A comprehensive analysis of 40 banks in Pakistan, 38 banks in India, and 27 banks in Bangladesh was conducted using panel data and the General Method of Moments (GMM) estimation technique to identify the determinants of non-performing loans (NPLs) The findings highlight the critical role of bank-specific factors such as inefficiency, profitability, capital ratio, and leverage ratio in influencing credit risk Additionally, macroeconomic variables were found to significantly impact NPLs, although results for Bangladesh indicated conflicting and insignificant effects at various levels The study underscores that the influence of these determinants on NPLs fluctuates over time, reflecting the banking sector's vulnerability to economic changes Recommendations for policymakers and regulators include reducing the frequency of stress tests and implementing effective strategies to manage bad debt, alongside enhancing the supervision of banks' management practices, performance, and risk management strategies to mitigate loan-related issues.
A research study conducted by Ph.D Nguyen Thi Hong Vinh in 2017 analyzed the causes and impacts of non-performing loans (NPLs) on Vietnam's commercial banking system, utilizing a sample of 34 banks from 2005 to 2015 The study, which relied on secondary data from annual reports and the State Bank of Vietnam, covered approximately 95% of the country's banking assets Findings revealed a significant increase in NPL levels from 2010 to 2012, with factors such as owner's equity, credit growth, and GDP negatively impacting NPLs Conversely, past NPL levels, bank size, outstanding debt on mobilized capital, inflation, exchange rates, interest rates, and real estate prices were found to positively influence NPLs.
The study recommends solution groups for credit institutions that address both specific and macroeconomic factors, alongside suggestions for the Government and the State Bank of Vietnam (SBV) to reform macroeconomic policies, the legal system, and banking supervision models Additionally, it advocates for the creation of a more effective bad debt resolution system to minimize bad debts and foster sustainable development within the banking sector.
A study titled "Factors Affecting Non-performing Loans in Vietnamese Commercial Banks" by Dr Tran Vuong Thinh and Nguyen Ngoc Hong Loan (2021) analyzed the determinants of non-performing loans (NPLs) across 22 commercial banks in Vietnam, encompassing state-owned, joint-stock, and foreign banks Utilizing secondary data from 2012 to 2020, the researchers applied linear multivariable regression methods to identify key factors influencing NPLs The findings revealed that micro factors, such as the loan loss reserve ratio, bank size, and credit growth rate, positively impacted NPLs, while return on equity had a negative effect Among macroeconomic factors, inflation was found to positively influence NPLs, whereas GDP growth rate showed no significant effect The study recommended that Vietnamese commercial banks enhance credit risk management, improve asset quality, and maintain adequate loan loss provisions, while also considering macroeconomic indicators like inflation and GDP growth in their credit policies and risk management strategies.
Previous studies have identified the determinants of Non-Performing Loans (NPLs), but they faced limitations such as inconsistent definitions and measurement of influencing factors Additionally, earlier research often utilized varied explanations and assessment techniques, leading to divergent results Furthermore, much of the available data is outdated, with most studies conducted prior to 2020.
23 when research is conducted, new and updated information about the topic may emerge, and this can affect the results of the study or provide new information for further research)
A theoretical framework is developed to assess the determinants of non-performing loans (NPLs) in Vietnamese commercial banks, incorporating insights from prior studies Key bank-specific factors include return on assets (ROA), loan loss reserve ratio (LLR), and bank size (SIZE) Additionally, macroeconomic factors such as economic growth (GDP) and inflation (INF) are also considered in this analysis.
Source: Followed by prior researches
Chapter 2 presents an overview of NPLs, the reality of NPLs in the Vietnamese banking sector, and mentions previous research on the issue of NPLs as well as identifies the determining factors of NPLs based on that research This chapter also proposes a theoretical framework by following prior studies The research framework identifies two groups of factors influencing NPLs: the bank-specific group, which includes ROA, size of the bank, and LLR; and the macro group, which includes GDP and inflation The next chapter will present the model and research methods, as well as set out the hypothesis of the dissertation in the study and carry out the identification and description of variables in the research
RESEARCH METHODOLOGY
Locale of the study
The research on "Determinants of Non-Performing Loans: Evidence from Vietnamese Joint-Stock Commercial Banks" highlights the crucial role of the banking sector in Vietnam's economy, particularly focusing on joint-stock commercial banks that represent a substantial share of the nation's banking assets This study examines the factors influencing non-performing loans within this vital sector, reflecting the significant transformations that have occurred in Vietnam's banking landscape over the years.
Research model
This study builds on experimental research and regression models of non-performing loans (NPLs), tailored to the unique characteristics of Vietnamese credit institutions It identifies and measures key determinants, categorized into two main groups: macroeconomic factors, including economic growth and inflation rates, and bank-specific factors, such as return on assets, loan loss reserves, and bank size.
This study utilizes a linear regression model to analyze the factors influencing non-performing loans (NPLs) in Vietnamese commercial banks, addressing the initial three objectives of the research Linear regression, a long-established statistical technique, is favored for its ease of implementation in software and calculations The model examines the relationship between NPLs (the dependent variable) and various independent variables, including return on assets (ROA), loan loss reserves (LLR), bank size (SIZE), gross domestic product (GDP), and inflation (INF) The choice of this model is justified by the significant impact these variables have on the NPL levels in banks Additionally, the linear nature of regression models simplifies the estimation process and allows for straightforward interpretation of results.
The study employs a linear regression model to evaluate the impact of independent variables on the bank's non-performing loans (NPL), aiming to draw meaningful conclusions that can inform strategies for reducing NPL ratios and credit risk The coefficients derived from the model reveal the strength and direction of the relationship between each independent variable and NPLs, providing insights that can enhance the bank's economic development Recommendations based on these findings are proposed to mitigate credit risk effectively.
NPLi,t=α0+ α1× ROAi, t + α2× LLRi, t+ α3× SIZEi, t + α4× GDPt+ α5× INFt + εi, t
The equation represents a regression model explains the variation in the NPL ratio of a bank i at time t The independent variables used are:
● ROA (return on assets) of the bank i at time t This variable measures the profitability of the bank
● LLR (loan loss reserves ratio) of the bank i at time t This variable measures the ratio of provision for loan losses made by the bank
● SIZE (size of the bank) i at time t This variable measures the total assets of the bank
● GDP (gross domestic product) at time t This variable measures the economic growth of the country
● INF (inflation rate) at time t inflation is measured by tracking changes in the prices of a large number of goods and services in an economy over a long period of time
● α0, α1, α2, α3, α4, α5: These are the regression coefficients used to calculate each explanatory variable's influence on the NPLi,t dependent variable The regression line's intercept, shown by the coefficient 0 is
● εi,t: This is the error term, sometimes referred to as the residual, which represents the variance in NPLi,t that cannot be explained by the explanatory factors
Non-Performing Loans (NPLs) are a critical financial metric, defined as the percentage ratio of non-performing loans to the total outstanding loans of a bank In Vietnam, NPLs are categorized based on the Classification of Debts Decision, encompassing outstanding debts classified in groups 3 to 5 on banks' balance sheets The calculation of NPLs follows a specific formula to assess the financial health of banking institutions.
NPL = (Debts in group 3+Debts in group 4+Debts in group 5)/total outstanding loans
Items in groups 3, 4, and 5 are taken from the notes to the financial statements and annual reports, while the total outstanding loans are collected from the balance sheet of banks
Return on Assets (ROA) is a key financial ratio that assesses a company's profitability by evaluating how effectively it utilizes its assets to generate earnings It serves as an important indicator of a company's operational efficiency and is calculated by dividing net income by total assets.
ROA= profit after tax/ total asset
Profit after tax is derived from the business operation results report, while total assets are obtained from the balance sheet Numerous empirical studies, including those by Kirui (2014), Kingu et al (2018), and Singh et al (2021), have consistently indicated a negative relationship between non-performing loans (NPLs) and bank profitability.
Highly profitable banks tend to avoid high-risk lending, while less efficient banks may resort to granting substandard loans to boost profits, leading to an increased occurrence of non-performing loans (NPLs) Since banks primarily generate profits through lending, high profitability correlates with high-quality loans and effective recovery of capital and interest, resulting in lower NPLs (K.T Nguyen, 2016) This observation supports the hypothesis that a bank's profitability is inversely related to the likelihood of NPLs.
H1: Return on assets has a negative impact on non-performing loans
The third variable is loan loss reserve ratio (LLR), which represents the ability to offset risks and is calculated using the following formula:
LLR ratio = Total provisions for credit losses / Total loans
The total provisions, derived from the income statement, and total loans from the balance sheet, significantly influence the Loan Loss Reserve (LLR) ratio, which directly impacts Non-Performing Loans (NPLs) LLR serves as an accounting estimate for potential losses in banks' credit activities, aiding in the assessment of credit portfolio quality and asset position However, since LLR is classified as an operating expense, it can adversely affect a bank's operating costs and profits Consequently, a higher LLR ratio indicates an increased level of NPLs faced by the bank Additionally, banks experiencing substantial capital losses may opt to raise their provisions to stabilize earnings and improve medium-term solvency (Messai & Jouini, 2013).
H2: Loan loss reserve ratio has a direct impact on non- performing loans
The fourth variable in the analysis is bank size (SIZE), which indicates the scale of the bank In regression analysis, the logarithm (Ln) of total assets is commonly utilized, as total assets are typically represented as an absolute value.
The total assets of banks, as reflected in their annual balance sheets, are believed to directly influence the non-performing loan (NPL) ratio This is rooted in the hypothesis that larger banks may engage in riskier lending practices due to their increased capital, leading to a higher incidence of NPLs Furthermore, government protections allow these banks to operate securely despite elevated levels of bad debts Consequently, a positive relationship between bank size and NPL ratios is anticipated, supported by research from Misra and Dhal (2010), Maude et al (2017), and Tran Vuong Thinh and Nguyen Ngoc Hong Loan (2021) This study proposes the following hypothesis:
H3: Size of banks has a positive impact on non - performing loans
Non-performing loans (NPLs) in the banking system are influenced not only by bank-specific factors but also by macroeconomic conditions While integrating macroeconomic variables into models can present challenges with micro data compatibility, neglecting these variables can result in overlooked factors contributing to NPLs Additionally, many research studies focus on a single country's context, utilizing macroeconomic variables to analyze the determinants of bad debts, as demonstrated by the work of Khemraj and Pasha.
A review of various studies highlights the influence of macroeconomic variables on non-performing loans (NPLs) in the banking sector Research includes Akinlo and Emmanuel's 2014 study on Nigerian banks, Rajha's 2016 examination of the Jordanian banking sector, and Umar and Sun's 2018 analysis of Chinese banks These findings underscore the importance of macroeconomic factors in assessing the performance of Vietnamese commercial banks.
The economic environment is primarily assessed through the growth rate of gross domestic product (GDP), which serves as a key macroeconomic indicator To calculate the economic growth rate, specific formulas are applied to measure changes in GDP over time.
Economic growth has a significant impact on non-performing loans (NPLs), as a thriving economy boosts business sales and investments, leading to increased credit demand and higher profits for enterprises This growth enhances individuals' and businesses' ability to repay debts Conversely, during economic stagnation or recession, consumer purchasing power declines, resulting in reduced business profits and sales, which hampers repayment capabilities and raises NPLs In Vietnam, where the economy heavily depends on credit expansion, a high growth rate correlates with improved loan repayment capacity, thereby lowering the NPL ratio This study aims to further elucidate this relationship.
H4: Economic growth has a negative impact on non- performing loans
Another macro factor is inflation The percentage change in the average annual CPI is applied to reflect the inflation rate The formula for calculating inflation is as follows:
High inflation leads to rising interest rates, which hampers the ability of economic agents within the banking system to repay debts, resulting in a rapid increase in non-performing loans (NPLs) (Mazreku et al., 2018; Khan et al., 2018) As inflation rises, consumers tend to reduce their spending due to higher prices, causing a decline in consumption and negatively impacting businesses through slowed economic activity This can lead to lower-than-expected profits or even losses, affecting a company's debt repayment capacity and contributing to the rise in NPLs for banks (Filip, 2015) Therefore, the following hypothesis is proposed:
H5: Inflation has a positive impact on non - performing loans
Table 3.1 Summary of variables and sign expectation
Determinants Symbol Calculation (Source) Previous studies
NPLs (Total NPLs/total gross loans )*100
ROA (Profit after tax/total assets)*100
Kirui (2014); Kingu et al.,(2018); Singh et al (2021); K.T
LLR (Total loan loss provision/ total loans)*100
Size of bank SIZE Ln (Total assets) Misra and Dhal
(2010); Boyd and Maude et al.,(2017);
Tran Vuong Thinh and Nguyen Ngoc Hong Loan (2021)
GDP GDP ratio*100 Messai et al,.(2013);
Dimitrious et al., (2016); Nguyen Thanh Dat, (2018)
Inflation INF Inflation ratio*100 Mazreku et al.,
Source: Compiled by the author
Research method
To address the second and third objectives of the study, which focus on the influence of bank-specific and macroeconomic factors on non-performing loans (NPLs) in the Vietnamese banking sector, a quantitative analysis was conducted This analysis utilized regression techniques on a comprehensive dataset, employing three distinct methods: Pooled Ordinary Least Squares (Pooled OLS), Fixed Effects Model (FEM), and Random Effects Model (REM), all executed using Stata 17.0 statistical software.
The Pooled Ordinary Least Squares (Pooled OLS) model is a fundamental statistical tool used in multivariate analysis, combining data from various groups or time periods to estimate a unified regression model (Vuko & Čular, 2014) This approach presumes a consistent relationship between the dependent variables, such as Return on Assets (ROA), Loan Loss Reserves (LLR), and SIZE, and the independent variables like GDP and inflation across all groups and time periods However, the lack of differentiation by year and subject may compromise the reliability of the regression results.
The Fixed Effect Model (FEM) is employed to analyze the relationship between a dependent variable and independent variables within grouped and varied data In FEM, each individual unit in the survey is assigned a fixed effect, indicating that its value remains constant over time (Merchant et al., 2014) This model is particularly useful for examining how independent variables influence the dependent variable for each individual unit in the study.
The Random Effect Model (REM) analyzes the relationship between dependent and independent variables, similar to the Fixed Effect Model (FEM) In REM, individual units within the survey exhibit random effects, indicating that each unit possesses a unique value.
The study utilizes a model incorporating random effects that fluctuate over time, assumed to follow a normal distribution with a mean of zero and constant variance This approach enables the estimation of both the coefficients for independent variables and the variance of the random effects (Merchant et al., 2014).
When selecting an estimation method for your analysis, it is crucial to determine the most suitable model, whether it be Pooled OLS, Fixed Effects Model (FEM), or Random Effects Model (REM) To assess the appropriateness of the chosen model, this study utilized a Lagrange multiplier test to differentiate between OLS and REM, as suggested by Baltagi.
2008) If P-value < 0.05, the model has random effects, or in other words, the REM is better than the OLS model
To determine the appropriate model between Random Effects Model (REM) and Fixed Effects Model (FEM), the Hausman test is utilized, as outlined by Baltagi (2008) and Gujarati (2021) A P-value of less than 0.05 indicates that FEM is preferable to REM To enhance the reliability of the research findings, additional tests are conducted to identify potential flaws in the regression model, particularly focusing on common issues in quantitative economics, such as changes in error variance and autocorrelation If flaws are detected, various corrective measures are implemented based on their nature and severity This study employs Feasible Generalized Least Squares (FGLS) regression to effectively address issues related to changing error variance and autocorrelation, thereby improving the regression model's performance.
Research data and sample
This study aims to analyze the influence of bank-specific and macroeconomic factors on non-performing loans (NPLs) by measuring the relationship between independent and dependent variables, making a quantitative approach suitable Secondary data was utilized, with macroeconomic data sourced from the World Bank and International Monetary Fund, while bank-specific information was collected from annual reports and financial statements of Vietnamese banks covering the period from 2014 to 2022.
The population consisted of 20 commercial joint-stock banks in Vietnam The reason for the number 20 is because of the use of Yamane's formula (1967) for the minimum
34 sample size required for a study when the population size is known exactly The formula is: n = N / (1 + N*e 2 )
n is the necessary sample size
e is the maximum allowable error, assuming e=0.1 (10%)
As of now, Vietnam has 31 joint-stock commercial banks With a 10% accuracy level and a 90% confidence level, the minimum sample size needed for the study is determined to be 20 banks These banks are selected from the category of credit institutions.
The study focuses on 20 major banks that collectively represent over 90% of the total assets in the commercial banking system, highlighting their significant role The research spans nine years, resulting in a balanced panel dataset comprising 180 observations Detailed data collection periods for each bank are provided in Appendix 1.
Validity and Reliability
Validity and reliability are essential criteria that ensure the quality of research findings Reliability refers to the consistency and dependability of a measurement tool across different samples and time periods, while validity measures how accurately a tool assesses its intended objective (Merriam, 1995) To uphold both correctness and consistency, the study meticulously evaluates the processes involved in data collection and analysis.
Since the research uses secondary data, the main concern is the source of that data Research on access and use of data in audited annual reports and financial statements
The study utilizes data from 35 commercial banks, alongside macroeconomic information sourced from reputable platforms such as the IMF and World Bank, ensuring high accuracy and reliability These trusted economic websites validate the integrity of the research findings.
After assessing the appropriateness of the research data, a research model and methodology are implemented Statistical software is utilized for data analysis, which minimizes bias and guarantees the accuracy of the study's findings.
To ensure the reliability of the research findings, various steps were taken
To ensure stable and consistent results over time, the data collection process spanned nine years, from 2014 to 2022 This extensive timeframe enabled the capture of any changes in the banking system, leading to more objective outcomes.
The reliability and consistency of the statistical methods employed in the analysis were carefully evaluated, with a focus on regression analysis to explore the relationship between dependent and independent variables The findings indicate a P-value that assesses the probability of the results being random rather than significant When the P-value is below the statistical significance threshold of 0.05, it confirms that the research results are both trustworthy and statistically significant.
This chapter focuses on analyzing the research model and methods employed to assess the impact of determinants on non-performing loans (NPLs) in commercial banks It introduces the key variables within the model and outlines the research hypotheses, emphasizing the primary objective of detailing the methodologies and models utilized in the study.
Pooled OLS, FEM and REM for analysing the impact of factors influencing NPLs
The next chapter will present the research results on the impact of factors on NPLs along with a discussion of the significance of the achieved results
RESULTS AND DISCUSSIONS
Results
4.1.1 Bank-specific and macro determinants of NPLs in the Vietnamese banking sector
4.1.1.1 Descriptive statistics for a research sample
STATA software is a powerful tool for data analysis, management, and visualization, enabling users to conduct statistical analyses The results, which include the number of observations, mean value, standard deviation, maximum value, and minimum value of the studied variables from 2014 to 2022, are summarized in Table 4.1 below.
Table 4.1: Descriptive statistics of the variables Variable Observation Mean Std dev Min Max
Source: extraction from Stata software
The average Non-Performing Loan (NPL) ratio of 20 commercial banks during the study period is 1.8315%, which is well below the allowable safety ratio of 3% as outlined in Articles 11, 12, and 19 of Circular 22/2019/TT-NHNN The standard deviation reflects the degree of value dispersion in the dataset; a low standard deviation indicates minimal fluctuation and tighter data dispersion, while a high standard deviation signifies greater volatility and a wider spread of data.
The analysis reveals a low volatility level of Non-Performing Loans (NPLs) in banks, with a deviation of only 0.9767%, indicating stability throughout the research period The lowest NPL ratio, recorded at 0.50%, was observed in 2020 at Vietnam Technological and Commercial Joint-Stock Bank (Techcombank), coinciding with a period of rapid economic growth in Vietnam Conversely, the highest NPL ratio of 6.68% was noted at Saigon Thuong Tin Commercial Joint Stock Bank (Sacombank) in 2016, a year marked by significant challenges due to global economic fluctuations.
In terms of bank-specific factors:
The average Return on Assets (ROA) across banks is 1.3278%, with a notable standard deviation of 2.2391%, indicating significant annual variability in ROA for each bank The lowest recorded ROA is 0.03% from Eximbank in 2014, while Techcombank achieved the highest at 23.93% in 2017 This variation highlights the disparities in profit generation between large and small banks within the banking system Notably, larger banks such as Vietcombank, Vietinbank, and BIDV have consistently demonstrated steady growth in their ROA.
The average LLR ratio stands at 1.3121%, with a standard deviation of 0.9923%, indicating minimal fluctuations in the credit risk provision ratio among banks over the years This stability reflects banks' consistent aim to keep this ratio as low as possible.
The analysis reveals that the average bank size is 19.07, with a standard deviation of 1.11, indicating notable volatility in bank scales over the years This volatility highlights an increasing disparity among banks, with the smallest size recorded at 15.72 for ABBank in 2014 and the largest at 21.47 for BIDV in 2022 In recent years, banks have focused on expanding their scale to enhance brand recognition and recover market share within the banking sector.
In terms of macro factors:
Vietnam's GDP growth rate has a mean of 6.1% and a standard deviation of 1.8436% The lowest recorded value was 2.6% in 2021, largely due to the lingering effects of the Covid-19 pandemic, which severely impacted the economy, leading to stagnant production and consumption In contrast, 2022 saw a remarkable GDP growth rate of 8%, marking a decade-high and showcasing a bright spot in the Vietnamese economy Experts attribute this impressive growth to strong domestic consumption, robust exports, and significant foreign direct investment (FDI) disbursement.
The average inflation rate stands at 2.82%, with a standard deviation of 0.99%, reflecting the government's efforts to stabilize inflation and maintain consistent consumption and product prices The inflation rate reached a low of 0.6% in 2015 and peaked at 4.1% in 2014 The Covid-19 pandemic significantly impacted the domestic business landscape, leading to a rapid increase in the inflation rate during this period.
4.1.1.2 Correlation and multicollinearity analysis of variables
The study employs a correlation matrix to assess the potential for multicollinearity within the model, where the correlation coefficient indicates the linear relationship between independent variables A correlation coefficient approaching 1 signifies a strong linear relationship, while values nearing 0 suggest that the explanatory variables are independent, enhancing the reliability of estimation results Table 4.2 illustrates the correlations among the variables in the research model.
Table 4.2: The correlation matrix among variables in the study
NPL ROA LLR SIZE GDP INF
Source: extraction from Stata software
Table 4.2 indicates that all independent variables exhibit no multicollinearity, as their values are below 0.8 (Farrar & Glauber, 1967), thus preventing issues related to spurious regression Furthermore, a multicollinearity test is performed to enhance the appropriateness and accuracy of the regression model results.
Table 4.3: Variance inflation factor index of the model
Source: extraction from Stata software
The Variance Inflation Factor (VIF) is a crucial metric in regression analysis that indicates the level of multicollinearity among independent variables It quantifies how the correlation between these variables inflates the variance of the estimated regression coefficients (O’Brien, 2007) A high VIF value, generally exceeding 5 or 10, implies that the independent variable in question may be redundant or excessively correlated with other variables.
A Variance Inflation Factor (VIF) score of 1 signifies no correlation between a variable and other independent variables, ensuring reliability in regression analysis Research by Craney and Surles (2002) suggests that the optimal VIF range for variables is between 1 and 5, as higher values can compromise the interpretability of regression results.
5 Table 4.3 shows all variables have a VIF below 2, so there is no multicollinearity Therefore, the model does not exhibit multicollinearity among independent variables
The Pooled OLS model was initially chosen to analyze the relationship between variables, but subsequent F and White tests revealed that while multicollinearity was absent (P-value = 0.000), heteroscedasticity was present (P-value = 0.5190) To enhance objectivity and accuracy, either the Fixed Effects Model (FEM) or the Random Effects Model (REM) will be employed instead of OLS The Hausman test will be utilized to compare the parameter estimates between the REM and FEM, aiding in the selection of the most appropriate model for analyzing the relationship between independent and dependent variables (Pace & LeSage, 2008).
Hypothesis H A : REM model is appropriate
Hypothesis H B : the FEM model is appropriate
Table 4.4: The result of Hausman test
Test of H A : difference in coefficients not systematic
Source: extraction from Stata software
The Hausman test results indicate a P-value of 0.2915, significantly exceeding the 0.05 threshold Consequently, we accept hypothesis H A and reject hypothesis H B, suggesting that the Random Effects Model (REM) is more appropriate for this study.
Afterwards, disability tests of the model including testing for multicollinearity using the Lagrangian test, testing for variance inflation phenomenon using the Wooldridge
The REM model was tested and revealed issues of multicollinearity and autocorrelation, indicated by P-values of 0.0000 and 0.0282, respectively, both below the 0.05 threshold To mitigate these issues, the FGLS method was employed to estimate the unknown parameters in the linear regression model, accommodating the correlation among the residuals The results of this regression analysis are presented in Table 4.5 below.
Table 4.5: The estimation results of FGLS method
Dependent variable Coefficient Std error P-value
Source: extraction from Stata software
Discussion
This study highlights the key determinants of non-performing loans (NPLs) in Vietnamese commercial banks, addressing a persistent issue in recent years It provides evidence that both bank-specific factors, such as return on assets and loan loss reserve ratio, as well as macroeconomic factors like inflation, significantly influence NPL levels Additionally, the size of the bank also plays a crucial role in determining the NPLs within Vietnam's banking sector.
First, the findings suggest that banks with higher ROA have lower levels of NPLs
According to the result in Table 4.6, The ROA variable is statistically significant at
A 1% increase in Return on Assets (ROA) is associated with a 5.38% decrease in Non-Performing Loans (NPL), aligning with findings from Louzis et al (2012), Klain (2013), and Singh et al (2021) This suggests that poor bank management leads to high NPLs due to risky activities and bad debts Conversely, banks demonstrating strong performance and profitability effectively manage operating costs and reduce NPL ratios When banks lower provisions, they enhance profits, indicating a decline in bad debts Consequently, bank managers should prioritize operational effectiveness to mitigate bad debt risks Additionally, a higher ROA enhances a bank's ability to attract investor funding, providing capital to manage NPLs and reducing dependence on costly government support.
Second, loan loss reserve ratio is also in accordance with the research expectations:
The analysis reveals a statistically significant positive relationship between the Loan Loss Reserve (LLR) ratio and Non-Performing Loans (NPLs) in Vietnam, indicating that a 1% increase in the LLR ratio correlates with a 22.2% rise in NPLs This suggests that as banks allocate more reserves for potential losses, their credit profitability may decline due to increased risky debts Factors contributing to this trend include the economic disruptions caused by the COVID-19 pandemic, which have led to higher loan defaults, and the rapid expansion of Vietnam's banking sector, resulting in more aggressive lending practices These findings align with previous research by Hasan and Wall (2004), Messai and Jouini (2013), and Ghosh (2015).
The study reveals that contrary to the initial hypothesis and previous research, larger banks tend to have a lower non-performing loan (NPL) ratio, with a 1% increase in bank size correlating to a 28.9% decrease in NPLs at a 1% significance level This finding suggests that larger banks possess superior risk management capabilities, a broader range of financial products, and a more diversified customer base, which collectively mitigate the impact of NPLs Additionally, economies of scale enable larger banks to achieve cost advantages and higher profits, while their ability to mobilize capital and maintain stronger reserves enhances their capacity to manage potential losses from bad debts Supporting this, Ahmed et al (2021) note that bank growth facilitates diversification of the credit portfolio, reducing reliance on single assets and lowering risk ratios Furthermore, larger banks can invest in advanced technologies for credit risk management, utilizing predictive tools and effective monitoring to bolster their risk prevention capabilities (Koju et al., 2018).
Fourth, for the GDP growth rate, while there may be some indirect relationship between GDP and NPLs, the impact of GDP on NPLs is not direct or straightforward
A country with a high GDP can still experience elevated non-performing loans (NPLs) if its financial sector is ineffective or if underlying economic issues hinder borrowers' repayment capabilities Conversely, a country with a low GDP may maintain low NPL levels if its financial sector operates efficiently, enabling borrowers to meet their obligations This study indicates that the economic growth rate does not significantly affect NPLs at Vietnamese commercial banks, particularly during the research period from 2014 to 2022.
The economy has shown a consistent recovery, marked by a steady GDP growth rate The government has implemented measures to support banks in reducing non-performing loans (NPLs) by establishing the Vietnam Asset Management Company (VAMC) This special tool aims to expedite the resolution of bad debts, enhance financial stability, mitigate risks for credit institutions, and foster sustainable credit growth within the economy.
The study confirms that inflation correlates with non-performing loans (NPLs), indicating that a 1-unit rise in inflation (INF) results in a 0.115 increase in NPLs High inflation rates contribute to increased loan defaults, aligning with previous research by Fofack (2005), Khemraj and Pasha (2009), Radivojevic and Jovovic (2017), and others In Vietnam, rising inflation prompts the State Bank of Vietnam (SBV) to enforce tighter monetary policies, leading to higher lending interest rates that elevate operational costs for businesses and hinder their repayment capabilities Stricter lending practices exacerbate liquidity shortages, stalling production and increasing bankruptcy risks, particularly for small and medium-sized enterprises Additionally, inflation can slow economic growth and reduce consumer spending, further complicating payment obligations for households and businesses Consequently, these factors collectively contribute to a rise in NPLs, as inflation makes debt repayment more challenging and negatively impacts the overall economy.
This chapter discusses the findings of a linear regression model that analyzes the factors affecting non-performing loans (NPLs) in Vietnamese commercial banks Key bank-specific factors positively correlated with NPLs include loan loss provisions.
The study reveals that the loan loss reserve ratio (LLR) is positively correlated with non-performing loans (NPLs), while return on assets (ROA) and bank size (SIZE) exhibit a negative correlation Additionally, inflation (INF) is positively linked to NPLs The findings are compared with previous research to underscore their significance and relevance in achieving the research objectives.
The next chapter will give a summary of findings, conclusion and suggest several recommendations for commercial banks and policymakers to control NPLs more effectively
SUMMARY OF FINDINGS, CONCLUSION AND
Summary of findings
This study highlights the critical relationship between bank-specific and macroeconomic factors influencing the non-performing loan (NPL) ratio of joint stock commercial banks in Vietnam Key bank-specific factors include return on assets (ROA), loan loss reserve ratio (LLR), and bank size (SIZE) The findings reveal that higher ROA and larger bank size correlate with lower NPL ratios, while a higher LLR is linked to increased NPL ratios Additionally, inflation demonstrates a strong inverse relationship with NPLs, indicating that banks in high inflation environments face higher NPL ratios compared to those in low inflation settings Conversely, the GDP growth rate shows no significant effect on the NPL ratios of these banks.
Conclusion
Non-performing loans (NPLs) pose significant threats to the commercial banking sector, leading to potential losses that can undermine financial stability and profitability An increase in NPLs can trigger a chain reaction, restricting banks' lending capabilities and ultimately dampening economic activity In light of the current economic challenges, it is crucial for banks and regulatory bodies to prioritize effective credit risk management Addressing the root causes of NPLs is essential for their mitigation, particularly within the Vietnamese banking system, where understanding these influencing factors is vital for future stability.
Policymakers and bank management must implement strategies to mitigate the factors leading to non-performing loans (NPLs) in Vietnam By addressing these issues, the banking system can enhance its resilience and effectively support the nation's economic growth and development.
Recommendations
To effectively manage and reduce non-performing loans in Vietnam's banking sector, it is essential to implement targeted recommendations for the Government and commercial bank managers These strategies aim to enhance the stability of the banking system and foster sustainable economic growth The State Bank of Vietnam should focus on monitoring key influencing factors to control bad debts, leveraging insights derived from the research findings.
Inflation has a direct impact on non-performing loans (NPLs) in Vietnam, necessitating government intervention to control prices and mitigate inflationary pressures By maintaining low inflation, the government can stabilize the economy, interest rates, and reduce NPLs Implementing a flexible and appropriate fiscal policy will be essential for effectively restraining inflation and promoting economic stability.
The government should strategically decrease budget expenditures, prioritizing investments in essential infrastructure projects that support national socioeconomic development Additionally, it is important to encourage private sector involvement in these initiatives through public-private partnership arrangements.
To effectively manage inflation in Vietnam, the government should prioritize macroeconomic stability over high GDP growth rates This may involve establishing lower growth targets to sustain an inflation rate of 1.5-2.5% in the coming years, ensuring steady and long-term economic growth.
Third, Government should consider and decide on cutting various taxes and fees related to petroleum, such as value-added tax (VAT) and special consumption tax.
Lowering taxes can decrease consumer spending costs, enhancing their quality of life and boosting market transactions This tax reduction would also enable the government to promote economic growth through increased production and consumption while effectively managing inflation and ensuring macroeconomic stability.
5.3.2 From the State Bank of Vietnam
The SBV also plays a vital role in directing and managing the banking system Below are some recommendations for the SBV:
To enhance Return on Assets (ROA) and reduce Non-Performing Loans (NPLs), the State Bank of Vietnam (SBV) should focus on promoting efficiency and cost control within banks These elements are essential for improving ROA, as banks can lower expenses through automation and process enhancements The SBV can facilitate this by offering guidelines and support for these initiatives Additionally, by considering and expanding the credit room for commercial banks, the SBV can make lending activities more dynamic and flexible, ultimately boosting profitability.
To enhance the stability of commercial banks, the State Bank of Vietnam (SBV) should actively monitor and manage the Loan Loss Reserve (LLR) ratio, ensuring that banks maintain sufficient provisions through regular reporting, on-site examinations, and stress testing Banks falling short in reserves may be mandated to raise their LLR ratios to adequately prepare for potential losses Additionally, the SBV could explore allowing the transfer of general provisions into tier-2 capital to encourage proactive risk management among bank managers It is also essential for the SBV to reassess collateral deduction rates and establish standardized guidelines for valuing collateral, which will aid credit institutions in their provisioning calculations.
Third, the SBV need implement bank restructuring, take strong measures against weak banks, and improve the quality and efficiency of bank operations Banks are
The banking sector is currently grappling with liquidity challenges, elevated non-performing loans (NPLs), and a heightened risk of failure, which collectively threaten the stability of the financial system, as exemplified by Sai Gon Joint Stock Commercial Bank (SCB) To mitigate these risks, it is crucial to restructure banks to rectify existing vulnerabilities, implement special oversight for underperforming institutions, and enhance the scale and competitiveness of banks to effectively reduce NPLs.
The State Bank of Vietnam (SBV) should revise regulations governing foreign investors' share purchases in Vietnamese credit institutions to align with international commitments and increase their ownership ratios This initiative aims to attract foreign capital, technology, and management expertise, thereby enhancing resource mobilization Additionally, streamlining regulatory processes, offering tax incentives, and promoting the advantages of investing in the Vietnamese banking sector will further encourage foreign investment.
Fourth, the SBV should employ monetary policy flexibly to control inflation
Monetary policy utilizes tools like open market operations and interest rate adjustments to manage the money supply effectively Open market operations involve the buying and selling of government bonds, where selling bonds withdraws money from circulation Adjusting interest rates, such as raising the rediscount and deposit rates, restricts commercial banks from discounting valuable papers and encourages individuals to deposit more money, reducing spending This ultimately lowers demand for goods and services, aiding in price control and inflation management However, higher interest rates can increase borrowing costs for individuals, necessitating a cautious and thoughtful approach to interest rate adjustments to ensure economic stability.
5.3.2 From joint-stock commercial banks
The following suggestions may partially help the managers of commercial banks handle this issue of NPLs:
First, to effectively improve their profitability, commercial banks should adopt a holistic approach that combines multiple strategies, including:
To mitigate credit risk, banks should prioritize low-risk loans over unsecured loans, which lack collateral and increase credit risk By focusing on loans with lower interest rates and default rates, banks can enhance their credit risk profile and reduce non-performing loans (NPLs) Lending to government agencies or established corporations with high credit ratings minimizes the likelihood of defaults Nonetheless, small and medium enterprises (SMEs) can still access loans if banks conduct thorough assessments of their financial health and repayment capacity This approach allows banks to manage risks effectively while also offering specialized financial products like microloans to support SME growth and development.
To enhance risk management, banks must implement comprehensive credit evaluation processes that assess customers' ability to repay loans This involves a thorough review of creditworthiness, including credit information, credit history, and financial capabilities prior to lending decisions Utilizing both traditional and modern risk measurement models in credit risk analysis enables bank leaders to accurately quantify credit risk levels, identify early warning signs, and pinpoint the primary causes of credit risk Additionally, effective coordination among departments and between headquarters and branches is essential for successful risk management implementation.
Effective risk management is often hindered by the perception that it is solely the responsibility of the Risk Management Department This misunderstanding can lead to delays and inefficiencies in risk management activities across various departmental units.
Second, commercial banks need to have a reasonable provisioning policy Several policies can be applied in order to improve loan loss reserves and reduce the incidence of NPLs as follows:
Diversifying the loan portfolio: Diversifying loan portfolios is one of the most effective method to mitigate credit risk To achieve this, banks need to:
+ Evaluate and consider industries: Banks should diversify loan allocations across different industries including real estate, transportation business, agriculture, forestry and mining services, public service business, etc
To mitigate risks, traditional banking theory emphasizes the importance of diversifying product offerings, particularly through technology-driven solutions Banks should focus on developing multi-channel, multi-utility payment products and services that leverage digital technology platforms By integrating modern payment technologies into the banking system, financial institutions can enhance customer experience and broaden their service capabilities.
To enhance stability and resilience, banks should prioritize diversifying their customer base by adopting a "Customer is the center of all activities" approach This involves catering to a wide range of clients, including individuals, small and medium-sized enterprises, and multinational corporations By building a diverse clientele, banks can mitigate risks associated with reliance on any single customer segment.