TOPIC FACTORS AFFECTING NON PERFORMING LOANS OF COMMERCIAL BANKS IN VIETNAM IN THE PERIOD 2010 2019

Rationale of the study

A robust financial system is essential for economic growth, as financial institutions, particularly commercial banks, play a key role in facilitating credit flow and enhancing business productivity through investment funding Consequently, an inefficient and unstable financial system hinders a country's ability to achieve sustainable economic development.

Over the past few decades, research has indicated that nonperforming loans (NPL) are a primary cause of banking failures and crises (Brownbridge, 1998) As commercial banks primarily focus on accepting deposits and issuing loans, they are inherently vulnerable to credit risk associated with NPLs These bad loans are often referred to as the "blood clot" of the economy, hindering economic development Additionally, NPLs can lead to liquidity risks, diminish operating profits, and tarnish a bank’s reputation among its customers.

The rise in non-performing loans (NPL) poses a significant threat to banks, potentially leading to bankruptcy and financial crises in both developing and developed nations, as highlighted by Barr and Siems (1944) and Khemraj and Pasha (2009).

Non-performing loans (NPLs) significantly impacted the profitability of Vietnamese banks during the economic slowdown of 2012, as evidenced by a sharp increase in the NPL ratio reported by most commercial banks From 2007 to 2012, the annual growth rate of NPLs surged, averaging around 43.11% per year This study aims to investigate the underlying causes of these NPLs, highlighting the scarcity of recent quantitative research on the issue in Vietnam.

A quantitative study utilized an econometric model to identify the key endogenous factors affecting the rate of change in non-performing loans (NPLs) within Vietnamese commercial banks.

This study aims to investigate the factors influencing the capital structure and address the issue of non-performing loans in commercial banks in Vietnam from 2010 to 2019 By analyzing these factors, the research seeks to provide valuable insights for policymakers and bank administrators, enabling them to develop effective strategies to minimize risks and reduce non-performing debts Ultimately, the findings aim to enhance the efficiency of banking operations in Vietnam.

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Research methodology…

To conduct the study, we used statistical,comparative and regression methods from annual financial data of 30 Vietnamese commercial banks in the period of 2010-

In 2019, a multivariate regression analysis utilizing the Ordinary Least Squares (OLS) method was conducted to explore the relationship between independent and dependent variables, specifically focusing on the factors influencing the non-performing loans ratio of commercial banks in Vietnam.

Goals and Purpose…

This study aims to identify the factors influencing the fluctuations in non-performing loans (NPLs) in commercial banks by analyzing the sensitivity of NPLs to macroeconomic conditions and bank-specific variables over the past decade The findings will assist policymakers and bank managers in formulating effective risk-reduction strategies to mitigate bad debt and enhance banking efficiency.

Research subject and scope

Research subject: Determinants affecting the ratio of NPLs in Vietnamese commercial banks in the period 2010-2019

In terms of Scope: Employing data of 30 commercial banks in Vietnam Banks are selected based on size diverse and having continuous operate from 2010 to 2019

In terms of Time: Collect data in the period of 10 recent years from 2010 to 2019.

The structure of report

The structure of study is organized into three main section as followings: Section 1: Overview of the topic

The first section aims to provide literature reviews related to in the research topics including definition, classifications, economic theories and research hypothesis used in the study.

In this section, we present research methodologies to collect and analyze data Section 3: Estimated results and statistical references

This part is to demonstrate the results of estimated model, tests for the model's possible problems and correct them.

Based on findings and results in the Section 3, give conclusions and some recommendations.

OVERVIEW OF THE TOPIC

Overview about commercial bank

A commercial bank is a financial institution that provides essential services such as accepting deposits, offering checking accounts, and granting various types of loans It also offers basic financial products, including certificates of deposit (CDs) and savings accounts, catering primarily to individuals and small businesses For many, a commercial bank serves as the primary location for their banking needs.

Commercial banks offer essential banking services and products to individuals and small to mid-sized businesses, including checking and savings accounts, loans, mortgages, and basic investment options like certificates of deposit (CDs) Additionally, they provide services such as safe deposit boxes to meet the financial needs of their customers.

Banks generate revenue through various service charges and fees associated with their products These fees can include monthly maintenance charges, minimum balance fees, overdraft fees, and non-sufficient funds (NSF) charges for accounts, as well as safe deposit box fees and late fees Additionally, many loan products come with fees that are separate from interest charges.

Banks generate revenue by lending out customer deposits, earning interest from borrowers They typically pay lower interest rates on deposits—such as 0.25% for savings accounts—compared to the higher rates charged on loans, like 4.75% for mortgages.

● The primary functions of commercial banks are:

Commercial banks offer a variety of deposit options to the public, including savings accounts, recurring deposit accounts, and fixed deposits These deposits are accessible to customers upon request or after a specified duration, ensuring flexibility and security for their funds.

Commercial banks offer a variety of loan products, including overdrafts, cash credit, bill discounting, and money at call They provide both demand and term loans to diverse clients, ensuring proper security is in place Additionally, these banks serve as trustees for their customers' wills, further enhancing their range of financial services.

Credit creation occurs through the role of credit and payment intermediaries, primarily commercial banks These banks utilize the deposits they gather to issue loans, which are then transformed into derivative deposits through check circulation and transfer settlements As a result, the derivative funds can increase several times the amount of the original deposits.

5 download by : skknchat@gmail.com greatly improves the driving force of commercial banks to serve the economic development.

Overview about non-performing loans

Non-performing loans are an inherent aspect of the banking and financial sectors, arising from the risks that financial institutions undertake When these risks materialize, loans can transition into non-performing status, reflecting the challenges within the lending process.

Non-performing loans, commonly referred to as bad debts or doubtful debts, are defined by the Bank for International Settlements (2004) as loans for which customers have failed to meet repayment obligations for over 90 days As outlined in Circular No 02/2013/TT-NHNN dated January 21, 2013, these bad debts fall into three categories: group 3 (non-standard loans), group 4 (doubtful loans), and group 5 (loss-making loans) This research utilizes data on bad debts specifically from these three groups.

● Reasons for Non-performing loans a Reduced attention to borrowers

The Hawthorne effect, observed in the 1920s at the Hawthorne Electric Company, revealed that factory workers' productivity increased due to the attention they received from researchers rather than changes in their working conditions such as lighting and heating This phenomenon suggests that when individuals feel observed and valued, their performance improves, a principle that can also apply to borrowers in financial contexts Additionally, lenders often lack effective strategies to manage associated risks.

Donor-funded credit programs often lack a clear focus on risk management, with microfinance initiatives showing little industry-wide concern for addressing potential vulnerabilities, aside from maintaining strict operational control The existing literature primarily emphasizes outreach, measured by borrower numbers and the coverage of administrative expenses Meanwhile, the performance of micro-lenders remains uncertain, as they are currently buoyed by substantial donor funding that supports even the least effective operations This neglect of risk is referred to as The Pollyanna Effect, as borrowers often explore the weaknesses within credit operations.

Credit programs are not infallible; unforeseen circumstances can hinder a borrower's ability to repay on time, even if they are determined to do so If lenders fail to follow up promptly with inquiries, borrowers will take notice.

Borrowers may exploit weaknesses in credit programs, particularly when some refuse to make timely payments or cleverly evade their obligations This behavior can be likened to the "Jurassic Park Effect," where the dinosaurs tested and ultimately overcame the containment measures of the park, leading to chaos and the downfall of their human captors As the situation deteriorated, the dinosaurs became increasingly aggressive, reflecting the unforeseen risks that arose when control was lost This concept underscores the importance of robust models in credit management to prevent similar pitfalls.

Lenders may lack familiarity with effective strategies for managing bad and doubtful debts, particularly in North and Central Asia, where commercial banking is relatively new compared to its role in economic planning This issue is also evident in countries like Bangladesh and Nepal, where state control over the banking system fosters a high tolerance for non-repayment due to the politicization of financial markets In these contexts, legal recourse is often distant, expensive, and uncertain, contributing to what can be termed the High Default Culture Effect.

● Non-performing loans issues in Vietnam

Vietnam's economy has faced significant challenges due to bad debts, particularly following the global crisis of 2007 Since 2010, the accumulation of these debts has had detrimental effects, highlighting the ongoing struggles within the financial system.

The management of old debts has been inadequate, leading to new challenges, particularly with the rising NPL ratios In 2020, despite a low overall NPL ratio, Group 4 and Group 5 debts surged significantly Techcombank, which reported the lowest NPL ratio in the banking system at just 0.47% and a 58% reduction in total bad debts to VND 1,295 billion, saw a 75% increase in Group 4 debt, totaling nearly VND 534 billion Similarly, NamABank maintained a bad debt ratio below 1% after a 44% decrease in total bad debts to VND 744 billion, yet Group 5 debt rose by 77% to nearly VND 468 billion Additionally, the bank's accrued interest doubled in 2020, reaching VND 2,632 billion.

Group 5 debt at BIDV increased by more than VND 5,000 billion, to VND 16,525 billion, equivalent to an increase of 46% compared to the beginning of the year MB's total bad debt was nearly VND 3,248 billion, of which Group 5 debt increased by 124%, accounting for VND 1,384 billion Another phenomenon is the remarkable debt at Group

2 at the end of 2020 at some banks which increased dramatically Specifically, OCB increased by 118%, VIB by 76%, and Vietcombank by 70% This is also a matter

7 download by : skknchat@gmail.com of concern because if customers continue to be unable to repay their debts, these debts will jump from Group 2 to bad debt groups.

The determinants affecting non-performing loans

The evolution of non-performing loans (NPLs) is influenced by two main sets of factors: external events, particularly macroeconomic conditions that impact borrowers' repayment abilities, and bank-level factors that account for the variability of NPLs across different banks Empirical evidence supports the significance of both external and internal influences on the levels of non-performing loans.

According to Circulars 02/2013/TT-NHNN:

NPL=(Debt group 3,4,5 /Total loans to customers)*100%

Financial analysts utilize the Non-Performing Loan (NPL) ratio to assess and compare the quality of loan portfolios across banks, identifying lenders with high NPL ratios as potentially engaging in riskier lending practices that may result in bank failures Economists analyze these ratios to forecast possible financial market instability, while investors consider NPL ratios to make informed investment decisions, often perceiving banks with lower NPL ratios as safer investment options compared to those with higher ratios.

Inefficiencies in the debt collection process contribute significantly to the accumulation of bad debts, which in turn leads to an excessive provision for unresolved debts This creates future financial burdens and results in a backlog of bad debts that adversely affects banks' balance sheets Consequently, banks face challenges in lending, while the tangible and intangible costs associated with managing bad debts continue to escalate.

The bad debt ratio of outstanding loans from banks is a key determinant of bad debt levels, as highlighted by various studies (Sinkey and Greenwalt, 1991; Keeton, 1999; Salas and Saurina, 2002; Jimenez and Saurina, 2006) This research utilizes the bad debt ratio from the previous period as a reference for assessing bad debt in year t-1.

Hypothesis H1: The bad debt ratio in the previous period has a positive effect on the current bad debt ratio (Louzis et al, 2010; Salas and Saurina, 2002).

The asset to equity ratio reveals the proportion of an entity’s assets that has been funded by shareholders, answering one question: What percentage of a company's assets do investors own?

Equity-to-Asset ratio= Net Worth / Total Assets

The equity-to-asset ratio is crucial for understanding the proportion of a company's assets that are owned by investors rather than being financed through debt This ratio highlights the extent to which a company's assets could be claimed by creditors, such as banks, in the event of bankruptcy.

A low asset-to-equity ratio signifies conservative financing, relying more on investor funding and less on debt, which is particularly important for businesses with variable cash flows that struggle to manage debt repayments Conversely, a higher ratio may be acceptable for companies with a stable history of cash flows that are projected to continue However, a high asset-to-equity ratio can limit a business's ability to secure additional debt financing, as lenders may hesitate to extend credit Additionally, businesses with high ratios are vulnerable to competitive pricing pressures, as they need to maintain elevated prices to generate sufficient cash flow for debt obligations.

Starting with the bank-level indicators, the estimations show that higher equity-to-assets ratio leads to lower NPLs.

Hypothesis H2: There is a negative relationship between EAR and NPL ( Kerlin 2013)

Return on Equity (ROE) is the measure of a company's annual return (divided by the value of its total shareholders’ equity.

ROE= Net income (annual) / Shareholders Equity

Return on Equity is a profitability metric that is used to compare the profits earned by a business to the value of its shareholders’ equity.

A higher Return on Equity (ROE) is desirable as it reflects management's efficiency in generating profits from invested capital Numerous empirical studies, including those by Klein (2013), Ghosh (2015), and Le and Mai, have demonstrated a negative correlation between bad debt and bank profitability.

In their 2015 study, KT Nguyen and Dinh Dinh highlighted that banks with high profitability have less motivation to engage in risky lending practices, while inefficient banks often resort to providing substandard credit, resulting in a higher likelihood of bad loans This trend is particularly relevant in the context of Vietnamese banks, where a significant portion of profits is derived from credit activities Consequently, when profits are robust, the quality of loans tends to improve, ensuring that capital and interest are adequately recovered, which ultimately leads to lower levels of bad debt (KT Nguyen & Dinh, 2016).

From 2003 to 2012, Abid, Ouertani, and Zouari-Ghorbel (2014) analyzed panel data from sixteen Tunisian banks, focusing on non-performing loans (NPLs) and their correlation with bank-specific and macroeconomic factors Their findings revealed a negative relationship between Return on Equity (ROE) and NPLs Similarly, research by Makri, Tsakanos, and Bellas (2014) confirmed this negative correlation in European countries, suggesting that inadequate management leads to riskier practices and diminished financial performance.

Hypothesis H3: there is a negative relationship between ROE and NPL ratio (Abid, Ouertani, and Zouari-Ghorbel (2014)

Credit growth is the rate of increase of this year's credit balance compared to the previous year

Credit Growth(t)= Loan balance(t)- Loan balance (t-1)/ Loan balance (t- 1)

Credit growth indicates the level of capital available in the economy and is crucial for evaluating a bank's lending performance over time As banks compete for market share, they often pursue aggressive credit growth strategies However, rapid credit expansion can lead to significant risks, particularly during economic downturns, as it may result in an increase in bad loans and future bad debt.

Salas and Saurina (2002) studied Spanish banks and found that loan balance growth is related to loan default Weinberg (1995) suggests that bad debts increase with an increase in credit.

In addition, the research results of Klein (2013), Do Quynh Anh and Nguyen Duc Hung (2013) and Nguyen Thi Hong Vinh (2015) also share the same opinion.

Keeton (1999) analyzes data from 1982 to 1996 using a vector autoregression model to examine the relationship between credit growth and loan delinquencies in the US The study finds a significant correlation between rapid credit growth, characterized by reduced credit standards, and increased loan losses in various states Loan delinquency is specifically defined in this research as loans that are overdue for more than 90 days or do not accrue interest.

Hypothesis H4: There is a positive relationship between the non-performing loan ratio and the credit growth of PCFs.

Gross Domestic Product (GDP) serves as a key indicator of a nation's economic health, reflecting various factors such as consumption and investment within the economy.

C = Private Consumption ;G = Government Investment; X = Exports;

The Gross Domestic Product (GDP) of a country is determined by summing personal consumption, private investment, government spending, and the balance of exports minus imports GDP can be represented in two forms: nominal GDP, which reflects current market prices, and real GDP, which adjusts for inflation to provide a more accurate economic analysis.

Nominal GDP measures the value of goods and services in an economy using current market prices, without adjusting for inflation or deflation It reflects the natural fluctuations in prices and provides insights into the gradual growth of an economy's overall value over time.

Real GDP accounts for inflation by considering the overall rise in price levels, making it a preferred measure for economists to compare a country's economic growth rate By using prices of goods and services from a base year instead of current prices, real GDP effectively adjusts for price changes, allowing economists to determine the genuine growth between consecutive years.

MODEL SPECIFICATION

Methodology in the study

Multiple Linear Regression is a statistical modeling technique that examines the relationship between a dependent variable and multiple independent variables In this research, we specifically analyze how non-performing loans are statistically influenced by factors such as last year’s non-performing loans, GDP growth, inflation rate, unemployment rate, equity-to-asset ratio, credit growth, and return on equity To derive the necessary estimates, we employ the Ordinary Least Squares (OLS) method.

Methodology to collect and analyze the data

This study examines the impact of bank-specific and macroeconomic factors on non-performing loans by analyzing panel data from the financial statements of 30 commercial banks in Vietnam, sourced from Vietdata and Vietstock, alongside macroeconomic indicators from World Bank datasets The research utilizes annual data from 2010 to 2019, resulting in a total of 300 observations (30 banks over 10 years).

Our team utilized Stata and Excel to analyze the dataset, focusing on the correlation matrix among variables Through histogram and scatter command analyses, we observed that non-performing loans (the dependent variable), along with the previous year’s NPL, equity to asset ratio, credit growth, and return on assets, exhibited long right-tailed distributions Consequently, we applied log-transformation to these variables, which clarified the relationships between independent and dependent variables.

Exhibit 2.1: The distribution of some variables

1, Before log-transformation After log-transformation non-performing loans NPL ln( non-performing loans) (lnNPL)

2, Before log-transformation After log-transformation

The previous year’s non-performing loans pNPL

3, Before log-transformation ln(The previous year’s non-performing loans) lnpNPL

Credit growth-Credit ln(Credit growth) lnCreG

Exhibit 2.2: The relationship between and the dependent variable(NPL) and some independent variables

1,Before log-transformation After log-transformation non-performing loans vs the previous year’s ln(non-performing loans) vs ln(the previous non performing loans year’s non performing loans)

NPL vs pNPL lnNPL vs lnpNPL

2, Before log-transformation After log-transformation

17 download by : skknchat@gmail.com non-performing loans vs equity-to-asset ln(non-performing loans) v s ln(equity-to- asset)

NPL vs EAR lnNPL v s lnEAR

Variab Variable description U Abbre Expe Previous studies le ni viation cted t sign

Non - % lnNPL perfo rmin g loans

(1991); Keeton, (1999); Salas and Saurina, (2002); and Saurina, (2006)

& Reininger, 2014; Jiménez & Saurina, 2006; Khemraj, Tarron

Hepşen (2015) Messai and Jouini (2013) Babouček and Jančar (2005)

Return ( ) % lnROE - Klein (2013), Ghosh on (2015), Le and Mai

Equity (2015), KT Nguyen and Dinh Dinh (2015)Makri, Tsakonas andBellas (2014)

Building the research model

This report utilizes a model grounded in public research and economic theories to analyze the impact of various factors on the non-performing loans (NPL) of commercial banks Key variables examined include Return on Equity (ROE), Effective Annual Rate (EAR), credit levels, and the ratio of problematic non-performing loans (pNPL), alongside macroeconomic indicators such as Gross Domestic Product (GDP), unemployment (UNE), and inflation (INF).

NPL=f( pNPL, GDP, UNE, INF, Credit, ROE ,EAR)

To demonstrate the relationship between movie revenue and other factors, the regression function can be constructed as below:

Where: 1: the intercept term of the model

2: the regression coefficient of lnpNPL

3: the regression coefficient of GDP

4: the regression coefficient of INF

5: the regression coefficient of UNE

6: the regression coefficient of lnEAR

7: the regression coefficient of lnCreG

8: the regression coefficient of lnROE

: the disturbance term of the model, represents other factors that affect NPL but not mentioned in the model i,t is for bank i at year t

: the estimator of - the residuals term i,t is for bank i at year t

Description of the data

2.4.1 Statistical description of the variables

Executing the sum command on the dataset reveals key statistics, including the number of observations (Obs), average value (Mean), standard deviation (Std Dev.), and the minimum (Min) and maximum (Max) values for each variable, as illustrated in the accompanying table.

Exhibit 2.3:Statistical description of the variables

Comment: Based on the 2 above tables, we can see that :

The Non-Performing Loan (NPL) ratios among various commercial banks exhibit significant variability, ranging from a high of 11.4% to a low of 0.34%, with an average of 2.3484% and a standard deviation of 1.477979 When the natural logarithm is applied to these values, the distribution becomes more uniform and approaches a normal distribution The transformed variable, lnNPL, shows a maximum value of 2.44361, a minimum of -1.07881, and an average of 0.6858951, with a standard deviation reflecting the adjusted data distribution.

0.5917907 and tended to be closer to the normal distribution.

● GDP: The value of GDP growth has the highest value of 7.075% and the lowest value of 5.247% , the average value is 6.31009% and s.d is 0.5949507

● UNE: The value of UNE growth has the highest value of 2.04% and the lowest value of 1% , the average value is 1.449% and s.d is 0.3848411

● INF: The value of INF growth has the highest value of 18.67% and the lowest value of 0.631% , the average value is 6.111543% and s.d is 5.025345

The Effective Annual Rate (EAR) varies significantly among commercial banks, ranging from a high of 25.13% to a low of -0.125%, with an average of 9.14% and a standard deviation of 3.99 By applying the natural logarithm to EAR, the distribution becomes more uniform and approaches a normal distribution The transformed variable, lnEAR, shows a maximum value of 3.22, a minimum of -0.07, and an average of 2.13, with a standard deviation of 0.41, further indicating its tendency towards normality.

The analysis of credit values among various commercial banks reveals significant variability, with the highest value reaching 265% and the lowest at -30.86% The average credit value stands at 24.45%, accompanied by a standard deviation of 28.19% Applying the natural logarithm to these values results in a more uniform distribution, approaching normality The transformed variable, lnCreG, shows a maximum value of 5.58, a minimum of 0.41, and an average of 2.98, with a standard deviation of 0.83, further indicating a tendency towards a normal distribution.

The analysis of Return on Equity (ROE) across various commercial banks reveals significant variability, with values ranging from a high of 41.8% to a low of -20.53%, and an average of 8.00% accompanied by a standard deviation of 6.99 Applying the natural logarithm to ROE results in a more uniform distribution, approaching normality Similarly, the lnNPL variable shows a maximum value of 3.73, a minimum of -2.99, an average of 1.63, and a standard deviation of 1.22, also trending towards a normal distribution.

Running the corr command in STATA, we have the result is the table of correlation matrix between variables:

Exhibit 2.4: Correlation matrix between variables

The correlation matrix confirms weak correlation between the variables This can be said that multicollinearity problems are either not severe or non-existent because all

The correlation coefficients are below 0.8, as noted by Farrar & Glauber (1967), suggesting a weak relationship Additionally, the sign of the correlation coefficient reveals the direction of the relationship between Non-Performing Loans (NPL) and other influencing factors.

● r(lnNPL, lnpNPL) is 0.6347, which is quite high The positive coefficient means that lnpNPL has a positive effect on lnNPL.

● r(lnNPL, GDP) is -0.3371, which is medium The negative coefficient indicates that GDP and lnNPL have a negative relationship.

● r(lnNPL, INF) is 0,1521, which is quite low The coefficient is positive so

INF has a positive effect on lnNPL.

● r(lnNPL, UNE) is -0,1989, which is quite low The negative coefficient means that UNE and lnNPL have a negative relationship.

● r(lnNPL, lnEAR) is 0,3146, which is medium The coefficient is positive so there is a positive relationship between lnNPL and lnEAR.

● r(lnNPL, lnCreG) is -0,2002, which is medium The negative coefficient suggests a negative relationship between lnNPL and lnCreG.

● r(lnNPL, lnROE) is -0,3090, which is medium The negative coefficient indicates that lnROE has a negative effect on lnNPL.

ESTIMATED RESULTS AND STATISTICAL INFERENCES

OLS regression and conclude the model

Running the reg command in STATA, we got this below result:

3.1.1 Testing the significance of an individual regression coefficient

At the level of significance α=0.05, if an independent variable has P-value< α →

Reject H0, it means that this independent variable affects the dependent variable Testing the hypothesis:

Table 3_1: Testing significance of variables

Variable P-value Reject/Do not reject H0 Affect/ Do not affect dependent variable(lnNPL) lnpNPL 0.000 Reject H0 Affect

UNE 0.672 Do not reject H0 Do not affect lnEAR 0.129 Do not reject H0 Do not affect lnCreG 0.000 Reject H0 Affect lnROE 0.003 Reject H0 Affect

H0: Model has no omitted variables

+)Running the estat ovtest command in STATA for the model which has 7 independent factors, we got the results:

Comment : P-value=0.0372< α=0.05→ Reject H0 >Model has omitted variables

⇒ We need to fix the model.

Based on the above result of coefficient’s significance test, we eliminated UNE and lnEAR (2 factors do not affect lnNPL) from the model : The new SRF is:

Running reg lnNPL lnpNPL GDP INF lnCreG lnROE and the estat ovtest again, we got a better result:

Comment : P-value=0.0563> α=0.05→ Do not Reject H0 >Model has no omitted variables

3.1.3 Sample regression model and explain the results

According to the estimated result from Stata using OLS method, we obtained the SRF as below: lnNPL = 1.545534 + 0.5613392*lnpNPL - 0.144781 *GDP + 0.0237141*INF -

The analysis encompasses data from 30 commercial banks over a span of 10 years, resulting in a total of 300 observations However, the OLS regression yields only 271 observations due to the presence of 29 missing values generated by the gen command.

● F(5,265)= 56.61 shows the value of F-test with 5 factors and 265 degrees of freedom

Pro>F=0.0000: P-value of F-test for overall significance is smaller than the level of significance 5%, thus 2 does not equal 0 In other words, the regression coefficients do

27 download by : skknchat@gmail.com not equal 0 simultaneously.==> The overall model is statistically significance level at 5%

● Explained sum of squares(ESS) indicates how much of the variation in the dependent variable NPL that the model can explain: ESS= 49.7975528 with degree of freedom is k-1=5

● Residuals sum of squares(RSS) shows how much of the variation in the dependent variable that the model cannot explain: RSSF.6181499, degree of freedom is n-k&5

● Total sum of squares (TSS) shows how much variation there is in the dependent variable of the model:TSS.4157026, degree of freedom is n-1'0

The equation 2 = 0.5165 indicates that 51.65% of the changes in lnNPL are accounted for by five independent variables within the model, while the remaining 48.35% may be influenced by other unconsidered factors Although this figure is not particularly remarkable, it remains within an acceptable range.

3.3.3.2 Testing the significance of an individual regression coefficient and explain the estimated coefficient:

At the level of significance α=0.05, if an independent variable has P-value< α →

Reject H0, it means that this independent variable affects the dependent variable Testing the hypothesis:

Table 3_2: Testing significance of variables of new model and explain the estimated coefficient

Variable P-value Coefficient Conclusion lnpNPL 0.000 α=0.05

=> Do not reject H0 => Model does not exist heteroskedasticity

The dataset used in Panel data so we don’t have to test for autocorrelation problem

We can visualize the disturbance’s distribution by drawing its histogram in Stata

The graph indicates a normal distribution of the data, showing neither significant skewness to the right or left nor an overly sharp or flat shape To assess the normality of the residuals, the Jarque-Bera test is employed.

H1: is not normally distributed The result is:

Comment: At the 5% level of significance, P-value=0.6301>0.05 so we have enough evidence to not reject H0, which means the residuals follow normal distribution.

So, the model: lnNPL = 1.545534 + 0.5613392*lnpNPL - 0.144781 *GDP + 0.0237141*INF - 0.1173223*lnCreG - 0.0669493* lnROE + can be accepted because:

+ All independent variables influence the dependent variable

+ Model is statistically significance level at 5%

+ Do not have omitted variables

+ Do not have multicollinearity error

+ Do not have heteroskedasticity error

Comparison to the literature and interpretation of result

Independent Coefficient Relationship Reject/ Do not Follow the variable with dependent reject literature or variable(lnNPL) hypothesis not? lnpNPL 0.5613392 positive Do not reject H1 Yes

GDP -0.144781 negative Do not reject H5 Yes

UNE 0.0237141 positive Do not reject H6 Yes lnCreG -0.1173223 negative Reject H4 No lnROE -0.0669493 negative Do not reject H3 Yes

Table 3_3: Hypothesis conclusion These results can be explained by below interpretation:

The study examined three bank-specific determinants to evaluate their impact on non-performing loans (NPLs) Notably, the previous year's NPLs (pNPL) demonstrated a significant positive correlation with current NPLs at a 5% level This indicates that inadequately managed bad debts from the prior period can adversely influence the subsequent period's bad debts The findings suggest that certain banks may have shortcomings in their credit processes and risk management, leading to increased lending-related bad debts This observation aligns with the research conducted by Louzis et al (2010), Salas and Saurina (2002), Klein (2013), Do and Nguyen (2013), and V T H Nguyen (2015).

The credit growth (Credit): As the results show, the effect of credit growth on

The negative and significant relationship of non-performing loans (NPLs) at the 5% level contradicts initial expectations but remains valid This finding indicates that bank lending primarily targets sectors such as invention, manufacturing, real estate, agriculture, and rural development The State Bank of Vietnam has effectively utilized monetary policy tools, requiring joint-stock commercial banks to enhance credit quality As a result, only effective projects receive funding, promoting operational efficiency and increased income for borrowers, who are then able to repay principal and interest on time, thereby reducing bad debts These results align with previous studies by Klein (2013), K T Nguyen & Dinh (2016), and Doan and Hoang (2016).

The analysis indicates a significant negative relationship between non-performing loans (NPLs) and return on equity (ROE), suggesting that an increase in ROE correlates with a decrease in NPLs ROE serves as a key indicator of bank profitability and contributes to enhancing overall bank value An effective bank aims to diversify its resources, maintain profitability, ensure quality control, manage credit operations efficiently, and minimize bad debts A higher ROE reflects greater profitability, which in turn boosts employee compensation, including salaries and additional income, leading to increased productivity in loan management and further reductions in NPLs This finding is consistent with the work of Messai and Jouini (2013).

The study shows two macroeconomic determinants that have significant influence on NPLs.

A significant negative relationship exists between GDP growth and non-performing loans (NPLs), indicating that robust GDP growth enhances income levels, thereby improving the ability of individuals and firms to repay debts due to increased economic activity Consequently, this leads to a reduction in NPLs In a growing economy, the demand for loans rises, prompting banks to extend credit to borrowers with lower credit quality Conversely, a slowdown in economic growth, characterized by low or negative GDP growth, typically results in an increase in NPLs, aligning with findings from studies by Ahmad and Bashir (2013) and Kumar and Kishore (2019).

Inflation (INF): The result shows that inflation positively relates to NPLs.

High inflation in Vietnam prompts the State Bank to implement monetary policy measures to manage it Increased interest rates can diminish the operational efficiency of firms reliant on borrowed capital, impacting their debt repayment capabilities Additionally, inflation can restrict the credit available for banks' lending activities Overall, a rising inflation rate escalates the debt burden on businesses, contributing to an increase in non-performing loans (NPLs), a finding consistent with Badar's research.

CONCLUSION AND RECOMMENDATION

Conclusion

This study analyzes data from 30 commercial banks in Vietnam between 2010 and 2019 to assess how macroeconomic factors and banking characteristics influence the bad debt ratio of joint stock commercial banks The findings indicate that the average non-performing loan ratio among these banks is relatively low, at 2.3484%, demonstrating significant efforts to maintain this figure below the State Bank's threshold of 3%.

The study examines the theoretical and empirical factors influencing bank non-performing loans (NPLs), revealing that bank-specific characteristics, such as previous year's NPLs, and macroeconomic variables, particularly inflation, positively impact NPLs Conversely, factors like return on equity (ROE), credit growth, and GDP growth negatively affect NPLs While the unemployment rate and equity-to-asset ratio also influence NPLs, their impact is not statistically significant The article concludes with policy implications aimed at controlling loan risks and reducing bad debt, ultimately enhancing the efficiency of bank operations and the overall banking system.

Recommendation

The study on the determinants of non-performing loans (NPLs) has significantly influenced state policies and bank management in Vietnam An increase in NPLs can hinder businesses and borrowers from obtaining necessary bank loans, which are crucial for sustaining production, business operations, and household expenditures, ultimately destabilizing economic growth The authors propose several policy implications aimed at reducing NPLs, targeting both commercial banks and the State Bank of Vietnam.

4.2.1 Recommendations for Vietnamese Commercial Banks

Non-performing loans (NPLs) from the previous year significantly influence the NPLs of the following year for commercial banks As joint-stock commercial banks experience continuous growth in asset size, it is essential for them to effectively manage credit risk to ensure financial stability and mitigate potential losses.

● Vietnamese commercial banks need to set up a task force to collect debts methodically and scientifically, ensuring a balance between profit and risks to reduce bad debts.

● In case, customers are late paying debts due to their insolvency or lack of cooperation, banks should coordinate with functional agencies to handle them according to regulations.

To effectively manage outstanding debts, businesses should promptly sell collateral, actively pursue customer payments, and consider options such as debt extension, loan restructuring, and debt reassessment Additionally, selling debts to trading companies or utilizing risk provisions can be strategic solutions for handling financial challenges.

It is of great importance that the NPL be checked to ensure that investors in the banking industry are not discouraged from investing or even divert their investments.

That a standard be set on the minimum value of collateral in relation to the amount of money being loaned to a customer.

To enhance risk management and ensure sustainable credit growth, it is essential to establish electronic platforms that verify whether an asset has been used as collateral by customers seeking loans from other sources By learning from global banks with robust risk management practices, financial institutions can strengthen their risk reduction and monitoring processes This involves implementing thorough supervision during the appraisal stage, making informed credit granting decisions, and conducting diligent post-credit monitoring.

Banks must refrain from excessive credit granting and lowering credit standards It is essential to establish and clearly define the risk appetite, which will enable the proactive development of a credit portfolio that aligns with targeted distribution proportions Additionally, selecting an appropriate strategy that balances profit objectives with acceptable loss tolerance is crucial for the bank's financial health.

Effective management of loans post-disbursement is crucial for banks, emphasizing the need for enhanced cross-supervision processes By leveraging advanced information technology, banks can significantly improve their ability to identify and address problem loans, ensuring a more efficient and proactive approach to loan management.

Macro factors significantly influence the credit granting processes and the debt repayment capabilities of customers Since these macro variables are typically beyond the control of commercial banks, it is essential for banks to proactively adapt to economic changes in order to safeguard their assets.

Banks need to pay more attention to macro variables This not only helps banks proactively respond to shocks of the economy, but also helps banks forecast risk

35 download by : skknchat@gmail.com provisions From there, banks can come up with reasonable development strategies, both ensuring profitability and preserving the bank's assets.

Improve business performance reflected in indicators such as ROE

The advantages for bank shareholders not only enhance investment appeal in the stock market but also bolster the institution's reputation, attracting more customers and contributing to a lower bad debt ratio.

4.2.2 Recommendations for Governments and the State Bank of Vietnam

● From the findings of this research, several suggestions for the management of the State Bank of Vietnam(SBV) are recommended.

The State Bank of Vietnam (SBV) must implement a suitable monetary policy aimed at maintaining inflation at manageable levels, which is essential for fostering economic growth and minimizing bad debts To achieve this, Vietnam should utilize macro-financial policies and tools to stabilize its economy, thereby preventing potential recessions or crises that could exacerbate bad debt issues within the banking sector.

Vietnam must adopt Basel standards for risk management to enhance the competitiveness of its commercial banking system This approach will facilitate a better understanding of bad debt regulations and help mitigate the risks associated with bad debt in the era of Industry 4.0.

● Second, the SBVneeds to require banks to implement a uniform interest rate policy as well as reduce costs to reduce lending rates and contribute to reducing bad debts.

The State Bank of Vietnam (SBV) must collaborate effectively with joint-stock commercial banks to develop long-term strategies for managing bad debts This includes enhancing transparency in public dealings related to bad debts and being prepared to phase out underperforming banks Such measures are essential to mitigate bad debts and stabilize the national financial system.

The government must ensure stable economic growth, as it enhances business performance, boosts investor returns, and raises income levels for both individuals and institutions This improved financial health enables borrowers to meet their loan obligations, ultimately lowering the bad debt ratio for banks Conversely, a robust banking system can further support this growth trajectory.

36 download by : skknchat@gmail.com the bad debt ratio, it will reduce operational risks; A healthy operating system is a capital transmission channel that promotes economic growth

Economic growth enhances borrowers' income, enabling them to repay debts more effectively This improvement in financial capability contributes to greater financial stability, which is essential for reducing non-performing loans (NPLs).

Abbas, S., Nguyen, V C., Yanfu, Z Y., & Nguyen, H T (2020) The Impact of China

Exchange Rate Policy on its Trading Partners: Evidence Based on the GVAR Model Journal of Asian Finance, Economics and Business, 7(8), 131-141

Beck, R., Jakubik, P., & Piloiu, A (2015) Key determinants of non-performing loans: new evidence from a global sample Open Economies Review,26(3), 525–550

Borad Sanjay "Bad Debts" (2021), Efinancemanagement, https://efinancemanagement.com/financial-accounting/bad-debts.

"Circular No 02/2013/TT-NHNN Classification Of Assets, Levels" (2013)

Thuvienphapluat.Vn, https://thuvienphapluat.vn/van-ban/Tien-te-Ngan-hang/Circular-No-02-2013-TT- NHNN-classification-of-assets-levels-169118.aspx.

In his 2012 article, Dinh, V T T examines the comparison of non-performing loans (NPLs), debt classification, and credit risk provisioning in Vietnam alongside international practices The study highlights the importance of effective debt management and the need for robust frameworks to mitigate credit risks, drawing parallels between Vietnam's strategies and global standards By analyzing these elements, the article provides insights into improving Vietnam's financial stability and compliance with international norms in banking practices.

Do and Nguyen (2013) conducted a practical analysis identifying the key factors influencing bad debt in Vietnamese commercial banks Their findings were presented at the Seminar on Economic Research and Policy, highlighting the critical determinants that contribute to the prevalence of non-performing loans in the banking sector This research provides valuable insights for financial institutions aiming to mitigate risks associated with bad debts in Vietnam.

07, Trung tâm Nghiên cứu Kinh tế và Chính sách tổ chức, Hanoi, Vietnam.

Filip, B F (2015) The quality of bank loans within the framework of

Globalization Procedia Economics and Finance, 20, 208-217

Ghosh, S (2005) Does leverage influence banks' non-performing loans?

Evidence from India Applied Economics Letters, 12(15), 913–918.

Keeton, W R (1999) Does faster loan growth lead to higher loan losses?

Economic Review-Federal Reserve Bank of Kansas City, 84(2), 57–76.

Klein, N (2013) Non-performing loans in CESEE: Determinants and Impact on Macroeconomic Performance IMF Country Report, No 13/72 Retrieved from

38 download by : skknchat@gmail.com https://www.imf.org/en/Publications/WP/Issues/2016/12/31/Non-Performing-Loans-in- CESEEDeterminants-and-Impact-on-Macroeconomic-Performance-40413

Le, H A C (2016) Macro-financial linkages and bank behaviour: Evidence from the second-round effects of the global financial crisis on East Asia Eurasian Economic Review, 6(3), 365–387.

List of commercial banks …

Vietnam Joint Stock Commercial Bank for 16 Tien Phong Commercial Joint Stock Bank Industry and Trade (VietinBank) (TPBank)

Joint Stock Commercial Bank for Foreign Trade

Vietnam International Commercial Joint Stock of Vietnam (Vietcombank) Bank (VIB)

The Vietnam Technological and Commercial 18 Petrolimex Group Commercial Joint Stock Bank Joint Stock Bank (Techcombank) (PG Bank)

Bank for Investment and Development of 19 Orient Commercial Joint Stock Bank (OCB) Vietnam (BIDV)

Vietnam Prosperity Joint-Stock Commercial 20 Bac A Commercial Joint Stock Bank (Bac A

Military Commercial Joint Stock Bank (MB) 21

An Binh Commercial Joint Stock Bank (ABBANK)

Sai Gon Thuong Tin Commercial Joint Stock

Dong A Commercial Joint Stock Bank (DongA

Saigon Joint stock Commercial Bank (SCB) 23 Viet Nam Thuong Tin Commercial Joint Stock

Asia Commercial Joint Stock Bank (ACB) 24

National Citizen Commercial Joint Stock Bank (NCB)

10 Vietnam Export Import Commercial Joint Stock 25 Viet A Commercial Joint Stock Bank

Sai Gon - Hanoi Commercial Joint Stock Bank

Nam A Commercial Joint Stock Bank (Nam A

Vietnam Maritime Commercial Joint Stock

Kien Long Commercial Joint Stock Bank

13 Ho Chi Minh City Development Joint Stock 28 Viet Capital Commercial Joint Stock Bank(Viet

Commercial Bank (HDBank) Capital Bank)

Vietnam Public Joint Stock Commercial Bank

Bao Viet Commercial Joint Stock Bank

Lien Viet Post Joint Stock Commercial Bank

Saigon Bank for Industry and Trade

Tiêu đề	Factors Affecting Non-Performing Loans Of Commercial Banks In Vietnam In The Period 2010-2019
Tác giả	Nguyễn Hà Vy, Nguyễn Thị Phương Thảo, Đỗ Thị Thu Hương, Nguyễn Thị Thu Hằng
Người hướng dẫn	Dr. Vũ Thị Phương Mai
Trường học	Foreign Trade University
Thể loại	Report
Năm xuất bản	2021
Thành phố	Hanoi

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