MINISTRY OF EDUCATION AND TRAINING HO CHI MINH UNIVERSITY OF BANKING ֎֎֎֎֎֎֎֎֎֎ GRADUATIONS THESIS THE FACTORS AFFECT TO THE NON- PERFORMING LOANS OF COMMERCIAL BANKS IN VIETNAM Major: Banking and Financial Code: 7 34 02 01 NGUYEN HOANG KIM NGAN Ho Chi Minh City - 2023 MINISTRY OF EDUCATION AND TRAINING HO CHI MINH UNIVERSITY OF BANKING ֎֎֎֎֎֎֎֎֎֎ GRADUATIONS THESIS THE FACTORS AFFECT TO THE NON- PERFORMING LOANS OF COMMERCIAL BANKS IN VIETNAM Major: Banking and Financial Code: 7 34 02 01 NGUYEN HOANG KIM NGAN 050607190289 HQ7 – GE04 SUPERVISOR: DR. DU THI LAN QUYNH Ho Chi Minh City - 2023 i ABSTRACT The study examines the factors influencing problematic loans at 30 commercial banks in Vietnam between 2012 and 2021. The study's objectives are to: (1) identify factors influencing bad debt. (2) By developing models based on past relevant studies' summarization, comparison, and statistical analysis.
(3) Finally, using the final research findings, provide policy implications for each element that influences non-performing loans. The systematization of core theories and associated studies is the first step in the research. The author proposes to build a research model based on the previously researched models that include the following 8 factors: non-performing loans in the previous period (NPL1), credit risk provision ratio (LLR), bank leverage (LEV), return on total assets (ROA), GDP growth rate (GDP_GR), inflation (INF), and global economic policy uncertainty (WUI). The study applied quantitative approaches to statistically represent the findings of earlier studies that identified characteristics that significantly influenced defaulted loans.
Furthermore, the author calculates variables within the bank using data from commercial banks' yearly financial statements. Macro variables are obtained from economic data from the General Statistics Office, state banks, and the Federal Reserve. The study then employs descriptive statistics, variable correlation matrices, and regression using the OLS, FEM, REM, and SGMM methods. The study's findings revealed that six factors out of eight were statistically significant.
Keywords: non-performing loans, the non-performing loans ratio, Join Stock Commercial Banks, Vietnam. ii ACKNOWLEDGEMENT First of all, I would like to express my appreciation to my supervisor Ms. Du Thi Lan Quynh for her sincere comments, her patient guidance, and useful critiques of this work. Also, I would like to thank all the members of staff at the High-quality department of Banking University, HoChi Minh City.
Without their assistance, this thesis would not be completed. In addition, I am very grateful to my family and friends for giving the biggest support and strength in difficult moments. iii AUTHOR’S DECLARATION I hereby confirm that this dissertation entitled: ―The factors affect to the non- performing loans of commercial banks in Vietnam‖, is my own study, and none of this work has been published before submission. Regards, Nguyen Hoang Kim Ngan iv TABLE OF CONTENTS ABSTRACT.
ii AUTHOR’S DECLARATION. iii LIST OF ABBREVIATIONS. viii LIST OF TABLES. ix LIST OF FIGURES.
The urgency of the study:. Subject and scope of research. Subjects of study. Scope of research.
Research methods and data. Meaning of the topic. Layout of the thesis .9 v CHAPTER 2: THEORETICAL FOUNDATIONS AND EMPIRICAL STUDIES. Theoretical basis of non-performing loans (NPLs).
The concept of non-performing loans (NPLs):. Non-performing loans classification:. Causes of non-performing loans (NPLs):. Determinants affect non-performing loans (NPLs).
Microelements inside the bank: .2 Macroeconomic factors of the economy:. Asymmetric Information Theory:. Pecking Order Theory. Economy of Scale Theory.
Keynesian Economics Theory. Empirical studies on NPLs. Synthesis of previous relevant studies. The research gap.
RESEARCH MODELS AND METHODS. Proposed model framework:. Model estimation methods:. Analyze regression models to choose the suitable one.
Inspection and remediation of defects of the selected model. Statistics describe and consider correlations. Linear multi-additive in the study sample. Testing the correlation between variables in the study model.
Multicollinearity inspection of the research model. Results of regression model estimation. OLS, FEM and REM synthetic regression model. Results of testing and selecting suitable models.
Inspection of selected model defects. The result of testing the autocorellation. The result of testing the heteroscedasticity. Overcoming model defects using SGMM method.
Discuss research results:. CONCLUSION AND RECOMMENDATIONS. The provision for credit risk:. Return on assets (ROA).
GDP growth rate and World Uncertainty Index (GDP_GR and WUI) .79 APENDIX 1: LIST OF COMMERCIAL BANKS USED IN MODEL. DATA OF COMMERCIAL BANKS. RESULTS OF MODEL FROM STATA 13. THE METHOD TO SOLVE THE NPLs OF COUNTRIES AROUND THE WORLD .100 viii LIST OF ABBREVIATIONS Abbreviations Full meaning DCC-GARCH Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity EAT Earning after tax EPU Economy policy uncertainty FEM Fixed effects model FRED The Federal Reserve economic data FSA Financial service authority GDP Gross domestic product GSO The General Statistics Office of Vietnam LEV Leverage LLR Loan loss reserve NPL Non-performing loans REM Random Effects model ROA Return on Assets ROE Return on Equity SBV State Bank of Vietnam SGMM System Generalized Method of moments WB World Bank WHO World Health Organization WUI World Uncertainty Index ix LIST OF TABLES Table 2.
Debt classification of some countries in the world. Classification of debt groups in Vietnam. Synthesis of related studies. Satistical table of research hypotheses.
Statistics describing variables in the research model. Correlation matrix between variables in the model. Test results for multicollinearity in the model. Regression model results according to OLS, FEM, REM.
The result of Fisher test. The result of Hausman accreditation. The result of testing autocorrelation. The result of heteroscedasticity.
The SGMM model estimation result. Compare research results with hypotheses. 67 x LIST OF FIGURES Chart 1. NPL ratio in the period 2012 – 2021.
Reserch model diagram. The urgency of the study: The banking sector plays an important role in the chain of economic links of a country, which is a bridge between the excessing capital and lacking capital. Banks utilize the depositor‘s funds in an efficient manner, share risk, play a significant role in growth of economy, are always critical to the whole financial system and remain at the centre of financial crisis (Franklin and Elena 2008). Financial institutions are responsible for operating the whole economy because they play an important role in transforming deposits into productive investments (Podder and Mamun 2004).
Therefore, stability of the banking system is very vital. One of the key components that result in financial instability or banking crisis is the NPL ratio. Rising non-performing loans lead to the collapse of the banking system and affect the entire economy as well as a country‘s politics. Speacifically, the NPL crisis in 2007 – 2012 occurred in Vietnam due to the impact of the global financial crisis.
Although after that, the state has had effective bad debt settlement policies and achieved good control of the bad debt ratio in 2019. However, at the end of 2019, the outbreak of the Covid-19 pandemic had a strong impact and changed many economic policies globally. This makes the NPL ratio tend to fluctuate and move closer to the same situation as the NPL crisis in 2012. To avoid a banking crisis and exacerbation of the post-pandemic economic downturn, bank managers need to identify factors affecting bad loans and come up with solutions to control this unit of risk measurement.
Besides, the financing costs of non-performing loans are significant. The settlement of non-performing loans is usually handled by asset management enterprises set up under state control. The main task of these enterprises is to receive and settle bad debts of financial institutions. This has the consequence of reducing the government's budget revenue.
Banks' NPL settlement costs account for between 10% and 20% of the country's total GDP (Boudriga et al. Therefore, 2 research on non-performing loans in order to manage and minimize financial costs has been a topic that has attracted a large number of economic researchers throughout the years. According to SBV data, the internal bad debt situation of the whole banking industry in Vietnam in 2012 – 2022 fluctuated quite a lot. The peak NPL ratio in 2012 jumped to 4.86% due to the heavy impact of the global financial crisis in 2007-2011.
Besides, it is due to the monetary easing policy in the period 2006-2007 and hot credit growth in 2009-2010. From 2013 to 2016, thanks to good management policies and the right guidance of the Party and the State, bad debt fluctuated on a downward trend and only reached 2. The next 3 years are the time when the country develops well, the banking system develops strongly, so bad debts continue to decrease to 1. However, bad debt tends to reverse after being afflicted by the COVID-19 epidemic in 2020, boosting the bad debt percentage to 1.
In 2021, bad debt rose to 1. When probable bad debts in 2021 are included, this percentage rises to 3. Bank debt settlement applies by Circular No. 03/03/2021/TT-NHNN and Circular 14/2021/TT-NHNN to ease and postpone debts to alleviate difficulties for customers affected by Covid 19.
Bad debts might reach 8.2% if instructions are not followed. After evaluating the situation and developments in Vietnam's economy in the post-epidemic period and economic slump, many economic analysts predicted that the bad debt ratio would fluctuate in 2022. According to the financial reports of the fourth quarter of 2022 of some banks, bad debts tend to increase. Specifically, VPBank was at a high level of 4.78%; Saigonbank increased from 1.97% at the beginning of the year to 2.12% at the end of 2022; TPBank increased from 2.88%; VIB increased from 2.88%; VIB increased from 2.45%; LienVietPostBank increased from 1.46%; Viet Capital Bank increased from 2.79%; PGBank increased from 2.
Although bad debts increased slightly in small and medium-sized banks, 7 banks kept the bad debt ratio 3 below 1% and 2 banks achieved the provision ratio of bad loans above and below 300% Chart 1. NPL ratio in the period 2012 – 2021 (% of total outstanding loans) Source: The State Bank of Vietnam (SBV) Previous studies on non-performing loans provide many perspectives on factors affecting bad debts in different periods of Vietnamese commercial banks. However, it is in the context of economic stability or recession due to the financial crisis. There is no presence of epidemic situations like the development of recent years 2019 to 2021.
Therefore, the author tends to carry out the study "THE FACTORS AFFECT TO THE NON-PERFORMING LOANS OF COMMERCIAL BANKS IN VIETNAM" including internal banking and macro factors to provide various perspectives for managers. From the research results, the author provides another approach to non-performing loans and proposes solutions for the bank manager. General objectives: 4 The general goal of the thesis is to understand and analyze the influencing factors to the NPL of Vietnam Joint Stock Commercial Banks. Based on research results.
The study proposes a number of recommendations to reduce the risk of bad debt appearing at the bank Vietnam Joint Stock Commercial Bank. Specific objectives: To achieve the general goal, the research needs to achieve specific goals following: Firstly, Identify the factors affecting the NPLs of Vietnamese joint stock commercial banks. From there, the author builds appropriate research models. Secondly, measure the direction and level of influence of factors affecting the NPLs based on the result of the study.
Lastly, the results suggest some measures to help limit and control the non- performing loans of Vietnam Joint Stock Commercial Banks. Research Questions The author fabricates a list of the following key questions to accomplish the research objectives: What factors impact the NPLs of Vietnamese commercial banks during the period 2012 - 2021? What direction do the factors in the research model impact the NPLs? From the research results obtained, what policy implications does the author propose to improve the NPLs situation? 1.