Factors affecting credit risk of joint stock commercial banks in vietnam 2022

1 MINISTRY OF EDUCATION AND TRANING THE STATE BANK OF VIETNAM HO CHI MINH UNIVERSITY OF BANKING ĐỖ NGUYỄN HIỀN HOA FACTORS AFFECTING CREDIT RISK OF JOINT STOCK COMMERCIAL BANKS IN VIETNAM GRADUATION T.

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MINISTRY OF EDUCATION AND TRANING THE STATE BANK OF VIETNAM

HO CHI MINH UNIVERSITY OF BANKING

ĐỖ NGUYỄN HIỀN HOA FACTORS AFFECTING CREDIT RISK OF JOINT STOCK

COMMERCIAL BANKS IN VIETNAM

GRADUATION THESIS MAJOR: FINANCE – BANKING

CODE: 7340201

HO CHI MINH CITY, 2022

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MINISTRY OF EDUCATION AND TRANING THE STATE BANK OF VIETNAM

HO CHI MINH UNIVERSITY OF BANKING

ĐỖ NGUYỄN HIỀN HOA FACTORS AFFECTING CREDIT RISK OF JOINT STOCK

COMMERCIAL BANKS IN VIETNAM

GRADUATION THESIS MAJOR: FINANCE – BANKING

CODE: 7340201

Instructor

DR LÊ HÀ DIỄM CHI

HO CHI MINH CITY, 2022

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ABSTRACT

“Factors affecting credit risk of joint stock commercial bank in Vietnam” using data

from 25 commercial banks on a panel data sample from 2010 to 2020 The independent variables are CAR (capital adequacy ratio), CAP (ratio of capital), ROA (ratio of profitability), SIZE (bank size), NPL (non-performing loan), GROW (credit growth), UPR (unemployment rate), GDP (GDP growth) and INF (inflation) are macro dependent variables

on credit risk The goal of this study was to examine the impact of common factors o the credit risk of commercial banks in Vietnam The S - GMM regression model is used in this study to examine the factors affecting the credit risk of 25 commercial banks in Vietnam from 2010 to 2020

Keywords: Credit risk, Commercial Bank, Vietnam

HCM City, 1 June, 2022

Author

DO NGUYEN HIEN HOA

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ACKNOWLEDGEMENT

First and foremost, I would like to thank my research supervisors, Dr Le Ha Diem Chi

Without her assistance and dedicated involvement in every step throughout the process, this

paper could have never been accomplished I am extremely grateful to you for supporting

and understanding me during the past time

I would also like to show gratitude to the teachers of Ho Chi Minh City University Of

Banking - who have imparted valuable knowledge to me during the four years of the

University Thank you for giving me the opportunity to meet and study at Ho Chi Minh City

University of Banking Getting through my dissertation required more than academic

support, and I have many, many teachers to thank for listening to and, at times, having to

tolerate me over the past four years

Finally, I would like to express my heartfelt appreciation to my family and friends,

who have always helped, accompanied, and supported me over the years

With limited knowledge and circumstances, this thesis cannot avoid many flaws As a

result, I am looking forward to receiving teacher guidance so that I can improve my

knowledge and serve my work in the future

Sincerely thank everyone!

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Contents

ABSTRACT 3

GUARANTEE 4

THANK YOU Error! Bookmark not defined. LIST OF ACRONYMS 10

LIST OF TABLES AND DIAGRAMS 11

CHAPTER 1: INTRODUCTION 12

1.1 REASONALE FOR RESEARCH 12

1.2 RESEARCH OBJECTIVES 12

1.2.1 Overall objectives 12

1.2.2 Detail objectives 12

1.3 RESEARCH QUESTIONS 13

1.4 OBJECTS AND RESEARCH SCOPE 13

1.4.1 Research object 13

1.4.2 Research scope 13

1.5 SCIENTIFIC AND PRACTICAL SIGNIFICANCE 13

1.5.1 Scientific significance 13

1.5.2 Practical significance 14

1.6 RESEARCH GAP 14

1.7 RESEARCH METHODS 14

1.8 CONTENT OF THESIS 15

CONCLUSION CHAPTER 1 17

CHAPTER 2: LITERATURE 18

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2.1 OVERVIEW OF CREDIT RISK OF JOINT STOCK COMMERCIAL BANKS

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2.1.1 Definition of credit risk 18

2.1.2 The indicators reflect the credit risk of commercial banks: 19

2.2 FACTORS AFFECTING THE CREDIT RISK OF JOINT STOCK COMMERCIAL BANKS 22

2.2.1 Macro factor 22

2.2.2 Bank specific 24

2.3 Overview of the studies to the thesis 26

2.3.1 Domestic studies 26

2.3.2 Forgein studies 27

CONCLUSION CHAPTER 2 31

CHAPTER 3: RESEARCH METHODS 32

3.1 DATA COLLECTION: 32

3.2 RESEARCH MODELS 33

3.3 DESCRIPTION VARIABLE AND RESEARCH HYPOTHESIS 34

3.3.1 Dependent variable 34

3.3.2 Independent variables 35

3.4 RESEARCH PROCESS 42

3.5 RESEARCH METHODS 44

3.5.1 Ordinary Least Squares (OLS) 44

3.5.2 Fixed Effect Model (FEM) 44

3.5.3 Random Effect Model (REM) 44

3.5.4 Feasible Generalized Least Square (FGLS) 44

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3.5.5 System Generalized Model of Moments (S-GMM) 45

3.5.6 Check for suitable model selection 45

CONCLUSION CHAPTER 3 48

CHAPTER 4: RESEARCH RESULTS AND DISCUSSION 49

4.1 DESCRIPTIVE STATISTICAL 49

4.2 CORRELATION ANALYSIS 51

4.3 MULTICOLLINEARITY TEST 52

4.4 ESTIMATED THE POOLED OLS, FEM, REM MODELS 53

4.5 SELECTION TEST OF 3 MODELS POOLED OLS AND FEM 55

4.6 ESTIMATED THE FGLS 56

4.7 ESTIMATING THE REGRESSION MODEL BY GMM 57

4.8 DISCUSSION 58

4.8.1 Bank size (SIZE) 60

4.8.2 Non-performing loan (NPL) 61

4.8.3 Ratio of Profitability (ROA) 62

4.8.4 Ratio of capital (CAP) 63

CONCLUSION OF CHAPTER 4 65

CONCLUSIONS AND RECOMMENDATIONS 66

5.1 CONCLUSION 66

5.2 RECOMENDATION 67

5.3 LIMITATIONS OF THE TOPIC AND RESEARCH DIRECTIONS 68

5.3.1 Limitations of the research 68

5.3.2 Further research directions 68

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CONCLUSSION CHAPTER 5 69REFERENCES 70APPENDIX 73

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LIST OF ACRONYMS

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LIST OF TABLES AND FIGURES

LIST OF TABLES

Table 3 1 List of Commercial banks in Vietnam 32

Table 3 2 Summary of research on credit risk 39

Table 4 1 Summary of Descriptive statistics 49 Table 4 2 Correlation between Credit risk and independent variables 51

Table 4 3 Multicollinearity VIF 52

Table 4 4 Multicollinearity VIF 53

Table 4 5 Estimated the FGLS 56

Table 4 6 Estimated the GMM 57

Table 4 7 Summary of research results 58

LIST OF FIGURES Figure 3 1 Process study 42

Figure 4 1 Relationship between CRI and SIZE 60 Figure 4 2 Relationship between CRI and NPL 61

Figure 4 3 Relationship between CRI and ROA 62

Figure 4 4 Relationship between CRI and CAP 63

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CHAPTER 1: INTRODUCTION 1.1 REASONALE FOR RESEARCH

The banking system plays important role in the dynamic of economic activities since banks function as an entity that allocates capital from fund sufficient party to fund deficient party Banks function to allocate capital follows by inherent risk which is credit risk thus a bank should have a proper criteria and procedure in allocating fund

Among other risks faced by banks, credit risk plays an important role on banks‘ profitability since a large chunk of banks‘ revenue accrues from loans from which interest is derived In addition, the banking system can make changes in the macroeconomic environment such as a decrease in growth, an increase in unemployment, interest rates and transport, which can affect credit risk Most for emerging economies need to focus on test risk signals credit risk needs to be studied more out some apartment solutions and limit risks

in particular, contributing to reducing credit risk to an acceptable level

Currently, researchers around the world are very interested in studying the risks in the operation of the banking system No exception with Vietnam, when credit is the main business of commercial banks, the study of credit risk becomes even more essentials

Stemming from the above reasons, the author has chosen to carry out the research topic

"Factors affecting the credit risk of joint stock commercial banks in Vietnam" to study

to show the factors that have affected the financial performance of the Bank, besides, there are proposed methods to limit the credit risk of commercial banks in Vietnam

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 Evaluate of influence of these factors on credit risk of joint stock companies of commercial banks in Vietnam Provide recommendations to limit credit risk for bank managers and policy makers

1.3 RESEARCH QUESTIONS

To achieve the research objectives, the thesis focuses on answering the research questions:

 What factors affect the credit risk of joint stock commercial banks in Vietnam?

 How influential are the factors affecting equity credit risk affect commercial banks in Vietnam?

 What solution to limit credit risk in the operation of joint stock companies commercial banks in Vietnam?

1.4 OBJECTS AND RESEARCH SCOPE

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1.5.2 Practical significance

Research results provide empirical evidence on the positive and negative effects of these factors on credit risk of joint stock commercial banks Thereby, giving some recommendations to bank managers to limit the future credit risk at joint stock commercial banks in Vietnam

1.6 RESEARCH GAP

At present, most the researchers are mainly conducted in foreign countries, there are relatively few domestic studies on this topic In addition, the research papers in Vietnam almost use the data series of commercial banks from 2000-2016 Therefore, there is still a lack of empirical evidence from the results of multivariate regression analysis to provide firmer evidence for the relationship between factors that can affect credit risk bank capital Based on those reasons, the author conducts

The study builds a theoretical basis system and provides empirical evidence related to the factors affecting the credit risk from 2010 to 2020

In addition, the author also conducts tests for multicollinearity, autocorrelation, variance and homogeneity Then present the results and model conclusions As a result, compare and contrast research results with reality, thereby proposing solutions to research problems

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1.8 CONTENT OF THE THESIS

CHAPTER 1 INTRODUCTION TO THE RESEARCH TOPIC

1.1 Resonables for research

1.2 Research objective

1.2.1 Overall objectives

1.2.2 Detail objectives

1.3 Research question

1.4 Object and scope of research

1.5 Scientific and practical significance

1.6 Research gap

1.7 Research methods

1.8 Content of thesis

CHAPTER 2 THEORETICAL BASIS AND OVERVIEW OF PRIOR STUDIES

2.1 Overview of credit risk of joint stock commercial banks

2.1.1 Definication of credit risk

2.1.2 The indicators reflect the credit risk of commercial banks

2.2 Factors affecting credit risk

4.4 Estimated the Pooled OLS, FEM, REM models

4.5 Selection test of 3 models of Pooled OLS, FEM AND FEM 4.6 Estimated the GLS

4.7 Estimated the regression model by GMM

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CONCLUSIONS OF CHAPTER 1

Chapter 1 gave an overview of the research topic After analyzing the necessity of the research, the author has outlined the research objectives, clearly defined the subject and scope of research, research methods and finally the layout of thesis including 5 chapter

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CHAPTER 2: LITERATURE

2.1 OVERVIEW OF CREDIT RISK OF JOINT STOCK COMMERCIAL BANKS

2.1.1 Definition of credit risk

Credit risk can be defined as ‗the potential that a contractual party will fail to meet its obligations in accordance with the agreed terms‘ Credit risk is also variously referred to as default risk, performance risk

According to the State Bank of Vietnam, credit risk in banking operations is the possibility of loss to the bank due to customers' failure to perform or inability to perform their obligations as committed (2005)

What is credit risk? Well, the easiest way to consider credit risk is to think of your

own situation Take the case where an acquaintance, someone you may have known at school or in a social situation, turns to you and asks you to lend them some money Not a trivial amount to pay for their bus fare home but a sufficient amount so that, if they do not repay you as promised, you are left significantly out of pocket Credit risk occurs when the borrower in a debt contract defaults or delays in repaying the debt either in whole or part Anderson (2013, 292) defines credit risk as ―the probability that a legally enforceable contract may become worthless (or at least substantially reduced in value) because the counterparty defaults and goes out of business.‖ In the words of Saunders and Cornett (2011, 186), it is the ―risk that the promised cash flows from loans and securities held by financial institutions may not be paid in full.‖ Thus, credit risk emerges due to default by debt issuers and counterparties in derivatives transactions (Hull 2012)

Bank credit risk can assessed through the bad debt ratio, is the ratio of total bad debt divided by total outstanding loans (Fadzlan Sufian & Royfaizal R Chong, 2008; Nguyen Thi Thai Hung, 2012; Rasidah M Said & Mohd H Tumin, 2011; Somanadevi Thiagarajan & ctg, 2011; Tobias Olweny & Themba M Shipo, 2011) Some other studies measure credit risk through the ratio of credit risk divided by total assets of the bank (Luc Laeven & Giovanni Majnoni (2002), Nabila Zribi & Younes Boujelbène (2011)) This point of view

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that outstanding loans account for mainly in total assets should be directly usable total assets

to calculate risk Daniel Foos & ctg (2010), Hess & ctg (2009), and Ong & Heng (2012) combines two ways of calculating above to calculate credit risk They measure risk credit risk by risk reserve ratio credit in year t compared to loan balance year t-1 This measurement criterion considers provision for losses may occur for each specific debt should more accurately reflect credit risk If the general comparison between prices bad debt

of different debt groups (groups 3, 4 and 5) with total outstanding loans from group 1 to 5 will not reflect the correct version credit risk quality Bank The State of Vietnam (2013) considers bad debt is debt in groups 3, 4 and 5, but Debts from group 2 onwards must be set aside risk room We measure credit risk using the method of Daniel Foos & ctg (2010), and Hess & ctg (2009), and is defined as follows:

Credit risk (i, t) =

Value of provision for credit risk used is the amount set up and accounted for into operating expenses to reserve for possible losses on debt of credit institutions, bank branches foreign Hedging includes specific and general provision Attend specific room is the amount set aside for provision for possible losses out for each debt at a specific rate like group 1: 0%; group 2: 5%; group 3: 20%; group 4: 50%; and group 5: 100% Attend shared room is the amount set aside for provision for possible losses out but not determined when extracting preventive The general reserve amount must deduction is determined as 0.75% of the total outstanding debts from group 1 to group 4, minus deposits (except payment deposits accounting) at a domestic credit institution, branches of foreign banks in Vietnam in accordance with the law and deposit at a foreign credit institution; and clause lending, buying with term valuable papers for with credit institutions, bank branches other foreigners

in Vietnam

2.1.2 The indicators reflect the credit risk of commercial banks:

 Direct indicators

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The criterias for assessing credit risk at commercial banks have a particularly important role because it directly reflects the credit risk of the bank, specifically:

 Outstanding debt: is the basic indicator reflecting credit risk Outstanding debt will arise when the borrower is unable to repay part or all of the loan to the lender Depending on how long it is past due, this debt will be identified as qualified debt, attention debt, subprime debt, doubtful debt, or potential default Outstanding debt is reflected through the following two criteria:

Outstanding debt =

Ratio of outstanding customers =

If a bank has a target of outstanding debt and a large number of customers with Outstanding debt, that bank is at high risk and vice versa  Non – performing debts: are loans to customers, which are difficult or impossible to recover due to loss of business or bankruptcy, increased liabilities, insolvency of enterprises Non – performing debts will reflect clearly the credit quality of the bank through the assessment of both the loan's overdue term and the loan's risk assessment criteria Non – performing debts is most clearly reflected in the following indicators:  Non – performing debts ratio= –

 Non – performing debts to equity ratio= –

 Ratio of Non – performing debts to loss provision fund= –

 Provision for credit risk: Provision for risk assesses the solvency of the bank when risks occur The purpose of using a bank's risk provision is to cover losses for the

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bank's debts that occur in the event that the customer is unable to pay due to dissolution, bankruptcy, death, missing, or when the debt is classified in category 5 Credit provision is calculated on the principal balance of the customer including: (i) Specific provision - to cover specific risks for each loan; (ii) General provision - insurance for unspecified general risks in the credit portfolio and all provisions are included in the operating expenses of the business

The use of provision is used on the principle that the specific provision is used for each debt first, the sale of security assets to recover the debt, and finally, if the sale of assets is not enough to recover the debt, the just used general backup Each bank needs to have a suitable provisioning method that is just enough to cover risks and avoid high costs affecting net income Indicators showing the provision for credit risk:

 Provision ratio for credit risk=

 Credit risk premium=

 Indirect indicators Although indirect indicators do not specifically reflect the bank's credit risk, there is a big change in this period compared to the previous period or compared to the average of the banking system is a sign reflecting the credit risk of the bank On that basis, the bank can consider adding other criteria to comprehensively assess the bank's credit risk  Credit size: Not a direct indicator of credit risk, but if the credit scale is too hot and does not correspond to the control ability of the bank, then the credit size will reflect credit risk Credit scale is clearly shown through the following indicators:  Debt balance to total assets=

 Average loan balance over number of credit officers =

 Credit structure: Reflects the degree of credit concentration in one industry, field, currency Therefore, although it does not directly reflect the level of risk, if the credit structure is too biased into risky sectors, will reflect potential credit risk Credit structure is divided into groups: Credit structure by industry (If you focus on lending

to high-risk industries, the risk of defaulting on bank loans is also high); Credit structure by type (state-owned enterprises, private enterprises, foreign-invested enterprises); Credit structure by currency (credit risk occurs when there is a strong or unfavorable fluctuation in exchange rates; the unresponsiveness of mobilized capital

in each currency for outstanding loans)

2.2 FACTORS AFFECTING THE CREDIT RISK OF JOINT STOCK COMMERCIAL BANKS

2.2.1 Macro factor

 Domestic and international political and social environments:

The bank is one of the organizations operating on a large scale, so it is also strongly influenced by external factors In which credit risk is greatly affected by the economic, political, cultural and social environment, etc So when the country's economy is stable, financial risks rarely occur and vice versa

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One of the common factors leading to credit risk is that borrowers encounter unpredictable changes in the business environment and the influence of the economic cycle During the high growth period, businesses do business favorably, so it's easy to collect loans and the credit risk is low On the contrary, during the recession, many businesses face difficulties, so loans are prone to risks, especially medium and long-term ones

 Regulatory environment:

This is a common risk factor in countries with unstable economic policies Changes in tax policies, regulations on real estate business will make it difficult for businesses to be proactive in their business strategies An unstable business environment will affect the borrower's financial position weakening as well as potentially higher credit risk

A stable and sustainable economy necessitates a strict legal system that is appropriate for the current situation Commercial banks' activities form various economic relationships and operate in accordance with the provisions of law, so inappropriate provisions of the law pose risks to business entities have an impact on commercial banks' financial performance

At the same time, Vietnam's recent quick monetization process necessitates the passage

of new laws and the amendment of existing laws that are no longer appropriate for the current economic scenario The new law establishes a comprehensive legal framework for resolving conflicts and complaints originating from business and social activity

Macro- and objective factors such as business environment, legal environment as well

as socio-political environment have different degrees of impact on each industry and business field due to the unique characteristics of each industry The professions are quite sensitive to the changes of the market, of objective factors such as securities, real estate, construction, etc., and there are professions with little or no impact of changes in the external environment External factors such as healthcare, education, consumer goods, etc Therefore, assessing the impact of external macro factors on credit risk when lending to a customer is necessary in the specific conditions of the customer each line of business

 Economic policy of the government

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The government is crucial to the growth of any industry in a country, particularly the banking sector Through the position of the Central Bank, as the owner, as the greatest debtor and creditor of the banks, the government has an impact on the banking industry as the manager and supervisor of the entire system Bank that is commercial The state's development policy orientations in each era have a direct impact on the macroeconomic environment, which has a significant impact on the bank's financial performance Commercial banks' operations are governed by state rules such as legal capital requirements, capital adequacy ratio requirements, and information transparency standards, among others Commercial banks must adhere to appropriate development directions as a result of these restrictions

 Competitive environment:

Competition is necessary for development, healthy competition can contribute to improving social benefits through reducing prices and enhancing service quality However, banking is a particular business, where a completely free market mechanism is not the most optimal choice because banking is a sensitive industry, the failure of a bank can affect affects many other banks, causing a banking crisis that has the ability to spread rapidly on a large scale and possibly turn into an economic crisis

A developed financial market with a diverse range of banks and non-bank financial institutions will increase competition among institutions The increasing level of competition will have an impact on bank profitability, requiring banks to develop development strategies, new products, and improve service quality for customers in order to sustain and increase the financial performance of their own organization The expansion of credit scale and excessive support policies through programs and preferential credit packages (both in terms of capital and interest rates) if not fully and timely identified and supported from Fiscal policy will affect the safety of the system in the medium-long term

2.2.2 Bank specific

 Applied science and technology:

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With the outstanding development of science and technology, in order to firmly survive, the bank needs to be equipped with new technology, including advanced equipment and quality personnel Applying technology in the creation of modern banking products and services to serve the needs of the developed port society With the development of science and technology, the bank's transaction processing becomes faster and easier, and the transactions are handled according to a strict process performed by machines instead of manual labor

 Qualification and quality of employees:

The more the economy develops, the more fierce the competition is, the higher the requirements for the qualifications of the workers A team of bankers with good professional skills, ethics, ability in management, appraisal and effective measures in debt recovery will help the bank avoid the following problems causing credit risk

 Management capacity factors:

 Organizational model of the bank's management:

The bank has a scientific organizational structure that will ensure close coordination among staff, departments within the bank, and between banks, creating conditions for the bank to promptly respond to requests of cutomer At the same time, it helps the bank to better manage mobilized capital as well as loans, thereby improving the quality of credit activities

 Internal control:

Through inspection and control, it helps the bank's leaders understand the ongoing business situation, thereby helping the bank's leaders to have appropriate guidelines and policies to solve difficulties and problems, promote favorable factors to improve credit quality

Development strategy of the bank

The bank's development strategy is one of the important factors affecting the credit quality of the bank If the bank builds a right and appropriate development strategy, it will

risk-Quý, V T & Toản, B N (2014) Studied the factors affecting credit risk on 26

commercial banks period 2009 – 2012 Panel data with GMM method are used for etching recover the first-order autocorrelation between errors and endogenous variables to ensure that the obtained estimates are robust and efficient Research results show that the risk Bank credit risk in the past with a one-year lag (LLRi, t-1), credit growth rate used in the past with

a one-year lag (LGi, t-1), and past GDP growth rates with a one-year lag (∆GDPi, t-1) has a significant impact on credit risk of Vietnamese commercial banks

Thuận, N V., & Ngọc, D H (2015) analyzed the factors affecting the credit risk provisions of Vietnam's commercial banks (including 27 commercial banks in Vietnam from 2008-2013) Based on relevant theories and previous research surveys, the most consistent models and assumptions are considered to be used for figuring out the factors impacting credit risk provisions in the Vietnam commercial banks The findings indicate that Marginal Net Interest Income, Bad Debts Ratio and Bank Size have positive movements with the ratio

of credit risk provisions, whereas Return on total Assets does negatively For further purpose, the findings would be useful for agencies and administrators to set policies related

to the credit risk of commercial banks in Vietnam

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Lê Thái, H (2017) The study "Analysis of factors affecting corporate credit risk at the Agribank Can Tho branch" is done in the period from April 2015 to January 2016 with the aims to find out the status of credit operations and the credit quality of corporate customers

to determine which factors affect corporate credit risk in the Can Tho city, on the basis of which set out measures to limit the risks of using loans as well as improving the efficiency

of using loans in this area This study uses the references on corporate credit risk in recent times to refer to the factors affecting credit risk in this area, the risks of using ineffective loans including objective and subjective reasons With 300 observations are collected from enterprises with formal credit loans in the districts of Can Tho city, the study used the probit regression model and Stata software to analyze the factors affecting the credit risk of the enterprises in the locality Analytical results from the regression model showed that the factors: Time of business operation, return on equity and speed of revenue growth have negative correlation relationship with the corporate credit risk and the factors: business, the history of relationship with the credit institutions and the gender of the corporate owner have positive correlation relationship with the credit risk Results obtained through quantitative researching, the study identified the factors affecting the credit risk and set out some solutions to improve the efficiency of loans and collect the repay loans on time, and

minimizing the risk

2.3.2 Forgein studies

A great deal of studies looks at the macroeconomic factors that affect the credit risk In particular, Salas and Saurina (2002), Jimenez and Saurina (2006), Jakubík (2007), Aver (2008), Bohachova (2008), Bonfim (2009), Kattai (2010) and Nkuzu (2011), among others, concentrate their research essentially on the influence of macroeconomic variables over the credit risk growth and stress that those variables should be included into the analysis since they have considerable influence on the changes of credit risk

Aver (2008) shows that the credit risk of the Slovenian banking loan portfolio depends

especially on the economic environment (employment and unemployment), long-term interest rates and on the value of the stock exchange index

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Salas and Saurina (2002) and Jakubík (2007), in studies for the Spanish and Czech

banking sectors respectively, also point out the GDP growth and changes in the interest rates

as the main macroeconomic factors affecting the credit risk In the same line, Bohachova (2008) concludes that the business cycle plays an important role in the evolution of the credit risk: in OECD countries, banks tend to hold higher capital ratios during business cycle highs; in non-OECD countries, periods of higher economic growth are associated with lower capital ratios (procyclical behavior) Thus, banks accumulate risks more rapidly in economically good times and some of these risks materialize as asset quality deteriorates during subsequent recessions

Nkuzu (2011) also analyses this issue for a sample of 26 advanced economies over the

period 1998-2009 using singleequation panel regressions and a panel vector autoregressive model and confirms the adverse link between macroeconomic developments and nonperforming loans model

Pesola (2005), Jimenez and Saurina (2006), Bohachova (2008) and Bonfim (2009)

conclude that the result of wrong decisions of financing will become apparent only during the period of recession of the economy and this will cause the growth of nonperforming loans and loan losses Other authors like, for example, Quagliariello (2006), Festic et al (2011) and Louzis et al (2012) combine the systematic and unsystematic credit risk factors Quagliariello (2006) uses a large panel of Italian banks over the period 1985-2002 to analyse the movements of loan loss provisions and new bad debts over the business cycle using both static fixed-effects and dynamic models His results confirm that banks‘ loan loss provisions and new bad debts are affected by the evolution of the business cycle but several bank-level indicators also play an important role in explaining the changes in the evolution of banks‘ riskiness In a dynamic panel data analysis for nine Greek banks over the period 2003-2009, Louzis et al (2012) finds that not only the real GDP growth rate, the unemployment rate and the lending rates have a strong effect on the level of nonperforming loans, but also some bank-specific variables such as performance and efficiency indicators possess additional explanatory power

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Festic et al (2011) considered a panel of five new EU member states (Bulgaria,

Romania, Estonia, Latvia, Lithuania), he also shows that the mix of slowdown in economic activity, growth of credit and available finance and lack of supervision are harmful to banking performance and deteriorate nonperforming loans dynamics The unsystematic credit risk factors are under the attention of a few studies

Zribi and Boujelbène (2011) provide an analysis for Tunisia estimating a panel model

controlling for random effects for ten commercial banks over the period 1995-2008 Despite they look at some macroeconomic factors, they take especially into account the impact of several microeconomic variables on credit risk Their results show that the main determinants of bank credit risk in Tunisia are ownership structure, prudential regulation of capital, profitability

Jimenes and Saurina (2004) and Ahmad and Ariff (2007) also focus their analysis

on the unsystematic factors While Jimenes and Saurina (2004) analyse the determinants of the probability of default of bank loans in several Spanish credit institutions, Ahmad and Ariff (2007) look at their impact on the credit risk using micro data from commercial banks

of some emerging and developed economies They emphasize that regulatory capital and management quality are critical to credit risk The role of collateral, type of lender, bankborrower relationship, the characteristics of the borrower and of the loan are also under the scope of Jimenes and Saurina‘s (2004) study They find that collateralised loans have a higher probability of default, loans granted by savings banks are riskier and that a close bankborrower relationship increases the willingness for banks taking more risk This survey

of the literature shows that, among the studies on banking credit risk determinants, the vast majority of them consider the macroeconomic environment as the most important factor in the determination of the credit risk Moreover, we also observe that they are mostly based on

a single country analysis Some provide a multi-country comparative analysis, but few use adequate dynamic panel data techniques Louzis et al (2012) make such analysis but at the bank level for a single country (Greece)

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Havidz, H B H., & Obeng-Amponsah, W (2020) This study analyzed the

determinants of bank‘s credit risk from macroeconomic and bank-specific perspective in Indonesia The analysis use panel data analysis by employing fixed effect, different GMM and system GMM approach to accommodate lagged determinant variable used in the model The use of lagged variable in the study is used to analyze the delayed response of bank‘s credit risk to its determinant because of the persistence nature in bank‘s credit risk The result shows that bank-specific variable have stronger influence to credit risk compare to macroeconomic variable Additionally, the study found that banks in this study maintain a prudent management in managing its credit risk thus further explain why bank-specific variable have higher significant compare to macroeconomic variable resulting to bank have more resistance to macroeconomics changes

Table 3 1 List of Commercial banks in Vietnam

1 An Binh Commercial Joint Stock

Joint Stock Commercial Bank For

Investment And Development of

Ho Chi Minh City Development

Joint Stock Commercial Bank

(HDBank)

18

Lien Viet Post Joint Stock Commercial Bank (LienVietPostBank)

6 Kien Long Commercial Joint Stock

Bank (Kienlongbank) 19

Vietnam International Commercial Joint Stock Bank (VIB)

8 Vietnam Maritime Commercial

Joint Stock Bank (MSB) 21

Vietnam Prosperity Joint Stock Commercial Bank (VPBank)

9 Nam A Commercial Joint Stock

Bank (Nam A Bank) 22

Viet Capital Commercial Joint Stock Bank (Viet Capital Bank)

Petrolimex Group Commercial

Joint Stock Bank (PG Bank) 24

National Citizen Commercial Joint

13 SaiGon Bank For Industry and

Source: Compiled by the author

3.2 RESEARCH MODELS

The dependent variable on financial performance is taken based on previous studies: Luc Laeven & Giovanni Majnoni (2002), Nabila Zribi & Younes Boujelbène (2011), Daniel Foos & ctg (2010), Hess & ctg (2009), và Ong & Heng (2012)

The research model is built as follows:

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= +

In which: is the credit risk of bank i, year t

α = Intercept

= Capital adequacy ratio of bank i at time t

= Ratio of capital of bank i at time t

= Ratio of profitability of bank i at time t

= Bank size of bank i at time t

= Non-Performing loan of bank i at time t

= Credit growth of bank i at time t

= Unemployment rate of bank i at time t

= GDP growth of bank i at time t

= Inflation rate of bank i at time t

= Error term where i is cross sectional and t time identifier

3.3 DESCRIPTION VARIABLE AND RESEARCH HYPOTHESIS

3.3.2.1 Capital adequacy ratio (CAR)

The sufficiency quantity of a bank's equity to absorb any shocks that the bank may suffer is referred to as capital adequacy The bank's ability to bear losses or financial risk is reflected in the CAR A bank with a high CAR has a strong ability to tolerate financial risk, which reduces the need for external funding and, as a result, increases profits Furthermore,

a well-capitalized bank can access more business prospects

CAR =

The studies of shows Ghenimi et al (2017); Rivai (2007) T Schilder, Havidz, H B H.,

& ObengAmponsah, W (2020) found that Capital Adequacy Ratio has a negative effect on credit risk but Shrieves and Dahl (1992) studied show that Capotal Adequacy Ratio has a positive impact on credit risk However, the hypothesis is formulated as follows:

Model:

H1: Captital Adequacy Ratio has a negative impact on credit risk

3.3.2.2 Ratio of capital (CAP)

Financial ratios mainly assess the capital capacity of the bank or financial stability As

a general rule, the higher the ratio, the stronger the bank A bank with a high asset-toequity ratio is more protected against business losses than a bank with a lower ratio, although this depends on the relative loss risk for each bank

CAP =

Hussain và Hassan (2004), Hussain và Hassan (2004); Furlong and Keely (1989,1990) and Dothan and Williams (1980) studied show that Ratio of capital has a negative impact on

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credit risk But Nor and Mohamed (2007) research shows that Ratio of Captital has a positive impact on credit risk However, the hypothesis is formulated as follows:

Model:

H2: Ratio of capital has a negative impact on credit risk

3.3.2.3 Ratio of profitability (ROA)

ROA is a measure of a company's profitability per dollar of assets

of Vietnam, the author hypothesizes as follows:

Model:

H3: Ratio of profitability has a negative impact on credit risk

3.3.2.4 Bank size (SIZE)

Bank size is measured by the natural logarithm of a bank's total assets which are taken from banks' balance sheets

SIZE= ln (Total Assets) The size of the bank is indicated by the total existing assets of the bank; increasing total assets indicate that the bank is in the expansion stage Previous empirical studies also show that bank size has the same negative effect on financial performance as the study of Jin-Li Hu & ctg (2004); Somanadevi Thiaga-rajan & ctg (2011); Hess & ctg (2008); Saunders et al (1990); Chen et al (1998); Cebenoyan et al (1999) and Megginson (2005)

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But Nguyễn Thùy Dương & Trần Thị Thu Hương, (2017) studied show that Collateral has a positive impact on credit risk Large banks may also be willing to take high risks due to the expectation that they will be protected by the government if there is a danger, leading to higher credit risk The opinions from scholars are different, but based on the fact of Viet Nam, large-scale banks often focus on lending to state-owned enterprises and large corporations, which always have an advantage in the borrowing relationship, so banks often simplify the process of review loan This has the potential risk of credit risk for these loans Therefore, the author hypothesizes as follows:

NPL =

* 100

Fungáčová, Z., & Poghosyan, T (2011); Yuga (2016); Havidz, H B H., & ObengAmponsah, W (2020) has a positive effect credit risk Therefore, the author hypothesizes as follows:

Model:

H5: Non – performing has a positive impact on credit risk

3.3.2.6 Credit Growth (GROW)

Credit growth is understood as the increase in credits provided by the banking system

to organizations, businesses and individuals in the economy The increase in bank credits is very necessary to meet the increasing demand for capital of organizations, businesses and individuals in the development process of the whole society

Model:

H6: Credit growth has a positive impact on credit risk

3.3.2.7 Unemployment rate (UPR)

Based on State agency reports

Jakubik & Schmieder (2008); Kucukozmen & Yuksel (2006); Altintas (2012); Yurdakul, F (2014); He, Z., & Xiong, W.(2012) studied shows a possitive effect on credit risk Based on that, the author develops the following hypothesis:

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Model:

H8: GDP Growth has a negative impact on credit risk

3.3.2.9 Inflation rate (INF)

The inflation rate is determined based on the consumer price index (CPI) Inflation is defined as the rate at which the general level of prices for goods and services in the economy rises over time Inflation reduces consumer purchasing power because we buy fewer goods and services with each unit of currency

INF=

Ramadan, I Z., Kilani,Q A., & Kaddumi, T.A (2011); Ghenimi et al (2017), Yurdakul (2014) studied show that inflation has a positive impact on credit risk However, Castro (2013) argued that higher inflation can make borrowers' ability to repay loans easier because inflation can reduce the real value of outstanding loans, so credit risk is lower Model:

H11: Inflation rate has a positive impact on credit risk

Table 3 2 Summary of research on credit risk

Number Variables Name Notation Research

Dependent variable

Ong & Heng (2012);

Daniel Foos & ctg (2010); Hess & ctg (2009)

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T Schilder, Havidz, H B.H., & ObengAmponsah, W (2020); Shrieves and Dahl (1992)

2 Ratio of Capital CAP (+/-)

Hussain and Hassan (2004); Furlong and Keely (1989,1990) and Dothan and Williams

(1980);

Nor and Mohamed (2007)

3 Ratio of Profitability ROA (+/-)

Saunders and al (1990); Chen