VIETNAM NATIONAL UNIVERSITY HO CHI MINH CITY
UNIVERSITY OF ECONOMICS AND LAW
FACULTY OF FINANCIAL AND BANKING
******************
BANK MANAGEMENT
FACTORS AFFECTING CREDIT RISK OF COMMERCIAL BANKS IN
VIETNAM PERIOD 2012-2021
Supervisor: Nguyễn Thị Diễm Hiền
Class: 221QT5401
HCMC – 11/2022
NATIONAL UNIVERSITY HO
VIETNAM NATIONAL
UNIVERSITY
CHI MINH
CITY HO CHI MINH CITY
UNIVERSITY OF ECONOMICS AND LAW
STUDENT LIST
Full Name
Students’ ID
Tasklist
Contribution
Nguyễn Lương Trí
Thanh
K204041192
Outline idea, introduction,
theoretical review, checking
25%
Mai Gia Kiệt
K204041164
Extant literature review,
research data, research
methodology
25%
Nguyễn Hồng Nhung
K204041181
Approach, result, conclusion
25%
Nguyễn Như Thành
K204041193
Recommendations, reference
25%
Comments of supervisor
Table of content
1. Introduction ............................................................................................................................................ 1
2. Literature review .................................................................................................................................. 2
2.1 Theoretical review .......................................................................................... 2
2.1.1 Dependent variable ...................................................................................2
2.1.2 Independent variables ..............................................................................2
2.2 Extant literature review ................................................................................. 7
2.2.1 Previous research related to the macro variables .................................7
2.2.2 Previous research related to the micro variables ..................................7
3. Methodology ........................................................................................................................................... 9
3.1 Research Data ................................................................................................. 9
3.1.1 Micro factors .............................................................................................9
3.1.2 Macro factors ..........................................................................................10
3.2 Research Methodology ................................................................................. 10
3.3 Approach ....................................................................................................... 12
4. Results ..................................................................................................................................................... 12
5. Conclusion ............................................................................................................................................. 15
5.1 Micro factors ................................................................................................. 15
5.2 Macro factors ................................................................................................ 16
6. Recommendations ............................................................................................................................. 17
7. Reference ................................................................................................................................................ 32
LIST OF TABLES
Table 1: Research variables and hypotheses in the research model ...........................6
Table 2: The bank's internal factors............................................................................9
Table 3: The Macroeconomics Factors ....................................................................10
Table 4: Variables in use and expected signs ...........................................................11
Table 5: Descriptive statistics table ..........................................................................12
Table 6: Correlation coefficient matrix ....................................................................13
Table 7: Table of Variance Inflation Factors (VIF) .................................................13
Table 8: Regression results .......................................................................................14
LIST OF SYMBOLS & ABBREVIATIONS
1. EXR
2. INF
3. INR (NIR)
4. LLP
5. LDR
6. NHNN
7. NII
8. NPL
9. SIZE
10. GDP
11. RGDP
12. ROE
13. ROA
14. ETA
15. REM
16. FEM
17. OLS
18. GMM
19. GLS
20. VIF
21. VAMC
Exchange Rate
Inflation Rate
Norminal Interest Rate
Loan Loss Provisions
Loan Deposit Rate
State Bank of Vietnam
Non-Interest Income
Non-Performing Loan
Bank Size
Gross Dometic Product
Regional Gross Dometic Product
Return On Equity
Return On Assets
Equity To Assets
Random Effects Model
Fixed Effect Model
Pool OLS Model
Generalized Method of Moments
Generalized Least Squares
Variance Inflation Factors
Vietnam Asset Management Company
NAME OF BANKS IN RESEARCH
1. ABB
An Binh Commercial Joint Stock Bank
2. ACB
Asia Commercial Joint Stock Bank
3. AGB
Vietnam Bank for Agriculture and Rural Development
4. BIDV
Joint Stock Commercial Bank for Investment and Development of VN
5. CTG
Vietnam Joint Stock Commercial Bank for Industry and Trade
6. KLB
Kien Long Commercial Joint Stock Bank
7. MB
Military Commercial Joint Stock Bank
8. MSB
Vietnam Maritime Commercial Joint Stock Bank
9. NAB
Nam A Commercial Joint Stock Bank
10. OCB
Orient Commercial Joint Stock Bank
11. SHB
Saigon Hanoi Commercial Joint Stock Bank
12. STB
Sai Gon Thuong Tin Commercial Joint Stock Bank
13. TCB
Vietnam Technological and Commercial Joint Stock Bank
14. VAB
Vietnam - Asia Commercial Joint Stock Bank
15. VCB
Joint Stock Commercial Bank for Foreign Trade of VN
16. VIB
Vietnam International Commercial Joint Stock Bank
17. VPB
Vietnam Prosperity Joint Stock Commercial Bank
ABSTRACT
The objective of the article is to empirically survey the impact of bank-specific microfactors and macroeconomic factors of the economy on the credit risks of Vietnamese
banks. Based on related theories and previous research surveys on credit risk provision
along with different estimation methods/models, the article has shown the factors
affecting credit risk provision of Vietnamese commercial banks.
The results of the study found that marginal net interest income, non-performing loan
ratio, and bank size have the same impact as the credit risk provision ratio, while income
on total assets has a negative impact on the bank's credit risk provision ratio. With the
results of the study, the study provided information on factors affecting the credit risk
provision of commercial banks. Since then, it has made useful contributions to agencies
and managers to propose policies to improve credit risks at Vietnamese commercial
banks.
Keywords:
Credit risk, bad debt, macro factors, internal factors of banks.
1
1. Introduction
______________________________________________________________________________________
According to Timothy & MacDonald (1995), credit risk is a potential change in net
income and market value of capital resulting from non-payment or late payment.
According to the Basel Supervisory Commission defines credit risk as the possibility that
a borrower or a bank's partner fails to comply with agreed repayment terms. Credit risk,
also known as default risk, arises from uncertainty associated with non-repayment of
debts on the part of customers to the bank. According to Circular No.11/2021/TT-NHNN,
credit risk in banking activities is a potential loss to debts of foreign credit institutions
and bank branches due to customers' failure to perform or inability to fulfill part or all of
their obligations as committed. In the period of 2007-2008, the world economy
experienced a dark period with a mass breakdown in the banking system, widespread
credit "hunger", falling stock prices and large-scale devaluation of currencies in the US
and many European countries.
The origin of this great recession was the real estate bubble along with the system of
monitoring and early warning of incomplete credit risks in the United States. The
collapse of these bubbles has led to many investors losing the ability to pay loans to
banks and financial institutions, leading to a widespread credit crisis.
In Viet Nam, many risks have been revealed during the period of hot credit growth,
since 2012, the State Bank of Vietnam (SBV) has begun to strictly control the credit
growth of the banking sector and considers this an important tool in managing monetary
policy. Since then, the financial situation and business activities of each commercial bank
have become the basis for the SBV to assign specific credit growth targets every year. On
the part of commercial banks in recent times, the structure of revenue sources has
expanded and the proportion of non-interest revenues has increased. However, with the
characteristics of financial intermediaries in banking business, lending is still a key
business of the bank and credit risk always plays a dominant role in the health and
operation of the bank. Appropriately controlled credit risk from a national perspective
will greatly support economic growth, from a banking perspective, it will help them
achieve good profits from the main business. Non-Performing loans in Vietnam over the
past time have been controlled quite well, always below 3% of the total outstanding debt,
but there is always a risk of increasing due to credit growth pressures, limitations in the
management capacity of commercial banks themselves, and external macro factors such
as inflation, exchange rate,...
In the context that lending is always an important channel of operation of banks and
the economy, and there are requirements for a transparent and unified credit management
mechanism, it is necessary to understand the factors affecting credit risk, in accordance
with the practice of state management and business development of social resources. This
2
fact shows the need to study the factors affecting the Credit Risk of Vietnamese
commercial banks, especially when the NPL has gradually risen recently. Stemming from
that important awareness of theory and practice, authors chose the topic “FACTORS
AFFECTING CREDIT RISK OF COMMERCIAL BANKS IN VIETNAM PERIOD
2012-2021” for this research.
2. Literature review
__________________________________________________________________
2.1 Theoretical review
2.1.1 Dependent variable
- Non-performing loans is one of the main causes of credit risk. They are debts that
are overdue for more than 90 days and are doubted about the ability to repay debts and
the ability to recover capital of creditors due to continuous losses of debtors, declaring
bankruptcy or having dispersed assets, insolvency... Non-performing loans will clearly
reflect the credit quality of the bank, based on the overdue time and repayment ability of
customers to classify bad debts into 3 groups: group 3 (subprime), group 4 (doubtful
debts) and group 5 (loss debts). Non-performing Loan Ratio is determined by dividing
the NPL, calculated by summing the three types of bad debts, by the total amount of
outstanding loans in the bank's portfolio, according to Decision 11/2021/TT-NHNN, this
ratio is used to evaluate the credit quality of credit institutions.
2.1.2 Independent variables
Macroeconomic variables
- Economic growth: A healthy economy makes it easier for people and businesses to
pay off their obligations or guarantees that they will be able to return their liabilities. The
ability of borrowers to repay debts declines or becomes insolvent owing to bankruptcy
and losses, however, when a slowdown in GDP growth denotes an economic downturn,
which increases credit risk.
- Inflation rate: According to the IMF: "Inflation is the rate of increase in prices over
a given period of time. Inflation is typically a broad measure, such as the overall
increase in prices or the increase in the cost of living in a country". When there is
significant inflation, the borrower's real income is reduced while the amount of interest
due rises because the interest rate is also high at this time, which could affect the
borrower's ability to repay the loan or force them to become insolvent, increasing the
number of bad debts.
- The nominal interest rate (NIR): The nominal interest rate is the amount of
domestic currency needed to buy a certain amount of foreign currency. Nominal currency
3
appreciation is the term used to describe a decline in this variable. Nominal currency
depreciation is the phrase for an increase in this variable. Interest rates have a direct
impact on the borrower's capacity to repay the loan; specifically, when the interest rate
rises, the borrower is put under more pressure to make repayments because the amount of
interest that must be paid also rises. Losses for the bank happen if the interest rises above
the borrower's capacity to repay the loan. Theoretically, INR has a favorable effect on
credit risk.
- Exchange rate: The exchange rate is the price at which at a time the currency of one
country or region can be converted to the currency of another country or region.
Accordingly, the exchange rate is calculated in the number of local units per unit of
foreign currency. When the local currency depreciates, the local currency is reduced in
the rain, as a result, the price of imports becomes more expensive, leading to an increase
in production costs and indirectly increasing the consumer price index, inflation is at risk
and businesses are highly dependent on raw materials, imported ancillary or intermediate
products will have to bear a debt burden when the cost of borrowing increases. From
there, Credit risk tends to increase. Therefore, it is assumed that EXR may have a similar
or diametrically opposed impact on credit risk.
Intrinsic variables
- Bank size: Bank size is the market value of the bank, studies are usually measured
by the logarithm of the bank's total assets to adjust this variable to the same value as other
variables in the model. Bank size can affect bad debts in both positive or negative ways.
Large banks are able to manage non-performing loans more effectively thanks to their
diversification of loan portfolios and superior Credit Risk management compared to
small banks (Das & Saibal, 2007). However, theoretically, large-scale banks frequently
have a high non-performing loan ratio because they believe that due to their size, they
cannot be at risk, making it simpler to make loans and resulting in a lot of bad debt.
According to the notion, bad debt and the size of the bank have similar effects.
- Capital structure: A combination of equity and loans from banks or financial
institutions to increase profit margins for businesses. Previous studies have often used the
ratio of equity to total assets as a proxy for the level of capitalization. Delis, Tran, and
Staikouras (2011) argue that higher financial leverage, due to stricter capital
requirements, implies that banks are being more cautious in their lending behavior. On
the other hand, low financial leverage leads to an increase in bad debt, as bank managers
have the ability to encourage moral hazard, by increasing loan portfolios when banks are
undercapitalized.
- Loan-to-Deposit Ratio: According to the State Bank of Vietnam: "LDR (Loan to
Deposit Ratio) is the ratio of credit balance to mobilized capital, the index is calculated
by dividing the number of loans to customers to divide by the bank's working capital".
4
LDR is one of the important indicators used to assess the safety of banks. When loans are
higher than bank deposits, to avoid showing that they are trying to attract capital from
outside, banks will have the incentive to reduce their risk provisioning ratio.
- Profitability of Banks: ROA is an important indicator of the profitability of the
bank. It shows the profit earned per dollar of assets and reflects the bank's ability to
manage resources to make a profit. There have been quite a few studies conducted and
the results prove the opposite relationship between profitability and bad debt. Dimitrios et
al. (2010) argue that mismanagement is associated with poor skills in credit scoring,
collateral appraisal, and commitment to monitoring borrowers. Meanwhile, Zribi et al.
(2011) argue that a highly profitable bank is less likely and motivated to generate income,
so it is less likely to engage in risky lending activities.
- Non-interest income (NII): is calculated as non-interest income divided by total
income. This strong indicator will lessen the pressure to maximize credit profit, which
will lessen Credit Risk. The influence of NII on Credit Risk is thought to be reversed.
- Loan Loss Provisions (LLP): An item on the income statement designated as a
reserve for unpaid loans and loan payments is known as a loan loss provision. This clause
is used to cover a variety of loan losses, including non-performing loans, bankruptcies of
customers, and loans that are renegotiated and have lower payments than originally
anticipated. Loan loss reserves, a balance sheet item that represents the entire amount of
loan losses deducted from a company's loans, are increased by loan loss provisions.The
indicator used by banks to ensure loan risks and control losses is Loan Loss
Provisions/Total Assets. High-risk provision indicates that the bank has a high credit risk
(represented by a bad debt ratio). Therefore, the hypothesis is that LLP has the same
impact as the bad debt ratio.
The level of provisioning is specified in Article 12 and Article 13 of Circular
11/2021/TT-NHNN as follows:
Specific provision: The specific level of provisioning is calculated according to the
formula:
, inside:
R: is the total amount of specific provisions that must be deducted from each
customer;
The sum of the pi chains where i runs from 1 - n: is the sum of the customer's specific
room from the letter balance 1 to the nth.
Ri: the total amount of specific provisions to be deducted by the customer for the
principal balance of the first debt. The formula for calculating Ri = ( Ai - Ci) x r. Where:
Who is the first principal balance. Ci is the collateral deduction value of the i. If Ai < Ci
5
then R = 0. r is a group-specific provisioning rate. The provisioning ratio for each debt
group is as follows:
- Debt in group 1, r = 0%
- Debt of group 2, r = 5%
- Debt in group 3, r = 20%
- Debt of group 4, r = 50%
- Debt of group 5, r = 100%
- General provision: The amount set aside for general provision is determined to be
equal to 0.75% of the total balance of debts from group 1 to group 4 except for the
following items:
+ The money is deposited at credit institutions, foreign bank branches in accordance
with the law or deposits at overseas credit institutions.
+ Loans or term purchases of valuable papers between credit institutions and foreign
bank branches in Vietnam.
+ Purchases of promissory notes, bonds, bills and certificates of deposit issued
domestically by credit institutions and branches of foreign banks in Vietnam.
+ The purchase and resale of Government bonds on the stock market shall comply
with the law on issuance, registration, depository, listing and trading of government debt
instruments on the stock market.
6
Table 1: Research variables and hypotheses in the research model
Classify Names of elements
Ampersand
Formula
RGDP
Gross Domestic Product
Growth Rate - Inflation
Expected
sign
A. Independent variables
Macro Economic growth
factors
(-)
Inflation
INF
The nominal interest rate
NIR
The nominal interest rate
(+)
Exchange Rate
EXR
USD price index at t-100
(-)
SIZE
Log(Total Assets)
(+)
Capital Structure
ETA
Total Equity/Total Assets
(-)
Loan to Deposit
LDR
Total Loan/Total Deposits
(-)
Non-Interest Income
Ratio
NII
Non-Interest Income/
Total Income
(-)
Return on Assets
ROA
Net Income /Average total
assets
(-)
Loan Loss Provisions
LLP
LLP/Total Assets
(+)
Micro Bank Size
factors
(+)
B. Dependent variable
Model
Non-Performing Loan
Ratio
NPL
(Sub-standard debts + Doubtful debts
+ Loss debts) /Gross loans
Source: Team of authors compiled from the study overview
7
2.2 Extant literature review
2.2.1 Previous research related to the macro variables
According to the theory of economic cycle and consumption model by Modigliani and
Miller (1967), economic growth, enterprises are easier to repay loans from commercial
banks due to opportunities for investment and business prospects more favorable.
However, the study of Schechman and Gaglianone (2011) shows a positive correlation,
suggesting that continued economic growth will probably make banks more dependent
and will lend more easily, the risk of credit risk increases.
Rising inflation reduces the real value of loans and reduces defaults (Jabra et al., 2017).
On the other hand, inflation devalues the currency, reducing the rate of return in general.
When inflation increases, the correlation leads to an increase in interest rates due to tight
monetary policy. Along with other costs, the cost of debt services also increases,
businesses and borrowers may have difficulty repaying (Le Ba Truc, 2018)
When real interest rates rise, borrowing costs increase, making the profitability of
investments lower, leading to an increase in bad debts, especially for loans. has a floating
interest rate, due to a decrease in the borrower's ability to meet its obligations. (Ahlem et
al., 2013)
When the domestic currency depreciates, the purchasing power of the domestic currency
decreases, making imported goods more expensive, which will increase production costs
and indirectly increase the consumer price index. occurs and businesses that depend on
imported raw materials, auxiliary materials or intermediate products will have to incur a
debt burden when the cost of borrowing increases. Since then, credit risk tends to
increase.
2.2.2 Previous research related to the micro variables
Das & Saibal (2007) supposed that large banks can manage bad debts more effectively
thanks to their superior ability to diversify their loan portfolios and credit risk
management capabilities compared to small banks. However, large banks may also be
willing to accept high risks due to the expectation that they will be protected by the
government if danger occurs, leading to a higher NPL ratio (Nguyen Thuy Duong & Tran
Thi Thu Huong, 2017).
Previous studies often used the ratio of equity to total assets as a proxy for the level of
capitalization. Delis, Tran and Staikouras (2011) suggest that higher financial leverage,
due to stricter capital requirements, implies that banks are more cautious in lending
behavior. In contrast, low financial leverage leads to an increase in bad debts, because it
is easy for bank managers to encourage moral hazard, increasing loan portfolios while
banks are not sufficiently capitalized.
8
To measure a bank's profitability, studies often use ROA (Return on Assets) or ROE
(Return on Equity), which implies the extent to which management is efficient in using
assets and equity to generate income. Many studies demonstrate a negative relationship
between profitability and bad debt. Dimitrios et al. (2010) suggest that poor management
is related to poor skills in credit scoring, collateral appraisal and commitment to
monitoring borrowers. Meanwhile, Zribi et al. (2011) argue that a bank with high
profitability has less incentive to generate income, therefore, is less constrained when
engaging in profitable lending activities risk.
Some other empirical studies by Berger and DeYoung (1997), Bikker and Hu (2002),
Pain (2003), Jimenez and Saurina (2006) and Quagliariello (2007) have shown bankspecific factors such as size, efficiency, and credit quality are important determinants of
bad debt, because they can cause loans to be risky.
Godlewski (2004) uses return on assets (ROA) as an efficiency indicator. He shows that
the impact of bank profitability is negative on NPL ratio. However, by studying 129
Spanish banks for the period 1993-2000, Garcıa-Marco and M. Dolores RoblesFernandez (2008) show a higher return on equity (ROE) followed by future risks. They
argue that the policy of profit maximization comes with a high degree of risk. Sinkey and
Greenwalt (1991) studied the losses of the banking industry in the United States. The
author asserts that internal and external factors explain the loss rates of these banks.
These authors found a significant positive relationship between loan loss rate and
intrinsic factors such as excessive lending, high-interest rates. Similarly, Pesola (2007)
suggested that loan losses are the main factor affecting the operation of credit institutions.
He used macroeconomic variables to explain the distribution of losses. Loan loss
provisions (LLPs) reflect the overall attitude of the banking system to control risk. LLP is
calculated according to the amount of provision for credit risk. Terms are determined
based on the agency's historical experience in the field. Hasan and Wall (2004) used a
sample of banks from 24 countries for the period 1993-2000; They found that higher
nonperforming loans were associated with higher LLPs. Boudriga et al. (2009) studied
the determinants of NPL and the impact of environmental monitoring for the period
2002-2006 for a sample of 59 countries. The authors found that a high contingency seems
to reduce bad debt levels. They also found a link between bad loans and bank-specific
variables such as the percentage of total assets that were rated as risky.
9
3 . Methodology
_____________________________________________________________________
3.1 Research Data
Research data were collected as follows:
Statistics were traced from Bankscope, Vietnam banks dataset (source: IBT), from
statistics report statistics of the State Bank of Vietnam (SBV), and from the audited
financial statements of banks in Vietnam from 2012 to 2021. Macro data was retrieved
from the World Bank, from the General Statistics Office of Vietnam, and from the SBV
during the same period.
3.1.1 Micro factors
Obtained from the audited financial statements of 17 commercial banks (see the list in
Appendix 2) in the research sample, annual report or Bankscope for bad debt (NPL)
element in case it is not found in the financial statement due to lack of explanation.
Research data was collected during the period from 2007 to 2018. As for the bank's
internal factors, after collecting, the author calculates as follows:
Table 2: Internal factors
Factors
Names of parameters collected on financial
statements of Vietnamese commercial banks
(1)
Measurement
(2)
NPL = Sub-standard
debts + Doubtful debts + Gross loans
Loss debts
(1)/(2)
Size
Total assets
Log(1)
Capital structure
Total Equity
Total Assets
(1)/(2)
Loan-to-Deposit
rate
Total loans
Total deposit
(1)/(2)
NII ratio
Non-interest income
Total income
(1)/(2)
ROA
Net income
Average total assets
(1)/(2)
NPL ratio
Intrinsic factors
Source: Team of authors compiled from the study overview
10
3.1.2 Macro factors
Data on macro factors are retrieved from the World Bank during the period 2012 - 2021
as follows
Table 3: The Macroeconomic Factors
Factors
Names of parameters collected on
financial statements of Vietnamese
commercial banks
Macro factors
(1)
(2)
Economic growth
Gross Domestic
Product Growth
Rate
Inflation
Inflation
Inflation
(1)
The nominal
interest rate
The nominal
interest rate
(1)
Exchange Rate
USD price index at
t-100
(1)
Measurement
(1)-(2)
Source: Team of authors compiled from the study overview
3.2 Research Methodology
Quantitative research methodology: Using Pooled OLS, FEM, and REM models and
using GLS method to solve heteroskedasticity and correlation errors and table data to
measure the influence of macro factors and intrinsic factors to NPL ratio. In addition, the
authors use the analytical method to interpret the results obtained from the quantitative
method to describe these effects better.
Model: NPLit = α + βjXi,t + vi + εi,t (1)
Include:
Dependent variable: NPLit: Risk is represented by bad debt variable
Independent variable includes:
+ Bank internal variables: LLPi,t, SIZEi,t, LDRi,t, ETAi,t, NII i,t, ROAi,t
+ Macroeconomic variables: RGDPt, INFt, RIt, EXRt
+ Coefficient of intercept: α
11
+ Xi,t is the vector of independent variables, including macro variables and internal
variables in the bank.
+ βj is the impact of the independent variable vector on the bad debt ratio
+ vi are unique characteristics not observed between banks.
+ i,t is the residual of the model
Table 4: Variables in use and expected signs
Variables
Measure
Expected
Sign
NPL ratio (NPL)
NPL /Total outstanding loans
Intrinsic variables
Capital structure (ETA)
Total Equity / Total Assets
(-)
Loan Loss Provisions (LLP)
LLP/Total Assets
(+)
Loan to Deposit (LDR)
Total loan/Total Deposits
(-)
Non-Interest Income Ratio (NII) Non-Interest Income/ Total Income
(-)
Size (SIZE)
Log(Total Assets)
(+)
Return on Assets (ROA)
Net Income /Average total assets
(-)
Gross Domestic Product Growth Rate
- Inflation
(-)
Macro variables
Economic growth (RGDP)
Inflation (INF)
(+)
The nominal interest rate (NIR)
The nominal interest rate
(+)
Exchange Rate (EXR)
USD price index at t-100
(+)
Source: Team of authors compiled from the study overview
12
3.3 Approach
- Based on previous research results, it is affirmed that the Credit Risk of Vietnamese
commercial banks is influenced by both macroeconomic factors and internal factors of
the bank.
- Consider the influence of macroeconomic and intrinsic factors on credit risks at
Vietnamese banks in the research period from 2012 to 2021.
4. Results
_____________________________________________________________________
The regression model follows the methodology in the Hasna Chaibi and Zied Ftiti (2015)
research model and takes the following form:
NPLit = α++ β1LLPi,t + β2ETAi,t + β3NIIi,t + β4SIZEi,t + β5ROAi,t + β6LDRi,t +
β7RGDPt + β8INFt + β9INRt + β10EXRt + vi + εi,t
First, the author uses quantitative methods to find the factors that influence and the level
of influence of these factors on credit risk at Vietnamese commercial banks.
To estimate the model, initially using the table estimation technique for Pooled models,
FEM model and REM model. But due to the heteroscedasticity appearance in the model,
GLS method regression on the table data will be used.
Table 5: Descriptive statistics table
Source: Team of authors compiled from the study overview
Results from the Correlation Coefficient Matrix Table show that the correlation
coefficient between variables is quite low (<0.8). So the authors assume that there is no
multicollinearity between variables. However, to ensure accuracy, the authors decided to
use the variance magnification factor to check.
13
Table 6: Correlation coefficient matrix
Source: Team of authors compiled from the study overview
The Variance Inflation Factors are used by the authors to test the phenomena of
multicollinearity. The authors came to the conclusion that there was no multilinear
phenomenon because the results showed that all variables had values lower than 10.
Table 7: Table of Variance Inflation Factors (VIF)
Source: Team of authors compiled from the study overview
The authors used the F-test to make a comparison between Pool and FEM. After running
the data, F-test results that all u_i=0: F(16, 143) = 2.26 Prob > F = 0.0059. Thus, the
authors concluded that the FEM model is better than the Pooled OLS model. Then, run
the Breusch_Pagan Lagrange Multiplier test with xttest0 command to compare between
the REM model and the Pooled, obtaining the result chibar2(01) = 5.80 Prob > chibar2 =
0.0080. Thus, the authors decided to choose the REM model.
14
Finally, the authors used the Hausman test to determine between FEM and REM. Prob >
chi2 = 0.7336 (greater than 10%). So the REM model is a suitable model for the research
of the authors. After selecting the REM model, the authors found that the model had
defects. It is the phenomenon of First order autocorrelation and Heteroskedasticity. The
authors used the GLS model to overcome the defects just mentioned.
The following regression results are obtained using the GLS model:
Table 8: Regression results
Source: Team of authors compiled from the study overview
(1) Pooled-Ols
(2) FEM Model
(3) REM Model
(4) GLS Model
15
According to the regression results table above, the intrinsic factors, such as Loan Loss
Provisions (LLP), Non-Interest Income (NII), Bank Size (SIZE), Equity to Assets (ETA),
Loan to Deposit (LDR), and Return on Assets (ROA), as well as the banks' macro
factors, such as GDP growth, inflation, and exchange rates, are both statistically
significant factors that affect credit risk.
- Bank Size has a negative impact on the current year's NPL at a significant 1%.
- ROA acts in the opposite direction on NPL with a meaningful level of 5%.
- LLP Ratio is the most impactful and in the same direction as NPL with a significance of
1%.
- NII Ratio has the same impact on NPL with a significance of 5%.
- GDP Growth has the same impact as the meaningful level of 1%.
- INF has a concurrent impact with a 1% significance level.
The regression results also showed that the LDR, ETA, NII, and EXR variables had little
impact and were not statistically significant.
5. Conclusion
_____________________________________________________________________
5.1 Intrinsic factors
The authors found that the Bank Size (SIZE) outcome had a relevant amount of 1%
negative influence on NPL after running the model. In other words, the credit risk lowers
as bank size grows. The authors' initial expectations were not met by this outcome. In
order to explain this outcome, the author claimed that the SBV had requested a
significant expansion of Vietnamese commercial banks, but that this expansion had not
raised credit risk because the SBV had also been successful in controlling credit growth
without affecting credit quality. On the other hand, in order to grow in size, banks must
also raise earnings. To this end, banks have increased their non-credit income, which has
eased the strain on credit and decreased credit risk.
Return on Assets (ROA) has a negative effect on NPL ratio (NPL) with a statistical
significance of 5% and the coefficient of influence is up to 0.122, that is, when the bank's
LLP is forecasted to grow by 1 %, bad debt decreased by 0.122%. The outcomes were
consistent with the authors' initial projections. Despite a reversal, Karimiyan et al.
(2013)'s research is consistent with the majority of earlier investigations, including
Dimitrios et al. (2012), Messai (2013), and Pham Xuan Quynh & Tran Duc Tuan (2019).
According to ROA, the more inefficiently a bank manages its operations, the less
profitable it is. Increased Credit Risk is frequently the outcome of banks with low rates of
return having subpar abilities in credit rating, collateral appraisal, and dedication to
monitoring borrowers. Because there are more uncollectible loans when banks increase
poor loans, interest collection falls. Additionally, the credit risk provisioning level has
16
increased as a result of the decline in loan portfolio quality. In contrast, the credit risk
tends to diminish for commercial banks with high profitability, good bad debt
management, or good cost management.
Loan loss provision (LLP) has the same effect on non-performing loan ratio (NPL) as a
statistical significance level of 1% and an influencing factor of up to 0.564, which means
that when the bank's LLP is forecasted to grow by 1%, NPL rise to 0.564%. LLP
influences the bad debt ratio in the same way; as bad debts rise, so does provisioning.
At a relevant level of 1%, non-interest income (NII) has the same impact as the nonperforming loan ratio (NPL). According to the study's findings, when NII climbed by 1%,
NPL increased by 0.0169%. Rising NII raises NPL, contradicting the assumption that
high NII relieves pressure on credit income, lowering credit risk. However, given the
situation of Vietnamese commercial banks, which are under pressure to achieve high
profits while their scale and credit growth are constrained by SBV regulations. Banks
have attempted to increase the proportion of NII, but due to ineffective cost management,
increasing NII leads to increased spending resulting in increased NPL.
The LDR, ETA, NIR, and EXR variables have an impact on NPL and are the same as
expected by the authors. However, according to the regression results, the statistical
significance of these variables has not been found.
5.2 Macro factors
Inflation rate is positively correlated with bad debt ratio. When the inflation rate in the
economy increases, the State Bank implements a tight monetary policy to combat
inflation, and the credit activities of commercial banks will also be affected. Specifically,
lending interest rates increased, input costs of enterprises were pushed up, which reduced
business efficiency of borrowing enterprises, thereby directly affecting their ability to
repay loans to banks. row. In addition, tightening lending by banks will lead to the
illiquidity of the economy, stagnant production and business activities, businesses
misappropriating capital from each other, insolvency, many Enterprises, especially small
and medium-sized enterprises, face the risk of bankruptcy, pushing the burden of bad
debt to banks.
GDP increased by 1%, bad debt ratio increased by 0.0584%. This can be explained that
when the economy grows continuously, because of competition for profits, banks will
become dependent and careless in reviewing loan applications, leading to reduced debt
quality. and the bad debt ratio increased. In the study of Inekwe and Murumba (2013),
the authors studied the banking system of Nigeria in the period 1995 - 2005 and
concluded that the bad debt ratio is positively related to real GDP.
17
6. Recommendations
_____________________________________________________________________
The results of the regression model have shown that the factors that affect credit risk at
Vietnamese commercial banks include macroeconomic factors and bank internal factors,
in which the factor has the strongest influence. to credit risk are provision for credit risk
(LLP), previous year's Non-Performing LoansLoans(L.NPL) and GDP growth (GGDP).
In addition, 5 other factors were also found to have an influence on credit risk and were
statistically significant, namely exchange rate (EXR), non-interest income (NII) and size
(SIZE), but because of the low level of influence of these factors on credit risk at
Vietnamese commercial banks, it is suggested to suggest solutions to limit credit risk so
the author will focus on solutions to affect 4 factors that have a strong influence on credit
risk at Vietnamese commercial banks. The author suggests the following solutions:

To SBV
Credit risk provision (LLP) is found to have a positive impact on credit risk, that is, if
provision is reduced, credit risk will also decrease. . In order to reduce provision for
credit risks, it is necessary to increase credit quality. When credit quality increases, it will
affect the bad debt ratio of the previous year (L.NPL) in a favorable direction because
when the credit quality is good, it will not increase the bad debt ratio each year, so next
year banks customers will not have to find a way to reduce the previous year's bad debt.
Some solutions suggested by the author to increase credit quality are as follows:
It is necessary to limit credit risks from the very beginning, to avoid cases due to lack of
attention and lack of close supervision so that when credit risks arise (as in years 2011 2012), they will pay attention and find ways to deal with them. Management will take a
lot of time, effort and may even encounter situations that cannot be handled. To do this,
SBV needs to:
- Complete the operational risk monitoring apparatus of commercial banks on the basis of
forming an independent department that does not participate in the risk creation process,
which has the function of risk management and supervision for commercial banks;
identify and detect risks; analyze and assess risk levels on the basis of established criteria
and criteria, and at the same time propose measures to prevent and minimize credit risks.
- Build an online information system connecting the data of banks with the data of the
state bank. Through this system, the State Bank can know which banks have abnormal
signs, they will organize unexpected inspection and supervision and take timely action to
intervene with that bank. If violations are detected, they must be strictly handled.
Currently, the updating of information into the information system of the SBV is mostly
18
done by the banks sending reports, then the SBV will update the system. Manual updates
like this are often slow and easy to falsify information, so it will be difficult for the
SBVto detect problematic situations to take timely intervention actions.
- Build an online credit scoring system for all customers of the whole Vietnamese
commercial banking system. Accordingly, the State Bank will play a central role, when
data is updated at a bank, it will also be updated immediately into the information system
of the state bank. Currently, the credit rating of customers, although it is complied with
the criteria of the SBV, but the implementation is done by each bank itself, there is no
general update data system in the whole banking system. Therefore, this scoring is still
subjective and has not had much impact on customers' sense of debt repayment.

To Commercial Banks
The cause of credit risk (i.e. poor credit quality) comes mainly from people. Therefore,
the first solution proposed by the author to increase credit quality is related to human
resources. Specifically, commercial banks need to have a solution for human resources,
first of all, banks must develop and complete a recruitment regulation and strictly comply
with this regulation, and before performing the operation, employees need to be trained in
both professionalism and professional ethics, specifically:
+ Focused training to provide foundation knowledge.
+ On-the-job training: hands-on work instructions.
+ Organize periodic and irregular professional knowledge checks for each title.
+ Organize training courses on professional ethics for bank staff, especially for
controlling, managerial and administrative positions.
+ To ensure compliance with regulations on professional ethics, besides communication,
commercial banks need to stipulate and enforce sanctions (should be strict to have a
deterrent effect) for violations related to ethics and professional ethics.
- Develop a system of internal documents and dispatches to provide detailed instructions
for each operation to ensure the correct, adequate and synchronous implementation of
regulations of the State Bank and the Government in credit granting activities and also
limit errors due to lack of synchronization, lack of information within the bank. When the
SBV or the Government issues new regulations, directions or orientations, commercial
banks need to organize communication and implementation guidance for the bank's
personnel, ensure the regulations, direction or orientation of the SBV and Government is
done right.
- Gradually shift credit operations mainly conducted by humans to machines and
technology to limit human-made errors, as before, credit application appraisal,
preparation of credit documents, etc. Credit records, which are manually manipulated by
19
employees on the computer, now need to be edited on the application program, all data is
updated, controlled from the beginning and performed and controlled on the program.
The use of technology in credit operations also helps to limit negative and fraudulent
actions in the credit granting process, so credit quality is more assured through
transparency in the appraisal and approval process, and limits errors in the preparation of
documents. The synchronous implementation of the above-proposed solutions will help
credit activities of Vietnamese commercial banks to limit risks, on that basis, create a
safe foundation for credit activities, even in the event of a crisis in the world economy
and affecting the Vietnamese economy.
In addition, by the end of 2021, the outstanding amount of bad debt has not been
completely resolved. The prolongation of the time for the final settlement of this bad debt
block hinders the operation and development of the banking system. Therefore, it is
necessary to quickly solve this bad debt block. Some solutions that, according to the
author, will help quickly resolve outstanding bad debts are:
- In addition to the main tool to solve bad debts, which is VAMC, it is necessary to
mobilize other resources in the society. Because the amount of bad debt outstanding is
still large, VAMC’s capital is limited, so it is difficult to completely solve bad debt
quickly. In order to mobilize the resources of society to solve bad debts, the author
believes that it should expand the scale of the debt trading market by allowing qualified
economic organizations to participate, especially foreign investors, as a potential channel.
In addition, the size of debt trading companies also needs to be commensurate to be able
to deal with the large bad debts of the whole Vietnamese banking system.
- Establishing a debt trading floor: According to the author, the debt trading floor will be
the place to introduce and provide the most complete and official information about debts
to investors. Like the stock exchange, the debt trading floor is responsible for developing
the trading infrastructure, setting standards for listing debt information, and managing
and developing market-making intermediaries. Investment organization, building
supervision mechanisms, and investor protection regulations. On the other hand, the
establishment of an exchange will increase the transparency and publicity of the debt
trading market.
20
APPENDIX 1: RESEARCH DATA SUMMARY TABLE (BANK INTERNAL
VARIABLES)
Bank Year NPL Ratio Bank size ROA LLP Ratio
LDR
ETA
NII Ratio
ABB 2012
2,83505 17,64445 0,86776
2,20523 65,27372 10,64954
3,22359
ABB 2013
6,74246 17,86951 0,24391
2,76347 63,63216 9,96825 10,29108
ABB 2014
3,96884 18,02712 0,17338
1,82393 57,57782 8,47164
ABB 2015
2,12199 17,98023 0,14179
1,24476 65,04390 8,99462 13,11106
ABB 2016
2,50527 18,12189 0,32885
1,47385 77,23723 7,87696 13,18834
ABB 2017
2,77026 18,25230 0,57848
1,58633 82,73618 7,24067 11,76316
ABB 2018
1,88607 18,31530 0,79436
1,24007 83,81665 7,63212 17,79954
ABB 2019
2,31041 18,44593 0,97573
1,29017 81,64357 7,64724 19,35095
ABB 2020
2,09152 18,57226 0,96046
1,11405 87,29100 7,65797 20,86944
ABB 2021
2,37155 18,57226 1,34057
1,18950 100,4914 10,07937 23,87921
ACB 2012
2,50058 18,98774 0,44470
1,46096 82,09846 7,16047 -3,45377
ACB 2013
3,02535 18,93110 0,49610
1,44415 77,61159 7,50557
ACB 2014
2,17775 19,00630 0,52993
1,35725 75,23534 6,90235 10,22725
ACB 2015
1,32110 19,12109 0,51040
1,14959 76,62507 6,34753
4,66343
ACB 2016
0,87903 19,26947 0,56732
1,11185 78,05044 6,01791
6,22729
ACB 2017
0,70658 19,46560 0,74499
0,93794 81,47246 5,63839 16,18447
ACB 2018
0,73469 19,61258 1,55983
1,11595 84,43863 6,38195 16,02485
ACB 2019
0,53680 19,75568 1,55205
0,93914 87,09677 7,36842 14,87123
ACB 2020
0,59559 19,91358 1,72648
0,95481 87,53541 7,96588 13,69079
ACB 2021
0,78623 20,08417 1,81949
1,64628 93,71718 8,50767 17,21141
AGB 2012
2,56000 20,23705 0,40319
1,59201 95,73797 5,61343
AGB 2013
2,35180 20,36250 0,24080
3,35493 94,39004 5,41741 10,61495
AGB 2014
2,73996 20,45354 0,23403
2,73996 85,12622 5,39310 11,16273
AGB 2015
1,96118 20,58951 0,27123
1,96118 82,59245 4,85913 15,12491
AGB 2016
2,06574 20,72573 0,29824
2,10515 86,49173 4,42166 14,35643
AGB 2017
2,04456 20,86555 0,34099
1,80998 87,36740 4,20925 15,57559
8,19349
9,65915
8,65158
21
AGB 2018
1,86050 20,86563 0,34097
1,84334 85,76004 4,20890 15,57559
AGB 2019
1,80261 20,98563 0,43760
1,41414 90,00000 4,46154 17,48554
AGB 2020
1,95057 21,09647 0,77444
1,79209 86,94256 4,76747 18,87489
AGB 2021
2,05370 21,17315 2,33616
2,05611 84,66946 4,66061 16,02662
BIDV 2012
2,69501 19,99922 0,53053
1,73996 112,16399 5,46520 10,36967
BIDV 2013
2,26051 20,12249 0,73871
1,57153 115,38288 5,84260 14,01946
BIDV 2014
2,03208 20,29301 0,76662
1,48599 101,18544 5,11598 16,07719
BIDV 2015
1,68000 20,56153 0,74962
1,25612 105,99583 4,97672 15,89900
BIDV 2016
1,99378 20,72963 0,61571
1,39072 99,67986 4,38353 16,94855
BIDV 2017
1,62237 20,90749 0,57770
1,30926 100,80236 4,06177 15,86003
BIDV 2018
1,90166 20,99561 0,57438
1,25462 99,90579 4,15460 16,72935
BIDV 2019
1,74543 21,12201 0,57369
1,30995 100,25448 5,21176 17,33539
BIDV 2020
1,75982 21,13979 0,47627
1,56930 98,99093 5,25136 20,94001
BIDV 2021
1,00669 21,28954 0,61539
2,16283 97,48151 4,90034 23,68987
CTG 2012
1,46690 20,03715 1,22528
1,10190 115,30611 6,67776
CTG 2013
1,00197 20,17226 1,00769
0,87705 103,23513 9,38196 10,11602
CTG 2014
1,11514 20,30963 0,86623
0,99268 103,69839 8,35687 14,70422
CTG 2015
0,91850 20,47414 0,73342
0,84555 109,15282 7,19838 15,63073
CTG 2016
1,05476 20,67046 0,71320
1,04210 101,05756 6,35767 12,67304
CTG 2017
1,13966 20,81408 0,68114
1,05008 105,01407 5,82299 13,95430
CTG 2018
1,58503 20,87550 0,46516
1,50399 104,73590 5,79298 13,57278
CTG 2019
1,15618 20,93895 0,76384
1,38417 104,75878 6,23471 14,69450
CTG 2020
0,94520 21,01701 1,02556
1,23717 102,52461 6,36715 16,87712
CTG 2021
1,26478 21,14957 0,92814
2,28140 97,31631 6,11454 20,51532
KLB 2012
2,92580 16,73765 1,88917
1,46511 91,00001 18,53974
1,54869
KLB 2013
2,47118 16,87760 1,46652
1,03149 91,16782 16,26281
1,57391
KLB 2014
1,95339 16,95551 0,76139
1,00913 81,62967 14,56076
1,59261
KLB 2015
1,12568 17,04719 0,65253
0,84966 80,76349 13,32168
2,02245
KLB 2016
1,06064 17,23163 0,39733
0,85898 86,35721 11,04688
6,82121
KLB 2017
0,83899 17,43522 0,54034
0,89075 94,49289 9,51487
3,83496
KLB 2018
0,95054 17,56053 0,54807
0,87355 100,03635 8,86354
8,94918
8,43171
22
KLB 2019
1,03050 17,74934 0,13243
0,89197 100,79854 7,42015
5,44209
KLB 2020
5,55675 17,86350 0,22052
0,85162 81,92511 6,84015
7,39242
KLB 2021
1,90996 18,24421 0,91893
0,96478 73,97334 5,58253
7,29446
MB
2012
1,84166 18,99122 1,31133
1,76258 63,25282 7,27092 10,60460
MB
2013
2,44587 19,02035 1,25485
2,01743 64,47475 8,31627 13,47415
MB
2014
2,72992 19,12848 1,23329
2,44881 60,00233 8,16010 16,77578
MB
2015
1,60661 19,22276 1,12642
1,62868 66,83467 10,39513 18,08076
MB
2016
1,31831 19,36967 1,11632
1,36019 77,37583 10,29328 16,30429
MB
2017
1,20402 19,57126 1,10455
1,15408 83,65495 9,36735 24,05062
MB
2018
1,35226 19,70805 1,67619
1,51839 88,12767 9,43155 28,99677
MB
2019
1,17252 19,83529 1,96084
1,29524 90,62013 9,69308 28,16564
MB
2020
0,97139 20,02879 1,72350
1,45969 95,92766 10,03322 32,56029
MB
2021
0,89335 20,23859 2,14632
2,40890 94,50538 10,14551 39,21193
MSB 2012
2,59332 18,51529 0,20597
2,59332 48,57413 8,26942
MSB 2013
2,70761 18,48941 0,30796
2,67510 41,85162 8,78734 12,54969
MSB 2014
5,15882 18,46344 0,13678
2,30936 37,18736 9,05030 15,31440
MSB 2015
3,41071 18,46289 0,11147
2,13999 44,86307 13,05348 12,31177
MSB 2016
2,24000 18,34386 0,15118
1,28713 60,98423 14,68588 29,53634
MSB 2017
2,18000 18,53614 0,10873
1,18419 63,70035 12,22565 27,97419
MSB 2018
2,99000 18,74109 2,75246
2,08066 75,19167 10,03142 23,45428
MSB 2019
2,03000 18,89068 2,30115
1,40642 77,77778 9,37500 21,36250
MSB 2020
1,93000 19,00847 3,09852
1,08078 88,63636 9,44444 24,41717
MSB 2021
1,66000 19,13199 3,91163
1,68891 105,55864 10,82058 37,84025
NAB 2012
2,47599 16,58861 1,12845
1,01666 78,46994 20,46981 11,83369
NAB 2013
1,47662 17,17525 0,46845
0,66091 84,58239 11,32129 24,12984
NAB 2014
1,39993 17,43432 0,50188
0,93065 81,84069 8,93258
6,96872
NAB 2015
0,91309 17,38420 0,54775
0,93491 85,62942 9,62680
5,63633
NAB 2016
2,93668 17,57325 0,07669
1,62043 70,53758 8,01154
7,39939
NAB 2017
1,94746 17,81261 0,43947
2,32034 91,18167 6,73597 13,85644
NAB 2018
1,54427 18,13379 0,78775
1,51921 93,77682 5,63568
2,95287
NAB 2019
1,97461 18,36609 0,77285
1,17501 95,47870 5,23869
5,20869
8,25439
23
NAB 2020
0,83409 18,71570 0,59536
0,95473 90,75619 4,91290
7,50030
NAB 2021
1,57144 18,84750 0,93604
1,24961 89,01649 5,23687
5,72367
OCB 2012
1,80644 17,12693 2,69992
1,80644 112,88313 13,92779 -2,92476
OCB 2013
1,01813 17,30579 2,59641
1,01813 105,56248 12,08950 -0,28808
OCB 2014
1,42602 17,48150 2,23419
1,42602 88,95964 10,27683
7,91810
OCB 2015
0,87192 17,71642 2,03470
0,87192 93,85784 8,54511
5,94956
OCB 2016
1,75424 17,97150 2,24624
0,86135 89,41746 7,38960
8,03897
OCB 2017
1,79436 18,24989 0,96895
0,83871 90,55965 7,28285
7,97910
OCB 2018
2,28766 18,42032 1,76166
1,00387 93,29680 8,80040 21,02384
OCB 2019
1,84164 18,58755 2,18537
1,01945 102,81838 9,73862 23,25502
OCB 2020
1,70834 18,84286 2,31744
1,06157 101,29530 11,43087 25,27129
OCB 2021
1,32242 19,03311 2,38763
1,09367 103,28539 11,81901 24,07208
SHB 2012
8,80662 18,57372 1,44783
2,19606 73,37733 8,15707
9,76298
SHB 2013
5,09416 18,78272 0,59166
1,55225 84,29794 7,21019
3,77195
SHB 2014
2,02475 18,94562 0,46780
1,00604 84,47434 6,19992
7,68861
SHB 2015
1,72162 19,13708 0,38844
1,08150 88,30759 5,49820
4,00045
SHB 2016
1,88000 19,29928 1,42089
1,10693 97,47861 5,49496
8,61473
SHB 2017
2,33000 19,47154 1,55100
1,43679 101,74498 5,13661 10,50460
SHB 2018
-1,3836 19,59402 0,51730
1,38359 96,34353 5,05219
6,25636
SHB 2019
-1,1806 19,71610 0,66197
1,18059 102,28553 5,06700
6,94029
SHB 2020
-1,1250 19,83818 0,63173
1,12495 100,67711 5,82443
8,90472
SHB 2021
-1,2797 20,04324 0,98838
1,27961 110,76395 7,01369 10,15506
STB
2012
2,04815 18,84017 0,65894
1,50167 89,64787 9,00531
6,82892
STB
2013
1,45610 18,89926 1,38130
1,22241 83,98809 10,57378
8,62697
STB
2014
1,18932 19,06150 1,16249
1,06934 78,50914 9,51683 13,59378
STB
2015
5,79744 19,49238 0,22187
1,21387 71,23393 7,56097 14,44982
STB
2016
6,91208 19,62071 0,02669
1,22277 68,18363 6,68385 15,71971
STB
2017
4,66690 19,72487 0,32067
1,23298 69,70141 6,30618 16,82631
STB
2018
1,58000 19,82196 0,44088
1,37269 73,44902 6,06648 17,25560
STB
2019
1,15000 19,93268 0,54122
1,34144 73,85155 5,89567 19,81512
STB
2020
1,69874 20,01504 0,54455
1,59097 79,50713 5,87925 19,78350
24
2021
1,50335 20,07149 0,65465
1,78313 90,76781 6,57459 23,96123
TCB 2012
2,69621 19,01433 0,42289
1,64827 61,14300 7,33993
TCB 2013
3,65172 18,89120 0,41171
1,68800 58,57321 8,69554 14,73278
TCB 2014
2,38313 18,99088 0,61170
1,19513 60,98237 8,47332 21,09873
TCB 2015
1,66960 19,07902 0,79168
1,04313 78,47731 8,52026 24,50033
TCB 2016
1,58000 19,28297 3,09225
1,04860 82,22363 8,26927 29,17299
TCB 2017
1,61000 19,41865 4,10793
1,17165 94,07981 9,92740 37,44001
TCB 2018
1,75282 19,58692 2,63997
1,49126 79,40798 16,13224 32,42453
TCB 2019
1,33356 19,76537 2,66516
1,26374 99,78610 16,17744 31,45655
TCB 2020
0,46669 19,90138 2,86223
0,79786 100,02377 16,97322 33,56217
TCB 2021
0,66041 20,15906 3,23460
1,07550 110,35376 16,35970 31,03167
VAB 2012
1,52550 17,01861 1,95951
1,52550 85,94646 14,35696 10,22053
VAB 2013
1,34056 17,11256 1,49229
1,34056 76,44451 13,27456
3,11122
VAB 2014
2,32585 17,38759 0,13345
1,19320 79,99077 10,21609
1,72634
VAB 2015
2,26360 17,55028 0,19572
1,12500 82,92912 9,35943 -2,58566
VAB 2016
2,14042 17,93398 -0,3331
1,34822 94,48884 6,53647
VAB 2017
2,67550 17,98115 -0,0279
0,95745 99,48792 6,38862 -1,07349
VAB 2018
2,56000 18,08229 0,16610
1,04909 90,70608 5,94023
2,43449
VAB 2019
2,45000 18,15211 0,27129
1,10465 88,89037 5,81093
5,55629
VAB 2020
2,32564 18,27599 0,43304
1,19339 80,65886 6,61501
9,89261
VAB 2021
1,90879 18,43096 0,65512
1,12304 79,55848 6,31259
8,42460
VCB 2012
2,40333 19,85521 1,05468
2,19466 84,79266 9,89906 15,33819
VCB 2013
2,72511 19,97976 0,92073
2,35161 82,56369 8,91503 19,34753
VCB 2014
2,31447 20,18547 0,78960
2,17825 76,58199 7,42266 23,21354
VCB 2015
1,84353 20,34201 0,78068
2,22390 77,34862 6,61377 23,26572
VCB 2016
1,50222 20,49514 2,11477
1,75491 78,04342 6,04827 22,20724
VCB 2017
1,14249 20,76576 2,01043
1,49292 76,69998 5,03715 20,91005
VCB 2018
0,98486 20,80422 1,34850
1,62906 78,79334 5,73441 23,35836
VCB 2019
0,78997 20,93283 1,50235
1,41782 79,13255 6,55913 21,00930
VCB 2020
0,62272 21,02001 1,37294
2,29139 81,36588 6,99345 22,80951
VCB 2021
0,63712 21,08836 1,52286
2,70369 84,62342 7,57419 24,27576
STB
7,90329
5,74198
25
VIB
2012
1,69435 17,99026 3,59355
1,69435 86,75399 12,87454
7,67893
VIB
2013
2,82055 18,15769 0,06536
2,62608 81,49626 10,38395 16,31652
VIB
2014
2,51419 18,20577 0,64798
2,32908 77,83344 10,53826 26,70579
VIB
2015
2,07040 18,25000 0,61804
1,57497 89,63129 10,21341 15,37843
VIB
2016
2,57516 18,46486 0,53746
1,68702 101,55033 8,36493 16,68494
VIB
2017
2,48758 18,62899 0,91287
1,18308 116,79854 7,13510 11,02119
VIB
2018
2,51891 18,75118 1,57648
0,91302 113,28748 7,66540 14,78267
VIB
2019
1,96327 19,03333 1,77011
0,99514 105,59246 7,27770 15,58850
VIB
2020
1,74460 19,31544 1,89734
1,03126 112,75108 7,34598 16,93676
VIB
2021
2,31750 19,55052 2,07089
1,19113 116,10456 6,87870 16,80094
VPB 2012
2,71869 18,45076 0,69428
1,03021 62,00762 6,51033
VPB 2013
2,80957 18,61846 0,83501
1,15239 62,58559 6,34016 10,79595
VPB 2014
2,53760 18,91760 0,76269
1,43296 72,33612 5,46364 12,19018
VPB 2015
2,69258 19,09168 1,22477
1,49119 89,66274 6,84442 13,94082
VPB 2016
2,90797 18,81312 2,65775
1,44461 116,87216 11,60178 13,57028
VPB 2017
3,39418 19,05441 3,41753
1,72304 136,77656 15,75682 20,72711
VPB 2018
3,49892 19,24707 3,21900
1,60693 129,91564 15,20760 24,21150
VPB 2019
3,42072 19,39298 3,12415
1,58801 120,20775 15,96431 19,99408
VPB 2020
3,41253 19,53281 3,42465
1,54623 124,58495 17,36158 22,09509
VPB 2021
4,57220 19,78495 2,93318
2,78397 146,90936 22,04980 32,81531
5,69036
APPENDIX 2: RESEARCH DATA SUMMARY TABLE OF MACROECONOMIC
VARIABLES
Bank Year GDP GROWTH INTEREST RATE EXCHANGE RATE INFLATION
ABB 2012
5,500
13,110
20,828
9,090
ABB 2013
5,550
12,680
20,933
6,590
ABB 2014
6,420
9,050
21,148
4,710
ABB 2015
6,990
9,870
21,698
0,880
ABB 2016
6,690
8,290
21,935
3,240
26
ABB 2017
6,940
6,110
22,370
3,520
ABB 2018
7,200
7,370
22,825
3,540
ABB 2019
7,150
8,530
23,050
2,790
ABB 2020
2,940
9,170
23,208
3,230
ABB 2021
2,589
6,750
23,160
1,840
ACB 2012
5,500
13,110
20,828
9,090
ACB 2013
5,550
12,680
20,933
6,590
ACB 2014
6,420
9,050
21,148
4,710
ACB 2015
6,990
9,870
21,698
0,880
ACB 2016
6,690
8,290
21,935
3,240
ACB 2017
6,940
6,110
22,370
3,520
ACB 2018
7,200
7,370
22,825
3,540
ACB 2019
7,150
8,530
23,050
2,790
ACB 2020
2,940
9,170
23,208
3,230
ACB 2021
2,589
6,750
23,160
1,840
AGB 2012
5,500
13,110
20,828
9,090
AGB 2013
5,550
12,680
20,933
6,590
AGB 2014
6,420
9,050
21,148
4,710
AGB 2015
6,990
9,870
21,698
0,880
AGB 2016
6,690
8,290
21,935
3,240
AGB 2017
6,940
6,110
22,370
3,520
AGB 2018
7,200
7,370
22,825
3,540
AGB 2019
7,150
8,530
23,050
2,790
AGB 2020
2,940
9,170
23,208
3,230
AGB 2021
2,589
6,750
23,160
1,840
BIDV 2012
5,500
13,110
20,828
9,090
BIDV 2013
5,550
12,680
20,933
6,590
BIDV 2014
6,420
9,050
21,148
4,710
BIDV 2015
6,990
9,870
21,698
0,880
27
BIDV 2016
6,690
8,290
21,935
3,240
BIDV 2017
6,940
6,110
22,370
3,520
BIDV 2018
7,200
7,370
22,825
3,540
BIDV 2019
7,150
8,530
23,050
2,790
BIDV 2020
2,940
9,170
23,208
3,230
BIDV 2021
2,589
6,750
23,160
1,840
CTG 2012
5,500
13,110
20,828
9,090
CTG 2013
5,550
12,680
20,933
6,590
CTG 2014
6,420
9,050
21,148
4,710
CTG 2015
6,990
9,870
21,698
0,880
CTG 2016
6,690
8,290
21,935
3,240
CTG 2017
6,940
6,110
22,370
3,520
CTG 2018
7,200
7,370
22,825
3,540
CTG 2019
7,150
8,530
23,050
2,790
CTG 2020
2,940
9,170
23,208
3,230
CTG 2021
2,589
6,750
23,160
1,840
KLB 2012
5,500
13,110
20,828
9,090
KLB 2013
5,550
12,680
20,933
6,590
KLB 2014
6,420
9,050
21,148
4,710
KLB 2015
6,990
9,870
21,698
0,880
KLB 2016
6,690
8,290
21,935
3,240
KLB 2017
6,940
6,110
22,370
3,520
KLB 2018
7,200
7,370
22,825
3,540
KLB 2019
7,150
8,530
23,050
2,790
KLB 2020
2,940
9,170
23,208
3,230
KLB 2021
2,589
6,750
23,160
1,840
MB
2012
5,500
13,110
20,828
9,090
MB
2013
5,550
12,680
20,933
6,590
MB
2014
6,420
9,050
21,148
4,710
28
MB
2015
6,990
9,870
21,698
0,880
MB
2016
6,690
8,290
21,935
3,240
MB
2017
6,940
6,110
22,370
3,520
MB
2018
7,200
7,370
22,825
3,540
MB
2019
7,150
8,530
23,050
2,790
MB
2020
2,940
9,170
23,208
3,230
MB
2021
2,589
6,750
23,160
1,840
MSB 2012
5,500
13,110
20,828
9,090
MSB 2013
5,550
12,680
20,933
6,590
MSB 2014
6,420
9,050
21,148
4,710
MSB 2015
6,990
9,870
21,698
0,880
MSB 2016
6,690
8,290
21,935
3,240
MSB 2017
6,940
6,110
22,370
3,520
MSB 2018
7,200
7,370
22,825
3,540
MSB 2019
7,150
8,530
23,050
2,790
MSB 2020
2,940
9,170
23,208
3,230
MSB 2021
2,589
6,750
23,160
1,840
NAB 2012
5,500
13,110
20,828
9,090
NAB 2013
5,550
12,680
20,933
6,590
NAB 2014
6,420
9,050
21,148
4,710
NAB 2015
6,990
9,870
21,698
0,880
NAB 2016
6,690
8,290
21,935
3,240
NAB 2017
6,940
6,110
22,370
3,520
NAB 2018
7,200
7,370
22,825
3,540
NAB 2019
7,150
8,530
23,050
2,790
NAB 2020
2,940
9,170
23,208
3,230
NAB 2021
2,589
6,750
23,160
1,840
OCB 2012
5,500
13,110
20,828
9,090
OCB 2013
5,550
12,680
20,933
6,590
29
OCB 2014
6,420
9,050
21,148
4,710
OCB 2015
6,990
9,870
21,698
0,880
OCB 2016
6,690
8,290
21,935
3,240
OCB 2017
6,940
6,110
22,370
3,520
OCB 2018
7,200
7,370
22,825
3,540
OCB 2019
7,150
8,530
23,050
2,790
OCB 2020
2,940
9,170
23,208
3,230
OCB 2021
2,589
6,750
23,160
1,840
SHB 2012
5,500
13,110
20,828
9,090
SHB 2013
5,550
12,680
20,933
6,590
SHB 2014
6,420
9,050
21,148
4,710
SHB 2015
6,990
9,870
21,698
0,880
SHB 2016
6,690
8,290
21,935
3,240
SHB 2017
6,940
6,110
22,370
3,520
SHB 2018
7,200
7,370
22,825
3,540
SHB 2019
7,150
8,530
23,050
2,790
SHB 2020
2,940
9,170
23,208
3,230
SHB 2021
2,589
6,750
23,160
1,840
STB
2012
5,500
13,110
20,828
9,090
STB
2013
5,550
12,680
20,933
6,590
STB
2014
6,420
9,050
21,148
4,710
STB
2015
6,990
9,870
21,698
0,880
STB
2016
6,690
8,290
21,935
3,240
STB
2017
6,940
6,110
22,370
3,520
STB
2018
7,200
7,370
22,825
3,540
STB
2019
7,150
8,530
23,050
2,790
STB
2020
2,940
9,170
23,208
3,230
STB
2021
2,589
6,750
23,160
1,840
TCB 2012
5,500
13,110
20,828
9,090
30
TCB 2013
5,550
12,680
20,933
6,590
TCB 2014
6,420
9,050
21,148
4,710
TCB 2015
6,990
9,870
21,698
0,880
TCB 2016
6,690
8,290
21,935
3,240
TCB 2017
6,940
6,110
22,370
3,520
TCB 2018
7,200
7,370
22,825
3,540
TCB 2019
7,150
8,530
23,050
2,790
TCB 2020
2,940
9,170
23,208
3,230
TCB 2021
2,589
6,750
23,160
1,840
VAB 2012
5,500
13,110
20,828
9,090
VAB 2013
5,550
12,680
20,933
6,590
VAB 2014
6,420
9,050
21,148
4,710
VAB 2015
6,990
9,870
21,698
0,880
VAB 2016
6,690
8,290
21,935
3,240
VAB 2017
6,940
6,110
22,370
3,520
VAB 2018
7,200
7,370
22,825
3,540
VAB 2019
7,150
8,530
23,050
2,790
VAB 2020
2,940
9,170
23,208
3,230
VAB 2021
2,589
6,750
23,160
1,840
VCB 2012
5,500
13,110
20,828
9,090
VCB 2013
5,550
12,680
20,933
6,590
VCB 2014
6,420
9,050
21,148
4,710
VCB 2015
6,990
9,870
21,698
0,880
VCB 2016
6,690
8,290
21,935
3,240
VCB 2017
6,940
6,110
22,370
3,520
VCB 2018
7,200
7,370
22,825
3,540
VCB 2019
7,150
8,530
23,050
2,790
VCB 2020
2,940
9,170
23,208
3,230
VCB 2021
2,589
6,750
23,160
1,840
31
VIB
2012
5,500
13,110
20,828
9,090
VIB
2013
5,550
12,680
20,933
6,590
VIB
2014
6,420
9,050
21,148
4,710
VIB
2015
6,990
9,870
21,698
0,880
VIB
2016
6,690
8,290
21,935
3,240
VIB
2017
6,940
6,110
22,370
3,520
VIB
2018
7,200
7,370
22,825
3,540
VIB
2019
7,150
8,530
23,050
2,790
VIB
2020
2,940
9,170
23,208
3,230
VIB
2021
2,589
6,750
23,160
1,840
VPB 2012
5,500
13,110
20,828
9,090
VPB 2013
5,550
12,680
20,933
6,590
VPB 2014
6,420
9,050
21,148
4,710
VPB 2015
6,990
9,870
21,698
0,880
VPB 2016
6,690
8,290
21,935
3,240
VPB 2017
6,940
6,110
22,370
3,520
VPB 2018
7,200
7,370
22,825
3,540
VPB 2019
7,150
8,530
23,050
2,790
VPB 2020
2,940
9,170
23,208
3,230
VPB 2021
2,589
6,750
23,160
1,840
32
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