Impact of Capital Structure on Risk-taking of Vietnamese Commercial
Banks
DAN THANH BUI
Faculty of Finance
Ho Chi Minh City University of Banking
39 Ham Nghi street, Nguyen Thai Binh Ward, 1st District, Ho Chi Minh City
VIETNAM
THANH HA DOAN
Faculty of Finance
Ho Chi Minh City University of Banking
39 Ham Nghi street, Nguyen Thai Binh Ward, 1st District, Ho Chi Minh City
VIETNAM
THI HONG NHUNG PHAM
Faculty of Finance and Accounting
Ho Chi Minh city College of Economics
33 Vinh Vien street, 2st Ward, District 10, Ho Chi Minh city
VIETNAM
HAI NAM PHAM
Faculty of Banking
Ho Chi Minh City University of Banking
39 Ham Nghi street, Nguyen Thai Binh Ward, 1st District, Ho Chi Minh City
VIETNAM
Abstract: - This study assesses the impact of capital structure on the risk-taking of Vietnamese commercial
banks in the period 2012–2020. The study uses the system GMM regression model (SGMM) to estimate the
results based on panel data collected by year from financial statements of 30 Vietnamese commercial banks.
The variable representing bank risk-taking is Z-score; the variables representing the capital structure of
commercial banks are customer deposits and non-deposit liabilities. Research results show that customer
deposits and non-deposit liabilities increase the risk-taking of commercial banks. From the findings of this
study, bank administrators will have a basis to decide on the appropriate capital structure and bring value to the
bank.
Key-Words: - capital structure, commercial bank, customer deposits, non-deposit liabilities, risk-taking, Z-
score.
Received: June 7, 2022. Revised: August 11, 2022. Accepted: September 2, 2022. Available online: September 21, 2022.
1 Introduction
Over the years, the banking industry has been
considered an essential industry. It plays an
important role in the financial system and economy
of each country, contributing to economic growth
and market stability. In addition, banks are also
considered as an active financial intermediary
channel, acting as an intermediary organization with
centralized functions, mobilizing temporarily idle
money and circulating it to where it is needed.
However, every business activity always faces
different risks. It so does the banking system, which
always faces various types of risks such as
bankruptcy risk, credit risk, liquidity risk, interest
rate risk, exchange rate risk. The financial risks
occurred, causing banks to fall into a state of a
liquidity shortage, business operations facing
difficulties, profits decreasing, making many banks
faced with the risk of collapse, people will lose
confidence in the banking system, leading to rushing
money withdrawals, switching to other investment
channels such as buying gold and foreign
currencies, which quickly leads to scarce of capital
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and other serious problems affecting the
development of the whole country [1] Batten and
Vo (2019). Once these risks occur regularly and
continuously, the bank is likely to lose capital,
profit, and decrease asset value, thereby directly
affecting the bank's operations and development [5]
Heffeman (2005). Enterprises in each industry have
their specific characteristics and have their unique
capital structure. Particularly for the banking sector,
a commercial bank is a remarkable enterprise in
which its capital is financed mainly from external
capital sources because the total assets financed are
mainly debt [3] Berlin (2011). Thus, capital
mobilization is one of the most basic and important
operations for any bank, considered an input in the
bank's business operations [18] Sealey and Lindley
(1977). However, commercial banks always operate
with a high level of risk and create risks for other
businesses in the economy, having a strong
influence on the entire economy in both positive and
negative terms [4] Gropp and Heider (2010).
In Vietnam, in recent years, the banking industry
has experienced rapid growth in both size and
operations. Besides, Vietnam is a country with an
open economy, so it is inevitable that the influence
of the world economy can be avoided, so to ensure
commercial banks are safe for business activities in
a volatile environment; the decision on the capital
structure is an important issue not only in
enterprises but also in the banking sector [8] Le et
al. However, the banking system is gradually
revealing its weaknesses, and the risk of system
breakdown is becoming more and more obvious.
The credit growth rate is high, and the focus on
lending to real estate and securities is very risky,
leading to the consequences are non-performing
loans increase rapidly, non-performing loans ratio is
high and difficult to handle, short-term capital
structure is the main. In contrast, medium and long-
term loans account for a large scale. Therefore, it
requires Vietnamese commercial banks to make
drastic changes to adapt to the internal and external
challenges of the economy [1] Batten and Vo
(2019). A reasonable capital structure not only helps
commercial banks achieve reasonable profits and
saves costs but also serves as a buffer against the
risk of bankruptcy when the economy has strong
fluctuations. In addition, building a reasonable
capital structure also helps the banks’ managers to
make decisions to prevent and limit risks at
commercial banks in Vietnam, which is a crucial
and urgent issue, to avoid any problems,
contributing to improving the reputation and
creating competitive advantages of commercial
banks [15] Pham and Bui (2019). Many empirical
studies have shown an impact of capital structure on
the risk-taking of commercial banks. However, the
results of the studies show that the direction of
impact of capital structure on the risk of commercial
banks is different. Some studies show that financial
leverage increases bank risk-taking [8] Le et al.,
2020; [15] Pham and Bui (2019); [13] Nguyen and
Nguyen (2015) or reduces the bank's risk-taking
[17] sSaif-Alyousfi and Saha (2020); [9] Mercan
(2021). The different results of domestic and foreign
economists may be due to the fact that studies
conducted in space, time, and different approaches
lead to different conclusions. In addition, the studies
use criteria such as equity to total assets, total debt
to total assets, short-term debt to total assets, and
long-term debt to total assets to represent the capital
structure of commercial banks. However, such
indicators are similar to those reflecting the capital
structure of non-financial enterprises. Meanwhile,
the capital structure of commercial banks is very
different from the capital structure of non-financial
enterprises [4] Gropp and Heider (2010); [19]
Sibindi (2018), showing the limitation of previous
studies. Therefore, the new point in this study
compared with previous studies is that the author
uses customer deposits and non-deposit liabilities to
represent the capital structure of Vietnamese
commercial banks. The results of the study are the
basis to help bank managers plan a more suitable
capital structure to better control risks and reduce
the risk of losses for banks. According to the
author's review, in the current studies in Vietnam in
the period from 2012 to 2020, no studies have been
conducted to measure the influence of capital
structure and other factors on the risk Vietnamese
commercial banks in this period. Therefore, this
study will help to build the foundation for future
research. In addition, this helps managers identify
the impact of capital structure on the risk of
Vietnamese joint stock commercial banks.
The following sections of the paper research include
part 2: theoretical background, part 3: research
methods and models, part 4: research results and
discussion, part 5: conclusion and policy
suggestions.
2 Literature Review
2.1 Theories
Trade-off theory
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This theory was initially proposed by [10]
Modigliani and Miller (1958) based on tax benefits,
bankruptcy costs, and agency costs, where there is
no offsetting cost of debt; therefore, firms can only
use debt financing in their capital structure. Hen, [7]
Kraus and Litzenberger (1973) argued about the
cost-benefit trade-off of debt to provide an optimal
capital structure. In this model, borrowing has an
advantage over equity because interest provides the
benefit of tax avoidance. However, borrowing can
increase financial risk, resulting in increased costs
compared to equity financing. Therefore, the trade-
off theory suggests that a bank's debt ratio is also
higher due to its advantage over equity, which in
turn leads to a higher risk for the bank.
Pecking order theory
Developed by [12] Myers and Majluf (1984) and
[11] Myers (1984), also called information
asymmetry theory. The idea behind this theory is
that a company follows an order of preference for
different sources of funding, from internal to
external sources. The theory is based on the
assumption that the cost of adverse selection is the
result of risk from external financing due to
asymmetric information, management optimism, or
both. To minimize the cost of adverse selection,
firms prefer to finance capital from internal sources
first and then external sources. If outside funding is
needed, their first preference is to issue debt and
then, a less popular option, issue a mix of securities
as a secondary debt and then equity as a source of
final funding.
Agency theory
According to [6] Jensen and Meckling (1976),
optimal capital structure is obtained by trading off
the management costs of debt with the benefits of
debt. Agency costs refer to costs incurred when
there is a conflict between executives, bondholders,
and company owners. Executives can choose
investments that are less riskier and have lower debt
levels to reduce the risk of bankruptcy. This may be
contrary to the desire of shareholders to maximize
the value of the company. Managers use the money
to expand their businesses, making their positions
more stable, salary and power more excellent, and
therefore higher debt ratio and risk. Executives are
also riskier in taking short-term profits to strengthen
their position. Therefore, agency theory implies that
a higher debt ratio of the bank creates a more
significant benefit for the manager and the risk of
the bank is also higher.
2.2 Empirical studies
[9] Mercan (2021) studies the factors affecting the
risk of commercial banks in George in the period
2006 - 2014. The study uses the OLS regression
method, unbalanced panel data by the method of
Random Effects Models and Fixed Effects Models.
Research results show that financial leverage
reduces bank risk-taking. In contrast, the size of the
bank, and the bank loan have the effect of increasing
the bank's risk-taking.
[16] Pricillia (2015) explores the factors affecting
the risk of Indonesian banks. Using a 2SLS
regression method, the study found seven factors
influencing bank risk-taking in Indonesia. Those
factors are capital adequacy ratio (CAR), financial
leverage, profit expectations, expectations of
inefficiency, industry concentration, the importance
of banks in the Indonesian economy, and the
movement of Bank Indonesia. In which financial
leverage increases the bank's risk-taking.
[17] Saif-Alyousfi and Saha (2020) conduct a study
to examine the impact of bank-specific financial
structures and macroeconomic factors on the risk-
taking and the profitability of banks in different
financial institutions in the Gulf Cooperation
Council (GCC) economy for the period 1998 to
2017. Using the SGMM method, the research results
show that banks with low financial leverage and
high loan growth rates have higher risk-taking and
returns. Larger commercial banks are less risky and
more stable, with better returns than smaller banks
before the global financial crisis. Islamic banks
perform better in terms of fee income,
capitalization, liquidity, asset quality, and have a
higher level of market concentration than
conventional banks.
The study by [8] Le et al. (2020) was conducted to
evaluate the factors affecting the risk-taking of
Vietnamese commercial banks in the period 2014 -
2018 by collecting data sets of 19 Vietnamese
commercial banks. Using panel data regression
models: OLS, fixed effect regression model, random
effects models. Research results show that NIM
increases the risk-taking of banks. The factors of
financial leverage, bank size, and bank loan reduce
risk-taking.
Research by [14] Nguyen and Duong (2020)
explores the impact of factors on the risk-taking of
27 Vietnamese commercial banks in the period 2010
- 2017. Using the GMM method, the author
concludes that financial leverage, non-performing
ratio, loan loss provisions, bank loans, bank size,
and GDP growth are factors that increase risk-
taking. In contrast, the rate of return, and inflation
have the effect of reducing risk-taking.
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[15] Pham and Bui (2019) conducted a study to
assess the impact of factors on risk-taking of 21
Vietnamese commercial banks in the period 2010 to
2018. Using the GLS method, the results show that
banks having higher financial leverage will increase
risk-taking. In addition, concentrated large
shareholders, institutional shareholders, and state
shareholders reduce risky behaviors that have a risk-
reducing effect
2.3 Research Hypothesis
Customer deposits
Commercial banks have large financial leverage
with capital mainly from outside. Customer deposits
indicate a bank's ability to raise capital regularly.
This source is the largest and most important source
of capital in the total capital structure of commercial
banks. As credit intermediaries, banks mobilize
deposits from depositors and lend money to those
short of cash, playing a key role in helping banks
maintain business operations, expand the scale, and
diversify banking services and credit products.
When the bank builds a reputation, good product
quality, and service, it will attract a large number of
depositors, thereby helping the bank to lend as well
as reach customers in other products and services.
However, when banks use too high financial
leverage, which can reduce the financial stability of
commercial banks, the risk-taking of commercial
banks will increase. Therefore, the author proposes
the hypothesis:
Hypothesis H1: Customer deposits have a negative
relationship with Z-score.
Non-deposit liabilities
Banks mobilize this source of capital at a lower and
infrequent cost than deposits to finance credit and
investment portfolios, meeting the bank's liquidity
needs. However, this is a highly stable source of
capital and is not required reserve. Moreover,
similar to customer deposits, when banks increase
non-deposit liabilities in total capital, their financial
leverage will also increase [4] Gropp and Heider
(2010), thereby increasing risk-taking for the bank.
Therefore, the author proposes the hypothesis:
Hypothesis H2: Non-deposit liabilities has a
negative relationship with Z-score.
3 Research Models and Methodology
3.1 Research Methodology
The study uses secondary data collected from
Vietnamese commercial banks' published annual
financial statements for the period 2012–2020. The
author has selected 30 banks as the research sample
based on the collected data. From there, calculate
and classify the independent variables, control
variables, and dependent variables of individual
banks. The data were then aggregated and formatted
as panel data for SGMM regression. The SGMM
regression method provides robust estimates for
multicollinearity, heteroskedasticity, and
endogenous or autocorrelation problems.
To show that the results obtained from the SGMM
method are reliable, the study conducted AR(2) and
Sargan tests. AR(2) considers the problem of
autocorrelation with the hypothesis H0: there is no
autocorrelation, and Sargan considers the validity of
the instrumental variable with the hypothesis H0:
the instrumental variables are not correlated with the
residuals of the model. In case both tests have a p-
value greater than 10% significance level, it shows
that the research results obtained from SGMM
method are reliable and can be analyzed.
3.2 Research Model
Based on the theoretical framework of capital
structure and inheriting the research of the reviewed
authors, the author proposes a specific research
model as follows:
Z-scoreit = β0 + β1DEPit + β2NONDEPit +
β3SIZEit + β4LOANit + β5OPEit + β6INFit+
β7GGDPit + 𝑢𝑖𝑡
where:
Z-scoreit: Risk-taking of bank i year t, with the Z-
score used as a proxy. However, the Z-score is often
skewed, so this variable will be corrected by taking
the natural logarithm of the Z-score.
DEPit: Customer deposits of the bank i year t,
representing the capital structure of the bank,
measured by dividing customer deposits by total
assets.
NONDEPit: Non-deposit liabilities of bank i year t,
representing a bank's capital structure, measured by
dividing non-deposit liabilities by total assets.
SIZE, LOAN, OPE, INF, GGDP are control
variables, detailed in Table 2.
For the banking industry, the Z-score is used to
assess a bank's probability of bankruptcy or default
risk. The Z-score considers a bank's likelihood of
bankruptcy in terms of the interaction between its
ability to generate income (ROA), business shocks
through ROA variability), and the bank's available
capital to deal with these shocks. The nature of the
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Z-score shows that the higher the Z-score, the more
resources the bank has (profit & capital) to absorb
shocks and cope with fluctuations in the business
better, so the ability to not low repayment (risk of
default).
4 Research Results and Discussion
4.1 Descriptive Statistics
Descriptive statistical results from Table 3 show an
overview of the financial situation of Vietnamese
commercial banks in the period 2012 - 2020. The
author uses the natural logarithm of the Z-Score to
represent the risk-taking of commercial banks in
Vietnam, through which we see that the variable
representing the level of risk-taking is the average
Z-score of 30 Vietnamese commercial banks in the
period 2012-2020 is 4.1249, with the lowest and
highest value are 1.2208 and 7.9168 respectively
and the standard deviation is 1.0798..
For the variables showing the capital structure of
commercial banks, the Customer Deposit (DEP)
variable has an average value of 67.4% and varies
between 17.46% and 92.82%, showing that
customer deposits of banks account for the majority
of the total capital of Vietnamese commercial banks.
Besides, Non-Deposit liabilitites (NONDEP) on
average accounted for 23.22%, the lowest value was
1.61%, the highest was 50.62% with a standard
deviation of 0.1018. That shows that Non-Deposit
liabilities plays an important role in the capital
structure of Vietnamese commercial banks. Control
variables belonging to bank characteristics include
Bank size (SIZE), bank loan (LOAN), and
Operating expenses (OPE). There is a significant
difference in bank size between banks, as shown by
the significant standard deviation (1.14). The
average size of banks is 32.4212, the most
significant value is 34.9553, and the smallest is
29.4911. The average bank loan (LOAN) of banks
was 56.85, varying between 21.62% and 78.80%.
Operating expenses (OPE) fluctuate between 0.67%
and 6.92% and have an average value of 1.71%.
The correlation coefficient matrix between the
variables in the model is presented in Table 4.
The correlation between the variables in the model
is shown through the correlation coefficient matrix
in Table 4, which can generally evaluate the
correlation
Table 1. Independent and dependent variables in the model
Table 2. Control variables used in the model
SIZE
Logarithm of total assets
LOAN
Bank loans/total assets
OPE
Operating expenses/total assets
INFLAT
Yearly inflation rate
GGDP
Yearly GDP growth rate
Var.
Previous studies
Expected
results
Calculation
Dependent
variable
Mercan (2021), Pricillia (2015), Saif-
Alyousfi va Saha (2020), Le et al.
(2020), Nguyen and Duong (2020),
Pham va Bui 2019)
𝑍 − 𝑠𝑐𝑜𝑟𝑒
=𝑅𝑂𝐴 + 𝐸/𝐴
𝛿(𝑅𝑂𝐴)
Independent
variables
Gropp and Heider (2010), Sibindi
(2018)
-
Customer
deposits/Total
assets
Gropp and Heider (2010), Sibindi
(2018)
-
Non-deposit
liabilities /Total
assets
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Table 3. Descriptive statistics of research variables in the period 2012 – 2020
Var.
Number of
obs.
Mean
Std.
Min
Max
lnZ-score
233
4.1249
1.0798
1.2208
7.9168
DEP
248
0.6740
0.1139
0.1746
0.9282
NONDEP
248
0.2322
0.1018
0.0161
0.5062
SIZE
248
32.4213
1.1431
29.4911
34.9553
LOAN
248
0.5685
0.1134
0.2162
0.7880
OPE
248
0.0171
0.0068
0.0067
0.0692
INFLAT
261
0.0422
0.0230
0.0063
0.0921
GGDP
261
0.0592
0.0123
0.0291
0.0708
Source: Calculation results from STATA software
Table 4. Matrix of correlation coefficients between variables
lnZSCORE
DEP
NONDEP
SIZE
LOAN
OPE
INFLAT
GGDP
lnZSCORE
1.00
DEP
0.17
1.00
NONDEP
-0.10
-0.93
1.00
SIZE
0.12
0.21
0.01
1.00
LOAN
0.04
0.46
-0.44
0.28
1.00
OPE
-0.33
-0.14
-0.04
-0.23
0.08
1.00
INFLAT
-0.27
-0.25
0.15
-0.22
-0.27
0.16
1.00
GGDP
0.06
0.05
-0.02
-0.03
0.04
0.01
-0.29
1.00
Source: Calculation results from STATA software
between the variables in the research model. The
correlation coefficient indicates a linear
relationship between two variables, regardless of
whether one variable depends on the other. The
larger the correlation coefficient, the stronger the
relationship between the two variables, and vice
versa, when the correlation coefficient is low, the
relationship between the two variables is not
strong. If the correlation coefficient between
variables in the regression model exceeds 0.8, it
will likely lead to high multicollinearity in the
estimated model. Then, the sign of the regression
coefficients in the model may be changed, leading
to skewed research results. In general, the
correlation coefficient between all pairs of
variables in the regression model has an absolute
value of less than 0.8. Therefore, multicollinearity
is not a severe problem affecting the estimated
results of the model.
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The results from Table 4 show that, for the
independent variables DEP and NONDEP, the
correlation coefficient is -0.93, showing a high
correlation between these two variables. Besides,
the correlation coefficients of the control variables
are all less than 60%, that is, the control variables
in the model have low correlation coefficients.
Therefore, it can be concluded that there is no
correlation of control variables in the model.
4.2 Results
The results of the estimation by SGMM method
show that the model has statistical significance, the
results of testing heteroskedasticity, autocorrelation
are overcome, p-value of AR(2) = 0.2828 >0.05.
Sargan's test shows that the SGMM model is
suitable by showing p-value = 0.9464 > 0.05.
Table 5. Regression results for dependent variable lnZ-score by SGMM method
Coef.
Std.
p>t
95% Conf. Interval
DEP
-14.4764
8.0236
0.071
-30.2025
1.2496
NONDEP
-13.6754
7.4388
0.0660
-28.2554
0.9044
SIZE
-0.0554
0.1455
0.7030
-0.3406
0.2297
LOAN
2.9136
1.008
0.0040
0.9376
4.8896
OPE
-101.0606
40.5336
0.1210
-180.5005
-21.6161
INF
-11.9292
2.5777
0.0000
-16.9811
-6.8772
GGDP
-1.1872
3.9983
0.7607
-9.0237
6.6493
con_
17.4721
4.7418
0.0000
8.1798
26.7644
Sargan test
0.9464
AR(2) p-value
0.2828
Source: Calculation results from STATA software
The results of the estimation by SGMM method
show that the model has statistical significance, the
results of testing heteroskedasticity, autocorrelation
are overcome, p-value of AR(2) = 0.2828 >0.05.
Sargan's test shows that the SGMM model is
suitable by showing p-value = 0.9464 > 0.05.
The regression coefficient of the independent
variable Customer deposits (DEP) representing
commercial banks' capital structure is -14.4764,
with a negative value, which means that it has a
negative effect on Z-score, which is positive for the
ability of bankruptcy of Vietnamese commercial
banks. This result shows that banks with higher
customer deposits will reduce the Z-score. In other
words, the higher a bank has a customer deposit
ratio, the higher the risk that banks face. This result
can be explained that if the bank receives more
deposits, the total assets will increase, putting
pressure on the effective use of the mobilized
capital. As a result, banks may be able to fund
portfolios or extend credit that are inefficient and
high-risk, leading to an increased risk of
bankruptcy. This result is consistent with the
proposed hypothesis, trade-off theory, agency
theory, and studies by [16] Pricillia (2015), [2]
Bhagat et al. (2015), [14] Nguyen and Duong
(2020), [8] Le et al. (2020), [15] Pham and Bui
(2019), [13] Nguyen and Nguyen (2015), [9]
Mercan (2021) but contrary to the research results
of [17] Saif-Alyousfi and Saha (2020).
The next variable representing the capital structure
of commercial banks is Non-Deposit liabilities
(NONDEP). Unlike customer deposits, non-deposit
liabilities has a negative effect on Z-score in the
regression model. This result shows that the higher
the bank's non-deposit liabilities, the lower the Z-
score of the bank. In other words, when non-
deposit liabilities is high, the bank's risk-taking will
increase. When commercial banks mobilize more
with non-deposit liabilities such as issuing bonds,
certificates of deposit, bank promissory notes, the
bank's financial leverage also increases
accordingly. Banks can use this borrowed money to
invest in high-risk portfolios or lend with long
terms for higher returns and higher risks, leading to
increased systemic risk. This result is consistent
with the proposed hypothesis, trade-off theory, and
agency theory and studies by [16] Pricillia (2015),
[9] Mercan (2021), [2] Bhagat et al. (2015), [14]
Nguyen and Duong (2020), [8] Le et al. (2020),
[15] Pham and Bui (2019), [13] Nguyen and
Nguyen (2015) but contrary to the research results
of [17] Saif-Alyousfi and Saha (2020).
For control variables, bank size (SIZE) has the
effect of reducing the lnZ-score. As banks increase
in size, but governance cannot keep up, banks may
face a higher risk of bankruptcy.
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Bank loan (LOAN) increases the LnZ-score, which
reduces the risk of bankruptcy of banks. Banks
with high lending rates also receive customers'
trust, thereby maintaining financial stability for the
bank. Operating expense (OPE) has the effect of
increasing the bank's risk. Inefficient management
of expenses to conduct banking activities will make
the bank unable to balance its profit. Costs that are
too high will reduce profits, putting the bank at risk
if this situation persists for a long time.
5 Conclusion and Policy Implications
The study was conducted to evaluate the effect of
capital structure on the risk-taking of Vietnamese
commercial banks in the period 2012 to 2020. The
variable representing bank risk-taking is Z-score,
and the variable representing the structure of
commercial banks is customer deposits and non-
deposit liabilities. By SGMM regression method,
the research results show that customer deposits
and non-deposit liabilities have an impact on
increasing the bank's risk-taking. The research
results will help state management agencies, and
bank administrators have a complete view of the
capital structure of each bank, assess their capital
management capacity, recognize the impact of
capital structure on the risk of banks, see the
importance of capital structure planning for the
stability of the banking system. From the above
research results, the author proposes some
recommendations for commercial banks and the
State Bank of Vietnam, specifically as follows:
Firstly, it is necessary to use customers' deposits
more effectively, and avoid taking short-term
capital for medium- and long-term loans, which
means the mismatch between loan and deposit
terms, especially small banks with low capital,
which can easily cause bankruptcy risk-taking for
banks. Research results show that customer
deposits have the effect of increasing risks for
commercial banks; therefore, it is important to
prioritize mobilizing deposits with low-interest
rates, avoiding the phenomenon of raising interest
rates too high to raise deposits. Vietnamese
commercial banks, especially small-sized banks,
often use high-interest rates to attract depositors.
However, this poses many risks for the bank itself
because in order to ensure profits and cover
expenses, banks are forced to increase lending
interest rates, making it difficult for customers to
borrow capital in the future, repayment of principal
and interest, causing instability to the entire
banking system. Therefore, using interest rate tools
is not an appropriate method for attracting
depositors, but banks need to actively seek and
mobilize deposits at a low cost.
Second, it is necessary to make more efficient use
of customer deposits. Vietnamese commercial
banks need to make a more thorough and
reasonable appraisal when lending to ensure the
quality of the bank's assets as well as the quality of
its loans. When a bank's assets increase but asset
quality deteriorates, it will lead to long-term
consequences such as the inability to recover
capital to meet customers' withdrawal needs,
increased liquidity risk, and default risk.
Third, commercial banks need to develop an
appropriate business strategy, and need to more
closely appraise investment projects as well as
long-term loans. Recently, banks have increased
strongly buying corporate bonds, especially
businesses in real estate and securities. Because
these businesses often offer very high-interest rates
on issued bonds, banks can reap large profits from
investing in these bonds.
However, the risks that banks face are very high
because the issuance of corporate bonds still has
many shortcomings, many major mistakes related
to the issuance of bonds, and the use of capital by
enterprises. Therefore, in order to reduce risks,
banks need to limit investment in corporate bonds;
when investing, it is necessary to be more cautious
and evaluate investments in corporate bonds more
carefully; It is necessary to focus on using the
mobilized capital to provide credit for production
and business activities, creating prosperity for the
economy.
Fourth, the State Bank of Vietnam should have a
mechanism to increase competition in temporarily
allocating idle budget funds deposited at
commercial banks. This source is a huge source of
capital, but currently, it is being deposited mainly
at state-dominated banks, creating inequality
among commercial banks. Therefore, this capital
needs to be reallocated throughout the banking
system based on competition mechanism, reducing
barriers created when bidding for this capital
source such as conditions on total asset size, total
equity, credit quality, and business results of each
bank.
Although the study has collected data from a
sample of 30 Vietnamese commercial banks in the
period from 2012 to 2020, the sample does not
include other types of banks, such as foreign banks
and joint venture banks. It limits the conclusions
that can be drawn from the estimation results and
does not cover the entire banking system. Further
studies need to supplement the data collected,
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2023.20.12
Dan Thanh Bui, Thanh Ha Doan,
Thi Hong Nhung Pham, Hai Nam Pham
E-ISSN: 2224-2899
120
Volume 20, 2023
thereby improving the quality and quantity of the
data.
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
Ha Thanh Doan built research framework.
Daan Thanh Bui analyzed research results.
Thi Hong Nhung Pham and Hai Nam Pham
conducted research overview, research model, data
collection.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2023.20.12
Dan Thanh Bui, Thanh Ha Doan,
Thi Hong Nhung Pham, Hai Nam Pham
E-ISSN: 2224-2899
121
Volume 20, 2023
Conflict of Interest
The authors have no conflicts of interest to declare
that are relevant to the content of this article.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
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Creative Commons Attribution License 4.0
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