La danghuyngan sum

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INTRODUCTION

longer appropriate in many cases. From the above reasons, the author chose the
thesis titled "Developing a default warning model for commercial joint stock banks
in Vietnam" to contribute to solving the issues presented by theories and reality.

1. Rationale
Banking system plays an important role in the economy, it is considered as
“vascular system” of the whole economy. As an inseparable factor in the
activities of commercial banks in the market, risk is always contained in any
banking activity. It can lead to bigger damages to the economy than other
business and the cost of reparation is huge. The early warning of default helps to
avoid the bank default, to minimize the loss of depositors, the deposit insurance
companies and the economy. The default of an inefficient bank can create a chain
failure in the banking system and badly affect the sustainable development of the
system. Therefore, it is important for to find out soon which banks are in
financial trouble and at high default risk in order to prevent crisis in the banking
system, and to maintain the stability of the financial market and macroeconomic.
The remarkable development of the system of joint stock commercial banks
in the period of 1991-1996 and the following period of 2006-2010, has contributed
significantly to the country's economic development. Apart from various
achievements, joint stock commercial banks have however exposed their
shortcomings and weaknesses. Many of them have become insolvent by the end of
2011. The Scheme of Credit Institutions Restructuring for the period of 2011-2015,
thus, was issued. The Resolution of the 3rd Plenum (XI Session) affirmed that one
of three pilars of economic restructuring is the financial system restructuring with
the banking system at the center. To accomplish a successful result, it is important to

identify and classify inefficient banks at the risk of default.
So far in the world, there have existed many theories and warning models of
default crisis such as: Univariate Analysis, Multiple Discriminant Analysis (MDA),
Logit Analysis (LA), Probit Analysis (PA), etc. The recent models, such as Artificial
Neural Networks (ANNs), Decision Tree (DT), Trait Recognition (TR), etc. have
been applied in default warning and have promised good results. The studies also
show that each method and model has its own advantages and disadvantages; each
model applied in different countries or different regions provides different variants.
It depends on the economic conditions of each country and each region. Many
models have been created to explain the causes as well as to forecast and to prevent
debt crisis. However, the unpredictable default of banks and financial institutions at
increasing scale and impact shows that default warning models should be paid
attention to and improved. Significant socio-economic changes, the unpredictability
of natural and socio-economic events make traditional and current methods no

2. Purpose
The purposes of the thesis are as follows:
- Develop and select the system of indicators applied in the evaluation of
the probability of default of joint stock commercial banks.
- Develop the empirical model of risk warning in Vietnamese joint stock
commercial banks.
- Propose some solutions to limit the default risk of Vietnamese joint stock
commercial banks.
Research questions:
- In Vietnam's context, what factors could characterize bank default; what
factors, indicators may affect the default risk of Joint stock commercial banks and
how?
- Each bank has specific characteristics making their own probability of
default. How to point out the differences?
- What method and model of default warning should be applied to

Vietnamese joint stock commercial banks?
- Implications for policy drawn from the model?
3. Object and scope of the thesis
- Research object: Object of this thesis is default risk, model of default
warning in Vietnamese joint stock commercial banks.
- Research scope: The research has been carried out on Vietnamese
joint stock commercial banks includes 35 Joint stock commercial banks,
including joint stock banks in which the State holds controlling shares such
as BIDV, MHB, Vietinbank and VCB. The study has been done in the period
from 2010 to 2015.
4. Research method
In order to be in line with the content, requirements and research objects,
the thesis applies the quantitative analysis and qualitative analysis. Some applied
models are Logit Analysis with array data, neural network model and decision
tree model to create a risk warning model for Vietnamese joint stock commercial
banks.


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The data in this thesis is collected from reports of State Bank of Vietnam,
the audited financial statements of Joint stock commercial banks in the period of
2010-2015.

Non-parametric models: Artificial Neural Networks, Decision Trees, character
analysis, genetic algorithms, etc.

5. The scientific and practical significance of the thesis

- The thesis builds the theoretical basis of the default warning model for
joint stock commercial banks.
- The thesis builds and selects the system of indicators of default warning
to be applied in banking system. Identification of factors and characters affecting
the default risk in Joint stock commercial banks.
- Quantifying the specific characteristics of each bank which affects the
probability of default.
- Develop a model of default warning for joint stock commercial banks.
- Propose some solutions to reduce the default risk of joint stock
commercial banks based on the analysis of the thesis.
6. Composition of thesis
Apart from the introduction, conclusions, appendices, tables and lists of
references, the content of the thesis is divided into 5 chapters as follows:
Chapter 1: Overview of bank default
Chapter 2: Theoretical background on default in commercial banks
Chapter 3: Situation of operations, default risk of Vietnamese joint stock
commercial banks in the period of 2009-2015.
Chapter 4: Creation of a default warning model for commercial banks in
Vietnam.
Chapter 5: Conclusions and policy recommendations.
CHAPTER 1: OVERVIEW OF BANK DEFAULT
1.1.

Definition of default and default in commercial banks
The definition of default, default in commercial banks, and the
consequence of bank default.

1.2.

Overview of international studies on default and bank default

1.2.1.

Overview of representative models and studies on default

Most of the current default warning studies in the world focus on two
branches: Parametric models and methods of analysis: Univariate Analysis,
Multivariate Analysis, Logit Analysis, Probit Analysis, Survival Analysis, etc;

Univariate Analysis: The main content of the univariate analysis in default
warning studies is: the examination of individual factors and comparison the
factors between two groups of insolvent companies and non-insolvent ones. In
case that the financial factors show signs of difference between these two groups,
they are used as predictors. The studies applying univariate analysis are: studies
by FitzPatrick (1932), Smith and Winakor (1935), Merwin (1942), etc. A widelyapplied study was the one by Beaver published in 1966. The advantages of the
univariate analysis method are: simplicity, quick and convenient application and
high rate of prediction. However, this method exposes three shortcomings.
To avoid the disadvantages of the univariate analysis, many researchers
applied the Multiple Discriminant Analysis (MDA) with Edward Atlman (1968).
as the representative author. Based on the data of bankrupt enterprises in the
United States, he identified the discriminant function which was widely used
later. Altman's MDA in 1968 became, for many years, an influential model to the
studies of default warning, especially the studies prior to 1980 such as the work
of Deakin (1972), Blum (1974), Altman and Edward, Haldeman, Narayanan
(1977), Norton and Smith (1979), Karel and Prakash (1987), etc. Howver when
the time and location of study changed, the observations in Altman’s sample
didn’t represent the market anymore. The estimated values were no longer
appropriate. Currently, many authors have developed their own discriminant
function for each country and each sector.
The Logit model and the Probit model appeared in the late 1970s and until

the late 1980s it became more common than the MDA method in the study of
default warning. The Logit and Probit model focus on the probability of
default/default of enterprises. Logit model and Probit model can be used to
evaluate the level of interpretation of independent variables. Ohson (1980) used
the Logit model to replace the MDA model to predict default in enterprises. The
LA model was also used by Platt (1991), Smith and Lawrence (1995), Koundinya
(2004), Prasad and associates (2005), etc.
Odom and Sharda (1990) were the first ones using neural network (ANN)
in their default warning studies. The other authors are Hawley, Johnson and Raina
(1990); Boritz and Kennedy (1995); Alam and associates (2000); Celik and
Karatepe (2007).
West (1985) used Logit model in combination with factor analysis to


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measure and to describe the financial and operational characteristics of banks.
The data was taken out from income reports, as well as reports of 1900
commercial banks in various US states. The important factors identified by the
Logit model in this study are similar to the factors used in the CAMELS model.
The study also showed that the combination of factor analysis and Logit Analysis
was useful for evaluate the bank performance.

Author/ Year of
publication

Approach/ Factors
divided in 2 groups.

Main result
sample and at 85.2% for test
sample.

Nowadays witness the recent trend of application of smart technics and the
computer technologies in default warning such as neural networks, decision trees,
character analysis, etc.

Odom and Sharda ANN/5;
MDA/5.
Data Rate of 100% for training
(1993)
collected from 38 insolvent sample and 77% for test sample.
enterprises and 36 noninsolvent ones.

The most international outstanding studies on default warning are
summarized in table 1.4.

Kolari
and TR and LA
associates (1996)

Table 1.4: Summary of international studies on default, default warning
Author/ Year of
publication
Beaver (1966)

Atlman (1968)

Martin (1977)

Hanweck (1977)

Ohson (1980)

Approach/ Factors

Main result

UDA/30. Data collected from Identification of 5 factors,
79 insolvent enterprises and 79 accuracy rate from 50% to 92%
non-insolvent ones in 38
sectors.
MDA/22. Data collected from Identification of 5 factors, the
33 enterprises for each group. accuracy rate of 95% per each
sample enterprise.
MDA; LA/25. Data collected Creation
of
6
models,
from American banks in the identification of 4 factors, the
period of 1970-1976.
best efficiency rate at 92.3%
PA/6. Data collected from 177 Out of 6 factors, 2 factors are
non-insolvent enterprises and statistically significant. The
32 insolvent ones.
accuracy rate of 83.8%, the test
pattern of 91.1%.
LA/9. Data collected from 1025 The accuracy rate of 96.3%

insolvent enterprises and 2000
non-insolvent ones.

Tam and Kiang ANN/19, MDA/19, LA/19. NN network with the accuracy
(1992)
Data collected from 118 banks rate of 96.2% for training

Lanine
Vander
(2006)

Rate of 98.6% for original
sample and of 95.6% for test
sample.

and TR model and Logit model in Accuracy rate of 91.6% for
Vennet major banks of Russia.
original data and and of 85.1%
for test data.

Ravi and Pramodh ANN/9;12. Data from Turk Rate of 96.6% for Turk banks
(2008)
banks and Spanish banks.
and 100% for Spanish banks.
In which: UDA- Univariate Discriminant Analysis; MDA- Multiple
Discriminant Analysis; ANN- Artificial Neural Network; TR- Trait Recognition;
DT- Decision Tree; LA- Logit Analysis; PA- Probit Analysis.
Source: Synthesis from references
1.2.2. Overview of criteria to determine default or high default risk in
existing studies.

1.2.3. Factors and variables in studies on default.
1.3. The studies on default warning and bank default in Vietnam.
The studies on default, bank default in Vietnam are summarized in Table 1.8.
Table 1.8: Various studies on default, bank default in Vietnam
Author/ Year
publication

of Approach/ Factors

Main content/Main results

Nguyen Trong Hoa MDA/37; LA/37. Data Estimating
the
discriminant
(2009)
collected
from
268 function, the Logit function to
enterprises in 2007.
calculate the probability of default
and ranking the observations of
the five selected samples.


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Nguyen
Quang MDA/40. Data collected Ranking the banks’ credit.

Dong (2009)
from 37 banks in 2008
Identifying two factors, the
accuracy rate of 90.6% for the
original data and 84.4% for the
test data.

the research gap, the author chose the following topic: "Developing a default
warning model for commercial joint stock banks in Vietnam".

Phan Hong
(2012)

Mai The model of
Vickers company

The Measure the risk of default of
construction
companies.
Identifying the cause of the
increased risk of default is the
poor asset management.

Nguyen Viet Hung Models of currency crisis Currency
crisis
forcast.
and Ha Quynh Hoa forecast.
Identifying 5 indicators reflecting
(2012)
the probability of economic

instability.
Nguyen Thi Luong Merton-KMV model. Data
(2014)
collected from 380 listed
companies in the period of
2011-2013.
Nguyen
(2015)

Phi

Measuring the default risk of
enterprises. Providing evidences for
the
reasonable
measurement
capacity of the method.

Lan Application of structure Evaluating the risk of system failure
model.
of financial institutions in Vietnam,
estimating the credit loss and the risk
of failure of the banking system.

Nguyen Thi Hong Array data model, GMM Determining micro and macro
Vinh (2015)
(Gaussian
mixture factors affecting bad debts in
model)
banks.

Source: Synthesis from references
Research gap: Default in joint stock commercial banks has not been fully
evaluated; the default in banks has not been monitored since the study was
carried out in only a year. The existing studies identify the factors leading to the
default risk in each case, but whether are those factors leading to the default risk
in Joint stock commercial banks in a certain period? In addition, each bank owns
their individual characteristics which affect the probability of default. At present,
there is no study to identify the measurement criteria. The macroeconomic factors
affecting the default risk in Vietnamese banks have not been verified yet. From

Chapter 1 conclusion
In chapter 1, the author presents the concept of default, bank default and the
consequences of bank default. The author summarizes the studies of default, bank
default in the world as well as in Vietnam. Specifically, the author provides a full
overview of the models and studies on default, from univariate discriminant
analysis to non-parametric smart techniques. The author presents the criteria for
default or high default risk in existing studies and systematically summarizes the
factors applied in those studies. By analyzing the main research methods in
representative studies, the advantages and disadvantages of each method, there is
still no model that is superior to others. Each one has its own pros and cons. The
number of predictors of default in those studies is diverse. The more or less
factors in a model do not affect the prediction rate. The summary helps the author
to find the research gap and to fill that gap, the author has set the object of the
thesis and carried out the study in the following chapters.

CHAPTER 2: THEORETICAL BACKGROUND ON DEFAULT IN
COMMERCIAL BANKS
2.1. Criteria to determine the default risk
In banking activities, credit risk is the biggest fear for managers. The
quality of credit reflects in the ratio of bad debt. Being a permanent problem in

the operation of every bank, bad debt causes some negative effects as follows:
Bad debt reduces the bank's profitability, liquidity, credibility and lead to default.
The increase of bad debt in the banking system is the most urgent
problem in the period of 2011-2015. Banks with a non-performing loan (NPL)
ratio of 3% or higher are placed under strict supervision by the State Bank.
Many studies in the world have shown the negative impact of high ratio of
non-performing loan on many aspects of banking operations; many studies
have demonstrated the relation between high ratio of non-performing loan and
bank default. In addition, the commercial bank is the monetary organization
whose biggest goal is profit and profit is the important indicator to evaluate the
success or failure of the management and operation of the bank. Profits are
also needed to make up for lost loans and to set up the full provision.


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Therefore based on the performance of banks, more grounds will be added into
categorizing bank risks. In particular, the author argues that banks of poor
credit (represented by high ratio of non-performing loans) are at high risk of
asset loss. In case of medium or poor performance, banks will not be able to
make up for the loss; the high level of vulnerability consequently lead to
default.

credit institutions. The factors in CAMELS (Capital adequacy, Assets,
Management Capability, Earnings, Liquidity - asset liability management,
Sensitivity to market risk, especially interest rate risk)

To evaluate the performance of banks, the thesis applies the DEA method

to estimate the technical efficiency of the banks, and then assess the operational
efficiency (through profitability). The banks are classified in 3 groups: (group A good performance, B - average performance, group C – weak performance).
On the basis of the above arguments, the author determines the default risk
as follows: the variable of default risk (Y) is assigned a value of 1 (high default
risk) if the bank has a bad debt ratio of 3% or more then it belongs to group C
when using DEA for grouping. The Y is assigned a value of 0 (low default risk)
in other cases.
The criteria selection to classify the defaults in this thesis is different from
other studies because of the following reasons: Many default studies in the world
use information about defaulted banks while in Vietnam, no default case as in
international practice is recorded. Few studies applied the coefficient of safety as
the criteria of categorization while in Vietnam, the State Bank made a mandatory
requirement on capital adequacy ratio so all commercial banks met this criteria.

a) Capital adequacy: 5 factors
b) Asset quality: Non-performance loan ratio/total outstanding loan; Bad
Debts / (Equity and Provisions for bad debts); Provision for bad debts / Bad
debts, provision for bad debts /outstanding loans. In addition, the following
indicators may be considered: loan rate/earning asset; interbank deposit and
loan/earning asset.
c) Management: 3 factors
d) Earnings: 13 factors
e) Liquidity: 6 factors
By analyzing the criteria in the CAMEL system, the author summarizes
and expects the sign of criteria affecting the default risk in Table 2.1.
2.3. Theoretical backgrounds for models applied in studies on default warning.
2.3.1. Logit Analysis, Logit Analysis with array data.

2.2. Factors affecting the default risk in commercial banks

Upon the advantages of array data, Logit Analysis and the purpose of the
thesis (The default risk in Vietnamese Joint stock commercial banks in the period
of 2010-2015), the author chooses the Logit Analysis with array data. The study
also experiments the Artificial Neural Network, Decision Tree to classify,
forecast the default risk in Joint stock commercial banks in Vietnam.

2.2.1. The macro factors affecting banking activities

2.3.2. Neural network

• Economic development.

2.3.3. Decision tree

• Legal, economic, financial and monetary policies of the State.

2.4. Data envelopment analysis (DEA) to evaluate the efficiency in Joint
stock commercial banks’ activities

• Competitive level
2.2.2. Micro factors affecting the default risk in JCBs
The factors in Atlman’s model (1968): 5 factors
Choudhy (2007), Pavlos Almanidis and Robin C. Sickles (2012) as well as
other authors has pointed out the financial ratios in CAMELS model are the
important criteria to evaluate the performance of financial institutions,
particularly the banks; these criteria is also important to forecast the bank default.
The CAMEL rating system by National Credit Union Administration (NCUA)
was created and has been applied from October 1987 as a tool to supervise the

2.5. The frame of the research thesis

Conclusion of Chapter 2
In chapter 2, the author presents the criteria to determine the default risk, clarify
the theoretical basis for the models of default risk warning in joint stock commercial
banks, analyze macro and micro factors affecting the risk of bank default. Based on
theoretical basis, the advantages and disadvantages of some risk warning models
matching the object of thesis as well as the existing data, the thesis chose to apply the
Logit Analysis with array data, Neural Network, and Decision Tree to build risk
warning model for Vietnamese joint stock commercial banks.


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dollars.
CHAPTER 3: CURRENT OPERATION, DEFAULT RISK OF JOINT
STOCK COMMERCIAL BANKS IN VIETNAM DURING 2009 - 2015
In Chapter 3, the thesis analyzed the macroeconomic context and monetary
policies during the period of 2009 – 2015. Clear analysis of current operation
condition in the joint stock commercial bank system of Vietnam during 2009 –
2015 is also provided for the following aspects: structure, scale, capital adequacy
level, profitability, management efficiency, and asset quality.
3.1. Macroeconomic context during 2009 - 2015

Inflation rate sharply increased with its peak in 2011, and then gradually
decreased after that thanks to the intervention and persistent inflation control of
the Government. The rate reached its lowest point in 2015.

6.42

5.98
5.25

• Open market operation
• Deposit rate
• Lending rate
3.3. Operation of banking industry
3.3.1. Structure, scale, and operational scope of banks
• Ownership structure, distribution of operations in commercial bank
system
3.3.2. Capital adequacy level and total asset scale of joint stock commercial
banks

6.68
6.24

5.4

• Interest policy

• Scale, operational scope

GDP
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3.2. Several monetary policies during 2009-2015
• Exchange rate policy

GDP growth remained quite stable with an average of 5.7%. However, this
is a low number in correlation with the economy potential. From 2010 to 2012,
GDP growth gradually decreased reflecting the turbulence in macroeconomic
condition. From 2012 to 2015, the economy started to recover resulting in the
improving GDP growth year after year.

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Budget income and spending: During 2009 – 2015, total income and
spending of the budget has been increasing over the year and showed constant
status of overspending. The period 2010 – 2015 witnessed the decrease of credit
growth within banking system. The credit growth constantly decreased during
2010 – 2013 reflected the difficult situation for businesses.

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GD P
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a) Equity capital and capital adequacy level of joint stock commercial
banks:
Period 2005-2011.

1
0
2009

2010

2011

2012

2013

2014

2015

Period 2011 to 2015.

Graphic 2.2: GDP growth 2009-2015 (%)
Source: Statistic bureau
Import and export: Export has become one of the most important factors
pushing for economic breakthrough during the period. Both import and export
growth showed strong increasing trend with approximately 25% increase
annually. Today, the volume for export and import has accounted for 80% GDP
of the whole economy, reflecting its importance toward general economic
growth. In 5 years from 2010 to 2014, export volume has been constantly higher
than import volume, leading to excess of export in the economy since 2012. For
the year 2014, the excess volume of export has reached approximately 2 billion

Graph 3.4: Capital adequacy ratio groups


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Source: Author’s calculation
Capital adequacy level of banks is guaranteed according to regulation from
the State Bank. However, the ratio is still quite low compared to other countries
in the region and should be increased to counter latent risks in the coming time.
Capital adequacy ratio of banks has been constantly decreasing since 2012
leading to instability within the commercial bank system.

• The growth of credit and deposit
• Liquidity
3.3.5. Asset quality, deficit level
Graph 3.9 showed the bad debt ratio of the joint stock commercial banks in
the research samples

b) Total asset scale of joint stock commercial banks
From 2008 to 2011, total assets of banks have shown increasing trend with
real breakthrough within the joint stock commercial bank section. Joint stock
commercial banks have actively been actively opening their branch networks
resulting in dramatic growth in raising capital and effectively exploit the capital
of citizens. In 2012, the total asset scale of joint stock commercial bank section
showed the declining trend. Till September 30, 2013, total assets from banking
sector reached 5,637 thousands in billion dongs. Till end of July, 2015, the total
asset of the whole financial credit system reached over 6.6 million in billion
dongs, increasing 150,097 billion dongs compared to end of 2014. Despite the
dramatic growth, banks in Vietnam are still of small asset scale comparing to
other countries in the region. Total assets need to be increased to sustain the
growing need of a developing economy.
3.3.3. Profitability, asset management efficiency

Graph 3.9: Bad debt ratio of joint stock commercial banks in Vietnam
during 2010 - 2015
Source: Author’s calculation
Bad debt ratio in Vietnam is currently at a high level comparing to other
countries in the region such as Thailand (2.7%), Indonesia (2.4%). By end of 2015,
bad debt ratio has been constraint at 2.9%; however, there remains a lot of
concerning issues.

ROE ratio of banks

Several weak points of the commercial bank system in Vietnam

Income distribution of banks

• Extremely high risk in banking operations
• Administrative ability and competitiveness are low
3.4. Risk of default of several typical joint stock commercial banks during the
period of 2009 - 2015
Weak banks with high probability of default include SCB Bank, Tin
Nghia Bank, Ficombank, Habubank, Dai Tin bank, Ocean Bank, Westernbank,
Dong A Bank, and GPBank. The author analyzed these weak banks to present
highlight characteristics of these banks.
Conclusion for Chapter 3
In Chapter 3, the thesis presented three key points as followed:

Graph 3.5: Indicators in profitability category
Source: Author’s calculation
3.3.4. Credit and deposit growth, liquidity

1) Analysis of macroeconomic context as well as major monetary policies

during 2009 – 2015. The global and regional economic conditions with great
instability ranging from global financial crisis to sovereign debt crisis have


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negatively affected Vietnam’s economy. During the period of 2010 – 2012,
macroeconomic environment showed many turbulences leading to the economic
growth decline to its lowest point within 10 years. Moving to the 2013 – 2015
period, the economy started to recover, however, sustainable growth has yet to be
achieved. The monetary policies during this period had many major movements
strongly affecting the banking operations.

Based on the assumption that all banks try to maximize their profits, input
indicators and outcome results are selected to run DEA model evaluating the
business performance of joint stock commercial banks.

2) Analysis of operations within banking system through following
indicators: structure, scale and scope of operation, capital adequacy ratio, total
asset scale, credit and deposit growth, financial efficiency and liquidity risk. The
author focused on analyzing several indicators in CAMEL model, bad debt
indicator, causes and negative effects of bad debts. At the same time, the research
pointed out the several weakness of commercial bank system in Vietnam
including: extremely high risk in banking operation of commercial bank system,
low capability in administration and competitiveness.
3) Analysis of default risk for several typical joint stock commercial banks
and pointing out highlight characteristics of weak banks. These weak banks are
those with low capability in risk management, low asset quality, high bad debt and

overdue debt rate, low profitability.
CHAPTER 4: DEVELOPING DEFAULT WARNING MODEL FOR
JOINT STOCK COMMERCIAL BANKS IN VIETNAM
4.1. Research design
4.1.1. Data
The banks included in the research consist of 35 joint stock commercial
banks. In detail, the number of banks over the year is indicated in table 4.1.
Table 4.1: Number of banks in the research
Year

2010

2011

2012

2013

2014

2015

Number of Banks

33

35

35

33

27

25

Financial indicators used to predict default probability of banks are
calculated from index and criteria within the audited financial statements by year
end of joint stock commercial banks in Vietnam from 2010 to 2014 with the total
of 163 observations. In 2015, there were 25 banks used to test out-of-sample
forecasting of the model.
4.1.2. Categorization of default probability level for the banks
a) Calculation of business performance of joint stock commercial banks:

Table 4.2: Selected Income / Outcome Variables
DEA Model (Profitability)
Income
• Total assets

Outcome
Pretax Profit

• Owner’s Equity
• Operation cost
Source: Synthesis from reference materials and model design by the author
After estimation results of business performance of joint stock commercial
banks from the DEA model, the thesis categorized the performance of joint stock
commercial banks into 3 categories.
c) Identification of default probability for joint stock commercial banks
For this indicator, the thesis calculated bad debt ratio of banks over the

years from their published financial statements and combined these with the
categories defined in section a) as well as the analysis of non-financial
information in order to identify the default probability of banks. Of the
observations in category C, there are 39 observations with bad debt ratio from 3%
and up. The rest 70 observations of category C have bad debt ratio lower than
3%. Analysis of these observations showed that, despite low business
performance, these banks all have credit quality indicators satisfying the
requirements from the State Bank and capital adequacy ratio CAR higher than
10%.
Thus, banks are considered having high default probability if they belong to
category C and have bad debt ratio from 3% and up – equivalent to status Y = 1
as opposed to Y = 0 in other status. Results showed 39 out of 163 observations
belonging to the high default probability group (Y = 1), accounting for 23.92% of
all observations. The rest of observations fallen under low default probability
group (Y = 0) equal 124 observations, accounting for 76.08% of total.
4.1.3. System of indicators affecting default probability
• Three indicators of macro element group: Gross domestic product;
inflation rate; credit growth.
•

Indicators of micro element group: The author selected and built a total


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of 39 indicators. These were initially categorized into 7 groups: Profitability (11
indicators); Deficit index (3 indicators); Asset management (4 indicators); Asset
quality (7 indicators); Adequacy index (4 indicators); Sustainable growth index

(4 indicators); and Liquidity (6 indicators).

The data used in neural network model consists of 163 observations. These
are randomly categorized into 3 sub-sets including: i) Training set of 115
observations; ii) Validation set of 24 observations; iii) Test set of 24
observations.

4.1.4. Statistical analysis

To identify the optimal number of neurons in hidden layer, the author used
the iterative process to review the number of neurons until finding the smallest
mean squared error (MSE). The author’s neural network structure consists of 21
input nodes corresponding to variables in Table 4.9 and Table 4.8, 10 nodes in
hidden layer, and 2 output nodes. The classification performance of neural
network on the samples is presented in Table 4.19.

The author conducted correlation analysis to identify indicators within
groups having ability to recognize level of risk. This resulted in 18 variables.
Then, the thesis applied correlation analysis between any two variables within
the groups.
4.2. The Logistic model with array data
From the set of 18 variables in Table 4.9 and 3 macro variables in Table
4.8, the research used the entry method in which predictors are entered one at a
time into the Logistic regression model with array data. Hausman test resulted in
the adequacy of fixed effect model.
^

After estimation results were out for β of β , the next step was to estimate
αi . Each bank has their characteristics represented by αi . With a data set

expanding 5 years from 2010 to 2015, the calculation of αi led to solving quintic,
quartic or cubic equations depending on the number of years we have data for
each bank. Matlab software is used to handle this work.
Model result:

ln(

p
) = α i -1.2955*RGDP + 1.0346 * d3 − 2.014 * e11 + 3.0769 * l3
1− p

Source: The author’s calculation
in which p is the probability of observation being in the high default risk
group.
+ Variable RGDP has negative effect on p at significance level of 6%,
while variable e11 has similar effect at significance level of 1%.
+ Variable d3 has positive effect on p at significance level of 1% while
variable l3 has similar effect at significance level of 6%.
Calculation of default probability and testing of the model efficiency shoed
the ratio of correct categorization for the Logistic model is at 87.71%.
Other intercept values showed characteristics of each bank affecting its
default probability indicate that four banks coded 22, 14, 19, and 7 in the research
have high intercept values or high latent default risk compared to others.
4.3. Neural network model

Table 4.19: Neural Network Performance
Samples

Training set

Validation set

Test set

Accuracy

95%

91.6%

91.6%

Source: The author’s calculation
Applied on the data set of 114 observations used to run Logistic regression
model with array data and fixed effect, the accuracy of classification for ANN model
is 92.98% compared to 87.71% of Logistic model. Moreover, class I error of ANN
model is also lower than Logistic model.
4.4. Decision tree model
The author experimentally constructed decision tree model to forecast
default probability for joint stock commercial banks using the data set of 163
observations. Independent variables for decision tree consist of 21 variables in
Table 4.9 and 4.8. The author used J48 algorithm on Weka software version 3.6.9
to create decision tree. The algorithm in decision tree returned 5 positive
indicators for the classification.
The accuracy level of classification using decision tree model on the 163observation data set is 96.93%, which is a considerably high number. With the
data set of 114 observations (previously used in Logistic regression model), the
decision tree model returned 95.61% accuracy rate.
The author has summarized and compared classification results returned by
the three models (including Logistic model, neural network model, and decision
tree model) on different data sets and concluded that using neural network or

decision tree model will raise the classification accuracy.


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default probability.

Conclusions for Chapter 4
In Chapter 4, the author has experimentally constructed default warning
model for joint stock commercial banks in Vietnam with following details:
calculation of business performance for banks, classification into performance
groups, and identification of default probability of banks. The thesis also
constructed 39 financial indicators categorized into 7 groups to use in default
prediction.

+ Variable e11 = (Receivable from interest – Payable from loan interest) /
Earning assets, has β$ 3 = − 2 .0 1 4 . Net interest margin shows the difference between

The Logistic model with array data and fixed effect has shown variables
with direct effect on default probability of banks including overdue debt/ debt
payable, net interest margin, net loan / total deposit. Variable GDP reflects the
growth of the economy and represent macro elements with negative effect on
default probability of banks.

represents the liquidity of banks and its higher value means the banks’ lower
liquidity. Bankrupt probability is under positive effect of this indicator.

The author also experimented with ANN and DT models in default

prediction for joint stock commercial banks. Results showed these two models
had higher classification accuracy comparing to the Logistic model. Decision tree
model showed 5 indicators used to alert of default risk in banks.

5.1. Obtained results
a) The Logistic model results with array data and fixed impact

β$ 3 = 3 .0 7

. This ratio

+ The intercept α i of banks are calculated in the model. These indicate the
differences and characteristics of banks which affecting their default probability.
According to calculation, there are four banks coded 22, 14, 19, and 7 with high
intercept values, meaning high default probability. The three banks coded 10, 21,
and 6 have lowest intercept values, meaning lower default probability under the
same condition of variables.

c) Summary and comparison of results of classification models:

From the initial 42 variables, then 18 variables, the final Logistic model
has only 4 variables left. During estimation, the author has tested the selection of
fixed impact versus random impact, and the fixed impact model has been
selected. Thus, the impact of variables RGDP, d3, e11, l3 toward default
probability is fixed impact. This also means that the above-mentioned variables
affecting the default probability of banks are of similar trend, fixed within the
studied period, and sharing similarity between banks.
+ Estimation result from variable RGDP model has

+ Variable l3 = Net loan / Total deposit with

b) Marginal effect of variables toward default probability p: Based on the
value of the coefficients on Logistic model, the author calculated the marginal
effect of these variables on default probability.

CHAPTER 5: CONCLUSIONS AND POLICY
RECOMMENDATIONS

β$1 = − 1.29 .

revenue from earning interest and cost from paying interest. The author expected
that variable e11 had negative effect on default probability. Regression test result
showed a negative value as expected.

This means

the RGDP variable has negative effect on default probability and Logistic model
result proves the impact of macroeconomic conditions, specifically the growth of
gross domestic product, on default probability of joint stock commercial banks in
Vietnam.
+ Variable d3 = Overdue debt / Debt payable in the model has

β$ 2 = 1 .0 3 ,

which is higher than 0. This variable indicates the deficit level of banks, and the
higher its value, the less safe the banks are. This variable has positive effect on

The author compares the effectiveness of models with the data set of 114
observations in 2015. In different data sets, neural network model and decision
tree model both have higher classification effectiveness compared to Logit

model. Especially, the number of observations wrongly classified by all models
are minimal at 1 observation, thus, the combination of three models will bring
higher classification effectiveness.
5.2. Classification of joint stock commercial banks
From the results of Logistic model with array data, fixed effect in section
4.2 and the regulation of bank classification standard according to Decision
06/2008 of the State Bank; the author has classified banks into 4 categories. The
author then compares the research classification results with the actual
classification results from the State Bank.
5.3. Several proposals and implied policies
After establishing and testing several default prediction models for the
joint stock commercial banks in Vietnam, the author proposes several actions as


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followed.
a) Proposals for joint stock commercial bank
+ According to results from the variable e11 model – Net interest margin
has negative effect on default probability. Thus, commercial banks should have
certain methods to raise their net interest margin such as: pushing for promotion
of brand, expanding market share, attracting low-cost deposits from individuals
and economic sectors, expanding credits, and finding potential clients. The
Overdue debt / Debt payable index has positive effect on default probability of
banks. Firstly, there should be correct evaluation and classification of loans to
identify the exact scope and severity of overdue debts. Then, the banks should
focus on lowering these numbers, especially bad debts, as soon as possible
through support of financial resource to establish allowance for bad debt and
doubtful receivable and make sure the amount could cover the worst case
scenario. Next step will be for banks to consider selling these bad debts to

businesses, organizations, or individuals with competency and authority in
handling said debts. On the other hand, the banks need to take measures to limit
the possibility of overdue debts right from the lending approval phase. Overdue
debt includes debt group 2, therefore, the banks should closely monitor these
debts to prevent them turning into bad debts. The variable of net loan/ total
deposit has positive effect on default probability and there should be thorough
review of possible causes for a high ratio between net loans against total deposits
from clients. From the review, necessary actions should be taken to lower the
ratio.
+ The model results showed variable RGDP having negative effect on
default probability of banks. Thus, as the macroeconomic overview, especially the
growth of GDP has shown negative signals, the banks must focus on the safety of
banking operation because these are when the default probability increases due to
macroeconomic element.
+ From the data collection and model establishment, the author realized
that a regression model result with high creditability and significance requires
correct and sufficient input data. Therefore, the joint stock commercial banks
should further complete their internal information system to ensure data is
updated in a correct and timely manner to support the analysis and management
of risks.
+ Four banks coded 22, 14, 19, and 7, as per the author’s calculation; have

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high intercept values implying the high latent default probability. Thus, there
must be thorough examination of all operational aspects of these banks to figure
out specific solutions to lower their probability of default.
b) Proposals for authorized management organization:
The State Bank is the governmental managing organization of banking
industry with the objective to monitor banking operation and ensure a stable and

healthy banking system. Results from the default prediction model for joint stock
commercial banks prompted the author to propose several actions for the State
Bank and other authorized organizations as followed:
+ Results from Logistic model of variable RGDP showed the growth of
gross domestic product having negative effect on default probability of banks. As
economic growth remains stable, banks will have favorable conditions to operate,
raise their revenue, and lower default probability. Thus, the Government should
maintain annual economic growth and pay closer attention to the safety of the
whole banking system as the economy declines because this is when the banks’
default probability increases due to economic regression. In its supervising role,
the State Bank need to build a script of annual economic growth and based on the
script to identify at-risk banks to timely alert and monitor these banks.
+ The Vietnamese Government should, along with creating favorable
environment for domestic joint stock commercial banks to operate, support banks
with legal matters and administration reform. Monetary policies issued should
take in consideration their impacts on the joint stock commercial banks,
especially those with lower capacity. At the moment, handling bad debts is a
matter of urgency and importance to lower default probability of joint stock
commercial banks. At the same time, efforts from the joint stock commercial
banks in quickly collecting their own bad debts and helping credit institutions to
minimize transaction cost and time should be met by the Government’s effort in
timely completing the secured asset handling procedure, shortening the time to
process secured assets. Additionally, the Government should consider some
policies to increase resources for participation in the handling of bad debts and
speeding up this process.
+ To avoid the risk of the whole banking system breakdown, the State
Bank should now encourage and later make it an obligation for all banks to apply
regulations according to international practices in operation, statistical data
information system, and prediction task. The supervision and monitoring of the

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State Bank must be conducted regularly and effectively. There should be a
compulsory mechanism to pressure banks into reporting their business operation
results in an honest and transparent manner.

+Fourthly: The thesis has proposed the default prediction model for joint
stock commercial banks in Vietnam being the Logistic regression model with
array data. This model helps to identify indicators and criteria affecting the
default probability and calculate the probability of at-risk categories for the banks
included in the sample data. Results provided by the model are economically
compatible and meet the required standard of a good model. Experimental results
from the thesis indicate that the neural network model and decision tree model –
being to branch model of the intelligence modeling technique used to improve the
effectiveness in classification.

c) Proposal of default prediction model and procedure to build the model
for joint stock commercial banks: The author proposes to use Logistic model
with array data of fixed effect to predict default probability for joint stock
commercial banks in Vietnam based on the collected experimental results. The
author also proposes a procedure to alert banks of the default risk.
CONCLUSIONS & APPROACH FOR FURTHER RESEARCH
Based on the necessity of default prediction task in joint stock commercial
banks along with the research gap among existing researches in Vietnam and the
world, this thesis has applied Logistic regression model with array data and
several nonparametric models (neural network, decision tree) to construct a
default prediction model for joint stock commercial banks in Vietnam. In order to

apply this model, the author has selected bad debt indicator in combination with
business performance analysis of banks as the key indicators measuring default
probability. The predictor variables are mainly built from indicators in CAMELS
model and calculated from financial statements of joint stock commercial banks
during the period of 2010 – 2015. The results are as followed:
+ Firstly: The thesis provides a systematic overview of the methodologies
and default prediction models applied for business, especially banks, ranging from
single variable analysis models to modern models using intelligent techniques
popularly used these days in default prediction. The pros and cons of each method
and model are analyzed to identify research room for selecting Logistic model with
array data to construct default prediction model for joint stock commercial banks
in Vietnam.

+ Fifthly: The thesis has quantified differences and distinctive
characteristics that affect default probability of each bank. It has also identified
four banks with latent high risk of default which require thorough examination.
+ Sixthly: From the experimental construction of default prediction model
in this thesis, the author has proposed a default alert procedure for joint stock
commercial banks in Vietnam.
+ Seventhly: From the collected results, the author has proposed several
solutions and actions for the banks and authorized managing organizations to
help minimize the default probability.
Approach proposal for further study
To further improve the default prediction model for joint stock commercial
banks in Vietnam, the author proposes several approaches for future study:
+ Firstly, due to limited accessibility to data sources for research model,
the author has used bad debt indicator and ranking of business performance as the
base to identify default probability. Other researches could look for classification
criteria, run tests and compare research results of these criteria.

+ Secondly: The thesis has built the theoretical framework to explain the
default probability of joint stock commercial banks in Vietnam. Current operation
capacity and default probability of joint stock commercial banks in Vietnam
during the period 2010 – 2015 were analyzed. The author provided analysis and
proposed criteria evaluating the default probability of the banks based on their
bad debts and business performance.

+ Secondly, other researches could experiment with models such as
survival analysis, characteristic analysis, or genetic algorithm… and compare to
select the compatible model. Combination of more than one methodology or
model could also be studied to improve the effectiveness of classification.

+ Thirdly: The thesis has built and selected a system of 39 micro indicators
and 3 macro indicators to use in the default prediction model. Variables with
direct positive effect on default probability of banks are: overdue debt/ debt
payable; net interest margin; net loan / total deposit. Research results have proven
the impact and quantified the impact level of RGDP variable upon default
probability of joint stock commercial banks.

+ Fourthly, after calculating the default probability and ranking the banks,
other researches could calculate the migration matrix of all banks or construct a
model to identify variables affecting the migration of banks.

+ Thirdly, further study of model construction and testing for the out-ofsample forecasting of the model should be carried out.