ɪ1

Dissertation submitted in partial fulfillment of the
Requirement for the MSc in Finance

FINANCE DISSERTATION ON
CREDIT RISK MANAGEMENT AT
BANK FOR INVESTMENT AND
DEVELOPMENT OF VIETNAM (BIDV) THANH DO BRANCH
DUONG THI THANH
ID No:
Intake 1

Supervisor: Prof. Dr. Nguyen Thi Hoai Thu

September 2018


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ACKNOWLEDGEMENT

After several months of hard work our thesis has been finished. Now it is time to thank
everyone warmly who provided their kind assistance to us. First of all, I would like
to thank my supervisor Prof. Dr. Nguyen Thi Hoai Thu for her guidance all through my
work. I would like to send our deepest gratitude to all lecturers, as well for giving us their
constructive suggestions. The same appreciations are given to the leadership and staffs of
the Bank for investment and development of Vietnam JSC (BIDV) - Thanh Do Branch
who have enthusiastically shared with me data and relevant documents to accomplish this

important dissertation.
The dearest appreciations are directed to our families and friends, for giving us great
support and help during these months. Without them, my thesis would not be finished.

September 2018
Student’s Signature

Duong Thi Thanh

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England

ABSTRACT

Credit risk management in banks has become more important not only because of the
financial crisis that the world is experiencing nowadays but also the introduction of Basel
II. Since granting credit is one of the main sources of income in commercial banks, the
management of the risk related to that credit affects the profitability of the banks. In my
study, I try to find out how the credit risk management affects the profitability in banks.
The main purpose of my study is to describe the impact level of credit risk management
on profitability in banks. The study is limited to identifying the relationship of credit
risk management and profitability of BIDV - Thanh Do Branch. The results of the study
are limited to the bank in the sample and are not generalized for the all the commercial

banks in Vietnam. Furthermore, my study uses both Secondary data collection methods
and quantitative research by analyzing the logistic regression model (Binary logistic)
was selected with the dependent variable is the binary variable (1. The enterprise falls
into the risky situation, 0. The business is not risky).
Key words: credit risk management, profitability, banks, regression model.

ABBREVIATION
S

DC

Design Consultancy

CI

Construction investment

F

Factory

E

Enterprise

JS

Joint-stock

OM


One member

L

Limited

CRM

Credit Risk Management

CBs

Commercial Banks

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ιv


TABLE OF CONTENTS
ACKNOWLEDGEMENT......................................................................................................ii
ABSTRACT.............................................................................................................................iii
ABBREVIATIONS.................................................................................................................iv
LIST OF TABLE, DIAGRAM............................................................................................viii
1. INTRODUCTION.......................................................................................................1
1.1 Background............................................................................................................1
1.2 Problem Discussion...............................................................................................2
1.3. Research purposes and tasks:...................................................................................3

1.4. Delimitation.................................................................................................................3
1.5. Disposition...................................................................................................................4
2. METHODOLOGY:.....................................................................................................5
2.1 Research approach................................................................................................5
2.2. Data collection and description................................................................................7
2.3. Tested Results of the research model.....................................................................10
2.3.1.

Correlation Matrix of thevariables in the model..............................................10

2.3.2.

Determination of research results....................................................................13

3. FRAMEWORK.........................................................................................................16
3.1. Previous studies........................................................................................................16
3.1.1.

Liturature review...........................................................................................16

3.1.2.

credit risk management Indicators...................................................................21

3.1.2.1

Credit size...................................................................................................21

3.1.2.2


Loan porfolio structure...............................................................................21

3.1.2.3

Overdue debt.............................................................................................. 22

3.1.2.4

Non-performing loans.................................................................................22

3.1.2.5.

Credit risk provision..................................................................................23

3.2. Theories.....................................................................................................................24
3.2.1.

Risk in Banks....................................................................................................24

3.2.2.

Credit risk managementin banks......................................................................25

3.2.3.

Credit risk classification.................................................................................. 25

3.2.4.

Loan porfolio structure....................................................................................27


3.2.5.

Overdue debt....................................................................................................27

3.2.6.

Non-performing loans......................................................................................28

3.3. Credit risk provision................................................................................................29

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3.4. Regulation..................................................................................................................30
3.5. Credit risk measurement.........................................................................................31
a. Qualitative model of credit risk management.......................................................31
b. Quantitative model of credit risk management........................................................32
c. Credit risk measurement model in Basel II.............................................................36
3.6. Lessons.......................................................................................................................37
4. EMPIRICAL FINDING AND ANALYSIS............................................................39
4.1. An overview of BIDV and Thanh Do Branch......................................................39
4.1.1

Establishment and development of BIDV Joint Stock Commercial Bank.........39

4.1.2


The formation and development of BIDV Thanh Do Branch........................... 40

4.2. Business performance of BIDV Bank - Thanh Do Branch................................41
4.2.1

Capital mobilization..........................................................................................41

4.2.2

Lending............................................................................................................. 43

4.2.3

Business performance.......................................................................................44

4.3. Credit at BIDV - Thanh Do branch.........................................................................45
4.3.1

Loans structure of BIDV- Thanh Do branch.....................................................45

4.3.2.

Credit risk of BID V - Thanh Do branch..........................................................48

4.3.3.

Credit risk management of BIDV- Thanh Do branch.......................................50

4.3.3.1


Risk management models of BIDV- Thanh Do branch...............................50

4.3.3.2.

Credit risk management policy of BIDV- Thanh Do branch......................53

a. Credit risk management policy for customers.........................................................53
b. Credit allocation policy...........................................................................................53
c. Jurisdiction..............................................................................................................54
d. Classifying debt, establishing and using credit loss provision policy.....................54
e. Regulations on reporting and checking credit risk..................................................54
4.3.3.3

Credit Risk Management of BIDV - Thanh Do branch...............................54

a. Identification of credit risk:.....................................................................................54
b. Credit risk measurement:.........................................................................................56
c. Risk control process:...............................................................................................59
d. Risk management operation:...................................................................................60
4.4. General evaluation of BIDV - Thanh Do branch in risk management...................62
4.4.1.

Achievements....................................................................................................62

4.4.2.

Limits and challenges.......................................................................................63

4.5 Solutions to improve credit risk management at BIDV Bank - Thanh Do branch ... 65


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4.5.1

Building an effective credit policy.................................................................... 65

4.5.2

Diversify credit portfolio to decrease credit risk.............................................. 68

4.5.3

Improve the quality of loan appraisal and analysis......................................... 68

4.5.4

Reinforcing internal credit supervision and inspection....................................69

4.5.5

Improve the quality of human resources...........................................................70

4.5.6

Use derivatives................................................................................................. 72

4.6.....................................................................................................Recommendations

..............................................................................................................................72
4.6.1

Recommendations to the SBV........................................................................... 72

4.6.2.

Recommendations to Head Office of BIDV......................................................73

CONCLUSION.......................................................................................................................75
REFERENCES.......................................................................................................................76

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1. INTRODUCTION

LIST OF TABLE, DIAGRAM
Table 1: Descriptive Statistics Business Credit risk.............................................................8

Table 2: Describe the indicators of financial ability and effectiveness in the operation of
enterprises............................................................................................................................10
Table 3: Correlation coefficient matrix...............................................................................11
Table 4: Summary table of the model.................................................................................12
Table 5: Summary of credit risk ratings by Standard & Poor's...........................................13
Table 6: Conclusion of model results..................................................................................14
Table 7: Capital mobilization results from 2015 to 2017...................................................41
Table 8: Several targets for credit activities in 2015 - 2017................................................43
Table 9: Results of business activities in 2015 - 2017........................................................44
Table 10. Loans structure of BIDV - Thanh Do branch in 2015 - 2017.............................45
Table 11: Overdue and bad debt of BIDV - Thanh Do branch in the period of 2015 to
2017
Fingure 1: Lending model without risk appraisal process..................................................50
Fingure 2: Lending model with risk appraisal process.......................................................51
In this chapter, I present the background of the thesis followed by the problem statement. The
discussion also contains the motivation for our thesis. Finally, we present the literature review,
the methodology, the purpose of this thesis and limit the area of the study.

1.1 Background
The world has experienced remarkable numbers of banking
the last thirty years. Caprio and Klingebiel (1997) have identified 112 systemic

and

financial

crises

during


banking crises
in 93 countries since the late 1970s (Ibid.). Demirguc-Kunt and
Detragiache (1998) have identified 30 major banking crises that are encountered from
early 1980s and onwards. Though most of those were experienced in the developing
countries, the authors have noted that Vietnam has also gone through similar crises in
the late 1980s and early 1990s1 2. Interestingly, the majority of the crises coincided with
the deregulatory measures that led to excessively rapid credit extension. In the long run,
continuous increases in asset prices created bubble 3. At some point, the bubble burst and
the asset markets experienced a dramatic fall in asset prices coupled with disruption.
Finally,
widespread
bankruptcies
accompanied
by
non-performing
loans,
credit
losses
and acute banking crises were observed.
Ironically,
reforms.

the

frequency

of

Why?


There

are

crises
many

did

not

decrease

contributing

despite

viii
factors,

the

mainly,

introduction
political

of

and


successive
economical


conditions. It can thus be self evident that the improved risk management does not
improve
the
banking
business.
Moreover,
Jean-Charles
Rochet
(2008)
states
that
key
factors to successful reform are independence and accountability of banking supervisors.
As long as banking supervisors represent political and economical interests of their
respective countries, it is not possible to implement international regulation successfully.
The
current
global
financial
crisis
indicates
that
risk
management
of

the
financial
institutions is not adequate enough. This leads to the failure of the banks in highly
challenging
financial
market.
Furthermore,
the
discussion
of
financial
crisis
in
mass
media and among scholars mentions the risk management as omissions or neglect of risk
measurement
signals.
They
state
that
more
attentive
participants
could
avoid
the
4
tremendous
affect
of

the
financial
meltdown .
Therefore,
Risk
Management
as
a
5
discipline is being taking seriously nowadays . Nevertheless, the financial storm teaches
several key lessons which can assist to improve the risk management in future. As a
result, risk has become a very challenging area of studies. This has motivated me to
conduct my thesis on this area of interest.


Systemic risk is the risk of collapse of an entire system or entire market and not to any one individual entity or
component of that system.
1

2

Steigum (1992) and Vihriala (1997) discusses on the Norwegian and Finnish cases

Englund P. (1999), The Swedish Banking crisis: Roots and Consequences, Oxford review of Economic Policy, Vol. 15,
no.3, pages 80-97.
Joe Nocera, “Risk management and financial crisis”, Herald Tribune, January 4, 2009
A.E. Feldman Associates, Inc., US Consulting firm report “On financial crisis and its effect”
Accessed February 9,2009
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1.1 Problem Discussion
The banking sector has become more complex over the last decades due to the
development of financial security market. As a result, banks are getting involved in
compound transactions without fully realizing the risk level. Consequently, the risk
bearing side gets blurred and risk exposure splits on everybody. This causes systemic
failure - the economical system of the countries breaks down. Government influences
the situation and tries to stabilize economy through the regulatory mechanisms.
In the trend of liberalization, economic globalization and internationalization of
financial flows have fundamentally changed the banking system; with the opening of the
financial market, the appearance of banks with investment capital, modern technology and
advanced management level are major challenges for domestic banks. Thus, the business
activities become more complex and the competitive pressure between the banks is larger
and along with it, the level of risk increases. Risks are almost present in each banking
operation. A bank wants to make a profit; it must have risky, meaning that it must live
together with the risks that arise in each of its operations. In the context of competition and
financial market integration and the financial services industry - the banking sector is
growing; it demands strong reforms to reduce operational risk.
Credit activities bring high income but also the riskiest. Over time, the nature of
credit risk also changes when Vietnamese enterprises are increasingly under pressure from
globalization and international integration. Therefore, the issue of improving credit risk
management to improve operational efficiency and competitiveness in the current period is

a serious problem for commercial banks.
BIDV Bank is a Joint Stock Commercial Bank with a wide network spreading
throughout the country and also reaching out to neighboring countries in the region as well
as having representative offices of some countries in the world. Thanh Do branch is an
affiliated unit is located at 463 Nguyen Van Linh Street, Phuc Dong Ward, Long Bien
District, Hanoi. The branch has 04 sub-branches. Over time, BIDV Thanh Do has made
significant contributions to the socio-economic development of Hanoi City in general and
for the development of BIDV in particular. Apart from the achievements, credit activities
still face many difficulties and challenges, the situation of overdue debts and bad debts
tend to increase due to the impact of the economic recession storm in the last period; it
requires BIDV Thanh Do to focus and improve risk management, especially credit risk, to
ensure the safety of the system, contributing to competition capacity building in the new
stage.
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In addition to the achievements, credit activities still face many difficulties, the
situation of overdue debt and bad debt still exist and tend to increase due to the impact of
economic recession in the past. The fact that, it requires BIDV Bank - Thanh Do Branch
has to focus on and improve risk management, especially credit risk management to ensure
safety, contributing to improving competitiveness that is an urgent requirement for JSCBs
in the current context. However, during the study, the author found there has not been
research work on credit risk management at BIDV Bank - Thanh Do Branch in the period
2015-2017 and suggested the direction to improve credit risk management quality.
Therefore, the author selected the topic “The Credit risk management at BIDV Bank Thanh Do Branch”

1.3.


>

Research purposes and tasks:

Research objects
The dissertation focuses on the current situation of credit risk management

activities at BIDV - Thanh Do Branch

>

Research scope:

-

Research is about the practical activities of credit risk management in
Thanh Do Branch and some features of other typical branches in the
BIDV system.

-

This research is focused in period 2015-2017

-

Research on credit risk management was conducted at BIDV Thanh Do
branch.

-


The process includes: analyzing the credit risk management situation,
assessing the weaknesses of credit risk management and proposing
solutions to improve the credit risk management in BIDV - Thanh Do
branch.

1.4.

Delimitation

The research is limited on identifying practical activities of credit risk

management in Thanh Do Branch and some features of other typical branches in the
BIDV system. Due to the unavailability of information in annual reports, my sample only
contains information of BIDV - Thanh Do branch. Considering the above mentioned
circumstance, the results of the study are limited to one commercial bank in the sample and
are not generalized for the commercial banks in Vietban.
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2. METHODOLOGY:

1.5. Disposition
University
University

In this chapter, I widely describe the HOWpart of our study. The chapter comprises research
approach, sampling, data collection, data anyzing instruments and the description of applied
regression model. The chapter is finalized by reliability and validity and limitation of my study.

Chapter 1

INTRODUCTION: In this chapter, I present the background of the thesis
followed by the problem statement. The discussion also contains the motivation for
our thesis. Finally, we present the literature review, the methodology, the purpose
of this thesis and limit the area of the study.

METHODOLOGY: In this chapter, I widely describe the HOW part of our study.
The chapter comprises research approach, sampling, data collection, data
anyzing instruments and the description of applied regression model. The chapter
is finalized by reliability and validity and limitation of my study.

FRAMEWORK: In this Chapter, I provide theoritical foundation to my study by
presenting relevant literature
Chapter 3

EMPRICAL FINDING AND ANALYSIS: In this chapter, I present overview of
BIDV - Thanh Do branch and the results of my regression model, sollutions and
recommendation for the bank.

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O1
Taking the e-log of Odds we have the Logistic regression model function:
Pi
Li = ln =

= Zi = -(β0 + β1X1 + β2X2 + ...+ βkXk)

(3.4)

1 - Pz
Impact of k:
ðPr (Y = 1/Xki)
2.1 Research approach
Bk using Logistic regression model;
(3.5) Data collection, data
1 (1 - P1)* by
My study is= Pconducted
ðXk1results of the test model including: correlation Matrix of variables in the
description and
model, regression results, relevance of the model. This is to explain the credit risk of the
business through the expected variables into the model.

Meaning: When Xk changes a unit, the probability that Y = 1 (also Pi) will change

Pi * (1 - Pi) * βk. Probability change according to this interpretation depends on two
factors.
The of

first
sign regression.
of the coefficient βk. If the coefficient is positive,
The method
ourfactor
study is
is the
Logistic
the
increase
in Xk will
increase
the probability
of Y =the
1 and
vice versa.
Theissecond
Logistic
regression
is a special
regression
model where
dependent
variable
a binary
variable
accepts onlychange
two values
1. This
regression

model
is used
to predict
factor
is that
the probability
for Yof=01and
when
the change
in Xk
is again
dependent
the probability of occurrence of an event based on information of Independent variables in
the model.

and the value of Xk means that the probability increase (decrease) Pi when the

change Xk is not fixed but will change corresponds to the value of the variable Xk
(1) Probability: The probability that something happens, denoted by P

and this change falls within the domain of the probability condition of 0 ≤ Pi ≤ 1.
(2) Odds is the ratio between two probabilities: probability of occurrence and probability
does not occur. Or more specifically the ratio between success and failure

The relationship between the marginal effect of the variables depends on probability
increases from P0 to P1 when change one unit of Xk:

When we have two dependent variables: Y = 1, Y = 0, and the probability that it happens
is denoted byPPO(Y = 1) = P. Statisticians often use a familiar quantity as Odds.


Odds
=
Odd =0 P/1-P
1 -(3.1)
PO

=

eZ0

(3.6)

Thus, according to the formula, Odds is a function of P. Odds> = 0, and Odds will not be
determined when P = 1.

In which, P1 is the probability when Xk is increased to a unit:

We have: P is the probability of occurrence of the event, then (1 - P) is the probability of

Zno1 =event
β1 + occurrence,
β2X2i + ...+probability
βk (Xki + 1)P is measured as follows:
1
From the two equations
we have: 1
P=
=
------ (3.2)
-Zi

1+e

*12+e

0

-(β + β X β X + -+β X)
11 +
2 2

kk

With Z = β0 + β1X1 + β2X2 + -+ βkXkZi ∈ (-∞, +∞), Pi ∈ (0,1), Xi (i=1,k)
Odds of the above case are:
1 + ezi

Pi
Odd =
1 - Pi
= eβk -> O1 = O0 eβk

=

= ezi
1+e

zi

5


OO

(3.3)


(3.7)

(3.8)

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West of
England

P
Replace Odd =

—
in (1), we have
1 - P0
P
------------------= O0


o

eβk

O0 eβk

,
-> P1 =

1 - P0

(3.9)
1 + O0 e

βk

From this relationship we can construct a scenario for the change of probability when
changing the unit of the variable Xk, this change by observing the difference of P0 and P1,
we take P1 - P0 will find the change of probability when changing a unit of Xk. The
advantages of this simulation show that there is a particular probability change, and the
probabilistic interpretation of the probability function in the previous section is qualitative.
The relationship between theory and research: A business classified as credit risk is an
expected value of the subject (called Y), and the business is not classified as a business risk
that is remainder value of the expected variable. Corporate credit risk is determined by the
variable explanatory system, which measures the ability of the firm to capitalize and the
efficiency of the firm's operations.
Evaluating the factors that affect the riskiness of the business, the model of assessing
businesses classified as risk, or not risk is used as the logit model (Binary logistics). Used
for the case where the dependent variable has only two values, usually these values are
encoded as "1" or "0". In which, each value represents a specific value of the dependent

variable. The definition of "1" or "0" belongs to any object, the value of the dependent
variable does not affect the results of the model.
The results of the above model building were conducted and concluded after the validation
of the model's usability, the verification of the collinearity between the explanatory
variables in the model, the evaluation of the level of interpretation of the model. At the
same time, research also aims at the feasibility of the model and the most accurate
assessment of the research objectives

2.2.

Data collection and description

Data collection method: Based on the selected research model, the collected data
was accessed and aggregated from the secondary data source at BIDV head office
and BIDV Thanh Do branch. Of which, 253 data of enterprises were collected and
processed for analyzing, clarifying the objectives of the topic on Credit Risk
Analysis of Vietnamese Enterprises, case study in banking system BIDV Thanh Do
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frequenc
y

%

Valid %

Cumulative

%

branch. As a result, the report focuses on the system of enterprises surveyed for
assesses the ability of enterprises to
Valid Non-risk research purposes,
30.4accordingly,
30.4 the report30.4
classify the group of enterprises to ensure the ability to pay debts and business
176

Risk

69.6

69.6

100.0

groups are difficult to repay (classified as credit risk).
100.0
On the 253
basis of 100.0
collected data
are enterprises, taking sources from the bank and

assessments of banks' ability to repay. Credit risk of enterprises in the thesis also
uses the view of credit risk for enterprises in view of the bank. According to the
above assessment, credit risk for enterprises classified as enterprises is difficult to
repay the bank's interest. Serving the ability to assess credit risk, the thesis uses a
set of indicators related to (i) the financial ability of the client and (ii) the

effectiveness of the client's operations. This information is described in detail as
follows:
The sample was conducted on 253 enterprises, 176/253 enterprises were ranked at
risk level, accounting for 69.6% of total surveyed enterprises. The number of
enterprises classified as risky is 77/253 enterprises, accounting for 30.4%.
Table 1: Descriptive Statistics Business Credit risk


At the general average level, enterprises are classified as non-risk indicators having
financial ratios at high level compared to enterprises classified as risk indicators. In
particular, the group of indicators of enterprises ranked in the form of no higher risk
than the group of risk indicators are classified as follows:
Group 1: The target of the non-risk group is higher than the group of risk indicators
are 9 indicators including retained earnings / total assets (X1), retained earnings /
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net revenues (X2), working capital / Total Assets (X3), Net profit margin (X4), Net
Profit / Equity, Net Revenues / Short-term Debt (X6) and Short-term liquidity ratio
(X7).

Table 2: Describe the indicators of financial ability and effectiveness in the operation

Group 2: The target of the non-riskofgroup
is lower than the risk group is Net sales /
enterprises
Total assets (X8)
The change ratio in Total Assets (X9) of a group of enterprises is considered as risk

has balance value in comparison with the non-risk group.
The above indicators reflect quite accurately the current status of enterprises are
classified into both risk and non-risk. In Group 1, most non-risk enterprises have a
higher return, liquidity and liquidity ratio than the risk group. In Group 2, the
indicators show that the non-risk group continues to perform more efficiently than
the risk group as shown by the Total Assets / Total Assets (X7) Net receivables
(X8) and Net sales / total assets (X12) of non-risk enterprises are lower than those
of risky group.

Non-risk

N

Avera

Risk

Standa
rd
deviati
on

N

e

g
X
1
X

2
X
3
X
4
X
5
X
6
X
7
X
8
X
9

7
7

,
065

7
7

,
158

7
7


7
7
7
7
7

117

2,3

7
7
7

766
39,0
86

1
1

253

,037

1,476

,080


,193

253

,133

,210

,008

,137

253

,018

,854

1
76

,451

253

,058

2,3

1,920


253

2,517

1,3

,859

253

1,632

1,1

,949

253

1,003

05

,
857

,051

68


2,1

1
76

2,0
63

,258

76
76

52
,

-,015

,115

Diff
e

5.9
10.1
3.1

1

3,1

85

79

,124

76
,

2,8

,011

Avera
ge
253
,027

76

42

57

1

1,5

,


N

Standa
rd
deviati
on

76

197

076

1

,

,
041

7

7

56

252

1


Standa
r
d
Avera deviati
ge
o

76
2,6

,

7
7

,
080

Total

1
76

06
38,825

1,826

253 9 38,90
4


5.2
,381 ___
1.5
2,381 ___
1.2
1,468 ___
1.8
,933
0.7
1,901
___
1.0


X2

X3

X4

X5

X1
X1

X2

X3


X4

X5

X6

X6

1 ___
**
.216
___«
1
.216
.,
*.46
2
*
.
116
___
**
.305

.100

. **
.629

045


016

.071

.046

.071

1

.045

.046

.045

__ .**
.284

.050

.
X7

X8

X9

**


* 127
.
.
118

-.0
07

.
049

.541

40

.
019

.038

.013

___*
-.156

.014

__
.**

.284

1

.025

.059

042

.038

**

.541

.050

.._*
.140

.102

.025

___
1 **
.261

.102


*

*

X9

___**
___
___
.118
.305
**
**
Table 3: Correlation coefficient matrix
.271
.208
___** .045
.016
___*
-.00
.629
.127
7

1

.

X8


.116

.100

.
___**
.271

. **
.462

X7

___
**
.261**
___
**
.707

.049

.019
___*
-.156

.013

.014


.059

-.04
2

___
**
.
707

___
**
-.288

-.11
1 6

9

1 ___
**
-.170

-.116

___
-.089
**
**

-.288

-.08

___
**
-.17
0

1

Source: Survey and synthesis of the author

2.3.
2.3.1.

Tested Results of the research model
Correlation Matrix of the variables in the model

Correlation of variables in the model is expressed through correlation matrix (Pearson,
Karl, Yule, G., Blanchard, Norman, Lee, Alice, 1903). Observing the pearson correlation
matrix, the relationship between the variables is relatively low. The maximum correlation
coefficient of the variables in the model is 0.707 which is the correlation between the
variables X6 and X8. The correlation between the independent variables in the model is
rather low, the degree of collinearity between the variables in the model is not high. Thus,
the expected independent variables are introduced into the modeling process.

10



UWE
Bristo
l

University
Ofthe
West of
England

Table 4: Summary table of the model
Step 1

-

a

X1

2.892

X2

7

2.4
39
-.03

3.658


.

X4

153

X5

126

X6

0

X7

8
X8

61

X9
Constant

.
-.37
-.05
1.3
-.17


4

34
211
700
133
132
380

.
.

525
032
63

1
1

.

1

7.7

1

.

1


195
12.846

1

4.0

1

11
3.5

00

.
055

.

.

.

087
8.1

15

.


236

1 763
12.524

.
.

.

.
091

1.0
.

1

06

123
-

X3

1.4

5.3


1

76

.
964

.
000
469
858
005
659
000

.
.
.
.
.
.

045
.

.
026
5
4
691

943

1.16
1.13
.
.
3.90

0

.
840
3345.835

020

**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed)

The result of logistic regression model (Binary logistic)
The research model with explanatory variables is that the indicators describing the
financial viability and efficiency of the model are concretized by the nine independent
variables (X1 to X9) mentioned in the preceding section which are further advanced. The
logistic regression model is designed to study the factors that impact on business risk. The
results of the modeling are specified in the following table:
11


No
.


Ranking

Rankin
g code
Aaa

High quality
Aa

3

A

4

(%)

Highest quality

1

2

Frequ
ency

Classif
ication
60.08


115

45.45

37

14.62

Table 5: Summary of credit risk ratings by Standard & Poor's
Quality above average

14

5.53

Baa

Medium quality

18

5

Ba

Medium quality, with speculation factor

7


2.77

6

B

Quality is below average

25

9.88

7.11

15.42

15.42

a. Variable(S) entered on step 1: X1, X2, X3, X4, X5, X6, X7, X8, X9.

Source: Summary from analytical data
The audited results with explanatory variables affect the model's credit risk show that with
9 expected variables affecting the model. The results can be divided into the two groups of
impact
The Group has less impact on the ability to forecast credit risk of the business include:
Retained Earnings / Total Assets (X1), Retained Earnings / Net Revenue (X2), Net Profit /
Net Income (X4), Net Profit / Equity (X5) and Short-term Liquidity Ratio (X7)
Variable groups are likely to impact the Company's credit risk including variable capital /
total assets (X3), net revenue / current liabilities (X6), turnover Net / Total Assets (X8) and
Changes in Total Assets (X9).

Based on the results of the study, the results of the credit risk classification are based on
Standard & Poor's. According to Standard & Poor's, 60.08% of enterprises surveyed and
forecasted according to the results of the high quality model of risk index, meaning that it
is impossible to fall into credit risk, 15.42% enterprises achieves average quality, 15.42%
of enterprises are below average quality and 9.09% of enterprises are in bad condition, they
are likely to default in short time.

12


7

Caa

Low quality

14

5.53

8

Ca

Speculative, can default

14

5.53
9.09


9

C

Total

Poor quality, bad prospects

9

3.56

253

100


UWE
Bristo
Variable l
No.

symbol

University
Ofthe
West of
England


Input
variables

Results from the
model

RE/TA= Retained Earnings
/ Total
Assets
Not
statistically
significant
Table
6: Conclusion
of model
results
1

X1

2

X2

3

X3

4


X4

5

X5

6

X6

7

X7

8

X8

9

X9

RE/NR= Retained Earnings / Net Revenue

Not statistically significant

WC/TA= Working capital / Total assets

Statistical significance


NPM= Net profit margin

Not statistically significant

ROE=Return on Equity

Not statistically significant

NR/STD= Net revenue / Short-term debt

Statistical significance

CR: Short-term liquidity ratio

Not statistically significant

NR/TA= Net revenue / Total assets

Statistical significance

(Log(TA): Log(Total assets))

Statistical significance

Source: Summary from the results of the analys

2.3.2.

Determination of research results


Based on the model results, the conclusions from the model can be summarized in the
following table. Of these, 9/ 9 variables are expected to be included in the model, 4 out
of 9 variables are able to explain the enterprise risk in the study and 5 out of 9 variables
do not show clear explanations, not significant in explaining the credit risk of the
enterprise. Specific results are as follows:

13