Luận văn thạc sĩ UEH construct credit scoring models using logistic regression, neural network and

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UNIVERSITY OF ECONOMICS

HO CHI MINH CITY

VIETNAM

INSTITUTE OF SOCIAL STUDIES

THE HAGUE

THE NETHERLANDS

VIETNAM – NETHERLANDS

PROGRAMME FOE M.A. IN DEVELOPMENT ECONOMICS

CONSTRUCT CREDIT SCORING MODELS USING

LOGISTIC REGRESSION, NEURAL NETWORK AND

THE HYBRID MODEL

BY

LE MINH TIEN

MASTER OF ARTS INDEVELOPMENT ECONOMICS

HO CHI MINH CITY, NOVEMBER 2015

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UNIVERSITY OF ECONOMICS

HO CHI MINH CITY

VIETNAM

INSTITUTE OF SOCIAL STUDIES

THE HAGUE

THE NETHERLANDS

VIETNAM – NETHERLANDS

PROGRAMME FOE M.A. IN DEVELOPMENT ECONOMICS

CONSTRUCT CREDIT SCORING MODELS USING

LOGISTIC REGRESSION, NEURAL NETWORK AND

THE HYBRID MODEL

A thesis submitted in partial fulfilment of the requirements for the degree of

MASTER OF ARTS INDEVELOPMENT ECONOMICS

By

LE MINH TIEN

Academic Supervisor:

DR. PHAM DINH LONG

HO CHI MINH CITY, NOVEMBER 2015

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Abstract

Viet Nam economy is facing many difficulties, the operation of enterprises is not effective

leading to the non performing loan ratio of Banks increases. In the period 2007 to 2014, Viet

Nam have seen a downtrend in credit growth from 53,89% in 2007 to 11,8% in 2014 without

signs of strong recovery in the next period. A decline of credit growth implies that enterprises are

facing difficult in approaching credit from lending institutions and those enterprises which

operate mainly base on credit will be strongest affected ones. Non performing loan ratio of

Banks in Viet Nam has increased in 2007 to 2014, from 2% in 2007 then reached 3,25% in 2014

(highest in 2012 at 4,08%). In this period, almost enterprises could not approach Banks’ loans

while Banks are afraid of non performing loan ratio increasing. However, Banks are competing

strongly with domestic and foreign ones to achieve shares and maintain profit at the current. Viet

Nam is known as a densely populated country (a market size of 90 million people and high

proportion of young people) which is considered as a potential retail market for Banks to expand

and develop in the next period. To increase the competitiveness of Banks and also improve

effective loan risk management, this study applied different methods that are common used to

build up credit scoring model such as logistic regression, neural network and hybrid model.

Credit scoring model is considered as an application which is developed and widely applied in

the sector of finance and banking in the last decades, it is useful in accelerating credit analysis

process of Banks. Final results confirmed that characteristics like age, education, marital status,

current living status, living time in the current place, type of job, working time in current job,

working time in current field, number of dependent people, historical payment have a

statistically significant effect on repayment capacity of a customer. Credit scoring models can

classify customers according to different strategic purposes of users. And the performance of

hybrid models seemed better and more reliable than separate ones.

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Content

CHAPTER 1: INTRODUCTION................................................................................................ 8

CHAPTER 2: LITERATURE REVIEW ................................................................................. 11

2.1 The concept of credit scoring model: ..................................................................................... 11

2.2 Judgmental analysis method and credit scoring model: ......................................................... 12

2.3 Advantages and disadvantages of credit scoring models: ...................................................... 13

2.4 Historical development of credit scoring models: .................................................................. 14

2.4.1 Development in credit card and instant loan markets:...................................................................16

2.4.2 Development in mortgage markets: ...............................................................................................17

2.4.3 Development in consumer credit market: ......................................................................................18

2.5 Common variables in constructing credit scoring models:..................................................... 20

2.6 Common techniques employed in credit scoring models: ...................................................... 23

CHAPTER 3: METHODOLOGY ............................................................................................ 26

3.1 Data:........................................................................................................................................ 26

3.1.1 Variables:.......................................................................................................................................26

3.1.2 Assumptions:..................................................................................................................................28

3.2 Methodology:.......................................................................................................................... 30

3.3 Logistic regression:................................................................................................................. 31

3.3.1 Theory:...........................................................................................................................................31

3.3.2 Odds ratio:......................................................................................................................................31

3.3.3 Information value:..........................................................................................................................32

3.3.4 Quality of the model: .....................................................................................................................32

3.3.4.1 Log-likelihood ratio (LR) test:................................................................................................32

3.3.4.2 Pearson Chi-Square test: .........................................................................................................33

3.3.4.3 Akaike Information Criterion (AIC):......................................................................................33

3.4 Neural Network: ..................................................................................................................... 34

3.4.1 Theory:...........................................................................................................................................34

3.4.2 Components of artificial neural network: ......................................................................................34

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3.4.3 Back Propagation Algorithm: ........................................................................................................37

3.5 The hybrid model:................................................................................................................... 38

3.6 Comparison of models:........................................................................................................... 38

CHAPTER 4: EMPIRICAL RESULTS................................................................................... 39

4.1 Data:........................................................................................................................................ 39

4.1.1 Dependent Variable: ......................................................................................................................39

4.1.2 Independent Variables: ..................................................................................................................40

4.2 Estimation results:................................................................................................................... 48

4.2.1 Construction of Logit models: .......................................................................................................49

4.2.1 Comparison of Logit models: .......................................................................................................50

4.2.1.1 Log-likelihood ratio (LR) test:................................................................................................50

4.2.1.2 Person Chi-square test:............................................................................................................51

4.2.1.3 Akaike Information Criterion (AIC):......................................................................................51

4.2.1.4 Classification tables: ...............................................................................................................52

4.2.1.5 Comparison summary: ............................................................................................................53

4.3 Neural network: ...................................................................................................................... 53

4.3.1 Measurement of Model performance:............................................................................................53

4.3.2 Importance of independent variables:............................................................................................54

4.4 Hybrid model:......................................................................................................................... 55

4.4.1 Hybrid model 1: .............................................................................................................................55

4.4.2 Hybrid model 2: .............................................................................................................................56

4.5 Summary comparison: ............................................................................................................ 57

CHAPTER 5: CONCLUSION .................................................................................................. 58

5.1 Research summary and implication:....................................................................................... 58

5.1.1 Research summary:........................................................................................................................58

5.1.2 Implication:....................................................................................................................................59

5.2 Limitations of the study:......................................................................................................... 60

References.................................................................................................................................... 62

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List of tables

Table 01 Common variables in previous studies…………………………………………….23

Table 02 Common methods in previous studies.…………………………………………….26

Table 03 Variables and their definitions…………………………………………….……….27

Table 04 Summary of selected variables in logit models……………………………………50

Table 05 Log-likelihood ratio (LR) test…..…………………………………………………. 50

Table 06 Person Chi-square test result………………………………………………………51

Table 07 Akaike Information Criterion (AIC) result……………………………………….51

Table 08 Classification table of logit models………………………………………………... 51

Table 09 Summary logit model comparison…………………………………………………52

Table 10 Neural network model summary………………………………………………….. 53

Table 11 Classification of Neural network model…………………………………………...53

Table 12 Importance of independent variables of Neural network model………………...54

Table 13 Hybrid model 1 summary…………………………………………………………..55

Table 14 Classification of Hybrid model 1………………………………………………….. 55

Table 15 Hybrid model 2 summary…………………………………………………………..56

Table 16 Classification of Hybrid model 2………………………………………………….. 56

Table 17 Selected model summary…………………………………………………………... 57

Table 18 Correlation Matrix…….…………………………………………………………... 65

Table 19 Collinearity Test……….…………………………………………………………....66

Table 20 Results of logit model 1...…………………………………………………………...66

Table 21 Results of logit model 2...…………………………………………………………...67

Table 22 Results of logit model 3...…………………………………………………………...68

Table 23 Results of Neural network model………………………………………………..... 72

Table 24 Results of Hybrid model 1.……………………………………………………….... 74

Table 25 Results of Hybrid model 2….………………………………………………….…... 78

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Table 26 Summary of Information value of variables ………...…………………………... 81

List of figures

Figure 01Viet Nam credit growth in 2006-2014…………………………………………… 09

Figure 02 Non performing loan ratio in 2006-2014……………………………………….. 10

Figure 03 Steps to construct Credit scoring model………………………………………… 30

Figure 04 Processing information in an Artificial Neuron………………………………… 34

Figure 05 Neural network with one hidden layer…………………………………………... 34

Figure 06 Example of Summation function………………………………………………… 35

Figure 07 Example of Sigmoid function of ANN…………………………………………… 36

Figure 08 Back propagation algorithm of single neuron………………………………….. 37

Figure 09 Ratio of good/bad customer of dataset………………………………………….. 40

Figure 10 Ratio of good/bad customer base on age of customer…………………………. 41

Figure 11 Ratio of good/bad customer base on Current living status……………………. 42

Figure 12 Ratio of good/bad customer base on Education level………………………….. 43

Figure 13 Ratio of good/bad customer base on Gender…………………………………… 44

Figure 14 Ratio of good/bad customer base on Marital status…………………………… 44

Figure 15 Ratio of good/bad customer base on Living time at current place…………… 45

Figure 16 Ratio of good/bad customer base on Type of job………………………….…… 45

Figure 17 Ratio of good/bad customer base on Working time in present job…………… 46

Figure 18 Ratio of good/bad customer base on Working time in current field….………. 47

Figure 19 Ratio of good/bad customer base on Number of dependent people…………… 48

Figure 20 Ratio of good/bad customer base on Historical payment ………...…………… 48

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CHAPTER 1: INTRODUCTION

In 2007, The Financial Crisis began from United States (US) by a decisive decline of home

prices, then affected entire the economy and spread through the world economy. A cut deep into

demand all over the world made Viet Nam economy facing many difficulties in export sector in

this time. Enterprises have to narrow down their operations result in the credit growth of the

banking system has slowed down in the recent period.

Figure 01: Viet Nam credit growth in 2006-2014

Source: The State Bank of Vietnam’s annual report 2006-2014.

Banks are afraid of losing their capital because of existing difficulties of the economy while the

sign of economic recovery is still very weak, thus they are careful in making their lending

decisions. Economists forecast this situation would still continue in the next few years. To

survive and develop in this period, some economists suggested that, in the coming period, the

retail banking segment will be the alternative strategy could help Banks developing their

businesses and maybe is the key growth because Viet Nam has a market size of 90 million

people (with a high proportion of young people) which will generate opportunities for Banks to

expand their services to help consumers increasing asset value and better businesses management

as well as carry out daily payment activities. Viet Nam with some typical characteristics of low￾income developing country such as dynamic young population, rising income and desire to

25,44%

53,89%

25,43%

37,53%

31,19%

12,00%

8,91%

12,51%

11,80%

-10%

0%

10%

20%

30%

40%

50%

60%

2006 2007 2008 2009 2010 2011 2012 2013 2014

Viet Nam credit growth in 2006-2014

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