Predicting Company Bankruptcy Using Data Mining｜國立清華大學博碩士論文庫

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研究生：	藍佳樂 Margaret Lungile Dlamini
論文名稱：	Predicting Company Bankruptcy Using Data Mining 應用資料探勘技術於公司倒閉預測之研究
指導教授：	魏志平 Chih-Ping Wei
口試委員:
學位類別：	碩士 Master
系所名稱：	科技管理學院 - 科技管理研究所 Institute of Technology Management
論文出版年：	2007
畢業學年度：	95
語文別：	英文
論文頁數：	50
中文關鍵詞：	bankruptcy prediction 、data mining 、decision tree induction 、support vector machines (SVM)
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Business is very important for the economic growth of every country. The most successful economies have been boosted by growth in the business sector in all industries. There have been mergers and acquisitions and an enormous growth in entrepreneurship in all the successful economies of the developed countries. The developing countries are also following this trend in order to succeed in the global arena. With all these business ventures going on, it is equally important to make sure that the business is carried out in a way that will benefit all stakeholders. But in some instances, the business venture fails because of circumstances beyond their control.

The main objective of bankruptcy prediction is early detection of decline in business activity, whether that decline is caused by factors within the company or outside. Prior studies highlight the most important factors (i.e., financial ratios) to be considered. However, most of them concentrate on the comparison of different statistical or data mining techniques at a single time point by using annual data. This study has addressed the longitudinal analysis of quarterly financial data on bankruptcy prediction. Using 210 companies listed in the Taiwan Stock Exchange (including 105 bankrupt and 105 solvent companies), our empirical results show that the predicting timings closer to the occurrence of actual events will lead to higher effectiveness on bankruptcy prediction. Specifically, prediction made at one or two quarters prior to bankruptcy generally has highest accuracy, precision, and recall rates. In addition, SVM outperforms J48 in the target prediction task. Finally, the inclusion of ratio-change variables has no positive effects on the effectiveness of bankruptcy prediction.

Keywords: Bankruptcy prediction, Data mining, Decision tree induction, Support vector machines (SVM)

Acknowledgements    ii
List of Tables    iv
List of Figures    v
Abstract    vi
Chapter 1  Introduction    1
1.1 Background    1
1.2 Research Motivation and Objectives    3
1.3 Organization of the Thesis    4
Chapter 2  Literature Review    5
2.1 Importance of Bankruptcy Prediction    5
2.2 Prior Studies on Bankruptcy Prediction    8
Chapter 3  Data, Variables, and Investigated Learning Techniques    18
3.1 Data Collection    18
3.2 Variables for Bankruptcy Prediction    20
3.3 Investigated Data Mining Techniques    22
Chapter 4  Data Analysis and Evaluation    25
4.1 Evaluation Criteria and Procedure    25
4.2 Analysis of Bankruptcy Prediction over Multiple Time Points    27
4.3 Effects of Ratio-Change Variables on Prediction Effectiveness    30
Chapter 5  Conclusions and Future Research    37
5.1 Conclusion    37
5.2 Limitations of the Study and Future Research Directions    38
References    40
Appendix A  Variables Used in Prior Bankruptcy Prediction Studies    43
Appendix B  Capital Ranges for Bankrupt Companies in Our Dataset    50



List of Tables

Table 2-1 Summary of Techniques Employed by Prior Bankruptcy Prediction Research

                 ……………………………………………………………………………….14

Table 3-1 Summary of Independent Variables………………………………………….21

Table 4-1 Effectiveness of Bankruptcy Prediction Using J48…………………………..27

Table 4-2 Significant test (p-value) between Different Time Points (Using J48)………28

Table 4-3 Effectiveness of Bankruptcy Prediction Using SVM………………………...29

Table 4-4 Significant test (p-value) between Different Time Points (Using SVM)…….29

Table 4-5 Significant test (p-value) between J48 and SVM…………………………….30

Table 4-6 Effectiveness of Bankruptcy Prediction across Different Time Points (J48 with Inclusion of Ratio-Change Variables)…………………………………………………..32

Table 4-7 Significant test (p-value) among Different Time Points (J48 with Inclusion of Ratio-Change Variables)……………………………………………………...................32

Table 4-8 Effectiveness of Bankruptcy Prediction across Different Time Points (SVM with Inclusion of Ratio-Change Variables)………………………………………..…....33

Table 4-9 Significant test (p-value) among Different Time Points (SVM with Inclusion of Ratio-Change Variables)………………………….………………………………….…34

Table 4-10 Significant test (p-value) between J48 and SVM (with Inclusion of Ratio-Change Variables……………………………………………………………………......35

Table 4-11 Significant test (p-value) on Effects of Inclusion of Ratio-Change Variables (Using J48)……………………………..………………………………………………..36

Table 4-12 Significant test (p-value) on Effects of Inclusion of Ratio-Change Variables (Using SVM)……………………………………………………………………………36





List of Figures


Figure 3-1 Example of Margin and Support Vectors in SVM………………………….. 24

                                

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