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研究生:阮孟濰
研究生(外文):Nguyen Manh Duy
論文名稱:強化灰色理論應用於營建公司財務風險預測之研究
論文名稱(外文):The Impact of Variables Quantity on the Accuracy of Grey System Theory in Bankruptcy Prediction
指導教授:曾惠斌曾惠斌引用關係
指導教授(外文):Tserng, Hui-Ping
口試委員:郭斯傑陳柏翰
口試委員(外文):Guo, Sy-JyeChen, Po-Han
口試日期:2013-06-28
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:土木工程學研究所
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:英文
論文頁數:75
中文關鍵詞:Logistic回歸灰色系統理論合成學位發病率財務比率ROC曲線建築行業
外文關鍵詞:Logistic RegressionGrey System TheorySynthetic Degree Incidencesfinancial ratioROC curveconstruction industry
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Bankruptcy Prediction has been a popular topic in business area. Once the firm goes bankrupt, it will bring the great loss to not only firm itself but also other stakeholders. The widely applied methods to predict the risk of business failure were based on financial ratio analysis; in which, applying Grey System Theory in the previous thesis for predicting default probability of construction firms, has brought some feasibility results, by relying on the 19 initial financial ratios. With the purpose of improving the Grey System Theory application, in this thesis, the authors would like to reduce the number of financial ratio before applying Grey System Theory, and then the results will be compared with previous thesis.
First, the Logistic Regression model, an accounting – based Model was applied to filter out the most important variables, before applying Grey Theory. Then, Synthetic Degree Incidences ρ of considered firms are calculated and combine these ρ values, the default probability of firms will be identified. Then, the other effected factors like as X zero (X0), theta θ and the key variables were considered. After that, using ROC curves to point out which one is the most favorable consequence data for model (correspond to the highest AUC value). Lastly, some comparisons as well as recommendations are suggested.


TABLE OF CONTENTS

ACKNOWLEDGEMENT ii
ABSTRACT iii
TABLE OF CONTENTS iv
LIST OF FIGURES vii
LIST OF TABLES viii
CHAPTER 1:INTRODUCTION 1
1.1 Background 1
1.2 Motivation and Problem statement 2
1.3 Research Objectives 3
1.4 Research Scope and Limitation 4
1.5 Thesis Structure 5
CHAPTER 2: LITERATURE REVIEW 7
2.1 Default Prediction Research 7
2.2 Default Pediction Research In the Construction Industry 12
2.3 Grey System Theory in Prediction Default Probability 15
2.4 Summary 18
CHAPTER 3:METHODOLOGY 19
3.1 Grey System Theory 19
3.1.1 Methods of Grey Numbers’ Generation Based on Average 20
3.1.2 Grey Incidence Analysis 22
3.2 The Accounting-based Models…………………………………………….…..27
3.2.1 The Logistic Regression 27
3.3 ROC Curve 29
3.3.1 Concept and Methodology of ROC Curve 29
3.3.2 Utilize ROC Curves to Validate the Model……………………………... 31
3.3 Summary 32
CHAPTER 4:DATA COLLECTION 33
4.1 Data Colection 33
4.1.1 Source and Validity of Data 33
4.1.2 Principles of Collecting Data…………………………………………... 34
4.1.3 Summary of the Input Data……………………………………………... 34
4.2 Data Clasification 36
4.3 Financial Ratios Definition 38
4.4 Variables Selection 42
4.5 Data Analysis Procedure 44
4.6 Summary 45
CHAPTER 5:DATA ANALYSIS AND RESULTS 47
5.1 Data Analysis 47
5.1.1 Example Analysis 47
5.2 Results 60
5.2.1Reasonable Data Consequence 60
5.2.2 Results of previous study (Le Quyen’s thesis).…………………..……... 61
5.2.3 Comparisons………………………………………………..……..…...... 63
5.3 Summary 65
CHAPTER 6:CONCLUSIONS 66
REFERENCES 68
APPENDICES…………………………………………………………………………71
A.1 Data Collection of Construction Firms 71
A.2 Data Collection of 15 Firms for Example Mathematics 73



LIST OF FIGURES

Fig.1.1The produce of research........................................................................................5
Fig.3.1 An example of ROC curve..................................................................................30
Fig.3.2 Schematic of a ROC……………………….....………...…………..........…….31
Fig.4.1 The algorithm chart of the data analysis process................................................46
Fig.5.1 Sum of the synthetic degree calculated based on matrix ρ..............…........…..58
Fig.5.2 Sum of the synthetic degree calculated based on matrix Q................................58
Fig.5.3 The synthetic degrees of firm No. 1……………………....…......……….........59
Fig.5.4: Summary of AUC value (846 samples, Ɵ = 0.5, 5 initial var.)…....….....…....61
Fig.5.5: Summary of AUC value of previous study …………….…....……....…....…64
Fig.5.6: Summary of AUC value of this study… …………….……....…..…..…....…64






LIST OF TABLES
Table 3.1 Types of error of ROC………………………....………....………...………..30
Table 4.1 Information of the defaulted companies …………......….……….…........….35
Table 4.2 Selected ratios’ classification … …………………....…………………...….37
Table 4.3 Definition and usage ratios …………………………....…….………………38
Table 4.4 Result of forward stepwise logistic regression process..……...….……….…43
Table 4.5 Correlation Matrix………………………………………...………….……...44
Table 5.1 Selected variables and their default probability correlation……..……..…....47
Table 5.2 5 year history data of firm No.1 ………………………….…………...….....48
Table 5.3 The absolute ɛj value of firm No.1………………………….…………...…...50
Table 5.4 The initial images value of firm No.1……………………...….………….....52
Table 5.5 The relative rj value of firm No.1…………………………............…………53
Table 5.6 The synthetic ρj value of firm No.1…………………………….…………....54
Table 5.7 The order of synthetic ρj value of firm No.1…….………………………59
Table 5.8 AUC value (846 samples, Ɵ = 0.5, 5 initial var.) …………………………60
Table 5.9 AUC value (846 samples, Ɵ = 0.5, 19 initial var) ………………………62


REFERENCES
1.Kagari, R., Farid, F., & Elgharib, H. (1992). Financial performance analysis for construction industry. Journal of Construction Engineering and Management, 118, (2), 349 – 361.
2.Kangari, R. (1988). Business failure in construction industry. Journal of construction Engineering and management , 172-190.
3.Russell, J. & Zhai, H.(1996). Predicting contractor failure using stochastic dynamics of economic and financial variables. Journal of Construction Engineering and Management, 122, (2), 183 – 191.
4.Edum-Fotwe, F., Price, A. & Thorpe, A. A review of financial ratio tools for predicting contractor insolvency. Construction Management and Economics (1996) 14, 189-19.
5.Altman, E.I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The journal of finance, 23, (4), 589-609.
6.Beaver, W.H. (1967). Financial ratios predictors of failure. Empirical research in accounting: selected studies 1966.Journal of Accounting Research 4(suppl.), 71–111.
7.Taffler, R.J. (1982). Forecasting company failure in the UK using discriminant analysis and financial ratio data. Journal of the Royal Statistical Society 145, 342–358 (part 3).
8.Kim, H. & Gu, Z. (2006). Predicting Restaurant Bankruptcy: A Logit Model in Comparison with a Discriminant Model. Journal of Hospitality & Tourism Research Vol. 30, No. 4, November 2006, 474-493.
9.Stein. R.M (2002). Benchmarking default prediction models: Pitfalls and remedies in model validation. Technical Report #020305.
10.Jame M.W Wong & S. Thomas. NG,(2010). Company failure in the construction industry: A critical review and a future research agenda.
11.David S. C (2000). Financial management and ratio analysis for corporative enterprises. Rural Business-Cooperative Service, U.S. Department of Agriculture. Research Report 175
12.A.Ohlson, J. (1980). Financial ratios and the probabilistic prediction of Bankruptcy. Journal of Accounting reseach , Vol.18, 109-131.
13.Deng, Ju long (1982). Control problems of Grey Systems, System and Control letters, Volume 1, number 5, 288-94.
14.Delcea, C. & Scarlat, E. The diagnosis of firm’s “Diseases” using the grey systems theory methods. Springer (106-120).
15.Liu, S.F., Lin, Y. (2006). Grey Information: Theory and Practical Applications. Springer, London.
16.Ping, J & Kejia, C. (2005). Application of Grey Incidence Analysis to Economic Index Time Difference Analysis. IEEE
17.Cheng, J. et al (2009). Business Failure Prediction Model based on Grey Prediction and Rough Set Theory. WSEAS transactions on information science and applications, Vol. 6.
18.Zweig MH, Campbell G (1993). Receiver-operating characteristic (ROC) plots a fundamental evaluation tool in clinical medicine.
19.Tserng, H.P., Liao, H.H., Tsai, L.K., Chen, P.C. (2011). "Predicting construction contractor default with option-based credit models-models'' performance and comparison with financial ratio models." Journal of Construction Engineering and Management, Vol. 137, No. 6, pp. 412-420
20.Tserng, H.P., Lin, G.F, Tsai, L.K., Chen, P.C. (2011). “An enforced support vector machine model for construction contractor default prediction,” Automation in Construction, Vol. 20, No. 8, pp. 1242-1249


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