# 臺灣博碩士論文加值系統

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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 CONTENTSACKNOWLEDGEMENT iiABSTRACT iiiTABLE OF CONTENTS ivLIST OF FIGURES viiLIST OF TABLES viiiCHAPTER 1:INTRODUCTION 11.1 Background 11.2 Motivation and Problem statement 21.3 Research Objectives 31.4 Research Scope and Limitation 41.5 Thesis Structure 5CHAPTER 2: LITERATURE REVIEW 72.1 Default Prediction Research 72.2 Default Pediction Research In the Construction Industry 122.3 Grey System Theory in Prediction Default Probability 152.4 Summary 18CHAPTER 3:METHODOLOGY 193.1 Grey System Theory 193.1.1 Methods of Grey Numbers’ Generation Based on Average 203.1.2 Grey Incidence Analysis 223.2 The Accounting-based Models…………………………………………….…..273.2.1 The Logistic Regression 273.3 ROC Curve 293.3.1 Concept and Methodology of ROC Curve 293.3.2 Utilize ROC Curves to Validate the Model……………………………... 313.3 Summary 32CHAPTER 4:DATA COLLECTION 334.1 Data Colection 334.1.1 Source and Validity of Data 334.1.2 Principles of Collecting Data…………………………………………... 344.1.3 Summary of the Input Data……………………………………………... 344.2 Data Clasification 364.3 Financial Ratios Definition 384.4 Variables Selection 424.5 Data Analysis Procedure 444.6 Summary 45CHAPTER 5:DATA ANALYSIS AND RESULTS 475.1 Data Analysis 475.1.1 Example Analysis 475.2 Results 605.2.1Reasonable Data Consequence 605.2.2 Results of previous study (Le Quyen’s thesis).…………………..……... 615.2.3 Comparisons………………………………………………..……..…...... 635.3 Summary 65CHAPTER 6:CONCLUSIONS 66REFERENCES 68APPENDICES…………………………………………………………………………71A.1 Data Collection of Construction Firms 71A.2 Data Collection of 15 Firms for Example Mathematics 73LIST OF FIGURESFig.1.1The produce of research........................................................................................5Fig.3.1 An example of ROC curve..................................................................................30Fig.3.2 Schematic of a ROC……………………….....………...…………..........…….31Fig.4.1 The algorithm chart of the data analysis process................................................46Fig.5.1 Sum of the synthetic degree calculated based on matrix ρ..............…........…..58Fig.5.2 Sum of the synthetic degree calculated based on matrix Q................................58Fig.5.3 The synthetic degrees of firm No. 1……………………....…......……….........59Fig.5.4: Summary of AUC value (846 samples, Ɵ = 0.5, 5 initial var.)…....….....…....61Fig.5.5: Summary of AUC value of previous study …………….…....……....…....…64Fig.5.6: Summary of AUC value of this study… …………….……....…..…..…....…64LIST OF TABLESTable 3.1 Types of error of ROC………………………....………....………...………..30Table 4.1 Information of the defaulted companies …………......….……….…........….35Table 4.2 Selected ratios’ classification … …………………....…………………...….37Table 4.3 Definition and usage ratios …………………………....…….………………38Table 4.4 Result of forward stepwise logistic regression process..……...….……….…43Table 4.5 Correlation Matrix………………………………………...………….……...44Table 5.1 Selected variables and their default probability correlation……..……..…....47Table 5.2 5 year history data of firm No.1 ………………………….…………...….....48Table 5.3 The absolute ɛj value of firm No.1………………………….…………...…...50Table 5.4 The initial images value of firm No.1……………………...….………….....52Table 5.5 The relative rj value of firm No.1…………………………............…………53Table 5.6 The synthetic ρj value of firm No.1…………………………….…………....54Table 5.7 The order of synthetic ρj value of firm No.1…….………………………59Table 5.8 AUC value (846 samples, Ɵ = 0.5, 5 initial var.) …………………………60Table 5.9 AUC value (846 samples, Ɵ = 0.5, 19 initial var) ………………………62
 REFERENCES1.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 17512.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. IEEE17.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. 412-42020.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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