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研究生:黃冠維
研究生(外文):Kuan-Wei Huang
論文名稱:企業失敗預測模式之建構
論文名稱(外文):Constructing a Business Failure Prediction Model
指導教授:林榮禾林榮禾引用關係
口試委員:吳泓怡池文海趙莊敏
口試日期:2007-07-23
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:商業自動化與管理研究所
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:英文
論文頁數:55
中文關鍵詞:企業失敗預測案例式推理分類迴歸樹
外文關鍵詞:Business failure predictionCBRCART
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近年來由於企業面臨競爭激烈與多樣且多變的經營環境,在經營上已不如往昔般的單純。企業也會在面臨競爭和管理成效不彰的情況下發生危機或倒閉,進而造成社會的不安定與金融秩序的嚴重衝擊。因此,研究企業經營失敗預測,能增進企業經營的成效與降低企業的營運成本,是目前企業決策者刻不容緩的要事。
本研究之目的在於建構一套企業預測模型,利用企業之財務與非財務指標偵測其體質並加以評判是否陷入失敗危機之中,期能提供企業經營者的參考。本研究提出整合性預測模型其中結合約略集合理論 (RST) 進行變數的篩選及灰關聯分析 (GRA) 作為權重計算方法,再分別運用案例式推理(CBR)及分類迴歸樹 (CART) 建構出兩套新的預測模式HBFP1 (Hybrid Business Failure Prediction 1) 模型與HBFP2 (Hybrid Business Failure Prediction 2) 模型來比較其預測準確率外,並分別偵測企業失敗的原因與規則。研究案例擷取自台灣經濟新報資料庫的資料及進行HBFP1、HBFP2、CBR與RST+CBR之方法比較,研究結果得知其準確率分別為83.3%、93.3%、43.2%與59.8%,以HBFP2模型之準確率最佳且有效降低型一誤差與型二誤差錯誤率。
本研究在實務上提出不同組合的混合預測模型, 最佳的HBFP2模型可提供企業決策者實際使用,進而採取適當的預防措施,防範企業危機發生或降低其影響的程度。
The business operational environment in contemporary society has become increasingly competitive and varied in recent years. Companies in this situation may have faced competition and ineffective management, resulting in bankruptcy, negative effects on societal stability, and financial problems. Consequently, exploring business operation failure predictio, strengthening business operation, and reducing operational cost are key issues for decision-makers. The purpose of this study is to develop an integrated model of predicting business failure, using business financial and non-financial factors to diagnose the status of business, thereby providing references for business operation. This study applied Rough Set Theory to extract key financial and non-financial factors and Grey Relational Analysis (GRA) as an approach to assigning weights. In addition, applying Case-Based Reasoning (CBR) and Classification and Regression Tree (CART) to propose two new hybrid models entitled Hybrid Business Failure Prediction 1 (HBFP1) and Hybrid Business Failure Prediction 2 (HBFP2). Not only compared with their accuracy rate in predicting failure. But also find out the considerable failed reasons and rules. After exploring Taiwan Economic Journal (TEJ) database and conducting various experiments with HBFP1, HBFP2, CBR and CBR, the results show that the accuracy rate of HBFP1, HBFP2, CBR and CBR predicts business failure with 83.3%, 93.3%, 43.2% and 59.8%, respectively. The HBFP2 with the highest accuracy rate and it effectively reduces Type I and Type II error rate as well. This study provides different hybrid models, especially HBFP2, for appropriate means to prevent business crises in the real world and lowers the degree of their influence.
ABSTRACT i
ABSTRACT IN CHINESE ii
ACKNOWKLEDGEMENT iii
CONTENTS iv
LIST OF TALBES vii
LIST OF FIGURES viii
Chapter 1 INTRODUCTION 1
1.1 Motivation and Background 1
1.2 Purposes of the Study 3
1.3 Organization of Research 4
Chapter 2 LITERATURE REVIEW 6
2.1 Statistical Methods 6
2.2 Rough Set Theory (RST) 7
2.2.1 Information System 7
2.2.2 Approximation of Sets 8
2.2.3 Approximation of a Partition of U 9
2.2.4 Reduction of Attributes 10
2.2.5 Decision Tables 10
2.2.6 RST Applications 11
2.3 Decision Trees 13
2.3.1 Classification and Regression Tree (CART) 14
2.4 Artificial Neural Network (ANN) 15
2.5 Grey Relational Analysis (GRA) 16
2.5 Case-Based Reasoning (CBR) 18
2.5.1 Case representation 19
2.5.2 Identifying the Case Attribute for CBR 20
2.5.3 Similarity Measurement 20
2.5.4 Retrieval Algorithm 21
2.5.5 CBR Applications 21
2.6 Financial Ratios and Other Variables 23
Chapter 3 METHODOLOGY 26
3.1 Experiment Design 26
3.1.1 Definition of Business Failure 27
3.1.2 Variable Selection 27
3.2 HBFP1 Model Construction 32
3.2.1 RST Attribute Reduction 33
3.2.2 GRA Weight Computation 33
3.2.3 CBR Failure Diagnosis 35
3.3 HBFP2 Model Construction 36
3.2.1 CART Rule Extraction 36
Chapter 4 EXPERIMENT RESULTS 38
4.1 Research Data 38
4.2 Model Evaluation 39
4.2.1 CBR Method 39
4.2.2 Combined RST with CBR model (RST+CBR) 40
4.2.3 ANN and combined RST with ANN Models (RST+ANN) 41
4.2.4 HBFP1 Model 41
4.2.5 HBFP2 Model 43
4.3 Discussion 44
Chapter 5 CONCLUSIONS AND SUGGESTIONS 46
5.1 Conclusions 46
5.2 Managerial Implications 46
5.3 Further Research 47
REFERENCE 48
APPEDIX A RST COMPUTATION 54
APPEDIX B GRA COMPUTATION 56
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