臺灣博碩士論文加值系統

在過去幾十年裡，財務受挫的預測是一項重要且具挑戰性的議題，許多的研究使用傳統的方式和人工智慧的技術建構模型來處理破產預測和財務危機的問題。財務受挫的資訊將影響投資者的決定，且人們會依賴分析師的意見和他們主觀的判斷，這可能會導致他們做出錯誤的決策。因此，本研究的目的為建構一個新穎且客觀的模型，並提供判斷公司財務狀況的規則給決策者做為參考。本研究使用六種屬性選取方法: (1)卡方檢定，(2)訊息增益，(3)判別分析，(4)羅輯迴歸，(5)支援向量機，及(6)本研究所提出的聯合屬性方法來減少高維度的資料。此外，本研究利用粗糙集分類器來分類財務受挫，為了驗證所提出的模型，本研究使用台灣經濟新報(TEJ)資料庫做為實驗資料，並且和決策樹、多層感知器及支持向量機比較分類正確率及型一、型二誤差。實驗結果顯示，羅輯迴歸與卡方屬性選取方法結合粗糙集分類器模型在型一、型二誤差及正確率優於其他所列出的方法。

After several decades, the prediction of financial distress is an important and challenging issue. Many researchers have constructed models to deal with bankruptcy prediction and financial crisis, including conventional approaches and artificial intelligence (AI) techniques. Financial distress information will influence the investors’ decision, and the investors depend on the analyst’s opinions and their subjective judgments, it will cause investors/decision-makers to make the wrong decision. Therefore, the objectives of this study is to construct a novel model, which can provide the rules of financial situation of company to decision-makers as references. This study employed six attribute selection methods to reduce high dimension data, which contain: (1) Chi square, (2) Information gain, (3) Discriminant analysis, (4) Logistic regression, (5) Support vector machine, and (6) the proposed Join method, then this study utilized rough set classifier to classify financial distress. For verifying proposed model, the TEJ dataset is employed as experimental data, and compare with Decision tree, Multilayer perceptron, Support vector machine in Type I and Type II error and accuracy. The experimental result shows that Logistic regression and Chi square attribute selection method combined rough set classifier outperforms the listing models in Type I and Type II error and accuracy.

摘要 i
Abstract ii
誌謝 iii
Content iv
List of Tables v
List of Figures vi
1. Introduction 1
1.1 Research background 1
1.2 Research motivations 1
1.3 Research objectives 2
1.4 Organization of the thesis 3
2. Literature review 5
2.1 Financial distress 5
2.1.1 Performance indicator 6
2.1.2 Financial ratio 6
2.2 Attribute selection 7
2.3 Rough set theory 8
2.3.1 The LEM2 rule extraction methods 10
2.4 Related data mining techniques 11
2.4.1 Decision tree C4.5 11
2.4.2 Multilayer perceptron 12
2.4.3 Support vector machine 13
3. Proposed model 14
3.1 Research concept 14
3.2 Proposed algorithm 16
4. Experiment and comparison 20
4.1 The collected dataset 20
4.2 Financial distress experiment 22
4.3 Moving windows experiment 26
4.4 Findings 30
5. Conclusion 33
Reference 35

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