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姓名 施冠安(Guan-an Shih)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 公司治理指標在財務危機預測: 以台灣上市上櫃公司為例
(Corporate government indicators apply in financial distressed problem: taking Taiwan-listed company for example)
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摘要(中) 財務危機預測問題長久以來都是一個重要且常被廣泛討論的主題,吸引了世界各地的投資者和研究學者的關注。以往大部分的學者單純使用財務比率來進行財務危機預測,而近年來開始有學者建議使用公司治理指標可以改善財務危機預測的準確率。
本研究對公司治理指標並與相關文獻比較,發現相關文獻所使用的特徵並不全面,因此我們想知道如果將所有公司治理指標的分類都考慮進來的話,對於財務危機預測是否有所幫助?
最後我們發現在台灣資料集下將所有的公司治理指標都考慮進來確實可以提升財務危機預測的準確率。其中solvency、profitability以及board structure都是重要的分類,因此如果要考慮加入公司治理指標這一類的特徵也需要將這幾類特徵都放到實驗裡面,這樣公司治理特徵才會有幫助。
摘要(英) Financial distress problem (FDP) has been important and widely studied topic. Financial distress prediction is receiving increasing attention of stakeholders and researchers in the worldwide. In the past, most researcher use financial ratios for FDP, recently some researchers proposed that corporate governance indicators may improve the accuracy of FDP.
Our research uses corporate government indicators and compares with related works. We find that these related works just use part of corporate government indicators. Thus when we use all the corporate government indicators in our research it′s still helpful for FDP?
Finally, we find that when we consider all the corporate government indicators will improve the accuracy of FDP in Taiwan dataset. Solvency, profitability, and board structure are important categories, so if we want to consider that using corporate governance indicator to improve the FDP, must use these categories and corporate governance indicators will be helpful.
關鍵字(中) ★ 特徵挑選方法
★ 分類器
★ 財務危機預測
★ 公司治理指標
關鍵字(英) ★ feature selection
★ classifier
★ financial distressed prediction
★ corporate governance indicators
論文目次 中文摘要 i
Abstract iii
表目錄 ix
一、緒論 1
1.1. 研究背景 1
1.2. 研究動機 3
1.3. 研究目的 6
1.4. 論文架構 6
二、文獻探討 7
2.1. CGIs相關研究文獻探討 7
2.2. 分類器介紹 9
2.2.1. 支持向量機 9
2.2.2. 最近鄰居分類 13
2.2.3. 分類回歸樹 14
2.2.4. 類神經網路 14
2.2.5. 單純貝式分類器 16
2.3. 特徵挑選方法 17
2.3.1. Genetic Algorithm (GA) 19
2.3.2. Recursive feature elimination (RFE) 22
2.3.3. Stepwise Discriminant Analysis (SDA) 22
2.3.4. Stepwise Logistic Regression (SLR) 23
2.3.5. t-Test 23
三、實驗設計 25
3.1. 資料來源 25
3.2. 資料前置處理方式 26
3.3. Misclassification cost和cost ratios 26
3.4. 實驗參數 28
3.5. 研究假說 31
3.6. 實驗架構 32
3.5.1. Filter approach實驗設計 33
3.5.2. Wrapper approach實驗設計 34
四、實驗流程與結果分析-1 36
4.1. 針對FRs、CGIs進行討論 36
4.2. 實驗流程 36
4.3. 實驗結果與分析 37
五、實驗流程與結果分析-2 39
5.1. 實驗流程 39
5.2. 實驗結果與結果分析 40
5.2.1. 完整資料集 40
5.2.2. Solvency 42
5.2.3. Profitability 45
5.2.4. Turnover ratios 47
5.2.5. 其他的FRs資料集 49
5.2.6. Board 51
5.2.7. Ownership 53
5.2.8. 其他的CGIs資料集 55
5.2.9. 針對完整資料集進一步討論 57
六、結論與未來展望 60
6.1. 結論 60
6.2. 未來展望 62
參考文獻 63
附錄一 67
附錄二 73
附錄三-其他FRs分類結果呈現 79
Cash flow 79
Growth 81
Others 83
附錄四-其他CGIs分類結果呈現 85
Key person retained 85
Others 87
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指導教授 梁德容(deron liang) 審核日期 2014-7-24
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