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論文中文名稱:公司財務危機之混合式離群值偵測方法 [以論文名稱查詢館藏系統]
論文英文名稱:A Hybrid Outlier Detection Approach for Corporation Financial Distress [以論文名稱查詢館藏系統]
院校名稱:國立臺北科技大學
學院名稱:管理學院
系所名稱:資訊與財金管理系碩士班
畢業學年度:103
畢業學期:第二學期
出版年度:104
中文姓名:張育瑜
英文姓名:Yu-Yu Chang
研究生學號:102938005
學位類別:碩士
語文別:中文
口試日期:2015/07/08
指導教授中文名:翁頌舜
指導教授英文名:Sung-Shun Weng
口試委員中文名:林榮禾;楊欣哲;吳瑞堯
中文關鍵詞:財務危機、資料探勘、離群值偵測
英文關鍵詞:Financial distress, Data mining, Outlier detection
論文中文摘要:在金融證券交易市場中,上市上櫃公司的財務資訊,往往因為投資標的(target)公司與投資人間存在資訊不對稱的關係或者公司有窗飾財務報表問題,若投資到財務危機公司,將會對投資相關利害關係人產生重大影響。本研究提出一個混合式離群值偵測方法應用於財務危機,使用非監督式與半監督式離群值偵測方法,偵測公司發生財務危機的可能性。主要研究重點在於如何有效於財務危機發生之前,運用財務報表所提供之財務資訊,透過提出之方法,偵測出潛在具有財務危機公司,以電子股之光電產業為例,實驗結果顯示危機發生前一年整體平均偵測效果精確率可達82.62%,能在公司發生財務危機前有效偵測出潛在危機公司。
論文英文摘要:In financial stock market, the financial information of listed and over-the-counter companies with mass data will affect stakeholders who involve in investing the companies with financial distress because there is information asymmetry between targets and investors or the problem about window dressing of financial statement. Therefore, this research provides a hybrid outlier detection approach. It uses the outlier detection method, including semi-supervised and unsupervised learning to detect the possibility for companies which may have financial distress. In our research, we make experiments of optoelectronics industry to emphasize on how to detect the financial distress for the companies which have potential problems for financial distress before it is released by financial statements. Through the method, launched by our research, the precision rate which resulted by the experiments for detecting the potentials companies is 82.62% and it proves that it can detect the financial distress of these companies effectively.
論文目次:摘要 i
ABSTRACT ii
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 ix
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究架構 4
第二章 文獻探討 5
2.1 財務危機 5
2.1.1 財務危機定義 5
2.1.2 財務特徵 6
2.2 離群值偵測 16
2.2.1 離群值偵測方法 17
2.3 電子產業 19
第三章 研究設計與方法 21
3.1 研究設計 21
3.2 特徵選取 23
3.2.1 資料前處理 25
3.2.2 單因子多變量分析 27
3.3 離群值偵測 28
3.3.1 區域離群因子偵測方法 28
3.3.2 以熵為基礎之離群值偵測方法 31
3.3.3 投票機制 35
3.3.4 驗證流程及方式 37
第四章 實驗結果 39
4.1 樣本選取與設計 39
4.2 資料前處理 40
4.3 特徵選取 41
4.4 實證結果 45
4.4.1 區域離群因子偵測方法 45
4.4.2 以熵為基礎之離群值偵測方法 46
4.4.3 混合式離群值偵測方法 47
4.4.4 案例實證小結 85
第五章 結論與未來研究方向 92
5.1 研究結論與貢獻 92
5.2 研究限制 94
5.3 未來研究方向 94
參考文獻 95
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