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研究生:王仁杰
論文名稱:以離群值偵測方法為基礎的公司財務危機預警模型之研究
論文名稱(外文):An Outlier Detection Based Predicting Model of Financial Distress
指導教授:陳安斌陳安斌引用關係
學位類別:碩士
校院名稱:國立交通大學
系所名稱:資訊管理研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2005
畢業學年度:93
語文別:中文
論文頁數:51
中文關鍵詞:財務預警模型局部離群值因子維度降低
外文關鍵詞:finance distresslocal outlier factordimension reduction
相關次數:
  • 被引用被引用:3
  • 點閱點閱:281
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:3
本研究提出一整合性的財務危機預警模型架構,以系統化建置此問題模型系統。並依循此架構,提出一個以離群值為基礎的財務危機預警模型,其以局部離群值因子(Local Outlier Factor)來衡量樣本與同業間離群程度,輔以2D低維平面投射技術(2D low dimension projection)與依特徵排序架構(Rank-By-Feature Framework)來解決高維度資料空間中離群值不易搜尋的問題。
本研究所針對的對象為台灣上市電子科技業中的五項子產業,對於此五子產業於不同時空環境下,分別找出最適判斷其離群分析的2D低維投射平面,即模型解釋變量。在實證研究中發現,事實中財務危機公司是否進行窗飾或惡意舞弊行為,其異常現象亦傾向發生於不同類型的2D低維投射平面。
實驗結果顯示,此模型對於企業財務危機預警能掌握其徵兆,並且可使用量化的局部離群值因子來描述此一異常程度。藉由實驗案例,我們亦發現雖然異常並不必然代表其為財務經營陷入困境,但其結果仍可提供使用者充分決策資訊。
摘要 III
ABSTRACT IV
致謝 IV
表目錄 VIII
圖目錄 X
第一章 緒論 1
1.1 問題背景 1
1.2 研究動機 2
1.3 研究目的 3
1.4 研究範圍與限制 3
1.5 研究流程 4
1.6 研究架構 4
第二章 文獻探討 5
2.1 財務危機預警 5
2.2 特徵選取與維度降低 13
2.3 離群值偵測與分析 15
第三章 研究方法 19
3.1 維度降低處理 19
3.2 局部離群值因子 21
第四章 實證研究 23
4.1 整合性財務危機預警模型架構 23
4.2 實驗流程 24
4.3 實證結果分析與討論 38
第五章 結論與未來展望 47
參考文獻 48
中文部分:
〔1〕 葉金成,我國股票上市優良與不優良企業財務特性之研究,碩士論文,國立政治大學企業管理研究所,民國六十七年。
〔2〕 何太山,運用區別分析建立商業放款信用評分制度,碩士論文,國立政治大學企業管理研究所,民國六十六年。
〔3〕 林建丞,「財務危機公司之預警偵測」,碩士論文,國立東海大學管理研究所,民國八十八年。
〔4〕 李立行,「運用現金流量預測企業財務危機之研究--以上市公司紡織業為例」,碩士論文,淡江大學管科所,民國七十七年。
〔5〕 陳肇榮,「運用財務比率預測企業財務危機之實證研究」,博士論文,國立政治大學財政研究所,民國七十二年。
〔6〕 潘玉葉,「台灣股票上市公司財務危機預警分析」,博士論文,淡江大學管理科學研究所,民國七十九年。
〔7〕 許瑞立,「台灣上電子公司財務預警模型」,碩士論文,義守大學管理科學研究所,民國八十九年。
〔8〕 施淑萍,「財務危機預警模式與財務危機企業財務特性之研究」,碩士論文,東吳大學會計學研究所,民國八十九年。
〔9〕 徐淑芳,「台灣上市公司財務危機預警-應用多變量CUSUM 時間序列分析」,碩士論文,國立東華大學企業管理研究所,民國八十七年。
〔10〕 王凱仁,「建設公司財務危機動態預警模型之研究」,博士論文,國立交通大學土木工程學系,民國九十二年。
〔11〕 賴麗月,「企業失敗的預測-比例危機模型應用」,碩士論文,東吳大學會計研究所,民國八十二年。
〔12〕 郭志安,「以Cox 模型建立財務危機預警模式」,碩士論文,逢甲大學統計與精算研究所,民國八十五年。
〔13〕 鄧志豪,「以分類樣本偵測地雷股-新財務危機預警模型」,碩士論文,國立政治大學金融學系,民國八十八年。
〔14〕 楊浚泓,「考慮財務操作與合併報表後之財務危機預警模式」,碩士論文,國立中央大學財務管理研究所,民國九十年。
〔15〕 施思佳,「電子業財務危機預警模式之研究--以現金流量觀點」,碩士論文,國立台北大學企業管理學系研究所,民國九十一年。
〔16〕 鄭國瑞,「多項財務危機預警模式之探討」,碩士論文,國立高雄第一科技大學金融營運所,民國九十年。


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〔8〕 Breunig, M. M., Kriegel, H. P., Ng, R. T., and Sander, J., "LOF: Identifying Density-Based Local Outliers," Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 93-104, 2000.
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〔10〕 Ester, M., Kriegel, H. P., Sander, J., and Xu, X., "A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise," Proceedings of the International Conference on Knowledge Discovery in Databases and Data Mining, pp. 226-231, 1996.
〔11〕 Fayyad, U., Shapiro, G. P., and Smyth, P., "Knowledge Discovery and Data Mining: Towards a Unifying Framework," Proceedings of International Conference on Knowledge Discovery and Data Mining, pp. 82-88. 1996.
〔12〕 Foster, B. P., Ward, T. J., and Woodroof, J., "An Analysis of the Usefulness of Debt Defaultsand Going Concern Opinions in Bankruptcy Risk Assessment," Journal of Accounting Auditing and Finance, pp. 351-371, 1998.
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〔14〕 Gavrilov, M., Anguelov, D., Indyk, P., and Motwani, R., "Mining The Stock Market: Which Measure Is Best ?," Proceedings of International Conference on Knowledge Discovery and Data Mining, pp. 487-496, 2000.
〔15〕 Gilson, S. C., "Management Turnover and Financial Distress," Journal of Financial Economics, vol. 25, pp. 241-262, 1989.
〔16〕 Guha, S., Rastogi, R., and Shim, K., "ROCK: A Robust Clustering Algorithm for Categorical Attributes," Proceedings of International Conference on Data Engineering, pp. 512-521, 1999.
〔17〕 Hawkins, D. M., "Identification of Outliers," Chapman and Hall, 1980.
〔18〕 Inselberg, A. and Dimsdale B., "Parallel Coordinates: A Tool for Visualizing Multidimensional Geometry," Proceedings of Conference on Visualization, pp. 361-375, 1990.
〔19〕 Kira, K. and Rendell, L., "A Practical Approach to Feature Selection," Proceedings of International Conference on Machine Learning, pp. 249-256, 1992.
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