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研究生:李偉銘
研究生(外文):Wei-Ming Li
論文名稱:以成長型階層式自映射網路模式及軌跡分析建構企業財務危機預測模式
論文名稱(外文):A corporate financial crisis forecasting model using growing hierarchical self-organizing map and trajectory analysis
指導教授:邱志洲邱志洲引用關係高淩菁高淩菁引用關係
指導教授(外文):Chih-Chou ChiuLing-Jing Kao
口試委員:呂奇傑蔡榮發
口試委員(外文):Chi-Jie LuJung-Fa Tsai
口試日期:2012-06-21
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:經營管理系碩士班
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:2012
畢業學年度:100
語文別:中文
論文頁數:63
中文關鍵詞:企業財務危機預測成長型階層式自組織映射圖網路軌跡分析
外文關鍵詞:Corporate financial crisis forecasting modelGrowing hierarchical self-organizing mapTrajectory analysis
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美國恩隆集團、日本活力門集團以及台灣博達和皇統等公司分別在2002年與2004年無預警地發生財務危機,這些財務危機的發生引發了一連串的跳票與違約交割,不僅波及上下游的企業,更造成銀行發生呆帳及投資人遭受損失等問題,產生極高的社會成本。因此,如何有效地預測企業未來是否可能發生財務危機以提早提出預警,已是一個非常重要的議題。
在企業財務危機預測的領域中,過去研究常以單變量模型、Z-SCORE模型、區別分析、羅吉斯迴歸和類神經網路等方法來進行模式的建立與預測。然而上述方法在短期預測中 (預測企業一年後是否發生財務危機) 雖有不錯的預測準確率,但在中長期的預測中 (預測企業二年後或三年後是否發生財務危機) 其準確率都不盡理想。為了提高長期預測的準確性,本研究以Dittenbach et al. (2002) 所提出之成長型階層式自組織映射圖網路(Growing hierarchical self-organizing map, GHSOM)結合軌跡分析(Trajectory analysis)來建立企業財務危機預測模式,期望能提高企業財務危機中長期預測的準確性以讓投資者及早認知企業財務體質狀況的變化,避開地雷企業。
本研究以臺灣上市櫃公司中的電子工程業、紡織纖維業與建築營造業等三個產業,在2000年到2009年間「發生財務危機企業」總計111家公司做為研究樣本,並以一比一的比例選取相對應的「未發生財務危機企業」總計111家公司做為對照組。再者,本研究根據Kumar and Ravi (2007) 所整理的1968年至2005年總計128篇相關文獻中最常被使用於財務危機預測的前十項財務指標做為本研究的研究變數。
研究結果顯示,本研究所提之方法可根據各企業財務指標形成的拓撲軌跡型態,以簡單圖形化的方式讓投資人了解企業整體財務體質的優劣變化,提供投資人有效的參考資訊。此外,在未來是否發生財務危機的長期預測上,本研究所提之模式在預測企業兩年後和三年後是否發生財務危機的整體預測準確率分別為77.3%和78.8%,皆比區別分析(71.2%和68.1%)和羅吉斯迴歸(74.2%和60.6%)來得理想。由此可知,本研究所提之方法相較於傳統方法,可提早向投資人提出有效的預警,讓投資人及早避開地雷企業,減少可能的損失。


In the field of forecasting corporate financial crisis, the univariate model、the discriminant analysis、the logistic regression and the neural networks were used to build forecasting models. Many literatures showed that these models perform better in short term forecasting, however, their accuracy rate sharply decline in long term forecasting. As a result, growing hierarchical self-organizing map and trajectory analysis are adopted in our research to overcome this drawback.
Two hundred and twenty two companies, including 111 financial crisis companies and 111 corresponding healthy companies, are used to be our empirical samples. Ten financial variables, according to Kumar and Ravi (2007), are selected to be our research variables.
The empirical results showed that the trajectories formed by the financial variables could clearly demonstrate the pattern of the company financial condition. Moreover, the two-year-ahead and the three-year-ahead accuracy rate of our proposed model are 77.3% and 78.8%. It also showed that our proposed model outperforms discriminant analysis model and logistic regression model in long term forecasting.


摘要 i
Abstract iii
誌謝 v
目錄 vi
表目錄 viii
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 3
第二章 文獻探討 5
2.1 企業財務危機預測模型 5
2.1.1 區別分析 5
2.1.2 羅吉斯迴歸 6
2.1.3 類神經網路 7
2.1.4 存活分析 8
2.2 自組織映射圖網路 16
2.3 成長型階層式自組織映射圖網路 17
第三章 研究方法 19
3.1 資料收集及前處理 20
3.2 GHSOM分群模式 23
3.3 軌跡分析 25
3.4 預測模式 26
第四章 實證結果與比較 28
4.1 實證結果 28
第五章 結論與建議 43
參考文獻 44
附錄 58

表2-1 2006年到2012年企業財務危機預測英文文獻彙總表 12
表2-2 企業財務危機預測中文文獻彙總表 14
表3-1 研究變數表 21
表4-1 財務危機公司十個財務指標之敘述統計表 28
表4-2 非財務危機公司十個財務指標之敘述統計表 29
表4-3 所有公司十個財務指標之敘述統計表 29
表4-4 財務危機公司財務指標之Shapiro-Wilk常態性檢定結果表 30
表4-5 非財務危機公司財務指標之Shapiro-Wilk常態性檢定結果表 30
表4-6 Mann-Whitney檢定結果表 31
表4-7 各大群內的財務指標之平均數與編號表 34
表4-8 「卓立」的軌跡型態與訓練樣本的軌跡型態之歐基里德距離節錄表 39
表4-9 「卓立」發生財務危機前三年的十個財務指標表 40
表4-10 「居易」的軌跡型態與訓練樣本的軌跡型態之歐基里德距離節錄表 41
表4 11 各方法整體預測正確率彙整表 42

圖1-1 研究架構圖 4
圖2-1 自組織映射圖網路架構圖 16
圖2-2 GHSOM網路架構圖 17
圖3-1 研究流程圖 19
圖3-2 GHSOM神經元生長圖 24
圖4-1 GHSOM分群結果架構圖 32
圖4-2 各神經元中非財務危機公司比例圖 34
圖4-3 各大群之分佈圖 35
圖4-4 第一大群中之六種軌跡型態圖 36
圖4 5 各大群軌跡型態圖 37
圖4-6 「卓立」發生財務危機前三年的軌跡型態與對照圖 39
圖4-7 「居易」前三年的軌跡型態與對照圖 41




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