臺灣博碩士論文加值系統

財務危機預警模式之建構的相關研究中主要以危機發生前一年度歷史資料來建構企業危機預警模式。然而企業財務狀況的惡化並非一夕之間所造成，應加入縱斷面之資訊，藉以將各年度樣本資料所蘊含動態時間趨勢之訊息納入考量。本研究應用灰色預測理論處理縱斷面歷史資料，針對資料的趨勢作預測，產生預測值。並結合K-means分群法和約略集合理論建構預測模型。以灰色預測增強資料的趨勢，分析危機發生前一到七年的歷史資料。預測模型利用K-mean分群法將同質性高的資料作群聚，再利用約略集合具有處理含糊性資料的特性對預測值做分類。藉由決策規則找出未來可能具有企業危機的公司。
研究設計階段建立了(1)灰預測約略集合預警模式、(2)K-mean約略集合預警模式以及(3)結合灰預測、K-mean和約略集合之預警模式。並建立傳統約略集合分類模式作為比較。資料收集民國60至97年間上市櫃財務危機公司之七年期資料作驗證。實證結果顯示，越接近危機發生年度，其預測準確度越高。而本研究建立之三個模型在準確度上皆優於傳統約略集合預警模式。當中又以結合灰預測、K-mean和約略集合之預警模式有最佳的表現。說明了加強資料的趨勢以及，先將資料依相似度作分類，有助於分類準確度的提升。

Operations in Companies are closely related to the stable of the society. When business is in failure, it will result a serious loss in the whole society. Hence, it is necessary to build a business failure prediction model. Previous researches, no matter traditional statistical techniques such as discriminant analysis or artificial intelligence algorithm such as rough set theory, were often constructed by using the samples of business failure happened one year ago(Year-1). However, the appearance of business failure was not suddenly, so that we should find clue in advance, and consider not only cross-section data but also vertical-section one in building business failure prediction model.
We addressed time-series data by grey prediction to generate predicted values as input data, and combined K-mean clustering and rough set theory to develop a grey-prediction business prediction model that identified what the probably company of business failure was. Grey-prediction used to emphasize the trends in historical data, K-mean clustering grouped data by homogeneity and found the failure companies according to decision rules in rough set theory.
In this research, we developed (1) combination of grey-prediction with rough set, (2) combination of K-mean with rough set, and (3) combination of grey-prediction, K-mean and rough set respectively and compare with traditional model that building by pure rough set. Experiment used data from 76 Taiwan public companies for the period 1971 to 2008 from TEJ database. According to the results, the closer to the financial distress announce, the higher the prediction accuracy is. The models developed of this research were all better than traditional prediction model using rough set. The model ,combines grey-prediction, K-mean and rough set, has the best performance. This shows that emphasizes the trends and groups the data by homogeneity can enhance the prediction accuracy.

目錄
中文摘要 i
英文摘要 ii
誌 謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
一、緒論 1
1.1 研究動機與目的 1
1.2 研究內容與方法 2
1.3 研究範圍與限制 3
1.4 研究流程 4
二、文獻回顧 5
2.1 灰色預測 5
2.1.1 預測的意義 5
2.1.1 灰色理論 5
2.1.2 灰色預測簡介 6
2.2 K-mean 分群法 10
2.3 約略集合理論 10
2.3.2 約略集合理論的基本概念 12
2.3.3 約略集合理論應用於分類的相關文獻 13
2.4 企業危機預警文獻回顧 15
2.4.1 傳統統計法建立之企業危機預警模式 15
2.4.2 約略集合理論在企業危機預警系統上的應用 17
三、研究設計 20
3.1 研究步驟 20
3.1.1 樣本資料收集與前處理 21
3.1.2 灰色預測 23
3.1.3 K-mean 分群法 23
3.1.4 約略集合理論 25
3.2 變數選取 26
3.3 小結 27
四、實證結果分析 29
4.1 灰色預測結合約略集合理論 29
4.1.1 傳統歷史值建立約略集合預警模式模式 30
4.1.2 灰色預測值建立約略集合預警模式模式 31
4.2 結合K-mean 分群與約略集合 34
4.2.1 K 值的選定 34
4.3 結合灰預測、K-mean 與約略集合 35
4.4 比較與討論 39
4.4.1 歷史值預警模式與預測值預警模式 39
4.4.2 約略集合預警模式與結合K-mean 之約略集合預警模式 39
4.4.3 結合灰預測、K-mean 分群與約略集合之預警模式 40
五、結論與建議 41
5.1 結論 41
5.2 貢獻 41
5.3 建議與後續研究 42
六、參考文獻 43
附錄1 : Excel 中計算灰預測之自訂函數 46

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