多分類器組合應用於財務危機預測

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姓名

楊順雄(YANG,SHUN-HSIUNG) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

多分類器組合應用於財務危機預測
(Financial distress prediction based on multiple Classifiers)

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摘要(中)

近年來，由於全球金融危機的爆發，許多經濟實體發生都會遭受巨大的損失，甚至導致破產，所以財務危機預測financial distress prediction（FDP）的問題一直以來都是被廣泛討論和持續研究的主題。在企業融資領域，要如何有效地預測財務危機，對於企業財務管理是一個很重要的問題。最近，使用多分類器組合來解決FDP問題越來越被重視。而本研究中就是在多分類器組合的基礎架構上提出了可提升FDP效果的方法。一般典型的財務預測基本上就是做二元分類（危機類和非危機類）的問題。然而，我們發現台灣證券交易所（TWSE）不僅定義了一間公司是否有發生財務危機，且還定義一間公司發生了哪種財務危機。因此，我們想試著建構各類財務危機的專精分類器再利用OR組合機制將所有危機種類分類器的預測結果做組合。我們會在本論文中會提出一個新的多分類器組合架構，並會在論文中試著證明我們所提出的預測模型相較以往傳統的預測模型有著更好的預測表現。

摘要(英)

In recent years, many economic entities have suffered great loss or even become bankrupt due to the breakout of global financial crisis. Financial distress prediction (FDP) has been widely studding. In the field of corporate finance, to predict financial distress effectively is an important issue in corporate financial management. Recently, some studies which take advantages of multiple classifiers combination to solve FDP problem have been noticed. This paper proposed a FDP method with increased efficiency based on parallel combination of multiple classifiers. Traditional approaches usually take financial prediction problem as Binary Classification (distressed class and non-distressed class) problem. However, we found that Taiwan Stock Exchange Corporation (TWSE) not only defined two classes for all companies but also defined subclasses for the distress companies. Hence, we wanted to construct specific classifier for each distress type and then combine all distress classifiers prediction results with the OR mechanism. We also proposed a new prediction model with multiple classifiers and showed that our method had outperformed the traditional approach.

關鍵字(中)

★ 財務危機預測
★ 多分類器
★ 資料探勘
★ 機器學習

關鍵字(英)

★ Financial Distress Prediction
★ Multiple Classifier
★ Data Mining
★ Machine Learning

論文目次

Abstract ii
誌謝 iii
圖目錄 v
表目錄 vi
一、緒論 1
1-1. 研究背景 1
1-2. 研究動機 2
1-3. 論文架構 3
二、文獻探討 4
2-1. 多分類器應用 4
三、實驗架構設計與結果 6
3-1. 資料來源 6
3-2. 資料前置處理 7
3-3. 實驗假設 7
3-4. Solution-1 【Parallel-OR】 8
3-4-1實驗設計 9
3-4-2實驗結果 14
3-4-3實驗討論 16
3-4-4延伸討論 -Degree of Divergence 17
3-5. Solution-2【Iterative Parallel-OR】 20
3-5-1實驗設計 20
3-5-2實驗結果與討論 21
四、結論及未來展望 23
4-1結論 23
4-2未來展望 24
參考文獻 27
附錄一 29
附錄二 35

參考文獻

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指導教授

梁德容(De-ron Liang)

審核日期

2012-10-19

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