應用約略集合理論建立二階段財務危機預警模式

CCUR > College of Business > Department of Accounting & Graduate Institute of Accounting > Thesis > Item 987654321/20125

Please use this identifier to cite or link to this item: https://irlib.pccu.edu.tw/handle/987654321/20125

Title:	應用約略集合理論建立二階段財務危機預警模式
Authors:	鄭凱元
Contributors:	會計學系
Keywords:	資料採礦財務危機約略集合
Date:	2010
Issue Date:	2011-11-02 13:37:50 (UTC+8)
Abstract:	企業發生財務危機的可能因整體經濟環境的快速變遷，而隨之逐年增加，建立一個有效且高預測性之財務危機預警模式，是當前迫在眉睫重要課題。應用在財務危機預警模式上的方法很多，但舊有之財務危機預警模式，常僅用單一特徵選取方法，搭配分類模式，鮮少使用多樣分類模式之結果相互進行比較，因此本研究運用約略集合之特徵選取，將篩選後變數以傳統方法，以及資料採礦方法加以篩選，並比較其危機模式之間的正確率。用以建立一較有效之兩階段財務危機預警模式。本研究主要以約略集合之方法對財務變數進行篩選，將篩選後之變數加以分類，結果發現，資料採礦方法對所考量之衡量財務危機指標進行分析，得知財務危機的主要特徵，最後應用資料採礦方法，所建構兩階段財務危機預警模式能確實有效降低早期財務危機預警上誤判情況，是以無論在財務理論或實務經驗上，對資訊使用者有其相當助益。 By the overall economic environment change fast, the enterprise whose possibility of financial crisis is increased year by year. Therefore, to build a forecasting model of financial crisis is the important subject currently. Forecasting model used in many ways on, but the old model of forecasting model, often only a single feature selection methods, with classification, rarely use the results of multiple classification models were compared with each other, so this study uses rough sets theory of feature selection, and using traditional method and data mining methods to classify and compare the accuracy between the forecasting model. To build a useful two-step financial crisis forecasting model. This study used the rough sets to select financial variables, and classified, the re-sults showed that data mining methods analysis financial crisis index, that the main fea-tures of the financial crisis, The empirical results interpret the application of data mining methods in two stages of financial crisis forecasting model can actually reduce the mis-judgment of the traditional model, therefore, both in academic research and empirical work, will be contributed to the information users.
Appears in Collections:	[Department of Accounting & Graduate Institute of Accounting] Thesis

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	406	View/Open

Loading...