碩士 / 元智大學 / 會計學系 / 92 / ABSTRACT
The purposes of this study is to use financial distress signals and new prediction method to develop a advanced distress prediction model。The distress early warning system developing can assist managers to avoid possible losses and wastage of resources, to act as an important investment guide for the investors as well。The study data are collected from the publication of those companies whose stocks are publicly traded on the Taiwan Stock Market between 1998 and 2003。Using 1：2 matched pairs sample method，distress and non-distress companies selection based on the same period time、same industry and the similar size company as matched sample。The study selects several financial ratios as independent variables including （1）Profitability；（2）Liquidity ；（3）Financial Structure；（4）Accruals；（5）Risk Control。Using both the Support Vector Machine and the Logistic Regression model to build the distress prediction models。Finally, we would compare both the prediction accuracy and the stability between the Support Vector Machine and the Logistic Regression Model。
The findings and conclusion of this study is listed below:
1. The empirical results indicating the association between the discretionary accruals and the company financial distress is positively correlated。Earnings manipulation is a common phenomenon whether the distress company or the non-distress company。
2. The findings also indicating the association between companies’ the extent of shares as collateral by the board of directors and financial distress of the company is positively correlated。The extent of the shares as collateral by the board of directors in the distress company’s will have a significant indicator when company is gradually approached the early distress stage。
3. Under the same financial ratio data, the classification accuracy is the best performance in the current season both using Support Vector Machine and the Logistic Regression Method 。
4. However, under the same financial data，both the predictive ability and the stability of the Support Vector Machine is better than The Logistic Regression Model。
| Identifer | oai:union.ndltd.org:TW/092YZU00385005 |
| Date | January 2004 |
| Creators | Yeh, Yi-Fang, 葉怡芳 |
| Contributors | Lin, Lee-Hsuan, 林利萱 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 94 |
Page generated in 0.0015 seconds