碩士 / 國立臺北科技大學 / 工業工程與管理研究所 / 97 / In recent years, our country experienced a financial tsunami, which may lead some firms force themselves suspend stock trading, shares or even became delivery companies. The financial crisis not only affected enterprises themselves, but also the general investors and external environment. From the financial turmoil, we can be found the uncertainty of investing, as well as the importance of accurate investing. Therefore, if we build an effective financial alert model for investors as a reference, investors can take early response accordingly.
If investors are able to notice crisis in advance, the loss of investing will be decreased and gain more profit. The most financial alert studies used classification models to determine whether it was a crisis company. In this study, the multiple methods is adoped which is more fair to give the guidelines for classifying. Then we discuss the accurate rates of binary logistic regression and multiple logistic regression model during different time period. And the values of classification of this study were substitured into the sequence mining respectively to forecast the financial crisis and discuss them.
The results showed that the classifying accurate rate of binary were better than multiple. But the time we ransformed the multiple into binary, that was we sacrificed a targer value, the accurate rate of multiple would surpass binary. Then the result substituting sequential mining showed that the accurate rates of binary were better than multiple. But the binary-classification had higher rates of non-definition. Therefore, the method we select depends on the types of the information as well as the approach of investion making.
| Identifer | oai:union.ndltd.org:TW/097TIT05031009 |
| Date | January 2009 |
| Creators | Hong-Lun Wang, 王宏綸 |
| Contributors | Shu-Jiang Luo, 羅淑娟 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 73 |
Page generated in 0.0142 seconds