| 期刊全稱 | Artificial Intelligence in Economics and Managment | | 期刊簡稱 | An Edited Proceeding | | 影響因子2023 | Phillip Ein-Dor | | 視頻video | http://file.papertrans.cn/163/162433/162433.mp4 | | 圖書封面 |  | | 影響因子 | In the past decades several researchers have developed statistical models for the prediction of corporate bankruptcy, e. g. Altman (1968) and Bilderbeek (1983). A model for predicting corporate bankruptcy aims to describe the relation between bankruptcy and a number of explanatory financial ratios. These ratios can be calculated from the information contained in a company‘s annual report. The is to obtain a method for timely prediction of bankruptcy, a so- ultimate purpose called "early warning" system. More recently, this subject has attracted the attention of researchers in the area of machine learning, e. g. Shaw and Gentry (1990), Fletcher and Goss (1993), and Tam and Kiang (1992). This research is usually directed at the comparison of machine learning methods, such as induction of classification trees and neural networks, with the "standard" statistical methods of linear discriminant analysis and logistic regression. In earlier research, Feelders et al. (1994) performed a similar comparative analysis. The methods used were linear discriminant analysis, decision trees and neural networks. We used a data set which contained 139 annual reports of Dutch industrial and trading comp | | Pindex | Conference proceedings 1996 |
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