A comparison of the ability of neural networks and logit regression models to predict levels of financial distress

被引：0
|
作者：
Zurada, JM [1 ]
Foster, BP [1 ]
Ward, TJ [1 ]
Barker, RM [1 ]
机构：
[1] Univ Louisville, Coll Business & Publ Adm, Dept Comp Informat Syst, Louisville, KY 40292 USA
来源：
SYSTEMS DEVELOPMENT METHODS FOR THE NEXT CENTURY | 1997年
关键词：
D O I：
暂无
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
In this study we compared the classification accuracy rates of neural networks to those from ordinal legit models for a multi-state response variable. The results indicate that with the multi-state response variable, neural networks produce higher overall classification rates than ordinal legit models, but do not more accurately classify distressed firms. As a result, we can not clearly state that neural networks are superior to regression when predicting more than one level of financial distress.
引用
收藏
页码：291 / 295
页数：5
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