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

页码：291 / 295

页数：5

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