Comparative analysis of artificial neural network models: Application in bankruptcy prediction

被引：80

作者：

Charalambous, C ^{[1
]}

Charitou, A ^{[1
]}

Kaourou, F ^{[1
]}

机构：

[1] Univ Cyprus, Dept Business Adm, CY-1678 Nicosia, Cyprus

来源：

ANNALS OF OPERATIONS RESEARCH | 2000年 / 99卷 / 1-4期

关键词：

Logistic Regression; Artificial Neural Network; Optimization Algorithm; Radial Basis Function; Conjugate Gradient;

D O I：

10.1023/A:1019292321322

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

This study compares the predictive performance of three neural network methods, namely the learning vector quantization, the radial basis function, and the feedforward network that uses the conjugate gradient optimization algorithm, with the performance of the logistic regression and the backpropagation algorithm. All these methods are applied to a dataset of 139 matched-pairs of bankrupt and non-bankrupt US firms for the period 1983-1994. The results of this study indicate that the contemporary neural network methods applied in the present study provide superior results to those obtained from the logistic regression method and the backpropagation algorithm.

引用

页码：403 / 425

页数：23