A Comparative Analysis of Artificial Neural Networks Using Financial Distress Prediction

Abstract

A bstract This paper examines the efficiency of a generalized adaptive neural network algorithm (GANNA) processor in comparison to earlier model‐based methods, a back‐propagation artificial neural network, and logistic regression approaches to data classification. The research uses the binary classification problem of discriminating between failing and non‐failing firms to compare the methods. The results indicate the potential in time savings and the successful classification results available from a GANNA processor.