This study makes a comparison between artificial neural networks (ANN) and the traditional statistical techniques of discriminant analysis (DA) and logistic regression (LR) in corporate failrue modelling. The comparison was made at every step of the corporate failure prediction mdoelling process, using principal component analysis (PCA) and self-organising feature maps (SOFM) in a variable reduction process, since a large number of financial ratios have been employed in financial risk measurement, when using DA and LR, As for using stepwise approaches in traditional classification techniques inorder to find best-fitted models, skeletonisation backporpagation was employed in order toestablish an optimum neural network structure. The main problem in ANN applications of cororate failure prediction has been the lack of understanding of the inanical data and stochastic properties. of dinancial ratios due to creative accounting practies, and failed companies. In this research, every step employed in cnventional financial failure studies was compared with the equivalent processes in the ANN field. The purpose is to establish a path for a fair coparison, and present ANNs as another tool in corporate failure prediction modelling.
展开▼
