Self organizing neural networks for financial diagnosis

摘要

A complete Decision Support System (DSS) for financial diagnosis based on Self Organizing Feature Maps (SOFM) is described. This is a neural network model which, on the basis of the information contained in a multidimensional space — in the case exposed, financial ratios — generates a space of lesser dimensions. In this way, similar input patterns — in the case exposed, companies — are represented close to one another on a map. The neural network has been complemented and compared with multivariate statistical models such as Linear Discriminant Analysis (LDA), as well as with neural models such as the Multilayer Perceptron (MLP). As the principal advantage, this DSS provides a complete analysis which goes beyond that of the traditional models based on the construction of a solvency indicator also known as Z score, without renouncing simplicity for the final decision maker.