A corporate solvency map through self-organising neural networks

Corporate failure prediction has been used in the application of both parametric classical classification and non-parametric artificial neural network techniques. Although discriminant and logistic regression analysis have been accepted as standard pattern recognition devices, different kinds of neural network technology have recently demonstrated promising outcomes, in terms of accuracy, when compared with results from classical pattern recognition techniques. Most of the neural net studies in corporate failure prediction have centred on implementing a large variety of supervised learning algorithms. Considering stochastic properties of financial ratios due to creative accounting practices, different accounting policies and deviant patterns of so-called healthy companies, little work has been conducted in identifying different patterns of both failed and solvent firms. Therefore, the purpose of the study is to extract solvency maps of UK listed manufacturing firms, by employing self-organising maps. The results obtained from this research indicate that there is marked difference between failed and non-failed firms in terms of financial characteristics although different financial structures exist amongst both bankrupt and solvent companies