Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)

This study analyzes the comparison between traditional statistical methodologies for distress classification and prediction, i.e., linear discriminant (LDA) or logit analyses, with an artificial intelligence algorithm known as neural networks (NN). Analyzing well over 1,000 healthy, vulnerable and unsound industrial Italian firms from 1982–1992, this study was carried out at the Centrale dei Bilanci in Turin, Italy and is now being tested in actual diagnostic situations. The results are part of a larger effort involving separate models for industrial, retailing/trading and construction firms. The results indicate a balanced degree of accuracy and other beneficial characteristics between LDA and NN. We are particularly careful to point out the problems of the ‘black-box’ NN systems, including illogical weightings of the indicators and overfitting in the training stage both of which negatively impacts predictive accuracy. Both types of diagnoslic techniques displayed acceptable, over 90%, classificalion and holdoul sample accuracy and the study concludes that there certainly should be further studies and tests using the two lechniques and suggests a combined approach for predictive reinforcement.