Forecasting with ARTMAP-IC neural networks an application using corporate bankruptcy data

Financial diagnosis and prediction of corporate bankruptcy can be viewed as a pattern recognition problem. This paper proposes a novel approach to solution based on ARTMAP-IC - a general-purpose neural network system for supervised learning and recognition. For a popular dataset, with proper preprocessing steps, the model outperforms similar techniques and provides prediction accuracy equal to the best one obtained by a backpropagation MLPs. An advantage of the proposed model over the MLPs is the short online learning, fast adaptation to novel patterns and scalability.