CAN ANTS PREDICT BANKRUPTCY? A COMPARISON OF ANT COLONY SYSTEMS TO OTHER STATE-OF-THE-ART COMPUTATIONAL METHODS

Abstract

In the current work, we consider the applicability of Ant Colony Systems (ACS) to the bankruptcy prediction problem. ACS are nature-based algorithms that mimic the functions of live organisms to find the best performing solution. In our work, ACS are used for the extraction of classification rules for bankruptcy prediction. An experimental study was conducted in order to evaluate the performance of the system and identify well performing parameters. Results were compared to the performance obtained by state-of-the-art methods for classification, namely the Artificial Neural Networks, the Support Vector Machines, the Partial Decision Trees and the Fuzzy Lattice Reasoning. Comparison indicates the high performance of the ACS which is further supported by their ability to extract classification rules, thus offering interpretation of the prediction results. The latter is of great importance in the field of corporate distress where no unified theory on distress prediction exists. Most studies with distress prediction have focused on increasing the accuracy of the model and have not always paid attention to the model interpretation.Ant colony systems, rule extraction, support vector machines, neural networks, decision trees, fuzzy lattice reasoning, bankruptcy prediction