Financial distress prediction model via GreyART network and grey model

Abstract

This study attempts to use GreyART network and grey model to construct a financial distress prediction model. The inputs used to train the network are the historical data containing 17 different financial ratios of 22 healthy and 5 distressed Taiwan's listed banks. With the help of the developed performance index, this study also proposes a growing extraction method for financial variables not only to further improve the classification ability in the training and testing phases, but also to use fewer extracted variables to build the financial distress prediction model. Simulation results show that the optimal condition is the one using four extracted variables as inputs and the vigilance threshold of 0.80. Under this condition, the proposed method generates only two clusters with corresponding classification hit rates of 96.30% and 95.24% for the training and testing results, respectively. © 2010 Springer-Verlag Berlin Heidelberg.