International Journal of Computational Intelligence Research

The recent East Asian economic crisis is a lesson one can learn from the absence of effective early warning systems. To serve as a sound early warning signal, the accuracy of a failure prediction model is as important as its robustness over time. This study analyses financial and ownership variables using principal component analysis. It can reduce huge number of financial data of the business bankruptcy prediction problem. Using neural networks for bankruptcy forecasting, the obtained features are fed into neural networks as the input data. Our experiments examine the predictive performance of three neural networks: Learning Vector Quantization, Probabilistic Neural Network, and Feedforward network with backpropagation learning. All these approaches are applied to data sets of 41 Thai financial institutions for the period 1993–2003.