Integration of Genetic Algorithm and Neural Network for Financial Early Warning System: An Example of Taiwanese Banking Industry

The applications of genetic algorithms and neural networks to financial early warning systems seem potential in the past works. Therefore genetic algorithm and neural network (GNN) are integrated to build a financial early warning system. An example of Taiwanese banking industry is discussed and the financial ratios of each bank were collected from 1998 to 2002. The performance of GNN is compared with other four early warning systems, namely, case-based reasoning (CBR), backpropagation neural network (BPN), logistic regression analysis (LR), and quadratic discriminant analysis (QDA). The result indicates that the GNN proposed in this research is a little superior to the two soft computing early warning systems (CBR and BPN). The GNN outperforms the statistical early warning systems (LR and QDA) at least 13%