Predicting Foreign Exchange Market Movement Direction Using a Confidence-Based Neural Network Ensemble Model

Neural network ensemble has been turned out to be an efficient strategy for achieving high classification performance, especially in fields where the development of a powerful single classifier system requires considerable efforts. Usually, neural network ensemble model outperforms the individual neural network models, whose performance is limited by the imperfection of feature extraction, learning/classification algorithms, and the inadequacy of training data. Due to these reasons, there is a growing research stream about neural network ensemble learning methods (Perrone and Cooper, 1993; Krogh and Vedelsby, 1995; Rosen, 1996; Tumer and Ghosh, 1996; Yang and Browne, 2004; Yu et al., 2005c, 2006d; Lai et al., 2006c, 2006d). For example, performance improvement can result from training the individual networks to be decorrelated with each other (Rosen, 1996) with respect to their errors. To achieve high classification performance, there are some essential requirements to the ensemble members and the ensemble strategy. First of all, a basic condition is that the individual neural network classifiers must have enough training data. Secondly, the ensemble members are diverse or complementary, i.e., classifiers show different classification properties. Thirdly, to obtain high performance, a wise ensemble strategy is also required on a set of complementary classifiers.

The motivation of this chapter is to formulate a multistage confidencebased neural network ensemble model for predicting foreign exchange market movement direction and compare its performance with other existing approaches. The rest of the chapter is organized as follows. The next section presents a formulation process of the multistage neural network ensemble model in detail. To verify the effectiveness of the proposed method, two real examples are performed in Section 12.3. For comparison, Section 12.4 reports the comparisons of three ensemble neural network models. Finally, some conclusions are drawn in Section 12.5.