Computational Intelligence Methods for Financial Forecasting

Forecasting the short run behavior of foreign exchange rates is a challenging problem that has attracted considerable attention. High frequency financial data are typically characterized by noise and non--stationarity. In this work we investigate the profitability of a forecasting methodology based on unsupervised clustering and feedforward neural networks and compare its performance with that of a single feedforward neural network and nearest neighbor regression. The experimental results indicate that the proposed combination of the two methodologies achieves a higher profit