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Forecasting Foreign Exchange Rates Using an Adaptive Back-Propagation Algorithm with Optimal Learning Rates and Momentum Factors

Part of the International Series in Operations Research & Management Science book series (ISOR, volume 107)

Foreign exchange rates prediction is regarded as a rather difficult and challenging task due to its high volatility and noisy market environment (Yu et al., 2005c). Usually, the difficulty in forecasting exchange rates is attributed to the limitation of many conventional forecasting models as many statistical and econometric models are unable to produce significantly better predictions than the random walk (RW) model (Ince and Trafalis, 2005; Chen and Leung, 2004), which has also encouraged academic researchers and business practitioners to develop more predictable forecasting models.

The rest of this chapter is organized as follows. In Section 4.2, an adaptive BP learning algorithm with optimal learning rate and momentum factor is first proposed in terms of the gradient descent rule and optimization techniques. For verification and illustration, Section 4.3 gives the experiments about three foreign exchange rates prediction and reports their results. Finally, conclusions and future directions are given in Section 4.4.

Keywords

Learning Rate Extended Kalman Filter General Regression Neural Network Momentum Factor Foreign Exchange Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2007

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