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A Nonlinear Combined Model Hybridizing ANN and GLAR for Exchange Rates Forecasting

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

Foreign exchange rates modeling and forecasting has been a common research stream in the last few decades. Over this time, the research stream has gained momentum with the advancement of computer technologies, which have made many elaborate computation methods available and practical (Yu et al., 2005c). However, it is not easy to predict exchange rates due to their high volatility and noise. But the difficulty in forecasting exchange rates is usually attributed to the limitation of many conventional forecasting models; this has encouraged academic researchers and business practitioners to develop more predictable forecasting models. As a result models using artificial intelligence such as artificial neural network (ANN) techniques have been recognized as more useful than conventional statistical forecasting models. Literature documenting the research shows this is quite diverse and involves different architectural designs. Some examples are presented. De Matos (1994) compared the strength of a multilayer feed-forward neural network (MLFNN) with that of a recurrent network based on the forecasting of Japanese yen futures. Kuan and Liu (1995) provided a comparative evaluation of the performance of MLFNN and a recurrent network on the prediction of an array of commonly traded exchange rates. Hsu et al. (1995) developed a clustering neural network model to predict the direction of movements in the USD/DEM exchange rate. Their experimental results suggested that their proposed model achieved better forecasting performance relative to other indicators.

The rest of the chapter is organized as follows. The next section describes the model building process in detail. In order to verify the effectiveness and efficiency of the proposed model, empirical analysis of the three main currencies’ exchange rates is reported in Section 8.3. The conclusions are contained in Section 8.4.

Keywords

Exchange Rate Artificial Neural Network Artificial Neural Network Model Forecast Model General Regression Neural Network 
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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