Hybridizing BPNN and Exponential Smoothing for Foreign Exchange Rate Prediction
A challenging task in financial market such as stock market and foreign exchange market is to predict the movement direction of financial markets so as to provide valuable decision information for investors (Lai et al., 2006b). Thus, many researchers and business practitioners have developed various kinds of forecasting methods. Of the various forecasting models, the exponential smoothing model has been found to be an effective forecasting method. Since Brown (1959) began to use simple exponential smoothing to forecast demand for inventories, the exponential smoothing models have been widely used in business and finance (Winters, 1960; Gardner, 1985; Alon, 1997; Leung, 2000). For example, Winters (1960) proposed exponentially weighed moving averages for sales forecasting and obtained good results. Gardner (1985) introduced exponential smoothing methods into supply chain management for forecasting demand, and achieved satisfactory results. Similarly, Alon (1997) found that Winters’ model forecasts aggregate retail sales more accurately than the simple exponential model. Leung et al. (2000) used an adaptive exponential smoothing model to predict stock indices, and achieved good forecasting performance for Nikkei 225.
In this chapter, a hybrid synergy model integrating both an exponential smoothing (ES) model and a BPNN model is proposed to take advantage of the unique strength of exponential smoothing and BPNN models in linear and nonlinear modeling. For testing purposes, two main exchange rates, EUR/USD and JPY/USD, are used. For comparison, individual exponential smoothing model and individual BPNN model are used as benchmark models.
The remainder of the chapter is organized as follows. Section 7.2 provides basic backgrounds about the exponential smoothing and neural network approaches to financial time series forecasting. Then the hybrid methodology combining the exponential smoothing and neural network model is introduced in Section 7.3. Subsequently, some experimental results are reported in Section 7.4. Finally, Section 7.5 concludes the study.
KeywordsRoot Mean Square Error Artificial Neural Network Model Exponential Smoothing Time Series Forecast Financial Time Series
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