Chinese Currency Exchange Rates Forecasting with EMD-Based Neural Network

Abstract

The Chinese currency, RMB, is developing as an international currency. Therefore, the effective strategy for trading RMB exchange rates would be attractive to international investors and policymakers. In this paper, we have constructed hybrid EMD-MLP models to forecast RMB exchange rates and developed a trading strategy based on these models. Empirical results show that the proposed hybrid EMD-MLP∗ model always performs best based on both NMSE and Dstat criteria when the forecasting period is greater than five days. Moreover, we compare the models’ performance using different horizons and find that accuracy will increase with the growth of the forecasting horizons; however, the NMSE will become larger. Lastly, we adopt the best performing model to develop trading strategies with longer forecasting horizons when considering the number of profitable trading activities. If we consider a 0.3% transaction cost, the developed strategy will bring an annual return exceeding 10%, as well as enough trading opportunities.