Forecasting Exchange Rates Using Integration of Particle Swarm Optimization and Neural Networks

Exchange rate forecasting involves many challenges in research. Due to the difficulty of selecting superior variables to design a good forecasting mode, few empirical studies have discussed the influence of explainable variables. In this paper, a new forecasting model is constructed; we adopt the particle swarm optimization (PSO) to select the optimal input layer neurons to predict NTD/USD exchange rates by the back propagation network (BPN), called PSOBPN model. There are several steps in this experiment: first, we divided the whole data into six periods of sliding windows. Second, we selected superior variables by the PSO method, and we selected 10 variables within the entire 27 variables. Finally, we forecasted the exchange rate by BPN with the selected variables. The results showed that the PSOBPN achieves the best forecasting performance and is closely matched with the actual exchange rate.