Neural networks, fuzzy system, and linear models in forecasting exchange rates: Comparison and case studies

Artificial neural networks and fuzzy systems, have gradually established themselves as popular tools in approximating complicated nonlinear systems and time series forecasting. This paper investigates the hypothesis that the nonlinear mathematical models of multilayer perceptron and radial basis function neural networks and the Takagi-Sugeno (TS) fuzzy system are able to provide a more accurate out-of-sample forecast than the traditional AutoRegressive Moving Average (ARMA) and ARMA Generalized AutoRegressive Conditional Heteroskedasticity (ARMA-GARCH) linear models. Using series of Brazilian exchange rate (R$/US$) returns with 15 min., 60 min., 120 min., daffy and weekly basis, the out-pf-sample one-step-ahead forecast performance is compared. Results indicate that forecast performance is strongly related to the series' frequency and the forecasting evaluation shows that nonlinear models perform better than their linear counterparts. In the trade strategy based on forecasts, nonlinear models achieve higher returns when compared to a buy-and-hold strategy and to the linear models.