Forecasting Exchange Rates with Fuzzy Granular Evolving Modeling for Trading Strategies

Abstract

This paper addresses a fuzzy set based evolving modeling (FBeM) approach and the task of forecasting exchange rates in order to perform trading strategies. FBeM is a granular computing technique that uses fuzzy information granules to model nonstationary functions providing functional and linguistic approximations. As an application, this work considers the BRL/USD exchange rate market data for the period from January 2000 to October 2012. Comparisons in terms of goodness of fit and based on trading performance indicators includes the granular model against a Multi-Layer Perceptron (MLP), an autoregressive moving average (ARMA), a naïve strategy and some state of the art evolving fuzzy systems. Computational results suggest that the FBeM model statistically outperforms the alternative approaches.