Fuzzy Granular Evolving Modeling for Trading Strategies with Exchange Rates

This work addresses a fuzzy set based evolving modeling (FBeM) approach and the task of trading strategies performance. FBeM is a granular computing technique that uses fuzzy information granules to model nonstationary functions providing functional and linguistic approximations. As an application, we consider the BRL/USD exchange rate for the period from January 2000 to October 2012. Caparisons 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 outperforms the alternative approaches.