Forecasting Exchange Rates with Fuzzy Granular Evolving Modeling for Trading Strategies
Leandro Maciel, Fernando Gomide, Rosangela Ballini
Available Online August 2013.
- https://doi.org/10.2991/eusflat.2013.40How to use a DOI?
- Granular Computing Evolving Systems Exchange Rates Trading Forecasting
- 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 for the period from January 2000 to October 2012. Caparisons 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Leandro Maciel AU - Fernando Gomide AU - Rosangela Ballini PY - 2013/08 DA - 2013/08 TI - Forecasting Exchange Rates with Fuzzy Granular Evolving Modeling for Trading Strategies BT - Proceedings of the 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-13) PB - Atlantis Press SP - 276 EP - 283 SN - 1951-6851 UR - https://doi.org/10.2991/eusflat.2013.40 DO - https://doi.org/10.2991/eusflat.2013.40 ID - Maciel2013/08 ER -