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Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization

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  • Sermpinis, Georgios
  • Theofilatos, Konstantinos
  • Karathanasopoulos, Andreas
  • Georgopoulos, Efstratios F.
  • Dunis, Christian

Abstract

The motivation for this paper is to introduce a hybrid neural network architecture of Particle Swarm Optimization and Adaptive Radial Basis Function (ARBF–PSO), a time varying leverage trading strategy based on Glosten, Jagannathan and Runkle (GJR) volatility forecasts and a neural network fitness function for financial forecasting purposes. This is done by benchmarking the ARBF–PSO results with those of three different neural networks architectures, a Nearest Neighbors algorithm (k-NN), an autoregressive moving average model (ARMA), a moving average convergence/divergence model (MACD) plus a naı¨ve strategy. More specifically, the trading and statistical performance of all models is investigated in a forecast simulation of the EUR/USD, EUR/GBP and EUR/JPY ECB exchange rate fixing time series over the period January 1999–March 2011 using the last 2years for out-of-sample testing.

Suggested Citation

  • Sermpinis, Georgios & Theofilatos, Konstantinos & Karathanasopoulos, Andreas & Georgopoulos, Efstratios F. & Dunis, Christian, 2013. "Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization," European Journal of Operational Research, Elsevier, vol. 225(3), pages 528-540.
    • Handle: RePEc:eee:ejores:v:225:y:2013:i:3:p:528-540
    DOI: 10.1016/j.ejor.2012.10.020
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    as
    1. Panda, Chakradhara & Narasimhan, V., 2007. "Forecasting exchange rate better with artificial neural network," Journal of Policy Modeling, Elsevier, vol. 29(2), pages 227-236.
    2. Yang, Jian & Cabrera, Juan & Wang, Tao, 2010. "Nonlinearity, data-snooping, and stock index ETF return predictability," European Journal of Operational Research, Elsevier, vol. 200(2), pages 498-507, January.
    3. Andreou, Panayiotis C. & Charalambous, Chris & Martzoukos, Spiros H., 2008. "Pricing and trading European options by combining artificial neural networks and parametric models with implied parameters," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1415-1433, March.
    4. Pesaran, M Hashem & Timmermann, Allan, 1992. "A Simple Nonparametric Test of Predictive Performance," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 561-565, October.
    5. Christian L Dunis & Jia Miao, 2005. "Optimal trading frequency for active asset management: Evidence from technical trading rules," Journal of Asset Management, Palgrave Macmillan, vol. 5(5), pages 305-326, February.
    6. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    7. Khurshid Kiani & Terry Kastens, 2008. "Testing Forecast Accuracy of Foreign Exchange Rates: Predictions from Feed Forward and Various Recurrent Neural Network Architectures," Computational Economics, Springer;Society for Computational Economics, vol. 32(4), pages 383-406, November.
    8. Adeodato, Paulo J.L. & Arnaud, Adrian L. & Vasconcelos, Germano C. & Cunha, Rodrigo C.L.V. & Monteiro, Domingos S.M.P., 2011. "MLP ensembles improve long term prediction accuracy over single networks," International Journal of Forecasting, Elsevier, vol. 27(3), pages 661-671.
    9. Bekaert, Geert & Wu, Guojun, 2000. "Asymmetric Volatility and Risk in Equity Markets," Review of Financial Studies, Society for Financial Studies, vol. 13(1), pages 1-42.
    10. S. D. Bekiros & D. A. Georgoutsos, 2008. "Direction-of-change forecasting using a volatility-based recurrent neural network," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(5), pages 407-417.
    11. Shapiro, Arnold F., 2000. "A Hitchhiker's guide to the techniques of adaptive nonlinear models," Insurance: Mathematics and Economics, Elsevier, vol. 26(2-3), pages 119-132, May.
    12. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    13. Bekiros, Stelios D., 2010. "Fuzzy adaptive decision-making for boundedly rational traders in speculative stock markets," European Journal of Operational Research, Elsevier, vol. 202(1), pages 285-293, April.
    14. Yang, Jian & Su, Xiaojing & Kolari, James W., 2008. "Do Euro exchange rates follow a martingale? Some out-of-sample evidence," Journal of Banking & Finance, Elsevier, vol. 32(5), pages 729-740, May.
    15. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    16. Adeodato, Paulo J.L. & Arnaud, Adrian L. & Vasconcelos, Germano C. & Cunha, Rodrigo C.L.V. & Monteiro, Domingos S.M.P., 2011. "MLP ensembles improve long term prediction accuracy over single networks," International Journal of Forecasting, Elsevier, vol. 27(3), pages 661-671, July.
    17. Huck, Nicolas, 2010. "Pairs trading and outranking: The multi-step-ahead forecasting case," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1702-1716, December.
    18. Christian Dunis & Jason Laws & Georgios Sermpinis, 2010. "Modelling and trading the EUR/USD exchange rate at the ECB fixing," The European Journal of Finance, Taylor & Francis Journals, vol. 16(6), pages 541-560.
    19. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
    20. José M. Matías & Juan C. Reboredo, 2012. "Forecasting Performance of Nonlinear Models for Intraday Stock Returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 31(2), pages 172-188, March.
    21. Wang, Ju-Jie & Wang, Jian-Zhou & Zhang, Zhe-George & Guo, Shu-Po, 2012. "Stock index forecasting based on a hybrid model," Omega, Elsevier, vol. 40(6), pages 758-766.
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