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Sermpinis, G.; Theofilatos, K.; Karathanasopoulos, A.; Dunis, C. (2013)
Publisher: Elsevier BV
Languages: English
Types: Article
Subjects:
The motivation for this paper is to introduce a hybrid Neural Network architecture of Particle\ud Swarm Optimization and Adaptive Radial Basis Function (ARBF-PSO), a time varying leverage\ud trading strategy based on Glosten, Jagannathan and Runkle (GJR) volatility forecasts and a\ud Neural Network fitness function for financial forecasting purposes. This is done by\ud benchmarking the ARBF-PSO results with those of three different Neural Networks\ud architectures, a Nearest Neighbors algorithm (k-NN), an autoregressive moving average model\ud (ARMA), a moving average convergence/divergence model (MACD) plus a naïve strategy.\ud More specifically, the trading and statistical performance of all models is investigated in a\ud forecast simulation of the EUR/USD, EUR/GBP and EUR/JPY ECB exchange rate fixing time\ud series over the period January 1999 to March 2011 using the last two years for out-of-sample\ud testing.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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