A Genetic Programming Approach for EUR/USD Exchange Rate Forecasting and Trading

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  author =       "Georgios A. Vasilakis and 
                 Konstantinos A. Theofilatos and Efstratios F. Georgopoulos and 
                 Andreas Karathanasopoulos and Spiros D. Likothanassis",
  title =        "A Genetic Programming Approach for EUR/USD Exchange
                 Rate Forecasting and Trading",
  journal =      "Computational Economics",
  year =         "2013",
  volume =       "42",
  number =       "4",
  pages =        "415--431",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 algorithms, Tournament selection, Exchange forecasting,
                 EUR/USD exchange rates, Financial trading strategies",
  ISSN =         "0927-7099",
  publisher =    "Springer",
  DOI =          "doi:10.1007/s10614-012-9345-8",
  URL =          "http://results.ref.ac.uk/Submissions/Output/1762292",
  size =         "17 pages",
  abstract =     "The purpose of this article is to present a novel
                 genetic programming trading technique in the task of
                 forecasting the next day returns when trading the
                 EUR/USD exchange rate based on the exchange rates of
                 historical data. Aiming at testing its effectiveness,
                 we benchmark the forecasting performance of our genetic
                 programming implementation with three traditional
                 strategies (naive strategy, MACD, and a buy & hold
                 strategy) plus a hybrid evolutionary artificial neural
                 network approach. The proposed genetic programming
                 technique was found to demonstrate the highest trading
                 performance in terms of annualised return and
                 information ratio when compared to all other strategies
                 which have been used. When more elaborate trading
                 techniques, such as leverage, were combined with the
                 examined models, the genetic programming approach still
                 presented the highest trading performance. To the best
                 of our knowledge, this is the first time that genetic
                 programming is applied in the problem of effectively
                 modelling and trading with the EUR/USD exchange rate.
                 Our application now offers practitioners with an
                 effective and extremely promising set of results when
                 forecasting in the foreign exchange market. The
                 developed genetic programming environment is
                 implemented using the C++ programming language and
                 includes a variation of the genetic programming
                 algorithm with tournament selection.",
  uk_research_excellence_2014 = "D - Journal article",

Genetic Programming entries for Georgios A Vasilakis Konstantinos A Theofilatos Efstratios F Georgopoulos Andreas S Karathanasopoulos Spiridon D Likothanassis