Gene Expression Programming and Trading Strategies

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{conf/ifip12/SermpinisFTK13,
  title =        "Gene Expression Programming and Trading Strategies",
  author =       "Georgios Sermpinis and Anastasia Fountouli and 
                 Konstantinos A. Theofilatos and 
                 Andreas S. Karathanasopoulos",
  bibdate =      "2013-09-04",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/ifip12/aiai2013.html#SermpinisFTK13",
  booktitle =    "Artificial Intelligence Applications and Innovations -
                 9th {IFIP} {WG} 12.5 International Conference, {AIAI}
                 2013, Paphos, Cyprus, September 30 - October 2, 2013,
                 Proceedings",
  publisher =    "Springer",
  year =         "2013",
  volume =       "412",
  editor =       "Harris Papadopoulos and Andreas S. Andreou and 
                 Lazaros S. Iliadis and Ilias Maglogiannis",
  isbn13 =       "978-3-642-41141-0",
  pages =        "497--505",
  series =       "IFIP Advances in Information and Communication
                 Technology",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, GEP",
  DOI =          "doi:10.1007/978-3-642-41142-7_5",
  abstract =     "This paper applies a Gene Expression Programming (GEP)
                 algorithm to the task of forecasting and trading the
                 SPDR Down Jones Industrial Average (DIA), the SPDR S&P
                 500 (SPY) and the Powershares Qqq Trust Series 1 (QQQ)
                 exchange traded funds (ETFs). The performance of the
                 algorithm is benchmarked with a simple random walk
                 model (RW), a Moving Average Convergence Divergence
                 (MACD) model, a Genetic Programming (GP) algorithm, a
                 Multi-Layer Perceptron (MLP), a Recurrent Neural
                 Network (RNN) and a Gaussian Mixture Neural Network
                 (GM). The forecasting performance of the models is
                 evaluated in terms of statistical and trading
                 efficiency. Three trading strategies are introduced to
                 further improve the trading performance of the GEP
                 algorithm. This paper finds that the GEP model
                 outperforms all other models under consideration. The
                 trading performance of GEP is further enhanced when the
                 trading strategies are applied.",
}

Genetic Programming entries for Georgios Sermpinis Anastasia Fountouli Konstantinos A Theofilatos Andreas S Karathanasopoulos

Citations