Grammar-mediated time-series prediction

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Anthony Brabazon and Katrina Meagher and 
                 Edward Carty and Michael O'Neill and Peter Keenan",
  title =        "Grammar-mediated time-series prediction",
  journal =      "Journal of Intelligent Systems",
  year =         "2004",
  volume =       "14",
  number =       "2--3",
  pages =        "123--143",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, time-series, high-frequency finance,
                 intra-day stock trading",
  ISSN =         "2191-026X",
  ISSN =         "0334-1860",
  DOI =          "doi:10.1515/JISYS.2005.14.2-3.123",
  size =         "20 pages",
  abstract =     "Grammatical Evolution is a data-driven,
                 model-induction tool, inspired by the biological
                 gene-to-protein mapping process. This study examines
                 the potential of Grammatical Evolution to uncover
                 useful technical trading rulesets for intra-day equity
                 trading. The form of these rule-sets is not specified
                 ex-ante but emerges by means of an evolutionary
                 process. High-frequency price data drawn from United
                 States stock markets is used to train and test the
                 model. The findings suggest that the developed rules
                 earn positive returns in holdout test periods, and that
                 the sizes of these returns are critically impacted by
                 the choice of position exit-strategy.",

Genetic Programming entries for Anthony Brabazon Katrina Meagher Edward Carty Michael O'Neill Peter Keenan