Grammar-mediated time-series prediction

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{brabazon:2005:GMTSP,

• 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

Citations