GP and the Predictive Power of Internet Message Traffic

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "James D. Thomas and Katia Sycara",
  title =        "{GP} and the Predictive Power of Internet Message
  booktitle =    "Genetic Algorithms and Genetic Programming in
                 Computational Finance",
  publisher =    "Kluwer Academic Press",
  year =         "2002",
  editor =       "Shu-Heng Chen",
  chapter =      "4",
  pages =        "81--102",
  keywords =     "genetic algorithms, genetic programming, Computational
                 Finance, Internet Message Boards",
  ISBN =         "0-7923-7601-3",
  URL =          "",
  DOI =          "doi:10.1007/978-1-4615-0835-9_4",
  abstract =     "This paper investigates the predictive power of the
                 volume of messages produced on internet stock-related
                 measure boards. We introduce a specialized GP learner
                 and demonstrate that it produces trading rules that
                 outperform appropriate buy and hold strategy benchmarks
                 in measures of risk adjusted returns. We compare the
                 results to those attained by using other relevant
                 variables, lags of price and volume, and find that the
                 the message board volume produces clearly superior
                 results. We experiment with alternative representations
                 for the GP trading rule learner. Finally, we find a
                 potential regime shift in the market reaction to the
                 message volume data, and speculate about future
  notes =        "part of \cite{chen:2002:gagpcf}",

Genetic Programming entries for James D Thomas Katia Sycara