Forecasting Stock Returns Using Genetic Programming in C++

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{kaboudan:1998:fsrGPC,
  author =       "M. Kaboudan",
  title =        "Forecasting Stock Returns Using Genetic Programming in
                 C++",
  booktitle =    "Proceedings of 11th Annual Florida Artificial
                 Intelligence International Research Symposium",
  year =         "1998",
  editor =       "Diane J. Cook",
  address =      "Sanibel Island, Florida, USA",
  month =        may # " 18-20",
  publisher =    "AAAI Press",
  keywords =     "genetic algorithms, genetic programming",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:10.1.1.532.2726",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.532.2726",
  URL =          "https://www.aaai.org/Papers/FLAIRS/1998/FLAIRS98-014.pdf",
  URL =          "http://aaaipress.org/Papers/FLAIRS/1998/FLAIRS98-014.pdf",
  ISBN =         "1-57735-051-0",
  size =         "5 pages",
  abstract =     "This is an investigation of forecasting stock returns
                 using genetic programming. We first test the hypothesis
                 that genetic programming is equally successful in
                 predicting series produced by data generating processes
                 of different structural complexity. After rejecting the
                 hypothesis, we measure the complexity of thirty-two
                 time series representing four different frequencies of
                 eight stock returns. Then using symbolic regression, it
                 is shown that less complex high frequency data are more
                 predictable than more complex low frequency returns.
                 Although no forecasts are generated here, this
                 investigation provides new insights potentially useful
                 in predicting stock prices.",
  notes =        "FLAIRS-98",
}

Genetic Programming entries for Mahmoud A Kaboudan

Citations