Fast and effective predictability filters for stock price series using linear genetic programming

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

@InProceedings{Wilson:2010:cec,
  author =       "Garnett Wilson and Wolfgang Banzhaf",
  title =        "Fast and effective predictability filters for stock
                 price series using linear genetic programming",
  booktitle =    "IEEE Congress on Evolutionary Computation (CEC 2010)",
  year =         "2010",
  address =      "Barcelona, Spain",
  month =        "18-23 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4244-6910-9",
  abstract =     "A handful of researchers who apply genetic programming
                 (GP) to the analysis of financial markets have devised
                 predictability pretests to determine whether the time
                 series that is being supplied to GP contains patterns
                 that can be predicted, but most studies apply no such
                 pretests. By applying predictability pretests,
                 researchers can have greater confidence that the GP
                 system is solving a problem which is actually there and
                 that it will be less likely to make questionable
                 investment decisions based on non-existent patterns.
                 Previous work in this area has applied regression to
                 randomised versions of time series training data to
                 create a functional model that is applied over a future
                 window of time. This work presents two types of
                 predictability filters with low computational overhead,
                 namely frequency-based and information theoretic, that
                 complement the previous function-based continuous
                 output predictability models. Results indicate that
                 either filter can be beneficial for particular trend
                 types, but the information-based filter involves a
                 greater chance of missing opportunities for profit. In
                 contrast, the frequency-based filter always
                 outperforms, or is competitive with, the filterless
                 implementation.",
  DOI =          "doi:10.1109/CEC.2010.5586297",
  notes =        "WCCI 2010. Also known as \cite{5586297}",
}

Genetic Programming entries for Garnett Carl Wilson Wolfgang Banzhaf

Citations