Computational Intelligence Algorithms for Risk-Adjusted Trading Strategies

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "N. G. Pavlidis and E. G. Pavlidis and 
                 M. G. Epitropakis and V. P. Plagianakos and M. N. Vrahatis",
  title =        "Computational Intelligence Algorithms for
                 Risk-Adjusted Trading Strategies",
  booktitle =    "2007 IEEE Congress on Evolutionary Computation",
  year =         "2007",
  editor =       "Dipti Srinivasan and Lipo Wang",
  pages =        "540--547",
  address =      "Singapore",
  month =        "25-28 " # sep,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "1-4244-1340-0",
  file =         "1953.pdf",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2007.4424517",
  abstract =     "This paper investigates the performance of trading
                 strategies identified through Computational
                 Intelligence techniques. We focus on trading rules
                 derived by Genetic Programming, as well as, Generalised
                 Moving Average rules optimised through Differential
                 Evolution. The performance of these rules is
                 investigated using recently proposed risk-adjusted
                 evaluation measures and statistical testing is carried
                 out through simulation. Overall, the moving average
                 rules proved to be more robust, but Genetic Programming
                 seems more promising in terms of generating higher
                 profits and detecting novel patterns in the data.",
  notes =        "CEC 2007 - A joint meeting of the IEEE, the EPS, and
                 the IET.

                 IEEE Catalog Number: 07TH8963C",

Genetic Programming entries for Nicos G Pavlidis Efthymios G Pavlidis Michael G Epitropakis V P Plagianakos Michael N Vrahatis