Multiobjective Algorithms for Financial Trading Multiobjective Out-trades Single-Objective

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  title =        "Multiobjective Algorithms for Financial Trading
                 Multiobjective Out-trades Single-Objective",
  author =       "Dome Lohpetch and David Corne",
  pages =        "192--199",
  booktitle =    "Proceedings of the 2011 IEEE Congress on Evolutionary
  year =         "2011",
  editor =       "Alice E. Smith",
  month =        "5-8 " # jun,
  address =      "New Orleans, USA",
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, buy and hold
                 strategy, economics, finance, financial trading,
                 frequent trading decision, infrequent trading strategy,
                 multiobjective algorithm, multiobjective out-trades
                 single-objective, multiobjective strategy, financial
  DOI =          "doi:10.1109/CEC.2011.5949618",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:",
  rights =       "Metadata may be used without restrictions as long as
                 the oai identifier remains attached to it.",
  URL =          "",
  URL =          "",
  size =         "8 pages",
  abstract =     "Genetic programming (GP) is increasingly investigated
                 in finance and economics. One area of study is its use
                 to discover effective rules for technical trading in
                 the context of a portfolio of equities (or an index).
                 Early work used GP to find rules that were profitable,
                 but were outperformed by the simple buy and hold
                 strategy. Attempts since then report similar findings,
                 except a handful of cases where GP has been found to
                 outperform BH. Recent work has clarified that robust
                 out performance of BH depends on, mainly, the adoption
                 of a relatively infrequent trading strategy (e.g.
                 monthly), as well as a range of other factors. Here we
                 add a comprehensive study of multiobjective approaches
                 to this investigation, and find that multiobjective
                 strategies provide even more robustness in
                 outperforming BH, even in the context of more frequent
                 (e.g. weekly) trading decisions.",
  notes =        "CEC2011 sponsored by the IEEE Computational
                 Intelligence Society, and previously sponsored by the
                 EPS and the IET.",

Genetic Programming entries for Dome Lohpetch David W Corne