Genetic programming application to generate technical trading rules in stock markets

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

  author =       "Akbar Esfahanipour and Somaye Mousavi",
  title =        "Genetic programming application to generate technical
                 trading rules in stock markets",
  journal =      "International Journal of Reasoning-based Intelligent
  year =         "2010",
  volume =       "2",
  number =       "3/4",
  pages =        "244--250",
  keywords =     "genetic algorithms, genetic programming, technical
                 trading rules, stock markets, tehran stock exchange,
                 TSE, Iran, decision making, stock trading",
  ISSN =         "1755-0564",
  bibsource =    "OAI-PMH server at",
  language =     "eng",
  URL =          "",
  DOI =          "doi:10.1504/IJRIS.2010.036870",
  abstract =     "Technical trading rules can be generated from
                 historical data for decision making in stock trading.
                 In this study, genetic programming (GP) as an
                 evolutionary algorithm has been applied to
                 automatically generate such technical trading rules on
                 individual stocks. In order to obtain more realistic
                 trading rules, we have included transaction costs,
                 dividends and splits in our GP model. Our model has
                 been applied for nine Iranian companies listed on
                 different activity sectors of Tehran Stock Exchange
                 (TSE). Our results show that this model could generate
                 profitable trading rules in comparison with buy and
                 hold strategy for companies having frequent trading in
                 the market. Also, the effect of the above mentioned
                 parameters on trading rule's profitability are
                 evaluated using three separate models.",

Genetic Programming entries for Akbar Esfahanipour Somayeh Mousavi