Using Genetic Programming with Lambda Abstraction to Find Technical Trading Rules

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

@InProceedings{RePEc:sce:scecf4:200,
  author =       "Tina Yu and Shu-Heng Chen",
  title =        "Using Genetic Programming with Lambda Abstraction to
                 Find Technical Trading Rules",
  booktitle =    "Computing in Economics and Finance",
  year =         "2004",
  address =      "University of Amsterdam",
  month =        "8-10 " # jul,
  organisation = "Society for Computational Economics",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "https://ideas.repec.org/p/sce/scecf4/200.html",
  abstract =     "Using GP with lambda abstraction module mechanism to
                 generate technical trading rules based on S&P 500
                 index, we find strong evidence of excess returns over
                 buy-and-hold after transaction cost on the testing
                 period from 1989 to 2002. The rules can be interpreted
                 easily; each uses a combination of one to four widely
                 used technical indicators to make trading decisions.
                 The consensus among GP rules is high, with most of the
                 time 80% of the evolved rules give the same decision.
                 The GP rules give high transaction frequency.
                 Regardless of market climate, they are able to identify
                 opportunities to make profitable trades and out-perform
                 buy-and-hold",
  notes =        "22 aug 2004
                 http://ideas.repec.org/p/sce/scecf4/200.html CEF 2004
                 number 200",
}

Genetic Programming entries for Tina Yu Shu-Heng Chen

Citations