Discovering effective technical trading rules with genetic programming: towards robustly outperforming buy-and-hold

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

@InProceedings{Lohpetch:2009:NaBIC,
  author =       "Dome Lohpetch and David Corne",
  title =        "Discovering effective technical trading rules with
                 genetic programming: towards robustly outperforming
                 buy-and-hold",
  booktitle =    "World Congress on Nature Biologically Inspired
                 Computing, NaBIC 2009",
  year =         "2009",
  month =        dec,
  pages =        "439--444",
  keywords =     "genetic algorithms, genetic programming, effective
                 trading rules, financial applications, fitness
                 function, profitable rules, research tool, stocks,
                 technical trading rules, financial management,
                 profitability, stock markets",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.457.845",
  URL =          "https://www.macs.hw.ac.uk/~dwcorne/lohpetchnabic.pdf",
  DOI =          "doi:10.1109/NABIC.2009.5393324",
  size =         "6 pages",
  abstract =     "Genetic programming is now a common research tool in
                 financial applications. One classic line of exploration
                 is their use to find effective trading rules for
                 individual stocks or for groups of stocks (such as an
                 index). The classic work in this area (Allen amp;
                 Karjaleinen, 99) found profitable rules, but which did
                 not outperform a straightforward buy and hold strategy.
                 Several later works report similar outcomes, while a
                 small number of works achieve out-performance of buy
                 and hold, but prove difficult to replicate. We focus
                 here on indicating clearly how the performance in one
                 such study (Becker amp; Seshadri, 03) was replicated,
                 and we carry out additional investigations which point
                 towards guidelines for generating results that robustly
                 outperform buy-and-hold. These guidelines relate to
                 strategies for organizing the training dataset, and
                 aspects of the fitness function.",
  notes =        "Also known as \cite{5393324}",
}

Genetic Programming entries for Dome Lohpetch David W Corne

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