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Edinburgh, UK

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  • [www.macs.hw.ac.uk]
  • [www.macs.hw.ac.uk]
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by David Corne
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BibTeX

@MISC{Corne_edinburgh,uk,
    author = {David Corne},
    title = {Edinburgh, UK},
    year = {}
}

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Abstract

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 & 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 & 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. Keywords- stock trading, technical trading rules, genetic programming

Keyphrases

genetic programming    becker seshadri    classic work    common research tool    keywords stock trading    effective trading rule    profitable rule    abstract genetic programming    financial application    straightforward buy    fitness function    classic line    hold strategy    towards guideline    allen karjaleinen    technical trading rule    report similar outcome    small number    additional investigation    individual stock    training dataset   

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