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

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@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 and Karjaleinen, 1999) 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 and Seshadri, 2003) 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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