Discovering effective technical trading rules with genetic programming: towards robustly outperforming buy-and-hold
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Lohpetch:2009:NaBIC,
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author = "Dome Lohpetch and David Corne",
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title = "Discovering effective technical trading rules with
genetic programming: towards robustly outperforming
buy-and-hold",
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booktitle = "World Congress on Nature Biologically Inspired
Computing, NaBIC 2009",
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year = "2009",
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month = dec,
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pages = "439--444",
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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",
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URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.457.845",
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URL = "https://www.macs.hw.ac.uk/~dwcorne/lohpetchnabic.pdf",
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DOI = "doi:10.1109/NABIC.2009.5393324",
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size = "6 pages",
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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.",
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notes = "Also known as \cite{5393324}",
- }
Genetic Programming entries for
Dome Lohpetch
David W Corne
Citations