Fast and effective predictability filters for stock price series using linear genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Wilson:2010:cec,
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author = "Garnett Wilson and Wolfgang Banzhaf",
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title = "Fast and effective predictability filters for stock
price series using linear genetic programming",
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booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
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year = "2010",
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address = "Barcelona, Spain",
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month = "18-23 " # jul,
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-4244-6910-9",
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abstract = "A handful of researchers who apply genetic programming
(GP) to the analysis of financial markets have devised
predictability pretests to determine whether the time
series that is being supplied to GP contains patterns
that can be predicted, but most studies apply no such
pretests. By applying predictability pretests,
researchers can have greater confidence that the GP
system is solving a problem which is actually there and
that it will be less likely to make questionable
investment decisions based on non-existent patterns.
Previous work in this area has applied regression to
randomised versions of time series training data to
create a functional model that is applied over a future
window of time. This work presents two types of
predictability filters with low computational overhead,
namely frequency-based and information theoretic, that
complement the previous function-based continuous
output predictability models. Results indicate that
either filter can be beneficial for particular trend
types, but the information-based filter involves a
greater chance of missing opportunities for profit. In
contrast, the frequency-based filter always
outperforms, or is competitive with, the filterless
implementation.",
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DOI = "doi:10.1109/CEC.2010.5586297",
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notes = "WCCI 2010. Also known as \cite{5586297}",
- }
Genetic Programming entries for
Garnett Carl Wilson
Wolfgang Banzhaf
Citations