Multiobjective Algorithms for Financial Trading Multiobjective Out-trades Single-Objective
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Lohpetch:2011:MAfFTMOS,
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title = "Multiobjective Algorithms for Financial Trading
Multiobjective Out-trades Single-Objective",
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author = "Dome Lohpetch and David Corne",
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pages = "192--199",
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booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary
Computation",
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year = "2011",
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editor = "Alice E. Smith",
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month = "5-8 " # jun,
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address = "New Orleans, USA",
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organization = "IEEE Computational Intelligence Society",
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publisher = "IEEE Press",
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ISBN = "0-7803-8515-2",
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keywords = "genetic algorithms, genetic programming, buy and hold
strategy, economics, finance, financial trading,
frequent trading decision, infrequent trading strategy,
multiobjective algorithm, multiobjective out-trades
single-objective, multiobjective strategy, financial
management",
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DOI = "doi:10.1109/CEC.2011.5949618",
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annote = "The Pennsylvania State University CiteSeerX Archives",
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bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
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language = "en",
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oai = "oai:CiteSeerX.psu:10.1.1.477.3567",
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rights = "Metadata may be used without restrictions as long as
the oai identifier remains attached to it.",
-
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.477.3567",
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URL = "http://www.macs.hw.ac.uk/~dwcorne/dldccec11.pdf",
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size = "8 pages",
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abstract = "Genetic programming (GP) is increasingly investigated
in finance and economics. One area of study is its use
to discover effective rules for technical trading in
the context of a portfolio of equities (or an index).
Early work used GP to find rules that were profitable,
but were outperformed by the simple buy and hold
strategy. Attempts since then report similar findings,
except a handful of cases where GP has been found to
outperform BH. Recent work has clarified that robust
out performance of BH depends on, mainly, the adoption
of a relatively infrequent trading strategy (e.g.
monthly), as well as a range of other factors. Here we
add a comprehensive study of multiobjective approaches
to this investigation, and find that multiobjective
strategies provide even more robustness in
outperforming BH, even in the context of more frequent
(e.g. weekly) trading decisions.",
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notes = "CEC2011 sponsored by the IEEE Computational
Intelligence Society, and previously sponsored by the
EPS and the IET.",
- }
Genetic Programming entries for
Dome Lohpetch
David W Corne
Citations