Evolutionary algorithms for financial trading
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @PhdThesis{Lohpetch:thesis,
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author = "Dome Lohpetch",
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title = "Evolutionary algorithms for financial trading",
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school = "Mathematical and Computer Sciences, Heriot-Watt
University",
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year = "2011",
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address = "UK",
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month = nov,
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keywords = "genetic algorithms, genetic programming, grammatical
evolution",
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URL = "http://www.ros.hw.ac.uk/bitstream/handle/10399/2510/LohpetchD_1111_macs.pdf",
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URL = "http://hdl.handle.net/10399/2510",
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size = "273 pages",
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abstract = "Genetic programming (GP) is increasingly popular as a
research tool for applications in finance and
economics. One thread in this area is the use of GP to
discover effective technical trading rules. In a
seminal article, Allen & Karjalainen (1999) used GP to
find rules that were profitable, but were nevertheless
outperformed by the simple buy and hold trading
strategy. Many succeeding attempts have reported
similar findings. This represents a clear example of a
significant open issue in the field of GP, namely,
generalization in GP [78]. The issue of generalisation
is that GP solutions may not be general enough,
resulting in poor performance on unseen data. There are
a small handful of cases in which such work has managed
to find rules that outperform buy and hold, but these
have tended to be difficult to replicate. Among
previous studies, work by Becker & Seshadri (2003) was
the most promising one, which showed outperformance of
buy-and-hold. In turn, Becker & Seshadri's work had
made several modifications to Allen & Karjalainen's
work, including the adoption of monthly rather than
daily trading. This thesis provides a replicable
account of Becker & Seshadri's study, and also shows
how further modifications enabled fairly reliable
outperformance of buy-and-hold, including the use of a
train/test/validate methodology [41] to evolve trading
rules with good properties of generalization, and the
use of a dynamic form of GP [109] to improve the
performance of the algorithm in dynamic environments
like financial markets. In addition, we investigate and
compare each of daily, weekly and monthly trading; we
find that outperformance of buy-and-hold can be
achieved even for daily trading, but as we move from
monthly to daily trading the performance of evolved
rules becomes increasingly dependent on prevailing
market conditions. This has clarified that robust
outperformance of B&H depends on, mainly, the adoption
of a relatively infrequent trading strategy (e.g.
monthly), as well as a range of factors that amount to
sound engineering of the GP grammar and the validation
strategy. Moreover, v we also add a comprehensive study
of multiobjective approaches to this investigation with
assumption from that, and find that multiobjective
strategies provide even more robustness in
outperforming B&H, even in the context of more frequent
(e.g. weekly) trading decisions. Last, inspired by a
number of beneficial aspects of grammatical evolution
(GE) and reports on the successful performance of
various kinds of its applications, we introduce new
approach for (GE) with a new suite of operators
resulting in an improvement on GE search compared with
standard GE. An empirical test of this new GE approach
on various kind of test problems, including financial
trading, is provided in this thesis as well.",
-
notes = "Supervisor David Wolfe Corne",
- }
Genetic Programming entries for
Dome Lohpetch
Citations