Generating trading rules on US Stock Market using strongly typed genetic programming
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- @Article{DBLP:journals/soco/VK20,
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author = "Kevin Michell and Werner Kristjanpoller",
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title = "Generating trading rules on {US} Stock Market using
strongly typed genetic programming",
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journal = "Soft Computing",
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volume = "24",
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number = "5",
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pages = "3257--3274",
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year = "2020",
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month = mar,
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keywords = "genetic algorithms, genetic programming, STGP,
Strongly typed genetic programming, Rule generation,
Stock market, Evolutionary computation, Portfolio
composition",
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timestamp = "Thu, 13 Feb 2020 00:00:00 +0100",
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biburl = "https://dblp.org/rec/journals/soco/VK20.bib",
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bibsource = "dblp computer science bibliography, https://dblp.org",
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URL = "https://doi.org/10.1007/s00500-019-04085-1",
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DOI = "doi:10.1007/s00500-019-04085-1",
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size = "18 pages",
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abstract = "Extracting rules from stock market data is an
important and exciting problem, where investment
decisions should be as clear and intuitive as possible
in order for investors to choose the composition of
their portfolios. Thus, it is important to guarantee
that this process is done with a good framework and
reliable techniques. In this context, portfolio
composition is a puzzle with respect to selecting the
appropriate assets and the optimal timing to invest.
There are several models and algorithms to make these
decisions, and in recent years, machine learning
applications have been used to solve this puzzle with
exceptional results. This technique allows a large
amount of data to be processed, resulting in more
informed recommendations on which asset to choose. Our
study uses strongly typed genetic programming to
generate rules to buy, hold and sell stocks in the US
stock market, considering a rolling windows approach.
We propose a different training approach, focusing the
fitness function on a ternary decision based on the
return prediction of each stock analyzed. The ternary
rule matches perfectly with the three decisions: buy,
hold and sell. Therefore, the rules are simple,
intuitive, and easy for investors to understand. The
results show that the proposed algorithm generates
higher profits than the classical optimization
approach. Moreover, the profits obtained are higher
than the buy-and-hold strategy and the return of the
indexes representative of the US stock market.",
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notes = "https://www.jpmorgan.com/global/research/machine-learning
Universidad Tecnica Federico Santa Maria, Avenida
Espana 1680, Valparaiso, Chile",
- }
Genetic Programming entries for
Kevin Michell
Werner Kristjanpoller
Citations