Taiwan Stock Investment with Gene Expression Programming
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- @InProceedings{conf/kes/LeeYC14,
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title = "Taiwan Stock Investment with Gene Expression
Programming",
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author = "Cheng-Han Lee and Chang-Biau Yang and Hung-Hsin Chen",
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booktitle = "18th International Conference in Knowledge Based and
Intelligent Information and Engineering Systems, {KES}
2014, Gdynia, Poland, 15-17 September 2014",
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publisher = "Elsevier",
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year = "2014",
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volume = "35",
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editor = "Piotr Jedrzejowicz and Lakhmi C. Jain and
Robert J. Howlett and Ireneusz Czarnowski",
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pages = "137--146",
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series = "Procedia Computer Science",
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keywords = "genetic algorithms, genetic programming, gene
expression programming, stock investment, majority
vote, technical indicator, strategy pool",
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bibdate = "2014-10-12",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/kes/kes2014.html#LeeYC14",
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URL = "http://www.sciencedirect.com/science/journal/18770509/35",
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DOI = "doi:10.1016/j.procs.2014.08.093",
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abstract = "In this paper, we first find out some good trading
strategies from the historical series and apply them in
the future. The profitable strategies are trained out
by the gene expression programming (GEP), which
involves some well-known stock technical indicators as
features. Our data set collects the 100 stocks with the
top capital from the listed companies in the Taiwan
stock market. Accordingly, we build a new series called
portfolio index as the investment target. For each
trading day, we search for some similar template
intervals from the historical data and pick out the
pertained trading strategies from the strategy pool.
These strategies are validated by the return during a
few days before the trading day to check whether each
of them is suitable or not. Then these suitable
strategies decide the buying or selling consensus
signal with the majority vote on the trading day. The
training period is from 1996/1/6 to 2012/12/28, and the
testing period is from 2000/1/4 to 2012/12/28. Two
simulation experiments are performed. In experiment 1,
the best average accumulated return is 548.97percent
(average annualised return is 15.47percent). In
experiment 2, we increase the diversity of trading
strategies with more training. The best average
accumulated return is increased to 685.31percent
(average annualized return is 17.18percent). These two
results are much better than that of the buy-and-hold
strategy, whose return is 287.00percent.",
- }
Genetic Programming entries for
Cheng-Han Lee
Chang-biau Yang
Hung-Hsin Chen
Citations