Analyze long \& mid-term trends of stock with Genetic Programming on moving average and turning points
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Zhao:2010:ICACC,
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author = "Erbo Zhao and Zhangang Han",
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title = "Analyze long \& mid-term trends of stock with Genetic
Programming on moving average and turning points",
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booktitle = "2nd International Conference on Advanced Computer
Control (ICACC)",
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year = "2010",
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month = "27-29 " # mar,
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volume = "3",
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pages = "87--91",
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abstract = "This paper employs Genetic Programming (GP) with
individuals of tree structure to form empirical
formulae in order to track the dynamic pattern of the
moving average curves of stock prices. We find that our
method tracks the 60-day moving average better than
other shorter period averages. In order to minimise the
effects of noise and other random events impacting on
the markets and maximise the effective information
abstracted from the origin data, two comparable data
preprocessing methods for turning points are proposed
to cooperate with GP for more stable long and mid-term
dynamic analysis and prediction. We use either discrete
data with fixed time intervals as long as 120 days or
data at local extreme by FFT. So, the formula finding
system tracks the next turning point with the
information of several previous turning points.
Simulations show that our method to track and predict
long and mid-term change trend of stock price is
practical.",
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keywords = "genetic algorithms, genetic programming, fast Fourier
transform, moving average curves, stock long term
trend, stock midterm trend, stock prices, time
intervals, tree structure, turning points, fast Fourier
transforms, moving average processes, pricing, stock
markets, trees (mathematics)",
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DOI = "doi:10.1109/ICACC.2010.5486744",
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notes = "Also known as \cite{5486744}",
- }
Genetic Programming entries for
Erbo Zhao
Zhangang Han
Citations