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:1.3872

@InProceedings{Zhao:2010:ICACC,
  author =       "Erbo Zhao and Zhangang Han",
  title =        "Analyze long \& mid-term trends of stock with Genetic
                 Programming on moving average and turning points",
  booktitle =    "2nd International Conference on Advanced Computer
                 Control (ICACC)",
  year =         "2010",
  month =        "27-29 " # mar,
  volume =       "3",
  pages =        "87--91",
  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.",
  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)",
  DOI =          "doi:10.1109/ICACC.2010.5486744",
  notes =        "Also known as \cite{5486744}",
}

Genetic Programming entries for Erbo Zhao Zhangang Han

Citations