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:1.8051

@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