A hybrid procedure for stock price prediction by integrating self-organizing map and genetic programming

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@Article{Hsu:2011:ESA,
  author =       "Chih-Ming Hsu",
  title =        "A hybrid procedure for stock price prediction by
                 integrating self-organizing map and genetic
                 programming",
  journal =      "Expert Systems with Applications",
  year =         "2011",
  volume =       "38",
  number =       "11",
  pages =        "14026--14036",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2011.04.210",
  URL =          "http://www.sciencedirect.com/science/article/B6V03-52T13T7-7/2/c2626c201c0da6cbc20628185936eaf3",
  keywords =     "genetic algorithms, genetic programming, Stock price
                 prediction, Self-organising map",
  size =         "11 pages",
  abstract =     "Stock price prediction is a very important financial
                 topic, and is considered a challenging task and worthy
                 of the considerable attention received from both
                 researchers and practitioners. Stock price series have
                 properties of high volatility, complexity, dynamics and
                 turbulence, thus the implicit relationship between the
                 stock price and predictors is quite dynamic. Hence, it
                 is difficult to tackle the stock price prediction
                 problems effectively by using only single soft
                 computing technique. This study hybridises a
                 self-organizing map (SOM) neural network and genetic
                 programming (GP) to develop an integrated procedure,
                 namely, the SOM-GP procedure, in order to resolve
                 problems inherent in stock price predictions. The SOM
                 neural network is used to divide the sample data into
                 several clusters, in such a manner that the objects
                 within each cluster possess similar properties to each
                 other, but differ from the objects in other clusters.
                 The GP technique is applied to construct a mathematical
                 prediction model that describes the functional
                 relationship between technical indicators and the
                 closing price of each cluster formed in the SOM neural
                 network. The feasibility and effectiveness of the
                 proposed hybrid SOM-GP prediction procedure are
                 demonstrated through experiments aimed at predicting
                 the finance and insurance sub-index of TAIEX (Taiwan
                 stock exchange capitalisation weighted stock index).
                 Experimental results show that the proposed SOM-GP
                 prediction procedure can be considered a feasible and
                 effective tool for stock price predictions, as based on
                 the overall prediction performance indices.
                 Furthermore, it is found that the frequent and
                 alternating rise and fall, as well as the range of
                 daily closing prices during the period, significantly
                 increase the difficulties of predicting.",
}

Genetic Programming entries for Chih-Ming Hsu

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