A genetic network programming with learning approach for enhanced stock trading model

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@Article{Chen200912537,
  author =       "Yan Chen and Shingo Mabu and Kaoru Shimada and 
                 Kotaro Hirasawa",
  title =        "A genetic network programming with learning approach
                 for enhanced stock trading model",
  journal =      "Expert Systems with Applications",
  volume =       "36",
  number =       "10",
  pages =        "12537--12546",
  year =         "2009",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2009.05.054",
  URL =          "http://www.sciencedirect.com/science/article/B6V03-4WC113D-2/2/a6c6277183e3b22cc3cc50ba71d7062f",
  keywords =     "genetic algorithms, genetic programming, Genetic
                 Network Programming, Sarsa Learning, Stock trading
                 model, Technical Index, Candlestick Chart",
  abstract =     "In this paper, an enhancement of stock trading model
                 using Genetic Network Programming (GNP) with Sarsa
                 Learning is described. There are three important points
                 in this paper: First, we use GNP with Sarsa Learning as
                 the basic algorithm while both Technical Indices and
                 Candlestick Charts are introduced for efficient stock
                 trading decision-making. In order to create more
                 efficient judgement functions to judge the current
                 stock price appropriately, Importance Index (IMX) has
                 been proposed to tell GNP the timing of buying and
                 selling stocks. Second, to improve the performance of
                 the proposed GNP-Sarsa algorithm, we proposed a new
                 method that can learn the appropriate function
                 describing the relation between the value of each
                 technical index and the value of the IMX. This is an
                 important point that devotes to the enhancement of the
                 GNP-Sarsa algorithm. The third point is that in order
                 to create more efficient judgment functions, sub-nodes
                 are introduced in each node to select appropriate stock
                 price information depending on the situations and to
                 determine appropriate actions (buying/selling). To
                 confirm the effectiveness of the proposed method, we
                 carried out the simulation and compared the results of
                 GNP-Sarsa with other methods like GNP with Actor
                 Critic, GNP with Candlestick Chart, GA and Buy&Hold
                 method. The results shows that the stock trading model
                 using GNP-Sarsa outperforms all the other methods.",
}

Genetic Programming entries for Yan Chen Shingo Mabu Kaoru Shimada Kotaro Hirasawa

Citations