Flexible neural trees ensemble for stock index modeling

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@Article{Chen:2007:N,
  author =       "Yuehui Chen and Bo Yang and Ajith Abraham",
  title =        "Flexible neural trees ensemble for stock index
                 modeling",
  journal =      "Neurocomputing",
  year =         "2007",
  volume =       "70",
  number =       "4-6",
  pages =        "697--703",
  month =        jan,
  note =         "Advanced Neurocomputing Theory and Methodology -
                 Selected papers from the International Conference on
                 Intelligent Computing 2005 (ICIC 2005), International
                 Conference on Intelligent Computing 2005",
  keywords =     "genetic algorithms, genetic programming, Flexible
                 neural tree, GP-like tree structure-based evolutionary
                 algorithm, Particle swarm optimisation, Ensemble
                 learning, Stock index",
  DOI =          "doi:10.1016/j.neucom.2006.10.005",
  abstract =     "The use of intelligent systems for stock market
                 predictions has been widely established. In this paper,
                 we investigate how the seemingly chaotic behaviour of
                 stock markets could be well represented using flexible
                 neural tree (FNT) ensemble technique. We considered the
                 Nasdaq-100 index of Nasdaq Stock MarketSM and the S&P
                 CNX NIFTY stock index. We analysed 7-year Nasdaq-100
                 main index values and 4-year NIFTY index values. This
                 paper investigates the development of novel reliable
                 and efficient techniques to model the seemingly chaotic
                 behaviour of stock markets. The structure and
                 parameters of FNT are optimised using genetic
                 programming (GP) like tree structure-based evolutionary
                 algorithm and particle swarm optimization (PSO)
                 algorithms, respectively. A good ensemble model is
                 formulated by the local weighted polynomial regression
                 (LWPR). This paper investigates whether the proposed
                 method can provide the required level of performance,
                 which is sufficiently good and robust so as to provide
                 a reliable forecast model for stock market indices.
                 Experimental results show that the model considered
                 could represent the stock indexes behaviour very
                 accurately.",
}

Genetic Programming entries for Yuehui Chen Bo Yang Ajith Abraham

Citations