An IP and GEP Based Dynamic Decision Model for Stock Market Forecasting

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Yuehui Chen and Qiang Wu and Feng Chen2",
  title =        "An {IP} and {GEP} Based Dynamic Decision Model for
                 Stock Market Forecasting",
  booktitle =    "4th International Symposium on Neural Networks
                 Advances in Neural Networks, ISNN 2007, Part I",
  year =         "2007",
  editor =       "Derong Liu and Shumin Fei and Zeng-Guang Hou and 
                 Huaguang Zhang and Changyin Sun",
  volume =       "4491",
  series =       "LNCS",
  pages =        "473--479",
  address =      "Nanjing, China",
  month =        jun # " 3-7",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, stock market forecasting,
                 dynamic decision model, application result, forecasting
                 model, favourable result, new method, static model,
                 hybrid immune programming, generalisation capacity,
                 artificial neural network, computational intelligence,
                 static environment, stock market index, new dynamic
                 decision forecasting model",
  isbn13 =       "978-3-540-72383-7",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:",
  URL =          "",
  broken =       "",
  DOI =          "doi:10.1007/978-3-540-72383-7_56",
  abstract =     "The forecasting models for stock market index using
                 computational intelligence such as Artificial Neural
                 networks (ANNs) and Genetic programming(GP), especially
                 hybrid Immune Programming (IP) Algorithm and Gene
                 Expression Programming (GEP) have achieved favourable
                 results. However, these studies, have assumed a static
                 environment. This study investigates the development of
                 a new dynamic decision forecasting model. Application
                 results prove the higher precision and generalisation
                 capacity of the predicting model obtained by the new
                 method than static models.",

Genetic Programming entries for Yuehui Chen Qiang Wu Feng Chen2