Stock Market Modeling Using Genetic Programming Ensembles

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Crina Grosan and Ajith Abraham",
  title =        "Stock Market Modeling Using Genetic Programming
  year =         "2006",
  booktitle =    "Genetic Systems Programming: Theory and Experiences",
  pages =        "131--146",
  volume =       "13",
  series =       "Studies in Computational Intelligence",
  editor =       "Nadia Nedjah and Ajith Abraham and 
                 Luiza {de Macedo Mourelle}",
  publisher =    "Springer",
  address =      "Germany",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-29849-5",
  URL =          "",
  DOI =          "doi:10.1007/3-540-32498-4_6",
  abstract =     "The use of intelligent systems for stock market
                 predictions has been widely established. This chapter
                 introduces two Genetic Programming (GP) techniques:
                 Multi-Expression Programming (MEP) and Linear Genetic
                 Programming (LGP) for the prediction of two stock
                 indices. The performance is then compared with an
                 artificial neural network trained using
                 Levenberg-Marquardt algorithm and Takagi-Sugeno
                 neuro-fuzzy model. We considered Nasdaq-100 index of
                 Nasdaq Stock Market and the S&P CNX NIFTY stock index
                 as test data. Empirical results reveal that Genetic
                 Programming techniques are promising methods for stock
                 prediction. Finally formulate an ensemble of these two
                 techniques using a multiobjective evolutionary
                 algorithm. Results obtained by ensemble are better than
                 the results obtained by each GP technique
  notes =        ",11855,5-146-22-92733168-0,00.html",
  size =         "17 pages",

Genetic Programming entries for Crina Grosan Ajith Abraham