Forecasting stock prices using Genetic Programming and Chance Discovery

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Alma Lilia Garcia-Almanza and Edward P. K. Tsang",
  title =        "Forecasting stock prices using Genetic Programming and
                 Chance Discovery",
  booktitle =    "12th International Conference On Computing In
                 Economics And Finance",
  year =         "2006",
  pages =        "number 489",
  month =        jul,
  organisation = "Society for Computational Economics",
  bibsource =    "OAI-PMH server at",
  description =  "Forecasting, Chance discovery, Genetic programming,
                 machine learning",
  identifier =   "RePEc:sce:scecfa:489",
  oai =          "oai:RePEc:sce:scecfa:489",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  URL =          "",
  URL =          "",
  abstract =     "In recent years the computers have shown to be a
                 powerful tool in financial forecasting. Many machine
                 learning techniques have been used to predict movements
                 in financial markets. Machine learning classifiers
                 involve extending the past experiences into the future.
                 However the rareness of some events makes difficult to
                 create a model that detect them. For example bubbles
                 burst and crashes are rare cases, however their
                 detection is crucial since they have a significant
                 impact on the investment. One of the main problems for
                 any machine learning classifier is to deal with
                 unbalanced classes. Specifically Genetic Programming
                 has limitation to deal with unbalanced environments. In
                 a previous work we described the Repository Method, it
                 is a technique that analyses decision trees produced by
                 Genetic Programming to discover classification rules.
                 The aim of that work was to forecast future
                 opportunities in financial stock markets on situations
                 where positive instances are rare. The objective is to
                 extract and collect different rules that classify the
                 positive cases. It lets model the rare instances in
                 different ways, increasing the possibility of
                 identifying similar cases in the future. The objective
                 of the present work is to find out the factors that
                 work in favour of Repository Method, for that purpose a
                 series of experiments was performed.",
  notes =        "CEF 2006",

Genetic Programming entries for Alma Lilia Garcia Almanza Edward P K Tsang