Detection of stock price movements using chance discovery and genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Alma Lilia Garcia-Almanza and Edward P. K. Tsang",
  title =        "Detection of stock price movements using chance
                 discovery and genetic programming",
  journal =      "International Journal of Knowledge-Based and
                 Intelligent Engineering Systems",
  year =         "2007",
  volume =       "11",
  number =       "5",
  pages =        "329--344",
  publisher =    "IOS",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1327-2314",
  broken =       "",
  DOI =          "doi:10.3233/KES-2007-11509",
  size =         "16 pages",
  abstract =     "The aim of this work is to detect important movements
                 in financial stock prices that may indicate future
                 opportunities or risks. The occurrence of such
                 movements is scarce, thus this problem falls into the
                 domain of Chance Discovery, a new research area whose
                 objective is to identify rare events that may represent
                 potential opportunities and risks.

                 In this work we propose to capture patterns of the rare
                 instances in different ways in order to increase the
                 probability of identifying similar cases in the future.
                 To generate more variety of solutions we evolve a
                 genetic program, which is an evolutionary technique
                 that is able to create multiple solutions for a single
                 problem. The idea is to mine the knowledge acquired by
                 the evolutionary process to extract and collect
                 different rules that model the positive cases in
                 several and novel ways.

                 Once an important movement in financial markets has
                 been discovered, human interaction is needed to analyze
                 the markets conditions and determine if that movement
                 could be a good opportunity to invest or could be the
                 principle of a bubble or another critical event that
                 represents a risk.

                 Standard decision trees methods capture patterns from
                 training data sets. However, when the chances are
                 scare, some of the patters captured by the best rules
                 may not repeat themselves in unseen cases. In this work
                 we propose Repository Method which comprises multiple
                 rules to form a more reliable classifier in rare

                 To illustrate our approach, it was applied to discover
                 important movements in stock prices. From experimental
                 results we showed that our approach can consistently
                 detect rare cases in extreme imbalanced data sets.",
  notes =        "Nov 2015
  bibdate =      "2008-08-06",
  bibsource =    "DBLP,

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