Investigating the effect of different GP algorithms on the non-stationary behavior of financial markets

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Kampouridis:2011:CIFEr,
  author =       "Michael Kampouridis and Shu-Heng Chen and 
                 Edward Tsang",
  title =        "Investigating the effect of different GP algorithms on
                 the non-stationary behavior of financial markets",
  booktitle =    "IEEE Symposium on Computational Intelligence for
                 Financial Engineering and Economics (CIFEr 2011)",
  year =         "2011",
  month =        "11-15 " # apr,
  address =      "Paris",
  size =         "8 pages",
  abstract =     "This paper extends a previous market microstructure
                 model, where we used Genetic Programming (GP) as an
                 inference engine for trading rules, and Self Organising
                 Maps as a clustering machine for those rules.
                 Experiments in that work took place under a single
                 financial market and investigated whether its behaviour
                 is non-stationary or cyclic. Results showed that the
                 market's behaviour was constantly changing and
                 strategies that would not adapt to these changes, would
                 become obsolete, and their performance would thus
                 decrease over time. However, because experiments in
                 that work were based on a specific GP algorithm, we are
                 interested in this paper to prove that those results
                 are independent of the choice of such algorithms. We
                 thus repeat our previous tests under two more GP
                 frameworks. In addition, while our previous work
                 surveyed only a single market, in this paper we run
                 tests under 10 markets, for generalisation purposes.
                 Finally, we deepen our analysis and investigate whether
                 the performance of strategies, which have not
                 co-evolved with the market, follows a continuous
                 decrease, as it has been previously suggested in the
                 agent-based artificial stock market literature. Results
                 show that our previous results are not sensitive to the
                 choice of GP. Strategies that do not co-evolve with the
                 market, become ineffective. However, we do not find
                 evidence for a continuous performance decrease of these
                 strategies.",
  keywords =     "genetic algorithms, genetic programming, agent-based
                 artificial stock market literature, financial markets,
                 genetic programming algorithm, market microstructure
                 model, nonstationary behaviour, self organising maps,
                 financial data processing, marketing data processing,
                 multi-agent systems, self-organising feature maps,
                 stock markets",
  DOI =          "doi:10.1109/CIFER.2011.5953568",
  ISSN =         "pending",
  notes =        "Also known as \cite{5953568}",
}

Genetic Programming entries for Michael Kampouridis Shu-Heng Chen Edward P K Tsang

Citations