The Market Fraction Hypothesis under Different Genetic Programming Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Michael Kampouridis and Shu-Heng Chen and 
                 Edward Tsang",
  title =        "The Market Fraction Hypothesis under Different Genetic
                 Programming Algorithms",
  booktitle =    "Information Systems for Global Financial Markets:
                 Emerging Developments and Effects",
  publisher =    "IGI global",
  year =         "2011",
  editor =       "Alexander Y. Yap",
  chapter =      "3",
  pages =        "37--54",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-61350-162-5",
  URL =          "",
  DOI =          "doi:10.4018/978-1-61350-162-7.ch003",
  abstract =     "In a previous work, inspired by observations made in
                 many agent-based financial models, we formulated and
                 presented the Market Fraction Hypothesis, which
                 basically predicts a short duration for any dominant
                 type of agents, but then a uniform distribution over
                 all types in the long run. We then proposed a two-step
                 approach, a rule-inference step, and a rule-clustering
                 step, to test this hypothesis. We employed genetic
                 programming as the rule inference engine, and applied
                 self-organising maps to cluster the inferred rules. We
                 then ran tests for 10 international markets and
                 provided a general examination of the plausibility of
                 the hypothesis. However, because of the fact that the
                 tests took place under a GP system, it could be argued
                 that these results are dependent on the nature of the
                 GP algorithm. This chapter thus serves as an extension
                 to our previous work. We test the Market Fraction
                 Hypothesis under two new different GP algorithms, in
                 order to prove that the previous results are rigorous
                 and are not sensitive to the choice of GP. We thus test
                 again the hypothesis under the same 10 empirical
                 datasets that were used in our previous experiments.
                 Our work shows that certain parts of the hypothesis are
                 indeed sensitive on the algorithm. Nevertheless, this
                 sensitivity does not apply to all aspects of our tests.
                 This therefore allows us to conclude that our
                 previously derived results are rigorous and can thus be

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