Market fraction hypothesis: A proposed test

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@Article{Kampouridis201241,
  author =       "Michael Kampouridis and Shu-Heng Chen and 
                 Edward Tsang",
  title =        "Market fraction hypothesis: A proposed test",
  journal =      "International Review of Financial Analysis",
  volume =       "23",
  pages =        "41--54",
  year =         "2012",
  note =         "Complexity and Non-Linearities in Financial Markets:
                 Perspectives from Econophysics",
  ISSN =         "1057-5219",
  DOI =          "doi:10.1016/j.irfa.2011.06.009",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1057521911000706",
  keywords =     "genetic algorithms, genetic programming, Market
                 Fraction Hypothesis, Self-Organizing Feature Map,
                 Time-Invariant Self-Organising Feature Map, Agent-based
                 financial model",
  abstract =     "This paper presents and formalises the Market Fraction
                 Hypothesis (MFH), and also tests it under empirical
                 datasets. The MFH states that the fraction of the
                 different types of trading strategies that exist in a
                 financial market changes (swings) over time. However,
                 while such swinging has been observed in several
                 agent-based financial models, a common assumption of
                 these models is that the trading strategy types are
                 static and pre-specified. In addition, although the
                 above swinging observation has been made in the past,
                 it has never been formalised into a concrete
                 hypothesis. In this paper, we formalise the MFH by
                 presenting its main constituents. Formalising the MFH
                 is very important, since it has not happened before and
                 because it allows us to formulate tests that examine
                 the plausibility of this hypothesis. Testing the
                 hypothesis is also important, because it can give us
                 valuable information about the dynamics of the market's
                 microstructure. Our testing methodology follows a novel
                 approach, where the trading strategies are neither
                 static, nor pre-specified, as in the case in the
                 traditional agent-based financial model literature. In
                 order to do this, we use a new agent-based financial
                 model which employs genetic programming as a
                 rule-inference engine, and self-organizing maps as a
                 clustering machine. We then run tests under 10
                 international markets and find that some parts of the
                 hypothesis are not well-supported by the data. In fact,
                 we find that while the swinging feature can be
                 observed, it only happens among a few strategy types.
                 Thus, even if many strategy types exist in a market,
                 only a few of them can attract a high number of traders
                 for long periods of time.",
}

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

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