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",
• keywords = "genetic algorithms, genetic programming, Market Fraction Hypothesis, Self-Organizing Feature Map, Time-Invariant Self-Organising Feature Map, Agent-based financial model",
• ISSN = "1057-5219",
• DOI = "doi:10.1016/j.irfa.2011.06.009",
• URL = "http://www.sciencedirect.com/science/article/pii/S1057521911000706",
• 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.",
• notes = "School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, CO4 3SQ, UK",

}

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

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