Multi-Fractality Analysis of Time Series in Artificial Stock Market Generated by Multi-Agent Systems Based on the Genetic Programming and Its Applications

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@Article{journals/ieicet/IkedaT07a,
  author =       "Yoshikazu Ikeda and Shozo Tokinaga",
  title =        "Multi-Fractality Analysis of Time Series in Artificial
                 Stock Market Generated by Multi-Agent Systems Based on
                 the Genetic Programming and Its Applications",
  journal =      "IEICE Transactions on Fundamentals of Electronics,
                 Communications and Computer Sciences",
  year =         "2007",
  volume =       "90-A",
  number =       "10",
  pages =        "2212--2222",
  keywords =     "genetic algorithms, genetic programming,
                 multi-fractal, artificial stock market,
                 multi-agent-based modeling",
  ISSN =         "0916-8508",
  DOI =          "doi:10.1093/ietfec/e90-a.10.2212",
  abstract =     "There are several methods for generating multi-fractal
                 time series, but the origin of the multi-fractality is
                 not discussed so far. This paper deals with the
                 multi-fractality analysis of time series in an
                 artificial stock market generated by multi-agent
                 systems based on the Genetic Programming (GP) and its
                 applications to feature extractions. Cognitive
                 behaviors of agents are modeled by using the GP to
                 introduce the co-evolutionary (social) learning as well
                 as the individual learning. We assume five types of
                 agents, in which a part of the agents prefer forecast
                 equations or forecast rules to support their decision
                 making, and another type of the agents select decisions
                 at random like a speculator. The agents using forecast
                 equations and rules usually use their own knowledge
                 base, but some of them use their public (common)
                 knowledge base to improve trading decisions. For
                 checking the multi-fractality we use an extended method
                 based on the continuous time wavelet transform. Then,
                 it is shown that the time series of the artificial
                 stock price reveals as a multi-fractal signal. We
                 mainly focus on the proportion of the agents of each
                 type. To examine the role of agents of each type, we
                 classify six cases by changing the composition of
                 agents of types. As a result, in several cases we find
                 strict multi-fractality in artificial stock prices, and
                 we see the relationship between the realizability
                 (reproducibility) of multi-fractality and the system
                 parameters. By applying a prediction method for
                 mono-fractal time series as counterparts, features of
                 the multi-fractal time series are extracted. As a
                 result, we examine and find the origin of multi-fractal
                 processes in artificial stock prices.",
  bibdate =      "2008-01-15",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/ieicet/ieicet90a.html#IkedaT07a",
}

Genetic Programming entries for Yoshikazu Ikeda Shozo Tokinaga

Citations