An Heterogeneous, Endogenous and Coevolutionary GP-Based Financial Market

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@Article{Martinez-Jaramillo:2009:ieeeTEC,
  author =       "Serafin Martinez-Jaramillo and Edward P. K. Tsang",
  title =        "An Heterogeneous, Endogenous and Coevolutionary
                 GP-Based Financial Market",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2009",
  month =        feb,
  volume =       "13",
  number =       "1",
  pages =        "33--55",
  keywords =     "genetic algorithms, genetic programming, economics,
                 multi-agent systems, pricing, series (mathematics),
                 statistical analysis, stock markets, Red Queen
                 principle, agent-based simulation, analytical models,
                 behavioral constraint, coevolutionary GP-based
                 financial market, economic learning, endogenous
                 artificial market, evolutionary computation, fitness
                 function, genetic programming based agents, homogeneous
                 investors, investment opportunity, noise traders,
                 perfect rationality, price generation, price series,
                 real financial markets, statistical property, stock
                 markets, technical traders",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2008.2011401",
  size =         "23 pages",
  abstract =     "Stock markets are very important in modern societies
                 and their behavior has serious implications for a wide
                 spectrum of the world's population. Investors,
                 governing bodies, and society as a whole could benefit
                 from better understanding of the behavior of stock
                 markets. The traditional approach to analyzing such
                 systems is the use of analytical models. However, the
                 complexity of financial markets represents a big
                 challenge to the analytical approach. Most analytical
                 models make simplifying assumptions, such as perfect
                 rationality and homogeneous investors, which threaten
                 the validity of their results. This motivates
                 alternative methods.

                 In this paper, we report an artificial financial market
                 and its use in studying the behavior of stock markets.
                 This is an endogenous market, with which we model
                 technical, fundamental, and noise traders.
                 Nevertheless, our primary focus is on the technical
                 traders, which are sophisticated genetic programming
                 based agents that co- evolve (by learning based on
                 their fitness function) by predicting investment
                 opportunities in the market using technical analysis as
                 the main tool.

                 With this endogenous artificial market, we identify the
                 conditions under which the statistical properties of
                 price series in the artificial market resemble some of
                 the properties of real financial markets. By performing
                 a careful exploration of the most important aspects of
                 our simulation model, we determine the way in which the
                 factors of such a model affect the endogenously
                 generated price. Additionally, we model the pressure to
                 beat the market by a behavioral constraint imposed on
                 the agents reflecting the Red Queen principle in
                 evolution. We have demonstrated how evolutionary
                 computation could play a key role in studying stock
                 markets, mainly as a suitable model for economic
                 learning on an agent- based simulation.",
  notes =        "also known as \cite{4769014}",
}

Genetic Programming entries for Serafin Martinez Jaramillo Edward P K Tsang

Citations