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Market Microstructure: A Self-Organizing Map Approach to Investigate Behavior Dynamics under an Evolutionary Environment

  • Michael Kampouridis
  • Shu-Heng Chen
  • Edward Tsang
Part of the Studies in Computational Intelligence book series (SCI, volume 380)

Summary

This chapter presents a market microstructure model, which investigates the behavior dynamics in financial markets. We are especially interested in examining whether the markets’ behavior is non-stationary, because this implies that strategies from the past cannot be applied to future time periods, unless they have co-evolved with the markets. In order to test this, we employ Genetic Programming, which acts as an inference engine for trading rules, and Self-Organizing Maps, which is used for clustering the above rules into types of trading strategies. The results on four empirical financial markets show that their behavior constantly changes; thus, agents’ trading strategies need to continuously adapt to the changes taking place in the market, in order to remain effective.

Keywords

Genetic Programming Trading Strategy Base Period Future Period Strategy Type 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Michael Kampouridis
    • 1
  • Shu-Heng Chen
    • 2
  • Edward Tsang
    • 1
  1. 1.School of Computer Science and Electronic EngineeringUniversity of EssexUK
  2. 2.AI-Econ Center, Department of EconomicsNational Cheng Chi UniversityTaipeiTaiwan

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