Artificial Life and Robotics

, Volume 12, Issue 1–2, pp 176–179 | Cite as

Interaction of agents in financial markets and informational method to quantify it

  • Aki-hiro SatoEmail author
Original Article


In this article, an informational method to quantify behavioral similarities of market participants is proposed regarding a financial market as a many-body system. An agent-based model of a financial market in which N market participants deal with M financial commodities is considered. In order to measure the agents’ interactions, the spectral distance defined by the Kullback-Leibler divergence between two normalized spectra of behavioral frequencies is introduced. The validity of the method is evaluated by using the behavioral frequencies obtained from the agent-based model. It is concluded that the perception and decision parameters of agents who treat two commodities tend to be similar when the behavioral frequencies are similar.

Key words

Agent-based modeling Kullback-Leibler divergence Behavioral frequencies 


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  1. 1.
    Haken H (1988) Information and self-organization: a macroscopic approach to complex systems. Springer, BerlinzbMATHGoogle Scholar
  2. 2.
    Aoki M (1996) New approaches to macroeconomic modeling: evolutionary stochastic dynamics, multiple equilibria, and externalities as field effects. Cambridge University Press, New YorkGoogle Scholar
  3. 3.
    Dacorogna MM, Gençay R, Müuller U et al. (2000) An introduction to high-frequency finance. Academic Press, San DiegoGoogle Scholar
  4. 4.
    Sato A-H (2006) Quantifying similarity between markets with application to high-frequency financial data. Phys Soc Jpn 75:084005-1–084005-5Google Scholar
  5. 5.
    Sato A-H (2006) Frequency analysis of tick quotes on foreign currency markets and the double-threshold agent model. Physica A, 369:753–764CrossRefGoogle Scholar
  6. 6.
    Granovetter M (1978) Threshold models of collective behavior. Am J Sociol 83:1420–1443CrossRefGoogle Scholar
  7. 7.
    CcCauley JL (2004) Dynamics of markets. Cambridge University Press, CambridgeGoogle Scholar
  8. 8.
    Veldhuis R, Klabbers E (2003) On the computation of the Kullback-Leibler measure for spectral distance. IEEE Trans Speech Audio Process 11:100–103CrossRefGoogle Scholar
  9. 9.
    Lin J (1991) Divergence measure based on the Shannon entropy. IEEE Trans Inform Theor 37:145–150zbMATHCrossRefGoogle Scholar

Copyright information

© International Symposium on Artificial Life and Robotics (ISAROB). 2008

Authors and Affiliations

  1. 1.Department of Applied Mathematics and Physics, Graduate School of InformaticsKyoto UniversityKyotoJapan

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