Advertisement

The Statistical Properties of Price Fluctuation by Computer Agent in U-Mart Virtual Futures Market Simulator

Conference paper

Abstract

Artificial Market is a growing research area where economist, scientist and engineers collaborate to understand real world’s market as complex systems. The shape of the distribution of price fluctuation in market is one of the active topics in the research of artificial market. The aim of this paper is to simulate the market from the bottom up and investigate it. We attempt to clarify some statistical properties of it using U-Mart virtual stock price index futures simulator — an agent-based economic simulator. Agent-based simulation is promising method for complex systems such as economics or mass psychology. U-Mart simulator is characterized by dealing with virtual stock price index futures of real stock price index in order to hold a connection between virtual and real world. We show that the high peaked and fat tailed distributions of price fluctuation can emerge from the agents whose price fluctuation on order is normal distribution. We show that kurtosis — the measure of high peak and fat tail — increases when all agents become more conservative. We also show that kurtosis increases when a small number of large traders exist in a large number of small traders.

Keywords

Artificial Market Agent-Based Simulation Price Fluctuation U-Mart Complex Systems 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Casti JL (1996) Would-Be Worlds: How Simulation Is Changing the Frontiers of Science, John Wiley and Sons, Indianapolis, IN.Google Scholar
  2. Waldrop M (1992) Complexity: the Emerging Science At the Edge of Order and Chaos, Touchstone Books, Carmichael, CAGoogle Scholar
  3. Simon HA (eds) (1992) Economics, Bounded Rationality and Cognitive Revolution, Edward Elgar Publishing, Northampton, MAGoogle Scholar
  4. Luna F, Stefannson B (2000) Economic Simulations in Swarm: Agent-Based Modelling and Object Oriented Programming, Kluwer Academic Publishers, Norwell, MAzbMATHCrossRefGoogle Scholar
  5. Epstein J, Axtell R (1996) Growing Artificial Societies: Social Science form the Bottom Up, MIT Press, Cambridge, MAGoogle Scholar
  6. Arthur WB, Holland J, LeBaron B, Palmer R, Tayler P (1997) Asset pricing under endogenous expectations in an artificial stock market. In: Arthur WB, Durlauf S, Lane D (eds) The Economy as an Evolving Complex System II, Addison-Wesley, MA, pp 15–44Google Scholar
  7. Mantegna RN, Stanley HE (1999) An Introduction to Econophysics: Correlations and Complexity in Finance, Cambridge University Press, Cambridge, UKCrossRefGoogle Scholar
  8. Sato H, Koyama Y, Kurumatani K, Shiozawa Y, Deguchi H (2001) U-Mart: A Test Bed for Interdisciplinary Research in Agent Based Artificial Market, Evolutionary Controversies in Economics, pp 179–190, SpringerGoogle Scholar
  9. Sato H, Matsui H, Ono I, Kita H. Terano T, Deguchi H, Shiozawa, Y (2002) U-Mart Project: Learning Economic Principles form the Bottom by Both Human and Software Agents, New Frontiers in Artificial Intelligence, Joint JSAI 2001 Workshop Post-Proceedings, pp. 121–131, Springer-Verlag, Heidelberg, Germany U-Mart web page, http://www.u-mart.econ.kyoto-u.ac.jp (Last Update: April 12th, 2002)Google Scholar
  10. Murphy J (1999) Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications, Prentice Hall Press, Upper Saddle River, NJGoogle Scholar

Copyright information

© Springer Japan 2003

Authors and Affiliations

  1. 1.Department of Computer ScienceNational Defense Academy of JAPANYokosuka, KanagawaJapan

Personalised recommendations