Advertisement

Multi-agent Simulation of Investor Cognitive Behavior in Stock Market

  • Zahra Kodia
  • Lamjed Ben Said
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 55)

Abstract

In this paper, we introduce a new model of Investor cognitive behavior in stock market. This model describes the behavioral and cognitive attitudes of the Investor at the micro level and explains their effects on his decision making. A theoretical framework is discussed in order to integrate a set of multidisciplinary concepts. A Multi-Agent Based Simulation (MABS) is used to: (1) validate our model, (2) build an artificial stock market: SiSMar and (3) study the emergence of certain phenomena relative to the stock market dynamics at the macro level. The proposed simulator is composed of heterogeneous Investor agents with a behavioral cognitive model, an Intermediary agent and the CentralMarket agent matching buying and selling orders. Our artificial stock market is implemented using distributed artificial intelligence techniques. The resulting simulator is a tool able to numerically simulate financial market operations in a realistic way. Preliminary results show that representing the micro level led us to build the stock market dynamics, and to observe emergent socio-economic phenomena at the macro level.

Keywords

Multi-agent based simulation Cognitive and behavioral modeling Stock market 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Arifovic, J.: The behavior of the exchange rate in the genetic algorithm and experimental economies. Journal of Political Economy 104, 510–541 (1996)CrossRefGoogle Scholar
  2. 2.
    Arthur, W., Durlauf, S., Lane, D.: The economy as an evolving complex system. Santa Fe Institute, vol. 27, pp. 15–44. Addison-Wesley, Reading (1997)Google Scholar
  3. 3.
    Ben Said, L., Bouron, T.: Multi-agent simulation of virtual consumer populations in a competitive market. In: SCAI 2001, pp. 31–43. IOS Press, Denmark (2001)Google Scholar
  4. 4.
    Dorsey, T.: Point & figure charting: The essential application for forecasting and tracking market prices, 3rd edn. Wiley Trading, Chichester (2007)Google Scholar
  5. 5.
    Fama, E.: The behavior of stock prices. Journal of Business (January 1965)Google Scholar
  6. 6.
    Gode, D., Sunder, S.: Allocative efficiency of markets with zero intelligence traders. Journal Of Political Economy 101(1), 119–137 (1993)CrossRefGoogle Scholar
  7. 7.
    Granovetter, M.: Les institutions économiques comme constructions sociales. Orléan André édition, Analyse économique des conventions, Paris, Presses Universitaires de France, chapitre, vol. 3, pp. 119–134 (2004)Google Scholar
  8. 8.
    Grossman, S., Stiglitz, J.: On the impossibility of informationally efficient markets. American Economic Review 70, 393–408 (1980)Google Scholar
  9. 9.
    Gutknecht, O., Ferber, J.: The madkit agent platform architecture. In: Agents Workshop on Infrastructure for Multi-Agent Systems, pp. 48–55 (2000)Google Scholar
  10. 10.
    Hoffmann, A., Jager, W.: The effect of different needs, decision-making processes and network-structures on simulating stock-market dynamics: a framework for simulation experiments. In: ISSA 2004 (2004)Google Scholar
  11. 11.
    Hoffmann, A., Jager, W., Eije, J.V.: Social simulation of stock markets: Taking it to the next. Level Journal of Artificial Societies and Social Simulation 10(2) (2007)Google Scholar
  12. 12.
    Kahneman, D., Tversky, A.: Prospect theory: An analysis of decision under risk. Econometrica 47(2), 263–292 (1979)zbMATHCrossRefGoogle Scholar
  13. 13.
    Karaken, J., Wallace, N.: On the indeterminacy of equilibrium exchange rates. Quarterly journal of economics 96, 207–222 (1981)CrossRefGoogle Scholar
  14. 14.
    LeBaron, B.: Agent-based computational finance: suggested readings and early research. Journal of Economic Dynamics and Control 24, 679–702 (2000)zbMATHCrossRefGoogle Scholar
  15. 15.
    Lettau, M.: Explaining the facts with adaptive agents: the case of mutual fund flows. Journal of Economic Dynamics and Control 21, 1117–1148 (1997)zbMATHCrossRefGoogle Scholar
  16. 16.
    Lux, T., Marchesi, M.: Volatility clustering in financial markets: A micro-simulation of interactive agents. In: 3rd Workshop on Economics and Interacting Agents, Ancona (1998)Google Scholar
  17. 17.
    Margarita, S., Beltratti, A.: Stock prices and volume in an artificial adaptive stock market. In: Mira, J., Cabestany, J., Prieto, A.G. (eds.) IWANN 1993. LNCS, vol. 686, pp. 714–719. Springer, Heidelberg (1993)Google Scholar
  18. 18.
    Neisser, U.: Cognitive psychology, New York. Appleton-Century-Crofts (1967)Google Scholar
  19. 19.
    Peyrard, J.: La Bourse. Edition Vuibert, collection Etreprise, 9 ème édition (2001)Google Scholar
  20. 20.
    Ricciardi, V.: Risk: Traditional Finance versus Behavioral Finance. John Wiley & Sons, Chichester (2008)Google Scholar
  21. 21.
    Routledge, B.: Artificial selection: genetic algorithms and learning in a rational expectations model. Technical report, GSIA, Carnegie Mellon, Pittsburgh, Penn (1994)Google Scholar
  22. 22.
    Sharpe, W.: Corporate pension funding policy. Journal of Financial Economics 3, 183–193 (1976)CrossRefGoogle Scholar
  23. 23.
    Steiner, P.: The sociology of economic knowledge. European Journal of Social Theory, 443–458 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Zahra Kodia
    • 1
  • Lamjed Ben Said
    • 1
  1. 1.Laboratoire d’Ingénierie Informatique Intelligente (LI3)Institut supérieur de Gestion de TunisLe BardoTunisia

Personalised recommendations