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Heterogeneity and Self-Organization of Complex Systems Through an Application to Financial Market with Multiagent Systems

Author

Listed:
  • Iris Lucas

    (RI2C - LITIS - Equipe Réseaux d'interactions et Intelligence Collective - LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes - ULH - Université Le Havre Normandie - NU - Normandie Université - UNIROUEN - Université de Rouen Normandie - NU - Normandie Université - INSA Rouen Normandie - Institut national des sciences appliquées Rouen Normandie - INSA - Institut National des Sciences Appliquées - NU - Normandie Université)

  • Michel Cotsaftis

    (ECE Paris)

  • Cyrille Bertelle

    (RI2C - LITIS - Equipe Réseaux d'interactions et Intelligence Collective - LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes - ULH - Université Le Havre Normandie - NU - Normandie Université - UNIROUEN - Université de Rouen Normandie - NU - Normandie Université - INSA Rouen Normandie - Institut national des sciences appliquées Rouen Normandie - INSA - Institut National des Sciences Appliquées - NU - Normandie Université)

Abstract

Multiagent systems (MAS) provide a useful tool for exploring the complex dynamics and behavior of financial markets and now MAS approach has been widely implemented and documented in the empirical literature. This paper introduces the implementation of an innovative multi-scale mathematical model for a computational agent-based financial market. The paper develops a method to quantify the degree of self-organization which emerges in the system and shows that the capacity of self-organization is maximized when the agent behaviors are heterogeneous. Numerical results are presented and analyzed, showing how the global market behavior emerges from specific individual behavior interactions.

Suggested Citation

  • Iris Lucas & Michel Cotsaftis & Cyrille Bertelle, 2017. "Heterogeneity and Self-Organization of Complex Systems Through an Application to Financial Market with Multiagent Systems," Post-Print hal-02114933, HAL.
  • Handle: RePEc:hal:journl:hal-02114933
    DOI: 10.1142/S0218127417502194
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    References listed on IDEAS

    as
    1. Lettau, Martin, 1997. "Explaining the facts with adaptive agents: The case of mutual fund flows," Journal of Economic Dynamics and Control, Elsevier, vol. 21(7), pages 1117-1147, June.
    2. Arthur, W.B. & Holland, J.H. & LeBaron, B. & Palmer, R. & Tayler, P., 1996. "Asset Pricing Under Endogenous Expectations in an Artificial Stock Market," Working papers 9625, Wisconsin Madison - Social Systems.
    3. Grossman, Sanford J & Stiglitz, Joseph E, 1980. "On the Impossibility of Informationally Efficient Markets," American Economic Review, American Economic Association, vol. 70(3), pages 393-408, June.
    4. M. Cristelli & V. Alfi & L. Pietronero & A. Zaccaria, 2010. "Liquidity crisis, granularity of the order book and price fluctuations," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 73(1), pages 41-49, January.
    5. Carl Chiarella & Giulia Iori, 2002. "A simulation analysis of the microstructure of double auction markets," Quantitative Finance, Taylor & Francis Journals, vol. 2(5), pages 346-353.
    6. LeBaron, Blake, 2006. "Agent-based Computational Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 24, pages 1187-1233, Elsevier.
    7. Thomas Lux & Michele Marchesi, 1999. "Scaling and criticality in a stochastic multi-agent model of a financial market," Nature, Nature, vol. 397(6719), pages 498-500, February.
    8. Margaret A. Peteraf & Mark E. Bergen, 2003. "Scanning dynamic competitive landscapes: a market‐based and resource‐based framework," Strategic Management Journal, Wiley Blackwell, vol. 24(10), pages 1027-1041, October.
    9. Gjerstad, Steven & Dickhaut, John, 1998. "Price Formation in Double Auctions," Games and Economic Behavior, Elsevier, vol. 22(1), pages 1-29, January.
    10. J. Doyne Farmer & Paolo Patelli & Ilija I. Zovko, 2003. "The Predictive Power of Zero Intelligence in Financial Markets," Papers cond-mat/0309233, arXiv.org, revised Feb 2004.
    11. Yamamoto, Ryuichi, 2011. "Order aggressiveness, pre-trade transparency, and long memory in an order-driven market," Journal of Economic Dynamics and Control, Elsevier, vol. 35(11), pages 1938-1963.
    12. G. Caldarelli & M. Marsili & Y. -C. Zhang, 1997. "A Prototype Model of Stock Exchange," Papers cond-mat/9709118, arXiv.org.
    13. W. Brian Arthur & Paul Tayler, "undated". "Asset Pricing Under Endogenous Expectations in an Artificial Stock Market," Computing in Economics and Finance 1997 57, Society for Computational Economics.
    14. Challet, Damien & Marsili, Matteo & Zhang, Yi-Cheng, 2001. "Minority games and stylized facts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 228-233.
    15. Chiarella, Carl & Iori, Giulia, 2009. "The impact of heterogeneous trading rules on the limit order book and order flows," Journal of Economic Dynamics and Control, Elsevier, vol. 33(3), pages 525-537.
    16. van den Berg, J.H. & van den Bergh, W.-M. & Kaymak, U., 2003. "Financial Markets Analysis by Probabilistic Fuzzy Modelling," ERIM Report Series Research in Management ERS-2003-036-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    17. Marco Bartolozzi, 2010. "A Multi Agent Model for the Limit Order Book Dynamics," Papers 1005.0182, arXiv.org, revised Oct 2010.
    18. Challet, Damien & Marsili, Matteo & Zhang, Yi-Cheng, 2001. "Stylized facts of financial markets and market crashes in Minority Games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 294(3), pages 514-524.
    19. Raberto, Marco & Cincotti, Silvano & Focardi, Sergio M. & Marchesi, Michele, 2001. "Agent-based simulation of a financial market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 319-327.
    20. Routledge, Bryan R, 1999. "Adaptive Learning in Financial Markets," Review of Financial Studies, Society for Financial Studies, vol. 12(5), pages 1165-1202.
    21. M. Bartolozzi, 2010. "A multi agent model for the limit order book dynamics," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 78(2), pages 265-273, November.
    22. Sandrine Jacob Leal & Mauro Napoletano, 2017. "Market Stability vs. Market Resilience: Regulatory Policies Experiments in an Agent-Based Model with Low- and High-Frequency Trading," Post-Print hal-01768876, HAL.
    23. B. LeBaron, 2001. "A builder's guide to agent-based financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 254-261.
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    Cited by:

    1. Iris Lucas & Michel Cotsaftis & Cyrille Bertelle, 2018. "Self-Organization, Resilience and Robustness of Complex Systems Through an Application to Financial Market from an Agent-Based Approach," Post-Print hal-02114928, HAL.

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