IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/4916.html
   My bibliography  Save this paper

Market dynamics and agents behaviors: a computational approach

Author

Listed:
  • Derveeuw, Julien

Abstract

We explore market dynamics generated by the Santa-Fe Artificial Stock Market model. It allows to study how agents adapt themselves to a market dynamic without knowing its generation process. It was shown by Arthur and LeBaron, with the help of computer experiments, that agents in bounded rationality can make a rational global behavior emerge in this context. In the original model, agents do not ground their decision on an economic logic. Hence, we modify indicators used by agents to watch the market to give them more economic rationality. This leads us to divide agents in two groups: fundamentalists agents, who watch the market with classic economic indicators and speculator agents, who watch the market with technical indicators. This split allows us to study the influence of individual agents behaviors on global price dynamics. In this article, we show with the help of computational simulations that these two types of agents can generate classical market dynamics as well as perturbed ones (bubbles and kraches).

Suggested Citation

  • Derveeuw, Julien, 2005. "Market dynamics and agents behaviors: a computational approach," MPRA Paper 4916, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:4916
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/4916/1/MPRA_paper_4916.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Johnson, Neil F. & Lamper, David & Jefferies, Paul & Hart, Michael L. & Howison, Sam, 2001. "Application of multi-agent games to the prediction of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 222-227.
    2. Blake LeBaron, "undated". "Experiments in Evolutionary Finance," Working papers _001, University of Wisconsin - Madison.
    3. Lux, Thomas, 1998. "The socio-economic dynamics of speculative markets: interacting agents, chaos, and the fat tails of return distributions," Journal of Economic Behavior & Organization, Elsevier, vol. 33(2), pages 143-165, January.
    4. Focardi, Sergio & Cincotti, Silvano & Marchesi, Michele, 2002. "Self-organization and market crashes," Journal of Economic Behavior & Organization, Elsevier, vol. 49(2), pages 241-267, October.
    5. Levy, Moshe & Levy, Haim & Solomon, Sorin, 1994. "A microscopic model of the stock market : Cycles, booms, and crashes," Economics Letters, Elsevier, vol. 45(1), pages 103-111, May.
    6. Neil F. Johnson & David Lamper & Paul Jefferies & Michael L. Hart & Sam Howison, 2001. "Application of multi-agent games to the prediction of financial time-series," OFRC Working Papers Series 2001mf04, Oxford Financial Research Centre.
    7. 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.
    8. N. F. Johnson & D. Lamper & P. Jefferies & M. L. Hart & S. Howison, 2001. "Application of multi-agent games to the prediction of financial time-series," Papers cond-mat/0105303, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Khaldoun Khashanah & Talal Alsulaiman, 2017. "Connectivity, Information Jumps, and Market Stability: An Agent-Based Approach," Complexity, Hindawi, vol. 2017, pages 1-16, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lux, Thomas & Alfarano, Simone, 2016. "Financial power laws: Empirical evidence, models, and mechanisms," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 3-18.
    2. Lux, Thomas, 2006. "Financial power laws: Empirical evidence, models, and mechanism," Economics Working Papers 2006-12, Christian-Albrechts-University of Kiel, Department of Economics.
    3. E. Samanidou & E. Zschischang & D. Stauffer & T. Lux, 2001. "Microscopic Models of Financial Markets," Papers cond-mat/0110354, arXiv.org.
    4. Chen, Fang & Gou, Chengling & Guo, Xiaoqian & Gao, Jieping, 2008. "Prediction of stock markets by the evolutionary mix-game model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(14), pages 3594-3604.
    5. LeBaron, Blake, 2000. "Agent-based computational finance: Suggested readings and early research," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 679-702, June.
    6. Li, Da-Ye & Nishimura, Yusaku & Men, Ming, 2014. "Fractal markets: Liquidity and investors on different time horizons," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 144-151.
    7. Staccioli, Jacopo & Napoletano, Mauro, 2021. "An agent-based model of intra-day financial markets dynamics," Journal of Economic Behavior & Organization, Elsevier, vol. 182(C), pages 331-348.
    8. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186, Elsevier.
    9. Zhang, H.S. & Shen, X.Y. & Huang, J.P., 2016. "Pattern of trends in stock markets as revealed by the renormalization method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 340-346.
    10. repec:hal:spmain:info:hdl:2441/5mqflt6amg8gab4rlqn6sbko4b is not listed on IDEAS
    11. Misha Perepelitsa, 2018. "A model of adaptive, market behavior generating positive returns, volatility and system risk," Papers 1809.09601, arXiv.org.
    12. Alfarano, Simone & Lux, Thomas & Wagner, Friedrich, 2008. "Time variation of higher moments in a financial market with heterogeneous agents: An analytical approach," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 101-136, January.
    13. Gou, Chengling, 2006. "Deduction of initial strategy distributions of agents in mix-game models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 371(2), pages 633-640.
    14. Groot, Robert D. & Musters, Pieter A.D., 2005. "Minority Game of price promotions in fast moving consumer goods markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 350(2), pages 533-547.
    15. Zhang, Mengqi & Jiang, Xin & Fang, Zehua & Zeng, Yue & Xu, Ke, 2019. "High-order Hidden Markov Model for trend prediction in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 1-12.
    16. Misha Perepelitsa & Ilya Timofeyev, 2020. "Self-sustained price bubbles driven by Bitcoin innovations and adaptive behavior," Papers 2012.14860, arXiv.org.
    17. Thomas Holtfort, 2019. "From standard to evolutionary finance: a literature survey," Management Review Quarterly, Springer, vol. 69(2), pages 207-232, June.
    18. E. Samanidou & E. Zschischang & D. Stauffer & T. Lux, 2007. "Agent-based Models of Financial Markets," Papers physics/0701140, arXiv.org.
    19. Troy Tassier, 2013. "Handbook of Research on Complexity, by J. Barkley Rosser, Jr. and Edward Elgar," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 39(1), pages 132-133.
    20. Guglielmo Maria Caporale & Antoaneta Serguieva & Hao Wu, 2009. "Financial contagion: evolutionary optimization of a multinational agent‐based model," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 16(1‐2), pages 111-125, January.
    21. Misha Perepelitsa, 2021. "Psychological dimension of adaptive trading in cryptocurrency markets," Papers 2109.12166, arXiv.org.

    More about this item

    Keywords

    multi-agent; finance; financial market; simulation; bubbles; kraches;
    All these keywords.

    JEL classification:

    • D58 - Microeconomics - - General Equilibrium and Disequilibrium - - - Computable and Other Applied General Equilibrium Models
    • D40 - Microeconomics - - Market Structure, Pricing, and Design - - - General
    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:4916. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.