IDEAS home Printed from
   My bibliography  Save this paper

Agent-Based Stock Market Model with Endogenous Agents' Impact


  • Jan A. Lipski
  • Ryszard Kutner


The three-state agent-based 2D model of financial markets as proposed by Giulia Iori has been extended by introducing increasing trust in the correctly predicting agents, a more realistic consultation procedure as well as a formal validation mechanism. This paper shows that such a model correctly reproduces the three fundamental stylised facts: fat-tail log returns, power-law volatility autocorrelation decay in time and volatility clustering.

Suggested Citation

  • Jan A. Lipski & Ryszard Kutner, 2013. "Agent-Based Stock Market Model with Endogenous Agents' Impact," Papers 1310.0762,, revised Dec 2013.
  • Handle: RePEc:arx:papers:1310.0762

    Download full text from publisher

    File URL:
    File Function: Latest version
    Download Restriction: no

    References listed on IDEAS

    1. M. Cristelli & L. Pietronero & A. Zaccaria, 2011. "Critical Overview of Agent-Based Models for Economics," Papers 1101.1847,
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. Johann Lussange & Ivan Lazarevich & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2021. "Modelling Stock Markets by Multi-agent Reinforcement Learning," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 113-147, January.

    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. Aleksejus Kononovicius & Vygintas Gontis, 2012. "Three-state herding model of the financial markets," Papers 1210.1838,, revised Jan 2013.
    2. Tseng, Jie-Jun & Li, Sai-Ping, 2012. "Quantifying volatility clustering in financial time series," International Review of Financial Analysis, Elsevier, vol. 23(C), pages 11-19.
    3. Aleksejus Kononovicius & Vygintas Gontis & Valentas Daniunas, 2012. "Agent-based Versus Macroscopic Modeling of Competition and Business Processes in Economics and Finance," Papers 1202.3533,, revised Jun 2012.
    4. Gualdi, Stanislao & Tarzia, Marco & Zamponi, Francesco & Bouchaud, Jean-Philippe, 2015. "Tipping points in macroeconomic agent-based models," Journal of Economic Dynamics and Control, Elsevier, vol. 50(C), pages 29-61.
    5. Kononovicius, A. & Gontis, V., 2014. "Control of the socio-economic systems using herding interactions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 80-84.
    6. Efstathios Panayi & Gareth W. Peters, 2015. "Stochastic simulation framework for the limit order book using liquidity-motivated agents," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 2(02), pages 1-52.
    7. Vygintas Gontis & Aleksejus Kononovicius, 2013. "Fluctuation analysis of the three agent groups herding model," Papers 1305.5958,
    8. Aleksejus Kononovicius & Vygintas Gontis, 2011. "Agent based reasoning for the non-linear stochastic models of long-range memory," Papers 1106.2685,, revised Aug 2011.
    9. Federico Garzarelli & Matthieu Cristelli & Andrea Zaccaria & Luciano Pietronero, 2011. "Memory effects in stock price dynamics: evidences of technical trading," Papers 1110.5197,
    10. Alexandru Mandes & Peter Winker, 2017. "Complexity and model comparison in agent based modeling of financial markets," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(3), pages 469-506, October.
    11. Pierre Blanc & Jonathan Donier & Jean-Philippe Bouchaud, 2015. "Quadratic Hawkes processes for financial prices," Papers 1509.07710,
    12. Vikram Krishnamurthy & Sujay Bhatt, 2015. "Sequential Detection of Market shocks using Risk-averse Agent Based Models," Papers 1511.01965,
    13. Aleksejus Kononovicius & Vygintas Gontis, 2014. "Herding interactions as an opportunity to prevent extreme events in financial markets," Papers 1409.8024,, revised May 2015.
    14. Jan A. Lipski & Ryszard Kutner, 2013. "Trust in foreseeing neighbours - a novel threshold model of financial market," Papers 1301.1824,
    15. Biondi, Yuri & Giannoccolo, Pierpaolo & Galam, Serge, 2012. "Formation of share market prices under heterogeneous beliefs and common knowledge," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5532-5545.
    16. Zeng, Yayun & Wang, Jun & Xu, Kaixuan, 2017. "Complexity and multifractal behaviors of multiscale-continuum percolation financial system for Chinese stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 364-376.
    17. Iori, G. & Porter, J., 2012. "Agent-Based Modelling for Financial Markets," Working Papers 12/08, Department of Economics, City University London.
    18. Jean-Philippe Bouchaud, 2012. "Crises and collective socio-economic phenomena: simple models and challenges," Papers 1209.0453,, revised Dec 2012.
    19. Pietro DeLellis & Franco Garofalo & Francesco Lo Iudice & Elena Napoletano, 2015. "Wealth distribution across communities of adaptive financial agents," Papers 1509.01217,, revised Sep 2015.
    20. Efstathios Panayi & Gareth Peters, 2015. "Stochastic simulation framework for the Limit Order Book using liquidity motivated agents," Papers 1501.02447,, revised Jan 2015.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    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:arx:papers:1310.0762. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (arXiv administrators). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.