IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1302.6477.html
   My bibliography  Save this paper

Signal amplification in an agent-based herding model

Author

Listed:
  • Adri'an Carro
  • Ra'ul Toral
  • Maxi San Miguel

Abstract

A growing part of the behavioral finance literature has addressed some of the stylized facts of financial time series as macroscopic patterns emerging from herding interactions among groups of agents with heterogeneous trading strategies and a limited rationality. We extend a stochastic herding formalism introduced for the modeling of decision making among financial agents, in order to take also into account an external influence. In particular, we study the amplification of an external signal imposed upon the agents by a mechanism of resonance. This signal can be interpreted as an advertising or a public perception in favor or against one of the two possible trading behaviors, thus periodically breaking the symmetry of the system and acting as a continuously varying exogenous shock. The conditions for the ensemble of agents to more accurately follow the periodicity of the signal are studied, finding a maximum in the response of the system for a given range of values of both the noise and the frequency of the input signal.

Suggested Citation

  • Adri'an Carro & Ra'ul Toral & Maxi San Miguel, 2013. "Signal amplification in an agent-based herding model," Papers 1302.6477, arXiv.org, revised Sep 2015.
    • Handle: RePEc:arx:papers:1302.6477
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1302.6477
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. S. Alfarano & M. Milakovic & M. Raddant, 2013. "A note on institutional hierarchy and volatility in financial markets," The European Journal of Finance, Taylor & Francis Journals, vol. 19(6), pages 449-465, July.
    2. D. Colander & H. Follmer & A. Haas & M. Goldberg & K. Juselius & A. Kirman & T. Lux & B. Sloth, 2010. "The Financial Crisis and the Systemic Failure of Academic Economics," Voprosy Ekonomiki, NP Voprosy Ekonomiki, issue 6.
    3. Alan Kirman, 1993. "Ants, Rationality, and Recruitment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 108(1), pages 137-156.
    4. Kononovicius, A. & Gontis, V., 2012. "Agent based reasoning for the non-linear stochastic models of long-range memory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1309-1314.
    5. 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.
    6. Simone Alfarano & Thomas Lux & Friedrich Wagner, 2005. "Estimation of Agent-Based Models: The Case of an Asymmetric Herding Model," Computational Economics, Springer;Society for Computational Economics, vol. 26(1), pages 19-49, August.
    7. Alfarano, Simone & Milakovic, Mishael, 2009. "Network structure and N-dependence in agent-based herding models," Journal of Economic Dynamics and Control, Elsevier, vol. 33(1), pages 78-92, January.
    8. Alan P. Kirman, 1992. "Whom or What Does the Representative Individual Represent?," Journal of Economic Perspectives, American Economic Association, vol. 6(2), pages 117-136, Spring.
    9. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    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. 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.

    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. Adri'an Carro & Ra'ul Toral & Maxi San Miguel, 2015. "Markets, herding and response to external information," Papers 1506.03708, arXiv.org, revised Jun 2015.
    2. Adrián Carro & Raúl Toral & Maxi San Miguel, 2015. "Markets, Herding and Response to External Information," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-28, July.
    3. David Vidal-Tomás & Simone Alfarano, 2020. "An agent-based early warning indicator for financial market instability," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(1), pages 49-87, January.
    4. Aleksejus Kononovicius, 2017. "Empirical Analysis and Agent-Based Modeling of the Lithuanian Parliamentary Elections," Complexity, Hindawi, vol. 2017, pages 1-15, November.
    5. Aleksejus Kononovicius & Julius Ruseckas, 2018. "Order book model with herd behavior exhibiting long-range memory," Papers 1809.02772, arXiv.org, revised Apr 2019.
    6. Aleksejus Kononovicius & Vygintas Gontis, 2014. "Herding interactions as an opportunity to prevent extreme events in financial markets," Papers 1409.8024, arXiv.org, revised May 2015.
    7. Kononovicius, Aleksejus & Ruseckas, Julius, 2019. "Order book model with herd behavior exhibiting long-range memory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 171-191.
    8. 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.
    9. Rytis Kazakevicius & Aleksejus Kononovicius & Bronislovas Kaulakys & Vygintas Gontis, 2021. "Understanding the nature of the long-range memory phenomenon in socioeconomic systems," Papers 2108.02506, arXiv.org, revised Aug 2021.
    10. Simon Cramer & Torsten Trimborn, 2019. "Stylized Facts and Agent-Based Modeling," Papers 1912.02684, arXiv.org.
    11. Aleksejus Kononovicius & Vygintas Gontis & Valentas Daniunas, 2012. "Agent-based Versus Macroscopic Modeling of Competition and Business Processes in Economics and Finance," Papers 1202.3533, arXiv.org, revised Jun 2012.
    12. S. Alfarano & M. Milakovic & M. Raddant, 2013. "A note on institutional hierarchy and volatility in financial markets," The European Journal of Finance, Taylor & Francis Journals, vol. 19(6), pages 449-465, July.
    13. Aleksejus Kononovicius & Vygintas Gontis, 2013. "Control of the socio-economic systems using herding interactions," Papers 1309.6105, arXiv.org, revised Feb 2014.
    14. Klein, A. & Urbig, D. & Kirn, S., 2008. "Who Drives the Market? Estimating a Heterogeneous Agent-based Financial Market Model Using a Neural Network Approach," MPRA Paper 14433, University Library of Munich, Germany.
    15. Vygintas Gontis & Aleksejus Kononovicius & Stefan Reimann, 2012. "The class of nonlinear stochastic models as a background for the bursty behavior in financial markets," Papers 1201.3083, arXiv.org, revised May 2012.
    16. Peralta, Antonio F. & Khalil, Nagi & Toral, Raúl, 2020. "Ordering dynamics in the voter model with aging," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 552(C).
    17. Torsten Trimborn & Philipp Otte & Simon Cramer & Maximilian Beikirch & Emma Pabich & Martin Frank, 2020. "SABCEMM: A Simulator for Agent-Based Computational Economic Market Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 707-744, February.
    18. Kononovicius, Aleksejus, 2021. "Supportive interactions in the noisy voter model," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    19. Alfarano Simone & Milakovic Mishael, 2012. "Identification of Interaction Effects in Survey Expectations: A Cautionary Note," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(4), pages 1-23, October.
    20. Lux, Thomas & Alfarano, Simone, 2016. "Financial power laws: Empirical evidence, models, and mechanisms," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 3-18.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:1302.6477. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.