IDEAS home Printed from https://ideas.repec.org/p/arx/papers/0810.4844.html
   My bibliography  Save this paper

Predator-Prey Model for Stock Market Fluctuations

Author

Listed:
  • Miquel Montero

Abstract

We present a dynamical model for the price evolution of financial assets. The model is based in a two level structure. In the first stage one finds an agent-based model that describes the present state of the investors' beliefs, perspectives or strategies. The dynamics is inspired by a model for describing predator-prey population evolution: agents change their mind through self- or mutual interaction, and the decision is adopted on a random basis, with no direct influence of the price itself. One of the most appealing properties of such a system is the presence of large oscillations in the number of agents sharing the same perspective, what may be linked with the existence of bullish and bearish periods in financial markets. In the second stage one has the pricing mechanism, which will be driven by the relative population in the different investors' groups. The price equation will depend on the specific nature of the species, and thus it may change from one market to the other: we will firstly present a simple model of excess demand, and subsequently consider a more elaborate liquidity model. The outcomes of both models are analysed and compared.

Suggested Citation

  • Miquel Montero, 2008. "Predator-Prey Model for Stock Market Fluctuations," Papers 0810.4844, arXiv.org, revised Jul 2009.
    • Handle: RePEc:arx:papers:0810.4844
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/0810.4844
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shalabh & W. Polasek, 2008. "Book reviews," Statistical Papers, Springer, vol. 49(1), pages 147-149, March.
    2. Unknown, 2008. "ARER Reviewers: September 2007-August 2008," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 37(2), pages 1-1.
    3. (Various), 2008. "Book Reviews," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 173-179.
    4. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    5. Jean-Philippe Bouchaud & J. Doyne Farmer & Fabrizio Lillo, 2008. "How markets slowly digest changes in supply and demand," Papers 0809.0822, arXiv.org.
    6. Oecd, 2010. "Country Review: Chinese Taipei," OECD Journal: Competition Law and Policy, OECD Publishing, vol. 10(2), pages 127-165.
    7. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    8. Unknown, 2008. "Book Review," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 21(1).
    9. Hall, Anthony D. & Hautsch, Nikolaus, 2007. "Modelling the buy and sell intensity in a limit order book market," Journal of Financial Markets, Elsevier, vol. 10(3), pages 249-286, August.
    10. 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.
    Full references (including those not matched with items on IDEAS)

    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. Li-Xin Wang, 2014. "Dynamical Models of Stock Prices Based on Technical Trading Rules Part I: The Models," Papers 1401.1888, arXiv.org, revised Feb 2016.
    2. Miquel Montero, 2021. "Predator–prey model for stock market fluctuations," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(1), pages 29-57, January.
    3. Li-Xin Wang, 2014. "Dynamical Models of Stock Prices Based on Technical Trading Rules Part II: Analysis of the Models," Papers 1401.1891, arXiv.org, revised Feb 2016.
    4. Stuart Birks, 2012. "Rethinking economics: Logical gaps – empirical to the real world," Working Papers 20121217, Department of Accounting, Economics and Finance, Bristol Business School, University of the West of England, Bristol.
    5. Deniz Erdemlioglu & Nikola Gradojevic, 2021. "Heterogeneous investment horizons, risk regimes, and realized jumps," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 617-643, January.
    6. Kubin, Ingrid & Zörner, Thomas O. & Gardini, Laura & Commendatore, Pasquale, 2019. "A credit cycle model with market sentiments," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 159-174.
    7. Westerhoff Frank H., 2008. "The Use of Agent-Based Financial Market Models to Test the Effectiveness of Regulatory Policies," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 228(2-3), pages 195-227, April.
    8. Waters, George A., 2009. "Chaos in the cobweb model with a new learning dynamic," Journal of Economic Dynamics and Control, Elsevier, vol. 33(6), pages 1201-1216, June.
    9. Cees Diks & Cars Hommes & Valentyn Panchenko & Roy Weide, 2008. "E&F Chaos: A User Friendly Software Package for Nonlinear Economic Dynamics," Computational Economics, Springer;Society for Computational Economics, vol. 32(1), pages 221-244, September.
    10. Serena Brianzoni & Roy Cerqueti & Elisabetta Michetti, 2010. "A Dynamic Stochastic Model of Asset Pricing with Heterogeneous Beliefs," Computational Economics, Springer;Society for Computational Economics, vol. 35(2), pages 165-188, February.
    11. Yamamoto, Ryuichi, 2019. "Dynamic Predictor Selection And Order Splitting In A Limit Order Market," Macroeconomic Dynamics, Cambridge University Press, vol. 23(5), pages 1757-1792, July.
    12. Yamamoto, Ryuichi & Hirata, Hideaki, 2013. "Strategy switching in the Japanese stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 37(10), pages 2010-2022.
    13. Richard Holt & J. Barkley Rosser & David Colander, 2011. "The Complexity Era in Economics," Review of Political Economy, Taylor & Francis Journals, vol. 23(3), pages 357-369.
    14. Pascal Seppecher & Isabelle Salle & Dany Lang, 2019. "Is the market really a good teacher?," Journal of Evolutionary Economics, Springer, vol. 29(1), pages 299-335, March.
    15. Daniele Giachini, 2018. "Rationality and Asset Prices under Belief Heterogeneity," LEM Papers Series 2018/07, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    16. Chueh-Yung Tsao & Ya-Chi Huang, 2018. "Revisiting the issue of survivability and market efficiency with the Santa Fe Artificial Stock Market," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(3), pages 537-560, October.
    17. Moulet, Sonia & Rouchier, Juliette, 2008. "The influence of seller learning and time constraints on sequential bargaining in an artificial perishable goods market," Journal of Economic Dynamics and Control, Elsevier, vol. 32(7), pages 2322-2348, July.
    18. Fischer, Thomas & Riedler, Jesper, 2014. "Prices, debt and market structure in an agent-based model of the financial market," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 95-120.
    19. He, Xue-Zhong & Li, Youwei, 2015. "Testing of a market fraction model and power-law behaviour in the DAX 30," Journal of Empirical Finance, Elsevier, vol. 31(C), pages 1-17.
    20. Frijns, Bart & Lehnert, Thorsten & Zwinkels, Remco C.J., 2010. "Behavioral heterogeneity in the option market," Journal of Economic Dynamics and Control, Elsevier, vol. 34(11), pages 2273-2287, November.

    More about this item

    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:0810.4844. 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.