IDEAS home Printed from https://ideas.repec.org/a/taf/jbemgt/v10y2009i4p329-341.html
   My bibliography  Save this article

Building an artificial stock market populated by reinforcement‐learning agents

Author

Listed:
  • Aleksandras Vytautas Rutkauskas
  • Tomas Ramanauskas

Abstract

In this paper we propose an artificial stock market model based on interaction of heterogeneous agents whose forward‐looking behaviour is driven by the reinforcement‐learning algorithm combined with some evolutionary selection mechanism. We use the model for the analysis of market self‐regulation abilities, market efficiency and determinants of emergent properties of the financial market. Distinctive and novel features of the model include strong emphasis on the economic content of individual decision‐making, application of the Q‐learning algorithm for driving individual behaviour, and rich market setup. Along with that a parallel version of the model is presented, which is mainly based on research of current changes in the market, as well as on search of newly emerged consistent patterns, and which has been repeatedly used for optimal decisions’ search experiments in various capital markets.

Suggested Citation

  • Aleksandras Vytautas Rutkauskas & Tomas Ramanauskas, 2009. "Building an artificial stock market populated by reinforcement‐learning agents," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 10(4), pages 329-341, September.
    • Handle: RePEc:taf:jbemgt:v:10:y:2009:i:4:p:329-341
    DOI: 10.3846/1611-1699.2009.10.329-341
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.3846/1611-1699.2009.10.329-341
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.3846/1611-1699.2009.10.329-341?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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. Tay, Nicholas S. P. & Linn, Scott C., 2001. "Fuzzy inductive reasoning, expectation formation and the behavior of security prices," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 321-361, March.
    3. Tomas Ramanauskas, 2009. "Agent-Based Financial Modelling: A Promising Alternative to the Standard Representative-Agent Approach," Bank of Lithuania Working Paper Series 3, Bank of Lithuania.
    4. Sethi, Rajiv & Franke, Reiner, 1995. "Behavioural Heterogeneity under Evolutionary Pressure: Macroeconomic Implications of Costly Optimisation," Economic Journal, Royal Economic Society, vol. 105(430), pages 583-600, May.
    5. E. Samanidou & E. Zschischang & D. Stauffer & T. Lux, 2007. "Agent-based Models of Financial Markets," Papers physics/0701140, arXiv.org.
    6. Lux, Thomas, 1995. "Herd Behaviour, Bubbles and Crashes," Economic Journal, Royal Economic Society, vol. 105(431), pages 881-896, July.
    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. Johann Lussange & Stefano Vrizzi & Stefano Palminteri & Boris Gutkin, 2024. "Modelling crypto markets by multi-agent reinforcement learning," Papers 2402.10803, arXiv.org.

    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. Ryuichi YAMAMOTO, 2005. "Evolution with Individual and Social Learning in an Agent-Based Stock Market," Computing in Economics and Finance 2005 228, Society for Computational Economics.
    2. 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.
    3. Michele Tumminello & Fabrizio Lillo & Jyrki Piilo & Rosario N. Mantegna, 2011. "Identification of clusters of investors from their real trading activity in a financial market," Papers 1107.3942, arXiv.org.
    4. 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.
    5. Georges, Christophre, 2008. "Staggered updating in an artificial financial market," Journal of Economic Dynamics and Control, Elsevier, vol. 32(9), pages 2809-2825, September.
    6. Maximilian Beikirch & Simon Cramer & Martin Frank & Philipp Otte & Emma Pabich & Torsten Trimborn, 2018. "Simulation of Stylized Facts in Agent-Based Computational Economic Market Models," Papers 1812.02726, arXiv.org, revised Nov 2019.
    7. Simon Cramer & Torsten Trimborn, 2019. "Stylized Facts and Agent-Based Modeling," Papers 1912.02684, arXiv.org.
    8. Chen, Shu-Heng, 2012. "Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 1-25.
    9. Sinha, Amit & Horvath, Philip A. & Beason, Tyler & Roos, Kelly R., 2019. "Simulation of a financial market: The possibility of catastrophic disequilibrium," Chaos, Solitons & Fractals, Elsevier, vol. 125(C), pages 13-16.
    10. Goldbaum, David, 2017. "Divergent Behavior in Markets with Idiosyncratic Private Information," Review of Behavioral Economics, now publishers, vol. 4(2), pages 181-213, September.
    11. 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.
    12. Torsten Trimborn & Philipp Otte & Simon Cramer & Max Beikirch & Emma Pabich & Martin Frank, 2018. "SABCEMM-A Simulator for Agent-Based Computational Economic Market Models," Papers 1801.01811, arXiv.org, revised Oct 2018.
    13. Musciotto, Federico & Marotta, Luca & Miccichè, Salvatore & Piilo, Jyrki & Mantegna, Rosario N., 2016. "Patterns of trading profiles at the Nordic Stock Exchange. A correlation-based approach," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 267-278.
    14. Hommes, C.H., 2005. "Heterogeneous Agent Models in Economics and Finance, In: Handbook of Computational Economics II: Agent-Based Computational Economics, edited by Leigh Tesfatsion and Ken Judd , Elsevier, Amsterdam 2006," CeNDEF Working Papers 05-03, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    15. Maximilian Beikirch & Simon Cramer & Martin Frank & Philipp Otte & Emma Pabich & Torsten Trimborn, 2020. "Robust Mathematical Formulation And Probabilistic Description Of Agent-Based Computational Economic Market Models," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 23(06), pages 1-41, September.
    16. David Goldbaum, 2013. "Learning and Adaptation as a Source of Market Failure," Working Paper Series 14, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    17. Jaqueson K. Galimberti & Sergio da Silva, 2012. "An empirical case against the use of genetic-based learning classifier systems as forecasting devices," Economics Bulletin, AccessEcon, vol. 32(1), pages 354-369.
    18. Gil Bazo, Javier & Moreno Muñoz, Jesús David & Tapia, Mikel, 2005. "Price dynamics, informational efficiency and wealth distribution in continuous double auction markets," DEE - Working Papers. Business Economics. WB wb057819, Universidad Carlos III de Madrid. Departamento de Economía de la Empresa.
    19. Smith, Peter, 2004. "Reworking the Standard Model of Competitive Markets: The Role of Fuzzy Logic and Genetic Algorithms in Modelling Complex Non-Linear Economic System," General Discussion Papers 30569, University of Manchester, Institute for Development Policy and Management (IDPM).
    20. Vishwesha Guttal & Srinivas Raghavendra & Nikunj Goel & Quentin Hoarau, 2016. "Lack of Critical Slowing Down Suggests that Financial Meltdowns Are Not Critical Transitions, yet Rising Variability Could Signal Systemic Risk," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-20, January.

    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:taf:jbemgt:v:10:y:2009:i:4:p:329-341. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TBEM20 .

    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.