IDEAS home Printed from https://ideas.repec.org/p/vnm/wpdman/2.html
   My bibliography  Save this paper

Learning to trade in an unbalanced market

Author

Listed:
  • Florian Hauser

    (Department of Banking and Finance, Universitat Innsbruck)

  • Marco LiCalzi

    (Department of Management, Università Ca' Foscari Venezia)

Abstract

We study the evolution of trading strategies in double auctions as the size of the market gets larger. When the number of buyers and sellers is balanced, Fano et al. (2011) show that the choice of the order-clearing rule (simultaneous or asynchronous) steers the emergence of fundamentally different strategic behavior. We extend their work to unbalanced markets, confirming their main result as well as that allocative inefficiency tends to zero. On the other hand, we discover that convergence to the competitive outcome takes place only when the market is large and that the long side of the market is more effective at improving its disadvantaged terms of trade under asynchronous order-clearing.

Suggested Citation

  • Florian Hauser & Marco LiCalzi, 2011. "Learning to trade in an unbalanced market," Working Papers 2, Department of Management, Università Ca' Foscari Venezia.
  • Handle: RePEc:vnm:wpdman:2
    as

    Download full text from publisher

    File URL: http://virgo.unive.it/wpideas/storage/2011wp2.pdf
    File Function: First version, 2011
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Marco LiCalzi & Paolo Pellizzari, 2008. "Zero-Intelligence Trading Without Resampling," Lecture Notes in Economics and Mathematical Systems, in: Klaus Schredelseker & Florian Hauser (ed.), Complexity and Artificial Markets, chapter 1, pages 3-14, Springer.
    2. Mikhail Anufriev & Jasmina Arifovic & John Ledyard & Valentyn Panchenko, 2013. "Efficiency of continuous double auctions under individual evolutionary learning with full or limited information," Journal of Evolutionary Economics, Springer, vol. 23(3), pages 539-573, July.
    3. Dhananjay K. Gode & Shyam Sunder, 1997. "What Makes Markets Allocationally Efficient?," The Quarterly Journal of Economics, Oxford University Press, vol. 112(2), pages 603-630.
    4. Shira Fano & Marco LiCalzi & Paolo Pellizzari, 2013. "Convergence of outcomes and evolution of strategic behavior in double auctions," Journal of Evolutionary Economics, Springer, vol. 23(3), pages 513-538, July.
    5. Curtis R. Taylor, 1995. "The Long Side of the Market and the Short End of the Stick: Bargaining Power and Price Formation in Buyers', Sellers', and Balanced Markets," The Quarterly Journal of Economics, Oxford University Press, vol. 110(3), pages 837-855.
    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. Anufriev, Mikhail & Arifovic, Jasmina & Ledyard, John & Panchenko, Valentyn, 2022. "The role of information in a continuous double auction: An experiment and learning model," Journal of Economic Dynamics and Control, Elsevier, vol. 141(C).
    2. Mikhail Anufriev & Jasmina Arifovic & John Ledyard & Valentyn Panchenko, 2013. "Efficiency of continuous double auctions under individual evolutionary learning with full or limited information," Journal of Evolutionary Economics, Springer, vol. 23(3), pages 539-573, July.
    3. Michiel Leur & Mikhail Anufriev, 2018. "Timing under individual evolutionary learning in a continuous double auction," Journal of Evolutionary Economics, Springer, vol. 28(3), pages 609-631, August.
    4. Ruijgrok, Matthijs, 2012. "A single-item continuous double auction game," MPRA Paper 42086, University Library of Munich, Germany.
    5. Lu, Dong & Zhan, Yaosong, 2022. "Over-the-counter versus double auction in asset markets with near-zero-intelligence traders," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    6. Marco LiCalzi & Lucia Milone & Paolo Pellizzari, 2011. "Allocative Efficiency and Traders’ Protection Under Zero Intelligence Behavior," Dynamic Modeling and Econometrics in Economics and Finance, in: Herbert Dawid & Willi Semmler (ed.), Computational Methods in Economic Dynamics, pages 5-28, Springer.
    7. Olga A. Rud & Jean Paul Rabanal, 2018. "Evolution of markets: a simulation with centralized, decentralized and posted offer formats," Journal of Evolutionary Economics, Springer, vol. 28(3), pages 667-689, August.
    8. Shira Fano & Marco LiCalzi & Paolo Pellizzari, 2013. "Convergence of outcomes and evolution of strategic behavior in double auctions," Journal of Evolutionary Economics, Springer, vol. 23(3), pages 513-538, July.
    9. Kazuto Sasai & Yukio-Pegio Gunji & Tetsuo Kinoshita, 2017. "Intermittent Behavior Induced By Asynchronous Interactions In A Continuous Double Auction Model," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 20(02n03), pages 1-21, March.
    10. Bester, Helmut, 2013. "Investments and the holdup problem in a matching market," Journal of Mathematical Economics, Elsevier, vol. 49(4), pages 302-311.
    11. Robin Nicole & Aleksandra Alori'c & Peter Sollich, 2020. "Fragmentation in trader preferences among multiple markets: Market coexistence versus single market dominance," Papers 2012.04103, arXiv.org, revised Aug 2021.
    12. Ewa Gałecka-Burdziak, 2012. "Labour market matching – the case of Poland," Bank i Kredyt, Narodowy Bank Polski, vol. 43(3), pages 31-46.
    13. Guillaume Rocheteau & Pierre‐Olivier Weill, 2011. "Liquidity in Frictional Asset Markets," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(s2), pages 261-282, October.
    14. Rong-Ruey Duh & Karim Jamal & Shyam NMI Sunder, 2001. "Control and Assurance in E-Commerce: Privacy, Integrity and Security at eBay," Yale School of Management Working Papers ysm170, Yale School of Management.
    15. Nuzzo, Simone & Morone, Andrea, 2017. "Asset markets in the lab: A literature review," Journal of Behavioral and Experimental Finance, Elsevier, vol. 13(C), pages 42-50.
    16. 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.
    17. Giuseppe Attanasi & Samuele Centorrino & Elena Manzoni, 2020. "Zero-Intelligence vs. Human Agents: An Experimental Analysis of the Efficiency of Double Auctions and Over-the-Counter Markets of Varying Sizes," Working Papers 05/2020, University of Verona, Department of Economics.
    18. Karim Jamal & Michael Maier & Shyam Sunder, 2019. "Aggregation of Diverse Information with Double Auction Trading among Minimally-Intelligent Algorithmic Agents," Cowles Foundation Discussion Papers 2182, Cowles Foundation for Research in Economics, Yale University.
    19. Li, Hongyan & Tesfatsion, Leigh, 2012. "Co-learning patterns as emergent market phenomena: An electricity market illustration," Journal of Economic Behavior & Organization, Elsevier, vol. 82(2), pages 395-419.
    20. Andrea Morone & Francesco Nemore & Simone Nuzzo, 2018. "Experimental evidence on tax salience and tax incidence," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 20(4), pages 582-612, August.

    More about this item

    Keywords

    Trading protocols; Market design; Allocative efficiency; Genetic Programming;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

    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:vnm:wpdman:2. 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: Marco LiCalzi (email available below). General contact details of provider: https://edirc.repec.org/data/mdvenit.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.