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The role of information in a continuous double auction: An experiment and learning model

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  • Anufriev, Mikhail
  • Arifovic, Jasmina
  • Ledyard, John
  • Panchenko, Valentyn

Abstract

We analyze trading in a modified continuous double auction market. We study how more or less information about trading in a prior round affects allocative and informational efficiency. We find that more information reduces allocative efficiency in early rounds relative to less information but that the difference disappears in later rounds. Informational efficiency is not affected by the information differences. We complement the experiment with simulations of the Individual Evolutionary Learning model which, after modifications to account for the CDA, seems to fit the data reasonably well.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:dyncon:v:141:y:2022:i:c:s0165188922000914
    DOI: 10.1016/j.jedc.2022.104387
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    1. Xue-Zhong He & Junqing Kang & Xuan Zhou, 2020. "The Fast and the Furious: Exchange Latency and Ever-fast Trading," Research Paper Series 419, Quantitative Finance Research Centre, University of Technology, Sydney.
    2. 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.
    3. Roberto Dieci & Xue-Zhong He, 2018. "Heterogeneous Agent Models in Finance," Research Paper Series 389, Quantitative Finance Research Centre, University of Technology, Sydney.
    4. Paolo Pellizzari & Dan Ladley, 2014. "The simplicity of optimal trading in order book markets," Working Papers 2014:05, Department of Economics, University of Venice "Ca' Foscari".
    5. Ben Greiner, 2015. "Subject pool recruitment procedures: organizing experiments with ORSEE," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 1(1), pages 114-125, July.
    6. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
    7. Dhananjay K. Gode & Shyam Sunder, 1997. "What Makes Markets Allocationally Efficient?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 112(2), pages 603-630.
    8. Bottazzi, Giulio & Dosi, Giovanni & Rebesco, Igor, 2005. "Institutional architectures and behavioral ecologies in the dynamics of financial markets," Journal of Mathematical Economics, Elsevier, vol. 41(1-2), pages 197-228, February.
    9. Vernon L. Smith, 1962. "An Experimental Study of Competitive Market Behavior," Journal of Political Economy, University of Chicago Press, vol. 70, pages 322-322.
    10. Mikhail Anufriev & Cars Hommes & Tomasz Makarewicz, 2019. "Simple Forecasting Heuristics that Make us Smart: Evidence from Different Market Experiments," Journal of the European Economic Association, European Economic Association, vol. 17(5), pages 1538-1584.
    11. Steven Gjerstad & Jason M. Shachat, 2007. "Individual Rationality and Market Efficiency," Purdue University Economics Working Papers 1204, Purdue University, Department of Economics.
    12. Arifovic, Jasmina & Ledyard, John, 2007. "Call market book information and efficiency," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 1971-2000, June.
    13. Dawid, Herbert, 1999. "On the convergence of genetic learning in a double auction market," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1545-1567, September.
    14. 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.
    15. Arifovic, Jasmina & Ledyard, John, 2011. "A behavioral model for mechanism design: Individual evolutionary learning," Journal of Economic Behavior & Organization, Elsevier, vol. 78(3), pages 374-395, May.
    16. 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.
    17. Urs Fischbacher, 2007. "z-Tree: Zurich toolbox for ready-made economic experiments," Experimental Economics, Springer;Economic Science Association, vol. 10(2), pages 171-178, June.
    18. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-137, February.
    19. Ekkehart Boehmer & Gideon Saar & Lei Yu, 2005. "Lifting the Veil: An Analysis of Pre‐trade Transparency at the NYSE," Journal of Finance, American Finance Association, vol. 60(2), pages 783-815, April.
    20. Jasmina Arifovic & John Ledyard, 2018. "Learning to alternate," Experimental Economics, Springer;Economic Science Association, vol. 21(3), pages 692-721, September.
    21. Vernon L. Smith, 1980. "Relevance of Laboratory Experiments to Testing Resource Allocation Theory," NBER Chapters, in: Evaluation of Econometric Models, pages 345-377, National Bureau of Economic Research, Inc.
    22. Easley, David & O’Hara, Maureen & Yang, Liyan, 2016. "Differential Access to Price Information in Financial Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 51(4), pages 1071-1110, August.
    23. Vriend, Nicolaas J., 2000. "An illustration of the essential difference between individual and social learning, and its consequences for computational analyses," Journal of Economic Dynamics and Control, Elsevier, vol. 24(1), pages 1-19, January.
    24. Shmuel Baruch, 2005. "Who Benefits from an Open Limit-Order Book?," The Journal of Business, University of Chicago Press, vol. 78(4), pages 1267-1306, July.
    25. Jasmina Arifovic & John Ledyard, 2004. "Scaling Up Learning Models in Public Good Games," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 6(2), pages 203-238, May.
    26. 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.
    27. Erik Snowberg & Leeat Yariv, 2021. "Testing the Waters: Behavior across Participant Pools," American Economic Review, American Economic Association, vol. 111(2), pages 687-719, February.
    28. Arifovic, Jasmina & Boitnott, Joshua F. & Duffy, John, 2019. "Learning correlated equilibria: An evolutionary approach," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 171-190.
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    More about this item

    Keywords

    Continuous double auction; Experiments; Individual evolutionary learning;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions

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