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Efficiency of continuous double auctions under individual evolutionary learning with full or limited information

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  • Mikhail Anufriev

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  • Jasmina Arifovic
  • John Ledyard
  • Valentyn Panchenko

Abstract

In this paper we explore how specific aspects of market transparency and agents’ behavior affect the efficiency of the market outcome. In particular, we are interested whether learning behavior with and without information about actions of other participants improves market efficiency. We consider a simple market for a homogeneous good populated by buyers and sellers. The valuations of the buyers and the costs of the sellers are given exogenously. Agents are involved in consecutive trading sessions, which are organized as a continuous double auction with order book. Using Individual Evolutionary Learning agents submit price bids and offers, trying to learn the most profitable strategy by looking at their realized and counterfactual or “foregone” payoffs. We find that learning outcomes heavily depend on information treatments. Under full information about actions of others, agents’ orders tend to be similar, while under limited information agents tend to submit their valuations/costs. This behavioral outcome results in higher price volatility for the latter treatment. We also find that learning improves allocative efficiency when compared to outcomes with Zero-Intelligent traders. Copyright The Author(s) 2013

Suggested Citation

  • 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.
  • Handle: RePEc:spr:joevec:v:23:y:2013:i:3:p:539-573
    DOI: 10.1007/s00191-011-0230-8
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    Citations

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    Cited by:

    1. Chiarella, Carl & He, Xue-Zhong & Wei, Lijian, 2015. "Learning, information processing and order submission in limit order markets," Journal of Economic Dynamics and Control, Elsevier, vol. 61(C), pages 245-268.
    2. 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.
    3. Florian Hauser & Marco LiCalzi, 2011. "Learning to trade in an unbalanced market," Working Papers 2, Department of Management, Università Ca' Foscari Venezia.
    4. Mikhail Anufriev & Cars Hommes & Raoul Philipse, 2013. "Evolutionary selection of expectations in positive and negative feedback markets," Journal of Evolutionary Economics, Springer, vol. 23(3), pages 663-688, July.
    5. repec:wsi:acsxxx:v:20:y:2017:i:02n03:n:s0219525917500059 is not listed on IDEAS
    6. Ruijgrok, Matthijs, 2012. "A single-item continuous double auction game," MPRA Paper 42086, University Library of Munich, Germany.
    7. Yosra Mefteh Rekik & Younes Boujelbene, 2015. "Price Dynamics and Market Volatility: Behavioral Heterogeneity under Switching Trading Strategies on Artificial Financial Market," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 6(2), pages 33-43, April.
    8. Lijian Wei & Wei Zhang & Xue-Zhong He & Yongjie Zhang, 2013. "Learning and Information Dissemination in Limit Order Markets," Research Paper Series 333, Quantitative Finance Research Centre, University of Technology, Sydney.

    More about this item

    Keywords

    Allocative efficiency; Continuous double auction; Individual evolutionary learning; D83; C63; D44;

    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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