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Efficiency of Continuous Double Auctions under Individual Evolutionary Learning with Full or Limited Information

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Author Info

  • Anufriev, M.

    ()
    (Universiteit van Amsterdam)

  • Arifovic, J.

    (Simon Fraser University)

  • Ledyard, D.

    (California Institute of Technology)

  • Panchenko, V.

    (University of New South Wales)

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 electronic 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 with to outcomes with Zero-Intelligent traders.

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File URL: http://www1.fee.uva.nl/cendef/publications/papers/iel_book_r1.pdf
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Bibliographic Info

Paper provided by Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance in its series CeNDEF Working Papers with number 10-01.

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Date of creation: 2010
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Handle: RePEc:ams:ndfwpp:10-01

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Postal: Dept. of Economics and Econometrics, Universiteit van Amsterdam, Roetersstraat 11, NL - 1018 WB Amsterdam, The Netherlands
Phone: + 31 20 525 52 58
Fax: + 31 20 525 52 83
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Web page: http://www.fee.uva.nl/cendef/
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  1. FOUCAULT, Thierry & KADAN, Ohad & KANDEL, Eugene, 2001. "Limit order book as a market for liquidity," Les Cahiers de Recherche 728, HEC Paris.
  2. Marco LiCalzi & Paolo Pellizzari, 2008. "Zero-Intelligence Trading without Resampling," Working Papers 164, Department of Applied Mathematics, Università Ca' Foscari Venezia.
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  11. 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.
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  17. 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 119-37, February.
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Cited by:
  1. Ruijgrok, Matthijs, 2012. "A single-item continuous double auction game," MPRA Paper 42086, University Library of Munich, Germany.
  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. 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.
  5. 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.

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