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

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  • Mikhail Anufriev

    ()

  • 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

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Bibliographic Info

Article provided by Springer in its journal Journal of Evolutionary Economics.

Volume (Year): 23 (2013)
Issue (Month): 3 (July)
Pages: 539-573

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Handle: RePEc:spr:joevec:v:23:y:2013:i:3:p:539-573

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Related research

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

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Cited by:
  1. Shira Fano & Marco Li Calzi & Paolo Pellizzari, 2010. "Convergence of outcomes and evolution of strategic behavior in double auctions," Working Papers 196, Department of Applied Mathematics, Università Ca' Foscari Venezia.
  2. Ruijgrok, Matthijs, 2012. "A single-item continuous double auction game," MPRA Paper 42086, University Library of Munich, Germany.
  3. Lijian Wei & Wei Zhang & Xue-Zhong He & Yongjie Zhang, 2013. "Learning and Information Dissemination in Limit Order Markets," Research Paper Series, Quantitative Finance Research Centre, University of Technology, Sydney 333, Quantitative Finance Research Centre, University of Technology, Sydney.
  4. Mikhail Anufriev & Cars Hommes & Raoul Philipse, 2013. "Evolutionary selection of expectations in positive and negative feedback markets," Journal of Evolutionary Economics, Springer, Springer, vol. 23(3), pages 663-688, July.
  5. Florian Hauser & Marco LiCalzi, 2011. "Learning to trade in an unbalanced market," Working Papers 2, Department of Management, Università Ca' Foscari Venezia.

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