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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

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    File URL: http://virgo.unive.it/wpideas/storage/2011wp2.pdf
    File Function: First version, 2011
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    References listed on IDEAS

    as
    1. Marco LiCalzi & Paolo Pellizzari, 2008. "Zero-Intelligence Trading without Resampling," Working Papers 164, Department of Applied Mathematics, Università Ca' Foscari Venezia.
    2. 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.
    3. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Trading protocols; Market design; Allocative efficiency; Genetic Programming;

    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

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