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

Author

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  • 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.

Suggested Citation

  • Anufriev, M. & Arifovic, J. & Ledyard, D. & Panchenko, V., 2010. "Efficiency of Continuous Double Auctions under Individual Evolutionary Learning with Full or Limited Information," CeNDEF Working Papers 10-01, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    • Handle: RePEc:ams:ndfwpp:10-01
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    Cited by:

    1. Bao, Te & Hommes, Cars & Pei, Jiaoying, 2021. "Expectation formation in finance and macroeconomics: A review of new experimental evidence," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    2. 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.
    3. Robin Nicole & Aleksandra Alori'c & Peter Sollich, 2020. "Fragmentation in trader preferences among multiple markets: Market coexistence versus single market dominance," Papers 2012.04103, arXiv.org, revised Aug 2021.
    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.
    5. 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.
    6. Yamamoto, Ryuichi, 2019. "Dynamic Predictor Selection And Order Splitting In A Limit Order Market," Macroeconomic Dynamics, Cambridge University Press, vol. 23(5), pages 1757-1792, July.
    7. Florian Hauser & Marco LiCalzi, 2011. "Learning to Trade in an Unbalanced Market," Lecture Notes in Economics and Mathematical Systems, in: Sjoukje Osinga & Gert Jan Hofstede & Tim Verwaart (ed.), Emergent Results of Artificial Economics, pages 65-76, Springer.
    8. Chernov, G. & Susin, I., 2019. "Models of learning in games: An overview," Journal of the New Economic Association, New Economic Association, vol. 44(4), pages 77-125.
    9. Qixuan Luo & Yu Shi & Xuan Zhou & Handong Li, 2021. "Research on the Effects of Institutional Liquidation Strategies on the Market Based on Multi-agent Model," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1025-1049, December.
    10. Sabiou M. Inoua & Vernon L. Smith, 2022. "Perishable goods versus re-tradable assets: A theoretical reappraisal of a fundamental dichotomy," Chapters, in: Sascha Füllbrunn & Ernan Haruvy (ed.), Handbook of Experimental Finance, chapter 15, pages 162-171, Edward Elgar Publishing.
    11. Olga A. Rud & Jean Paul Rabanal, 2018. "Evolution of markets: a simulation with centralized, decentralized and posted offer formats," Journal of Evolutionary Economics, Springer, vol. 28(3), pages 667-689, August.
    12. Lijian Wei & Xiong Xiong & Wei Zhang & Xue-Zhong He & Yongjie Zhang, 2017. "The effect of genetic algorithm learning with a classifier system in limit order markets," Published Paper Series 2017-3, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    13. 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.
    14. Anufriev, Mikhail & Duffy, John & Panchenko, Valentyn, 2024. "Individual evolutionary learning in repeated beauty contest games," Journal of Economic Behavior & Organization, Elsevier, vol. 218(C), pages 550-567.
    15. Kazuto Sasai & Yukio-Pegio Gunji & Tetsuo Kinoshita, 2017. "Intermittent Behavior Induced By Asynchronous Interactions In A Continuous Double Auction Model," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 20(02n03), pages 1-21, March.
    16. Giamattei, Marcus & Huber, Jürgen & Lambsdorff, Johann Graf & Nicklisch, Andreas & Palan, Stefan, 2020. "Who inflates the bubble? Forecasters and traders in experimental asset markets," Journal of Economic Dynamics and Control, Elsevier, vol. 110(C).
    17. 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).
    18. Ruijgrok, Matthijs, 2012. "A single-item continuous double auction game," MPRA Paper 42086, University Library of Munich, Germany.
    19. 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.
    20. 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.
    21. Lu, Dong & Zhan, Yaosong, 2022. "Over-the-counter versus double auction in asset markets with near-zero-intelligence traders," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).

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    More about this item

    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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