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Learning and Evolution of Trading Strategies in Limit Order Markets

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Abstract

How do traders process and learn from market information, what trading strategies should they use, and how does learning affect the market? This paper proposes a learning model of an artificial limit order market with asymmetric information to address these issues. Using a genetic algorithm as a learning mechanism, we show that learning, in particular the learning from uninformed traders, improves market informational efficiency and has a significant impact on the stylized facts of limit order markets, order submission, liquidity supply and consumption, the hump shaped order book near the quote, and the bid-ask spread. Moreover, the learning affects the evolution process of the trading strategies for all traders. The model provides some insights into market efficiency, the interaction of traders, the dynamics of limit order books, and the evolution of trading strategies.

Suggested Citation

  • Carl Chiarella & Xue-Zhong He & Lijian Wei, 2013. "Learning and Evolution of Trading Strategies in Limit Order Markets," Research Paper Series 335, Quantitative Finance Research Centre, University of Technology, Sydney.
  • Handle: RePEc:uts:rpaper:335
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    File URL: https://www.uts.edu.au/sites/default/files/qfr-archive-03/QFR-rp335.pdf
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    References listed on IDEAS

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    1. Chiarella, Carl & Iori, Giulia, 2009. "The impact of heterogeneous trading rules on the limit order book and order flows," Journal of Economic Dynamics and Control, Elsevier, vol. 33(3), pages 525-537.
    2. Lukas Menkhoff & Carol L. Osler & Maik Schmeling, 2010. "Limit-Order Submission Strategies under Asymmetric Information," CESifo Working Paper Series 3054, CESifo Group Munich.
    3. Yan, Yuxing & Zhang, Shaojun, 2012. "An improved estimation method and empirical properties of the probability of informed trading," Journal of Banking & Finance, Elsevier, vol. 36(2), pages 454-467.
    4. Burton Hollifield & Robert A. Miller & Patrik Sandås & Joshua Slive, 2006. "Estimating the Gains from Trade in Limit‐Order Markets," Journal of Finance, American Finance Association, vol. 61(6), pages 2753-2804, December.
    5. William Lin, Hsiou-Wei & Ke, Wen-Chyan, 2011. "A computing bias in estimating the probability of informed trading," Journal of Financial Markets, Elsevier, vol. 14(4), pages 625-640, November.
    6. Keim, Donald B. & Madhavan, Ananth, 1995. "Anatomy of the trading process Empirical evidence on the behavior of institutional traders," Journal of Financial Economics, Elsevier, vol. 37(3), pages 371-398, March.
    7. Kluger, Brian D. & McBride, Mark E., 2011. "Intraday trading patterns in an intelligent autonomous agent-based stock market," Journal of Economic Behavior & Organization, Elsevier, vol. 79(3), pages 226-245, August.
    8. Chakravarty Sugato & Holden Craig W., 1995. "An Integrated Model of Market and Limit Orders," Journal of Financial Intermediation, Elsevier, vol. 4(3), pages 213-241, July.
    9. 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.
    10. Dugast, J., 2013. "Limited attention and news arrival in limit order markets," Working papers 449, Banque de France.
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    Cited by:

    1. Vivien Lespagnol & Juliette Rouchier, 2014. "Trading volume and market efficiency: an Agent Based Model with heterogenous knowledge about fundamentals," AMSE Working Papers 1419, Aix-Marseille School of Economics, France, revised May 2014.
    2. Rossella Agliardi & Ramazan Gençay, 2017. "Optimal Trading Strategies With Limit Orders," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(01), pages 1-16, February.

    More about this item

    Keywords

    Limit order book; evolution; genetic algorithm learning; asymmetric information; trading strategy;

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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