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Order aggressiveness, pre-trade transparency, and long memory in an order-driven market

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  • Yamamoto, Ryuichi

Abstract

Recent empirical research has documented that the state of the limit order book influences stock investors' strategies. Investors place more aggressive orders when the same side of the order book is thicker, and less aggressive orders when it is thinner. We conjecture and demonstrate that this behavior is related to long memories of trading volume, volatility, and order signs in stock markets. We investigate our conjecture in two types of artificial stock markets: a transparent market, in which agents observe all limit orders on both sides of the book and order volumes at those prices before they trade; and a less transparent market, in which agents observe only the best five bid and ask quotes with the depth available at these limit prices. The first market structure resembles certain actual stock exchanges in the level of pre-trade transparency, such as the Australian Stock Exchange, NYSE OpenBook, and the London Stock Exchange, whereas the second market structure is consistent with stock exchanges such as Euronext Paris, the Toronto Stock Exchange, the Tokyo Stock Exchange, and Hong Kong Exchanges and Clearing. We demonstrate that our long memory results are robust with different levels of pre-trade transparency, implying that the strategy constructed by the state of the order book is key for explaining long memories in many actual stock exchanges.

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  • Yamamoto, Ryuichi, 2011. "Order aggressiveness, pre-trade transparency, and long memory in an order-driven market," Journal of Economic Dynamics and Control, Elsevier, vol. 35(11), pages 1938-1963.
  • Handle: RePEc:eee:dyncon:v:35:y:2011:i:11:p:1938-1963
    DOI: 10.1016/j.jedc.2011.06.009
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    Cited by:

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    2. 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.
    3. Vivien Lespagnol & Juliette Rouchier, 2015. "What Is the Impact of Heterogeneous Knowledge About Fundamentals on Market Liquidity and Efficiency: An ABM Approach," Lecture Notes in Economics and Mathematical Systems, in: Frédéric Amblard & Francisco J. Miguel & Adrien Blanchet & Benoit Gaudou (ed.), Advances in Artificial Economics, edition 127, pages 105-117, Springer.
    4. Kovaleva, Polina & Iori, Giulia, 2015. "The impact of reduced pre-trade transparency regimes on market quality," Journal of Economic Dynamics and Control, Elsevier, vol. 57(C), pages 145-162.
    5. 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.
    6. Yuki Sato & Kiyoshi Kanazawa, 2023. "Exact solution to a generalised Lillo-Mike-Farmer model with heterogeneous order-splitting strategies," Papers 2306.13378, arXiv.org, revised Nov 2023.
    7. Marina Balboa & Paulo M. M. Rodrigues & Antonio Rubia & A. M. Robert Taylor, 2021. "Multivariate fractional integration tests allowing for conditional heteroskedasticity with an application to return volatility and trading volume," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 544-565, August.
    8. Erdinc Akyildirim & Shaen Corbet & Guzhan Gulay & Duc Khuong Nguyen & Ahmet Sensoy, 2019. "Order Flow Persistence in Equity Spot and Futures Markets: Evidence from a Dynamic Emerging Market," Working Papers 2019-011, Department of Research, Ipag Business School.
    9. Vivien Lespagnol & Juliette Rouchier, 2018. "Trading Volume and Price Distortion: An Agent-Based Model with Heterogenous Knowledge of Fundamentals," Computational Economics, Springer;Society for Computational Economics, vol. 51(4), pages 991-1020, April.
    10. Bellia, Mario & Pelizzon, Loriana & Subrahmanyam, Marti G. & Uno, Jun & Yuferova, Darya, 2017. "Low-latency trading and price discovery: Evidence from the Tokyo Stock Exchange in the pre-opening and opening periods," SAFE Working Paper Series 144, Leibniz Institute for Financial Research SAFE, revised 2017.
    11. Iris Lucas & Michel Cotsaftis & Cyrille Bertelle, 2018. "Self-Organization, Resilience and Robustness of Complex Systems Through an Application to Financial Market from an Agent-Based Approach," Post-Print hal-02114928, HAL.
    12. Roberto Mota Navarro & Hernán Larralde, 2017. "A detailed heterogeneous agent model for a single asset financial market with trading via an order book," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-27, February.
    13. Vivien Lespagnol & Juliette Rouchier, 2015. "Fair Price And Trading Price: An Abm Approach With Order-Placement Strategy And Misunderstanding Of Fundamental Value," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 18(05n06), pages 1-14, August.
    14. Kyubin Yim & Gabjin Oh & Seunghwan Kim, 2016. "Understanding Financial Market States Using an Artificial Double Auction Market," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-15, March.
    15. Yamamoto, Ryuichi, 2012. "Intraday technical analysis of individual stocks on the Tokyo Stock Exchange," Journal of Banking & Finance, Elsevier, vol. 36(11), pages 3033-3047.
    16. Al-Shboul, Mohammad & Alsharari, Nizar, 2019. "The dynamic behavior of evolving efficiency: Evidence from the UAE stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 73(C), pages 119-135.
    17. Xiao, Xijuan & Yamamoto, Ryuichi, 2020. "Price discovery, order submission, and tick size during preopen period," Pacific-Basin Finance Journal, Elsevier, vol. 63(C).
    18. Pham, Thu Phuong & Westerholm, P. Joakim, 2013. "A survey of research into broker identity and limit order book," Working Papers 17212, University of Tasmania, Tasmanian School of Business and Economics, revised 16 Oct 2013.
    19. Iris Lucas & Michel Cotsaftis & Cyrille Bertelle, 2017. "Heterogeneity and Self-Organization of Complex Systems Through an Application to Financial Market with Multiagent Systems," Post-Print hal-02114933, HAL.
    20. Mathieu, Philippe & Morvan, Rémi, 2019. "A deterministic behaviour for realistic price dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 33-49.
    21. Lien, Donald & Hung, Pi-Hsia & Chen, Hung-Ju, 2021. "Who knows more and makes more? A perspective of order submission decisions across investor types," The Quarterly Review of Economics and Finance, Elsevier, vol. 79(C), pages 381-398.
    22. Ming-Chang Wang & Yu-Jia Ding & Pei-Han Hsin, 2018. "Order Aggressiveness and the Heating and Cooling-off Effects of Price Limits: Evidence from Taiwan Stock Exchange," Journal of Economics and Management, College of Business, Feng Chia University, Taiwan, vol. 14(2), pages 191-216, August.
    23. Tóth, Bence & Palit, Imon & Lillo, Fabrizio & Farmer, J. Doyne, 2015. "Why is equity order flow so persistent?," Journal of Economic Dynamics and Control, Elsevier, vol. 51(C), pages 218-239.
    24. Vivien Lespagnol & Juliette Rouchier, 2018. "Trading Volume and Price Distortion: An Agent-Based Model with Heterogenous Knowledge of Fundamentals," Post-Print hal-02084910, HAL.
    25. Tseng, Yi-Heng & Chen, Shu-Heng, 2015. "Limit order book transparency and order aggressiveness at the closing call: Lessons from the TWSE 2012 new information disclosure mechanism," Pacific-Basin Finance Journal, Elsevier, vol. 35(PA), pages 241-272.

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    More about this item

    Keywords

    Long memory; Order aggressiveness; Pre-trade transparency; Market microstructure; Agent-based modeling;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions

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