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Automated Liquidity Provision

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Abstract

Traditional market makers are losing their importance as automated systems have largely assumed the role of liquidity provision in markets. We update the model of Glosten and Milgrom (1985) to analyze this new world: we add multiple securities and introduce an automated market maker who prices order flow for all securities contemporaneously. This automated participant transacts the majority of orders, sets prices that are more efficient, reduces spreads, and increases informed and decreases uninformed traders' transaction costs. The model's predictions match very well with recent empirical findings and are difficult to replicate with alternative models.

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  • Austin Gerig & David Michayluk, 2014. "Automated Liquidity Provision," Research Paper Series 345, Quantitative Finance Research Centre, University of Technology, Sydney.
    • Handle: RePEc:uts:rpaper:345
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    Cited by:

    1. Edward W. Sun & Timm Kruse & Yi-Ting Chen, 2019. "Stylized algorithmic trading: satisfying the predictive near-term demand of liquidity," Annals of Operations Research, Springer, vol. 281(1), pages 315-347, October.
    2. Bellia, Mario & Pelizzon, Loriana & Subrahmanyam, Marti & Uno, Jun & Yuferova, Darya, 2017. "Coming early to the party," SAFE Working Paper Series 182, Leibniz Institute for Financial Research SAFE.
      • Mario Bellia & Loriana Pelizzon & Marti G. Subrahmanyam & Jun Uno & Darya Yuferova, 2020. "Coming early to the party," Working Papers 2020:11, Department of Economics, University of Venice "Ca' Foscari".
    3. Kin‐Yip Ho & Wai‐Man Liu & Jing Yu, 2018. "Public News Arrival and Cross‐Asset Correlation Breakdown," International Review of Finance, International Review of Finance Ltd., vol. 18(3), pages 411-451, September.
    4. Hee Su Roh & Yinyu Ye, 2015. "Market Making with Model Uncertainty," Papers 1509.07155, arXiv.org, revised Nov 2015.
    5. Robert Litzenberger & Jeff Castura & Richard Gorelick, 2012. "The Impacts of Automation and High Frequency Trading on Market Quality," Annual Review of Financial Economics, Annual Reviews, vol. 4(1), pages 59-98, October.
    6. Austin Gerig, 2012. "High-Frequency Trading Synchronizes Prices in Financial Markets," Papers 1211.1919, arXiv.org.
    7. Moriyasu, Hiroshi & Wee, Marvin & Yu, Jing, 2018. "The role of algorithmic trading in stock liquidity and commonality in electronic limit order markets," Pacific-Basin Finance Journal, Elsevier, vol. 49(C), pages 103-128.
    8. Hasbrouck, Joel & Saar, Gideon, 2013. "Low-latency trading," Journal of Financial Markets, Elsevier, vol. 16(4), pages 646-679.
    9. Eun Jung Lee, 2015. "High Frequency Trading in the Korean Index Futures Market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 35(1), pages 31-51, January.
    10. Daniel Fricke & Austin Gerig, 2018. "Too fast or too slow? Determining the optimal speed of financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 18(4), pages 519-532, April.
    11. Kun Li, 2018. "Do high-frequency fleeting orders exacerbate market illiquidity?," Electronic Commerce Research, Springer, vol. 18(2), pages 241-255, June.
    12. Zhang, Wei & Huang, Ke & Feng, Xu & Zhang, Yongjie, 2017. "Market maker competition and price efficiency: Evidence from China," Economic Modelling, Elsevier, vol. 66(C), pages 121-131.
    13. Benjamin Myers & Austin Gerig, 2013. "Simulating the Synchronizing Behavior of High-Frequency Trading in Multiple Markets," Papers 1311.4160, arXiv.org.

    More about this item

    Keywords

    algorithmic trading; automated trading; high-frequency trading; market making; specialist; statistical arbitrage;

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G19 - Financial Economics - - General Financial Markets - - - Other

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