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Automated liquidity provision

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  • Gerig, Austin
  • Michayluk, David

Abstract

Over the last decade, the task of liquidity provision in many markets has shifted from traditional market makers to autonomous, computerized trading systems. These automated systems collect, process, and react to market-wide information quicker and more comprehensively than the humans they have replaced. Here, we update the model of Glosten and Milgrom (1985) to analyze how the automation of liquidity provision affects market quality, the transaction costs of market participants, and volatility. To Glosten and Milgrom's original model, we add multiple securities and introduce an automated market maker who prices order flow for all securities contemporaneously. We find that the automated market maker transacts the majority of orders, sets prices that are more efficient, increases informed and decreases uninformed traders' transaction costs, and has no effect on volatility. The model's predictions match very well with recent empirical findings and are difficult to replicate with alternative models.

Suggested Citation

  • Gerig, Austin & Michayluk, David, 2017. "Automated liquidity provision," Pacific-Basin Finance Journal, Elsevier, vol. 45(C), pages 1-13.
  • Handle: RePEc:eee:pacfin:v:45:y:2017:i:c:p:1-13
    DOI: 10.1016/j.pacfin.2016.05.006
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    Cited by:

    1. 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".
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Hee Su Roh & Yinyu Ye, 2015. "Market Making with Model Uncertainty," Papers 1509.07155, arXiv.org, revised Nov 2015.
    7. Austin Gerig, 2012. "High-Frequency Trading Synchronizes Prices in Financial Markets," Papers 1211.1919, arXiv.org.
    8. 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.
    9. Kun Li, 2018. "Do high-frequency fleeting orders exacerbate market illiquidity?," Electronic Commerce Research, Springer, vol. 18(2), pages 241-255, June.

    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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