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Multiple markets, algorithmic trading, and market liquidity

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  • Upson, James
  • Van Ness, Robert A.

Abstract

Using a sample of NYSE firms from the first quarter of 2012, we show that the National Best Bid and Offer (NBBO) depth is negatively affected by quote competition between exchanges and by excess algorithmic trading (AT) activity, but positively impacted by volume fragmentation. Trade execution quality also decreases with higher quote competition and AT activity but is better with higher volume fragmentation. In addition, we find that the U-shaped pattern of spreads is an S-shape, with higher spreads at the open and lower spreads at the close. NBBO depth has an inverse pattern to that of spreads.

Suggested Citation

  • Upson, James & Van Ness, Robert A., 2017. "Multiple markets, algorithmic trading, and market liquidity," Journal of Financial Markets, Elsevier, vol. 32(C), pages 49-68.
    • Handle: RePEc:eee:finmar:v:32:y:2017:i:c:p:49-68
    DOI: 10.1016/j.finmar.2016.05.004
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    2. Hatch, Brian C. & Johnson, Shane A. & Wang, Qin Emma & Zhang, Jun, 2021. "Algorithmic trading and firm value," Journal of Banking & Finance, Elsevier, vol. 125(C).
    3. Wilkoff, Sean & Yildiz, Serhat, 2023. "The behavior and determinants of illiquidity in the non-fungible tokens (NFTs) market," Global Finance Journal, Elsevier, vol. 55(C).
    4. Hung, Pi-Hsia & Lien, Donald, 2019. "Trading aggressiveness, order execution quality, and stock price movements: Evidence from the Taiwan stock exchange," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 60(C), pages 231-251.
    5. Chakrabarty, Bidisha & Pascual, Roberto, 2023. "Stock liquidity and algorithmic market making during the COVID-19 crisis," Journal of Banking & Finance, Elsevier, vol. 147(C).
    6. Justin Cox & Bonnie Van Ness & Robert Van Ness, 2022. "The dark side of IPOs: Examining where and who trades in the IPO secondary market," Financial Management, Financial Management Association International, vol. 51(4), pages 1091-1126, December.
    7. Ritesh Kumar Dubey & A. Sarath Babu & Rajneesh Ranjan Jha & Urvashi Varma, 2022. "Algorithmic Trading Efficiency and its Impact on Market-Quality," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 29(3), pages 381-409, September.
    8. Jurich, Stephen N., 2021. "Does off-exchange trading decrease in the presence of uncertainty?," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 201-213.
    9. Eduard Silantyev, 2019. "Order flow analysis of cryptocurrency markets," Digital Finance, Springer, vol. 1(1), pages 191-218, November.
    10. Cox, Justin & Woods, Donovan, 2023. "COVID-19 and market structure dynamics," Journal of Banking & Finance, Elsevier, vol. 147(C).
    11. Yildiz, Serhat & Van Ness, Bonnie & Van Ness, Robert, 2020. "VPIN, liquidity, and return volatility in the U.S. equity markets," Global Finance Journal, Elsevier, vol. 45(C).
    12. Bogoev, Dimitar & Karam, Arzé, 2017. "Detection of algorithmic trading," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 168-181.
    13. Ramos, Henrique Pinto & Perlin, Marcelo Scherer, 2020. "Does algorithmic trading harm liquidity? Evidence from Brazil," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
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    More about this item

    Keywords

    Algorithmic traders; High-frequency traders; HFT; Market liquidity; Intraday liquidity; Latency;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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