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Do Algorithmic Traders Improve Liquidity When Information Asymmetry is High?

Author

Listed:
  • Archana Jain

    (Saunders College of Business, Rochester Institute of Technology, Rochester, NY 14623, USA)

  • Chinmay Jain

    (SUNY Geneseo, Geneseo, NY 14454, USA3Ontario Tech University, Oshawa, Ontario, L1H 7K4 Canada)

  • Revansiddha Basavaraj Khanapure

    (Jindal School of Management, University of Texas at Dallas, 800 W Campbell Rd, JSOM 14.218, Richardson, TX 75080, USA)

Abstract

Hendershott et al. (2011, Does Algorithmic Trading Improve Liquidity? Journal of Finance 66, 1–33) show that algorithmic traders improve liquidity in equity markets. An equally important and unanswered question is whether they improve liquidity when information asymmetry is high. We use days surrounding earnings announcement as a period of high information asymmetry. First, we follow Hendershott et al. (2011, Does Algorithmic Trading Improve Liquidity? Journal of Finance 66, 1–33) to use introduction of NYSE autoquote as a natural experiment. We find that increased algorithmic trading (AT) as a result of NYSE autoquote does not improve liquidity around earnings announcements. Next, we use trade-to-order volume % and cancel rate as a proxy for algorithmic trading and find that abnormal spreads surrounding the days of earnings announcement are significantly higher for stocks with higher AT. Our findings indicate that algorithmic traders reduces their role of liquidity provision in markets when information asymmetry is high. These findings shed further light on the role of liquidity provision by algorithmic traders in the financial markets.

Suggested Citation

  • Archana Jain & Chinmay Jain & Revansiddha Basavaraj Khanapure, 2021. "Do Algorithmic Traders Improve Liquidity When Information Asymmetry is High?," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 11(01), pages 1-32, March.
    • Handle: RePEc:wsi:qjfxxx:v:11:y:2021:i:01:n:s2010139220500159
    DOI: 10.1142/S2010139220500159
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    Citations

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    Cited by:

    1. Salman Bahoo & Marco Cucculelli & Xhoana Goga & Jasmine Mondolo, 2024. "Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis," SN Business & Economics, Springer, vol. 4(2), pages 1-46, February.
    2. Yamada, Masahiro, 2022. "Profitability and liquidity provision of HFTs during large price shocks: Does relative tick size matter?," Finance Research Letters, Elsevier, vol. 46(PA).
    3. Karkowska, Renata & Palczewski, Andrzej, 2023. "Does high-frequency trading actually improve market liquidity? A comparative study for selected models and measures," Research in International Business and Finance, Elsevier, vol. 64(C).

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