IDEAS home Printed from https://ideas.repec.org/a/eee/jbfina/v147y2023ics0378426622000152.html
   My bibliography  Save this article

Stock liquidity and algorithmic market making during the COVID-19 crisis

Author

Listed:
  • Chakrabarty, Bidisha
  • Pascual, Roberto

Abstract

Much of the liquidity supply in modern markets comes from algorithmic traders (ATs). Prompted by concerns of fragility induced by such voluntary market making, we examine ATs’ liquidity-provision role during the COVID-19 crisis. We find that amidst the turmoil as market liquidity declined, ATs did not (disproportionately) withdraw liquidity supply. Stocks with the highest algorithmic trading (AT) experienced lower liquidity reduction compared to stocks with the lowest AT activity. High AT stocks did not experience greater reduction in either competition for liquidity provision or price improvements than low AT stocks. Multiple tests indicate that high AT did not associate with any greater deterioration in price efficiency vis-à-vis low AT stocks. Stocks in the industries hardest hit by COVID-19 did not see any less AT competition for liquidity supply or price efficiency than stocks in the least affected ones. Overall, our results allay some concerns that the current levels of AT make markets more susceptible to liquidity withdrawal in times of crises.

Suggested Citation

  • Chakrabarty, Bidisha & Pascual, Roberto, 2023. "Stock liquidity and algorithmic market making during the COVID-19 crisis," Journal of Banking & Finance, Elsevier, vol. 147(C).
    • Handle: RePEc:eee:jbfina:v:147:y:2023:i:c:s0378426622000152
    DOI: 10.1016/j.jbankfin.2022.106415
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378426622000152
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jbankfin.2022.106415?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Craig W. Holden & Stacey Jacobsen, 2014. "Liquidity Measurement Problems in Fast, Competitive Markets: Expensive and Cheap Solutions," Journal of Finance, American Finance Association, vol. 69(4), pages 1747-1785, August.
    3. Thierry Foucault & Ailsa Röell & Patrik Sandås, 2003. "Market Making with Costly Monitoring: An Analysis of the SOES Controversy," The Review of Financial Studies, Society for Financial Studies, vol. 16(2), pages 345-384.
    4. Ricardo J Caballero & Alp Simsek, 2021. "A Model of Endogenous Risk Intolerance and LSAPs: Asset Prices and Aggregate Demand in a “COVID-19” Shock [Financial intermediaries and the cross-section of asset returns]," The Review of Financial Studies, Society for Financial Studies, vol. 34(11), pages 5522-5580.
    5. Baron, Matthew & Brogaard, Jonathan & Hagströmer, Björn & Kirilenko, Andrei, 2019. "Risk and Return in High-Frequency Trading," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 54(3), pages 993-1024, June.
    6. Mark S. Seasholes & Terrence Hendershott, 2007. "Market Maker Inventories and Stock Prices," American Economic Review, American Economic Association, vol. 97(2), pages 210-214, May.
    7. Albert J. Menkveld & Marius A. Zoican, 2017. "Need for Speed? Exchange Latency and Liquidity," Review of Financial Studies, Society for Financial Studies, vol. 30(4), pages 1188-1228.
    8. Andrei Kirilenko & Albert S. Kyle & Mehrdad Samadi & Tugkan Tuzun, 2017. "The Flash Crash: High-Frequency Trading in an Electronic Market," Journal of Finance, American Finance Association, vol. 72(3), pages 967-998, June.
    9. Hendershott, Terrence & Moulton, Pamela C., 2011. "Automation, speed, and stock market quality: The NYSE's Hybrid," Journal of Financial Markets, Elsevier, vol. 14(4), pages 568-604, November.
    10. Andreas Schrimpf & Hyun Song Shin & Vladyslav Sushko, 2020. "Leverage and margin spirals in fixed income markets during the Covid-19 crisis," BIS Bulletins 2, Bank for International Settlements.
    11. Harris, Lawrence E. & Panchapagesan, Venkatesh, 2005. "The information content of the limit order book: evidence from NYSE specialist trading decisions," Journal of Financial Markets, Elsevier, vol. 8(1), pages 25-67, February.
    12. Valentin Haddad & Alan Moreira & Tyler Muir, 2020. "When Selling Becomes Viral: Disruptions in Debt Markets in the COVID-19 Crisis and the Fed’s Response," NBER Working Papers 27168, National Bureau of Economic Research, Inc.
    13. Stefan Nagel, 2012. "Evaporating Liquidity," The Review of Financial Studies, Society for Financial Studies, vol. 25(7), pages 2005-2039.
    14. Amihud, Yakov, 2002. "Illiquidity and stock returns: cross-section and time-series effects," Journal of Financial Markets, Elsevier, vol. 5(1), pages 31-56, January.
    15. Goodell, John W. & Huynh, Toan Luu Duc, 2020. "Did Congress trade ahead? Considering the reaction of US industries to COVID-19," Finance Research Letters, Elsevier, vol. 36(C).
    16. O'Hara, Maureen & Zhou, Xing (Alex), 2021. "Anatomy of a liquidity crisis: Corporate bonds in the COVID-19 crisis," Journal of Financial Economics, Elsevier, vol. 142(1), pages 46-68.
    17. Conrad, Jennifer & Wahal, Sunil & Xiang, Jin, 2015. "High-frequency quoting, trading, and the efficiency of prices," Journal of Financial Economics, Elsevier, vol. 116(2), pages 271-291.
    18. Comerton-Forde, Carole & Putniņš, Tālis J., 2015. "Dark trading and price discovery," Journal of Financial Economics, Elsevier, vol. 118(1), pages 70-92.
    19. Ready, Mark J, 1999. "The Specialist's Discretion: Stopped Orders and Price Improvement," Review of Financial Studies, Society for Financial Studies, vol. 12(5), pages 1075-1112.
    20. Nilabhra Bhattacharya & Bidisha Chakrabarty & Xu (Frank) Wang, 2020. "High-frequency traders and price informativeness during earnings announcements," Review of Accounting Studies, Springer, vol. 25(3), pages 1156-1199, September.
    21. Anand, Amber & Venkataraman, Kumar, 2016. "Market conditions, fragility, and the economics of market making," Journal of Financial Economics, Elsevier, vol. 121(2), pages 327-349.
    22. Brogaard, Jonathan & Carrion, Allen & Moyaert, Thibaut & Riordan, Ryan & Shkilko, Andriy & Sokolov, Konstantin, 2018. "High frequency trading and extreme price movements," Journal of Financial Economics, Elsevier, vol. 128(2), pages 253-265.
    23. Kewei Hou & Tobias J. Moskowitz, 2005. "Market Frictions, Price Delay, and the Cross-Section of Expected Returns," Review of Financial Studies, Society for Financial Studies, vol. 18(3), pages 981-1020.
    24. Brian M. Weller, 2018. "Does Algorithmic Trading Reduce Information Acquisition?," Review of Financial Studies, Society for Financial Studies, vol. 31(6), pages 2184-2226.
    25. Malceniece, Laura & Malcenieks, Kārlis & Putniņš, Tālis J., 2019. "High frequency trading and comovement in financial markets," Journal of Financial Economics, Elsevier, vol. 134(2), pages 381-399.
    26. Jonathan Brogaard & Terrence Hendershott & Ryan Riordan, 2014. "High-Frequency Trading and Price Discovery," The Review of Financial Studies, Society for Financial Studies, vol. 27(8), pages 2267-2306.
    27. Cao, Charles & Choe, Hyuk & Hatheway, Frank, 1997. "Does the Specialist Matter? Differential Execution Costs and Intersecurity Subsidization on the New York Stock Exchange," Journal of Finance, American Finance Association, vol. 52(4), pages 1615-1640, September.
    28. Terrence Hendershott & Charles M. Jones & Albert J. Menkveld, 2011. "Does Algorithmic Trading Improve Liquidity?," Journal of Finance, American Finance Association, vol. 66(1), pages 1-33, February.
    29. Pagano, Marco & Roell, Ailsa, 1996. "Transparency and Liquidity: A Comparison of Auction and Dealer Markets with Informed Trading," Journal of Finance, American Finance Association, vol. 51(2), pages 579-611, June.
    30. Hendrik Bessembinder & Jia Hao & Kuncheng Zheng, 2015. "Market Making Contracts, Firm Value, and the IPO Decision," Journal of Finance, American Finance Association, vol. 70(5), pages 1997-2028, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cox, Justin & Woods, Donovan, 2023. "COVID-19 and market structure dynamics," Journal of Banking & Finance, Elsevier, vol. 147(C).
    2. Dodd, Olga & Frijns, Bart & Indriawan, Ivan & Pascual, Roberto, 2023. "US cross-listing and domestic high-frequency trading: Evidence from Canadian stocks," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 301-320.
    3. Ekinci, Cumhur & Ersan, Oğuz, 2022. "High-frequency trading and market quality: The case of a “slightly exposed” market," International Review of Financial Analysis, Elsevier, vol. 79(C).
    4. Sánchez Serrano Antonio, 2020. "High-Frequency Trading and Systemic Risk: A Structured Review of Findings and Policies," Review of Economics, De Gruyter, vol. 71(3), pages 169-195, December.
    5. Thierry Foucault & Roman Kozhan & Wing Wah Tham, 2017. "Toxic Arbitrage," The Review of Financial Studies, Society for Financial Studies, vol. 30(4), pages 1053-1094.
    6. Kemme, David M. & McInish, Thomas H. & Zhang, Jiang, 2022. "Market fairness and efficiency: Evidence from the Tokyo Stock Exchange," Journal of Banking & Finance, Elsevier, vol. 134(C).
    7. Anagnostidis, Panagiotis & Fontaine, Patrice, 2020. "Liquidity commonality and high frequency trading: Evidence from the French stock market," International Review of Financial Analysis, Elsevier, vol. 69(C).
    8. 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).
    9. Rif, Alexandru & Utz, Sebastian, 2021. "Short-term stock price reversals after extreme downward price movements," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 123-133.
    10. Brogaard, Jonathan & Carrion, Allen & Moyaert, Thibaut & Riordan, Ryan & Shkilko, Andriy & Sokolov, Konstantin, 2018. "High frequency trading and extreme price movements," Journal of Financial Economics, Elsevier, vol. 128(2), pages 253-265.
    11. 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).
    12. Aït-Sahalia, Yacine & Brunetti, Celso, 2020. "High frequency traders and the price process," Journal of Econometrics, Elsevier, vol. 217(1), pages 20-45.
    13. Aliyev, Nihad & Huseynov, Fariz & Rzayev, Khaladdin, 2022. "Algorithmic trading and investment-to-price sensitivity," LSE Research Online Documents on Economics 118844, London School of Economics and Political Science, LSE Library.
    14. Nicholas Hirschey, 2021. "Do High-Frequency Traders Anticipate Buying and Selling Pressure?," Management Science, INFORMS, vol. 67(6), pages 3321-3345, June.
    15. Foucault, Thierry & Moinas, Sophie, 2018. "Is Trading Fast Dangerous?," TSE Working Papers 18-881, Toulouse School of Economics (TSE).
    16. Rzayev, Khaladdin & Ibikunle, Gbenga & Steffen, Tom, 2023. "The market quality implications of speed in cross-platform trading: evidence from Frankfurt-London microwave," LSE Research Online Documents on Economics 119989, London School of Economics and Political Science, LSE Library.
    17. Chordia, Tarun & Miao, Bin, 2020. "Market efficiency in real time: Evidence from low latency activity around earnings announcements," Journal of Accounting and Economics, Elsevier, vol. 70(2).
    18. Karolis Liaudinskas, 2022. "Human vs. Machine: Disposition Effect among Algorithmic and Human Day Traders," Working Paper 2022/6, Norges Bank.
    19. S. Sarah Zhang, 2018. "Need for speed: Hard information processing in a high‐frequency world," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(1), pages 3-21, January.
    20. Yang, Haijun & Ge, Hengshun & Luo, Ying, 2020. "The optimal bid-ask price strategies of high-frequency trading and the effect on market liquidity," Research in International Business and Finance, Elsevier, vol. 53(C).

    More about this item

    Keywords

    COVID-19; Algorithmic trading; Liquidity; Efficiency; Competition;
    All these keywords.

    JEL classification:

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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jbfina:v:147:y:2023:i:c:s0378426622000152. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbf .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.