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

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  • 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
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    as
    1. 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.
    2. Stefan Nagel, 2012. "Evaporating Liquidity," The Review of Financial Studies, Society for Financial Studies, vol. 25(7), pages 2005-2039.
    3. Kewei Hou & Tobias J. Moskowitz, 2005. "Market Frictions, Price Delay, and the Cross-Section of Expected Returns," The Review of Financial Studies, Society for Financial Studies, vol. 18(3), pages 981-1020.
    4. 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.
    5. 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.
    6. Brian M. Weller, 2018. "Does Algorithmic Trading Reduce Information Acquisition?," The Review of Financial Studies, Society for Financial Studies, vol. 31(6), pages 2184-2226.
    7. 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.
    8. 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.
    9. 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.
    10. Albert J. Menkveld & Marius A. Zoican, 2017. "Need for Speed? Exchange Latency and Liquidity," The Review of Financial Studies, Society for Financial Studies, vol. 30(4), pages 1188-1228.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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).
    21. 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.
    22. 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.
    23. 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.
    24. Comerton-Forde, Carole & Putniņš, Tālis J., 2015. "Dark trading and price discovery," Journal of Financial Economics, Elsevier, vol. 118(1), pages 70-92.
    25. Ready, Mark J, 1999. "The Specialist's Discretion: Stopped Orders and Price Improvement," The Review of Financial Studies, Society for Financial Studies, vol. 12(5), pages 1075-1112.
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
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    Cited by:

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    2. Karim, Sitara & Shafiullah, Muhammad & Naeem, Muhammad Abubakr, 2024. "When one domino falls, others follow: A machine learning analysis of extreme risk spillovers in developed stock markets," International Review of Financial Analysis, Elsevier, vol. 93(C).
    3. An Pham Ngoc Nguyen & Martin Crane & Thomas Conlon & Marija Bezbradica, 2024. "Herding Unmasked: Insights into Cryptocurrencies, Stocks and US ETFs," Papers 2407.08069, arXiv.org, revised Dec 2024.
    4. Jiang, Christine & Zhang, Xiaori & Hu, Bill, 2024. "Government reporting credibility as immunity: Evidence from a public health event," Journal of Multinational Financial Management, Elsevier, vol. 73(C).
    5. Petar-Pierre Matek & Maša Galiæ, 2024. "The impact of designated market-makers on liquidity in frontier markets: Evidence from Zagreb and Ljubljana Stock Exchanges," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 42(1), pages 95-121.

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    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

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