IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v15y2022i7p308-d862960.html
   My bibliography  Save this article

Market Quality and Short-Selling Ban during the COVID-19 Pandemic: A High-Frequency Data Approach

Author

Listed:
  • Sandra Ferreruela

    (Department of Accounting and Finance, University of Zaragoza, 50005 Zaragoza, Spain)

  • Daniel Martín

    (Department of Accounting and Finance, University of Zaragoza, 50005 Zaragoza, Spain
    CESTE International Business School, 50012 Zaragoza, Spain)

Abstract

The recent emergence of COVID-19 and the subsequent short-selling restriction (SSR) imposed on some equity markets provide us with a unique framework to analyze the effects of this kind of measure on market quality in the context of increasingly automated equity markets. We contribute to the literature by analyzing the microstructure and quality parameters of the Spanish equity market during COVID-19 and SSR. We study four subperiods, namely pre-crisis, turmoil, SSR, and first de-escalation periods, by means of a tick-by-tick dataset and the complete limit order book (LOB). We observe the following impact of the SSR on the constituents of IBEX 35: (1) the SSR did comply partially with its aim at an intraday level regarding volatility, but liquidity was reduced; (2) liquidity deterioration affected more the sell than the buy side of the LOB; (3) high-frequency activity (HFT) diminished during SSR, reinforcing volatility; (4) negative effects on liquidity and HFT diminished and disappeared as the ban was lifted; (5) HFT unidirectionally Granger causes 1 min realized volatility while the natural logarithm of the slope of the LOB bidirectionally Granger causes 1 min realized volatility.

Suggested Citation

  • Sandra Ferreruela & Daniel Martín, 2022. "Market Quality and Short-Selling Ban during the COVID-19 Pandemic: A High-Frequency Data Approach," JRFM, MDPI, vol. 15(7), pages 1-29, July.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:7:p:308-:d:862960
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/15/7/308/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/15/7/308/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Friederich, Sylvain & Payne, Richard, 2015. "Order-to-trade ratios and market liquidity," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 214-223.
    2. Anton Golub & John Keane & Ser-Huang Poon, 2012. "High Frequency Trading and Mini Flash Crashes," Papers 1211.6667, arXiv.org.
    3. Feng, Xunan & Chan, Kam C., 2016. "Information advantage, short sales, and stock returns: Evidence from short selling reform in China," Economic Modelling, Elsevier, vol. 59(C), pages 131-142.
    4. Ozkan, Oktay, 2021. "Impact of COVID-19 on stock market efficiency: Evidence from developed countries," Research in International Business and Finance, Elsevier, vol. 58(C).
    5. Ramazan Gençay & Nikola Gradojevic & Richard Olsen & Faruk Selçuk, 2015. "Informed traders’ arrival in foreign exchange markets: Does geography matter?," Empirical Economics, Springer, vol. 49(4), pages 1431-1462, December.
    6. 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.
    7. Kaddour Hadri, 2000. "Testing for stationarity in heterogeneous panel data," Econometrics Journal, Royal Economic Society, vol. 3(2), pages 148-161.
    8. Gianluca Piero Maria Virgilio, 2019. "High-frequency trading: a literature review," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 33(2), pages 183-208, June.
    9. MacKinnon, James G & Haug, Alfred A & Michelis, Leo, 1999. "Numerical Distribution Functions of Likelihood Ratio Tests for Cointegration," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(5), pages 563-577, Sept.-Oct.
    10. Oussama Tilfani & Paulo Ferreira & My Youssef El Boukfaoui, 2021. "Dynamic cross-correlation and dynamic contagion of stock markets: a sliding windows approach with the DCCA correlation coefficient," Empirical Economics, Springer, vol. 60(3), pages 1127-1156, March.
    11. Ekkehart Boehmer & Juan (Julie) Wu, 2013. "Short Selling and the Price Discovery Process," The Review of Financial Studies, Society for Financial Studies, vol. 26(2), pages 287-322.
    12. 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.
    13. Ma, Rui & Anderson, Hamish D. & Marshall, Ben R., 2018. "Market volatility, liquidity shocks, and stock returns: Worldwide evidence," Pacific-Basin Finance Journal, Elsevier, vol. 49(C), pages 164-199.
    14. Muhammad Sheraz & Imran Nasir, 2021. "Information-Theoretic Measures and Modeling Stock Market Volatility: A Comparative Approach," Risks, MDPI, vol. 9(5), pages 1-20, May.
    15. Jonathan Brogaard & Björn Hagströmer & Lars Nordén & Ryan Riordan, 2015. "Trading Fast and Slow: Colocation and Liquidity," The Review of Financial Studies, Society for Financial Studies, vol. 28(12), pages 3407-3443.
    16. Lorenzo Dall’amico & Antoine Fosset & Jean-Philippe Bouchaud & Michael Benzaquen, 2019. "How does latent liquidity get revealed in the limit order book?," Post-Print hal-02323373, HAL.
    17. Lorenzo Dall’amico & Antoine Fosset & Jean-Philippe Bouchaud & Michael Benzaquen, 2019. "How does latent liquidity get revealed in the limit order book?," Post-Print hal-02283821, HAL.
    18. Ftiti, Zied & Ben Ameur, Hachmi & Louhichi, Waël, 2021. "Does non-fundamental news related to COVID-19 matter for stock returns? Evidence from Shanghai stock market," Economic Modelling, Elsevier, vol. 99(C).
    19. Jakub Kubiczek & Marcin Tuszkiewicz, 2022. "Intraday Patterns of Liquidity on the Warsaw Stock Exchange before and after the Outbreak of the COVID-19 Pandemic," IJFS, MDPI, vol. 10(1), pages 1-16, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Luu, Ellie & Xu, Fangming & Zheng, Liyi, 2023. "Short-selling activities in the time of COVID-19," The British Accounting Review, Elsevier, vol. 55(4).

    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. Aggarwal, Nidhi & Panchapagesan, Venkatesh & Thomas, Susan, 2023. "When is the order-to-trade ratio fee effective?," Journal of Financial Markets, Elsevier, vol. 62(C).
    2. Nidhi Aggarwal & Venkatesh Panchapagesan & Susan Thomas, 2019. "When do regulatory interventions work?," Working Papers id:13040, eSocialSciences.
    3. Hagströmer, Björn, 2021. "Bias in the effective bid-ask spread," Journal of Financial Economics, Elsevier, vol. 142(1), pages 314-337.
    4. Meng, Qingbin & Huang, Haozheng & Li, Xinyu & Wang, Song, 2023. "Short-selling and corporate default risk: Evidence from China," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 398-417.
    5. Brogaard, Jonathan & Hendershott, Terrence & Riordan, Ryan, 2017. "High frequency trading and the 2008 short-sale ban," Journal of Financial Economics, Elsevier, vol. 124(1), pages 22-42.
    6. Donald B. Keim & Massimo Massa & Bastian von Beschwitz, 2018. "First to \"Read\" the News: New Analytics and Algorithmic Trading," International Finance Discussion Papers 1233, Board of Governors of the Federal Reserve System (U.S.).
    7. 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).
    8. Daniel Ladley, 2019. "The Design and Regulation of High Frequency Traders," Discussion Papers in Economics 19/02, Division of Economics, School of Business, University of Leicester.
    9. Thanh Huong Nguyen, 2019. "Information and Noise in Stock Markets: Evidence on the Determinants and Effects Using New Empirical Measures," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 7-2019.
    10. Zura Kakushadze & Juan Andrés Serur, 2018. "151 Trading Strategies," Springer Books, Springer, number 978-3-030-02792-6, December.
    11. Cox, Justin & Woods, Donovan, 2023. "COVID-19 and market structure dynamics," Journal of Banking & Finance, Elsevier, vol. 147(C).
    12. Ladley, Daniel, 2020. "The high frequency trade off between speed and sophistication," Journal of Economic Dynamics and Control, Elsevier, vol. 116(C).
    13. 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.
    14. Bastian von Beschwitz & Donald B Keim & Massimo Massa, 2020. "First to “Read” the News: News Analytics and Algorithmic Trading," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 10(1), pages 122-178.
    15. Helder Rojas & Artem Logachov & Anatoly Yambartsev, 2023. "Order Book Dynamics with Liquidity Fluctuations: Asymptotic Analysis of Highly Competitive Regime," Mathematics, MDPI, vol. 11(20), pages 1-24, October.
    16. Hu, Ting & Chi, Yanzhe, 2019. "Can short selling activity predict the future returns of non-shortable peer firms?," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 165-185.
    17. Lin, Tse-Chun & Liu, Jinyu & Ni, Xiaoran, 2022. "Foreign bank entry deregulation and stock market stability: Evidence from staggered regulatory changes," Journal of Empirical Finance, Elsevier, vol. 69(C), pages 185-207.
    18. Geraci, Marco Valerio & Gnabo, Jean-Yves & Veredas, David, 2023. "Common short selling and excess comovement: Evidence from a sample of LSE stocks," Journal of Financial Markets, Elsevier, vol. 65(C).
    19. Fr'ed'eric Bucci & Iacopo Mastromatteo & Michael Benzaquen & Jean-Philippe Bouchaud, 2019. "Impact is not just volatility," Papers 1905.04569, arXiv.org.
    20. Seddon, Jonathan J.J.M. & Currie, Wendy L., 2017. "A model for unpacking big data analytics in high-frequency trading," Journal of Business Research, Elsevier, vol. 70(C), pages 300-307.

    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:gam:jjrfmx:v:15:y:2022:i:7:p:308-:d:862960. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.