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

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  • 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
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    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.
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    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).

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