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Modelling Financial Markets during Times of Extreme Volatility: Evidence from the GameStop Short Squeeze

Author

Listed:
  • Boris Andreev

    (Data Engineering Department, GfK Bulgaria, 47A Tsarigradsko Shosse Blvd, 2nd Floor, 1124 Sofia, Bulgaria)

  • Georgios Sermpinis

    (Department of Accounting and Finance, Adam Smith Business School, University of Glasgow, University Avenue, West Quadrangle, Gilbert Scott Building, Glasgow G12 8QQ, UK)

  • Charalampos Stasinakis

    (Department of Accounting and Finance, Adam Smith Business School, University of Glasgow, University Avenue, West Quadrangle, Gilbert Scott Building, Glasgow G12 8QQ, UK)

Abstract

Ever since the start of the coronavirus pandemic, lockdowns to curb the spread of the virus have resulted in an increased interest of retail investors in the stock market, due to more free time, capital, and commission-free trading brokerages. This interest culminated in the January 2021 short squeeze wave, caused in no small part due to the coordinated trading moves of the r/WallStreetBets subreddit, which has rapidly grown in user base since the event. In this paper, we attempt to discover if coordinated trading by retail investors can make them a market moving force and attempt to identify proactive signals of such movements in the post activity of the forum, to be used as a part of a trading strategy. Data about the most mentioned stocks is collected, aggregated, combined with price data for the respective stock and analysed. Additionally, we utilise predictive modelling to be able to better classify trading signals. It is discovered that despite the considerable capital that retail investors can direct by coordinating their trading moves, additional factors, such as very high short interest, need to be present to achieve the volatility seen in the short squeeze wave. Furthermore, we find that autoregressive models are better suited to identifying signals correctly, with best results achieved by a Random Forest classifier. However, it became apparent that even the best performing model in our experimentation cannot make accurate predictions in extreme volatility, evidenced by the negative returns shown by conducted back-tests.

Suggested Citation

  • Boris Andreev & Georgios Sermpinis & Charalampos Stasinakis, 2022. "Modelling Financial Markets during Times of Extreme Volatility: Evidence from the GameStop Short Squeeze," Forecasting, MDPI, vol. 4(3), pages 1-20, July.
    • Handle: RePEc:gam:jforec:v:4:y:2022:i:3:p:35-673:d:866295
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    References listed on IDEAS

    as
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    4. Umar, Zaghum & Gubareva, Mariya & Yousaf, Imran & Ali, Shoaib, 2021. "A tale of company fundamentals vs sentiment driven pricing: The case of GameStop," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    5. Thomas Renault, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03205113, HAL.
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    7. Nam, Kiseok & Pyun, Chong Soo & Avard, Stephen L., 2001. "Asymmetric reverting behavior of short-horizon stock returns: An evidence of stock market overreaction," Journal of Banking & Finance, Elsevier, vol. 25(4), pages 807-824, April.
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    Cited by:

    1. Suchanek, Max, 2024. "Social interactions in short squeeze scenarios," International Review of Economics & Finance, Elsevier, vol. 91(C), pages 898-919.
    2. David Alaminos & M. Belén Salas-Compás & Estefanía Alaminos, 2026. "High-Frequency Trading, Short Squeeze and ARMA-GARCH-Fractal Neural Networks," Computational Economics, Springer;Society for Computational Economics, vol. 68(2), pages 1097-1154, August.

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