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

Boris Andreev, Georgios Sermpinis and Charalampos Stasinakis
Additional contact information
Boris Andreev: Data Engineering Department, GfK Bulgaria, 47A Tsarigradsko Shosse Blvd, 2nd Floor, 1124 Sofia, Bulgaria
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

Forecasting, 2022, vol. 4, issue 3, 1-20

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.

Keywords: retail trading; WallStreetBets; short squeeze; GameStop; machine learning (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2571-9394/4/3/35/pdf (application/pdf)
https://www.mdpi.com/2571-9394/4/3/35/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jforec:v:4:y:2022:i:3:p:35-673:d:866295

Access Statistics for this article

Forecasting is currently edited by Ms. Joss Chen

More articles in Forecasting from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().