Reddit and the GameStop Short Squeeze

In January 2021 a mass coordination on the subreddit r/wallstreetbets (WSB) was able to trigger a short squeeze of GameStop shares. This event represents an unique example of collective action on social media with significant impact on financial markets. 

WSB is a community whose users are retail investors but also non-skilled traders with a gambling attitude, who discuss trading strategies and share their gains and losses using an irreverent jargon and edgy humor. In late 2020 these users started to discuss the case of GameStop (NYSE:GME), a video game retailer which was struggling in recent years due to competition from digital distribution services and economic effects of the COVID-19 pandemic. GME stock price reached an all-time low of $2.57 on April 3, 2020, leading many hedge funds to short sell the stock — meaning they would profit from its further decrease in price. On the contrary WSB users, likely driven by the opportunity to make profit and possibly anger towards institutional investors, coordinated with the intent to trigger a short squeeze: a rapid increase in the stock price due to the excess of demand and lack of availability. The resulting large-scale mass coordination (buying and holding GME shares) succeeded in driving up the price of GME, attracting even more users and forcing short sellers to cover their positions at large losses, thus further promoting the price rally. On January 28, 2021 GME shares reached an astounding high price of $483.00; more than 1 million of its shares were deemed failed-to-deliver, which sealed the success of the short squeeze.

Such a highly coordinated financial operation received a huge attention from the media, financial stakeholders and the academic community. Many scholars tried to understanding whether WSB activity, conversation sentiment and user interactions could be used to predict retail trading activity and GME returns, using linear regression models or machine-learning approaches. A few empirical works instead address the more fundamental question of how consensus could spontaneously emerge in this context. In this paper we followed this direction by analysing discussions on WSB and measuring user engagement towards GME through the occurrence and mean sentiment of GME-related conversations. We found that the frequency of GME in conversations peaks in correspondence to the major events in the GameStop saga,  while sentiment towards GME increases significantly far before January, providing an early sign of the collective action.

Mean sentiment of comments containing ‘GME’, with respect to the same quantity computed on all comments. GME closing price is also reported for illustrative purposes.

Taking inspiration from such empirical evidence we assumed that a high and widespread engagement with the GME collective operation increases the likelihood that users themselves become committed and will actively participate to the short squeeze --- since its success strongly depends on the number of participants. A possible way to include this mechanism in classical models of opinion dynamics is through a self-induced global field that drives users towards collective unity. Therefore we proposed to model opinion dynamics in this scenario considering that users form their opinions either by interacting with peers or by following a global field, which is self-induced by the current status of the community and whose strength is determined by the mean level of user engagement.

Schematic representation of the model: at each time step, a user takes on the opinion of either a randomly chosen neighbor or is influenced (according to a control parameter c) by the current level of consensus in the community, namely the magnetization m.

Analytical mean-field solutions of the model display a phase transition from a disordered state (where no opinion prevails) to full consensus as user engagement grows. Model simulations on statistically validated social networks of WSB users, extracted from their `reply-to' interaction patterns, feature a broader transition that implies a non negligible level of consensus even when engagement is low. However the transition becomes abrupt when, as data suggests, the community grows together with the level of consensus reached.

Sample stochastic realizations of the model dynamics for different values of the parameter c setting the strength of the global feedback.
Phase transition of the extensive order parameter of the model (total magnetization for a community that grows as an exponential of m) according to the mean-field solution and to numerical simulations on the January user network.

To fully validate the model we would need access to confidential information on actual purchases of GME shares by WSB users that actual laws prohibits to collect. Therefore the model stands as a minimal yet solvable framework that offers a possible way to qualitatively reproduce the explosive dynamics of the GameStop event. Nevertheless the assumption of a self-induced field, which activates when user engagement grows (as supported by the empirical analysis), leads to a spontaneous phase transition from disorder to order, which is seldom found in models of opinion dynamics.