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Risk modeling lessons learned from GameStop mania

3 techniques to better forecast the unexpected and mitigate risk

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  • Written by  Anthony Mancuso, Head of Risk Modeling and Decisioning at SAS
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  • Comments:   DISQUS_COMMENTS
Risk modeling lessons learned from GameStop mania

“I want to buy GameStop stock,” my 15-year-old son announced from the hallway. Hearing those words sparked in me a vague awareness that something important was happening in the market, something that had made a real impression on the popular consciousness. That something – the GameStop “short squeeze” as it’s come to be called – ultimately holds an important lesson for financial services firms.

Setting the stage: What’s all the hoopla about anyway?

To start, let’s align on the basics. GameStop is a US-based purveyor of video games and gaming accessories. As online gaming has accelerated in recent years, sales at the retailer’s bricks-and-mortar stores have languished, and GameStop’s stock price has reflected its tepid revenue performance. This prompted institutional investors, largely hedge funds, to place significant bets against GameStop stock (ticker GME) by taking substantial short positions (to the tune of approximately $11B USD).

Enter Reddit’s r/wallstreetbets (WSB), a raucous online forum where non-professional investors discuss stock and option trading. Recognizing how heavily shorted GME had become, WSB’s community of traders purchased GME in droves in the early weeks of 2021. Surging demand generated a feedback loop, where hedge funds and other institutional investors that held short positions were forced to purchase additional shares to cover their shorts, spurring further demand and driving prices ever higher. In the end, the combination of factors drove GME’s price from $17.25/share on January 4 (the first trading day of 2021) to an all-time high of $483/share on January 27.

Like many Wall Street frenzies, GME’s price per share is already reverting to a market-determined value (though, at the time of this writing, it is still markedly up from pre-mania levels). Still, the impacts are significant. One hedge fund, Melvin Capital, lost more than half of its assets in January, requiring a $2.75 billion infusion by two larger firms, Citadel LLC and Point72 Asset Management. Further, many late-to-the-party retail investors – most of them amateurs – lost much of their personal savings. The hysteria has also prompted a congressional hearing and raised questions about the proper role of government and regulation in financial markets.

Mitigating risk: Applying lessons learned

Setting aside those questions, perhaps the key takeaway lies in if banks can model and effectively respond to similar events in the future. Fundamentally, a statistical model is a simple object. Historical data is fed into an algorithm, which creates a set of parameters. These parameters define the relationship between input values and one or more outputs. If projected values for the inputs are fed into the model, the result will be a prediction of the output.

What the GME short squeeze brings to the fore is that not every event can be predicted. That is, the future may bring about completely unforeseen events. In statistical terms, these unanticipated events are truly exogenous, and models (based on historical observations) will be hard-pressed to forecast them with any degree of accuracy.

However, that lack of definability does not mean that one cannot prepare. There are three concrete, achievable steps that can help protect financial services organizations against volatile future events. We will examine each through the lens of “GameStop mania.”

  1. Early detection. It’s as true in the markets as it is in life: the sooner, the better. The earlier an unforeseen event can be detected, the faster a firm can respond. Detection mechanisms must be near real-time, easily configurable to address emerging threats, and able to incorporate a variety of information (i.e., different data types from various sources). To that end, machine learning and other advanced analytic techniques can be brought to bear. For example, monitoring stock prices is obvious, but a robust detection process might also have incorporated social media sentiment analysis, gauged the popularity of certain search terms, or monitored online activity in investor forums.
  1. Rapid response. Information is necessary but, alone, is not sufficient to develop a response system. First, mover advantage is real – and it could mean the difference between limited losses and an existential threat. Once a threat is detected, it must be properly analyzed to enable a clear course of action. Rapid response requires systems capable of dynamic reporting, lightning fast analytics, and rapid updating. Getting information into the hands of decision makers could have reduced the time to remediation (e.g., closing positions or entering into offsetting hedges) and dampened the financial impact.
  1. Scenario analysis. To the extent there is a way to predict exogenous events, scenario analysis will be involved. Scenarios can consider events outside the realm of observed data and/or incorporate discrete events. For those holding short positions, scenarios could have included drastic swings in stock price, absent any assumptions about why those may change. Alternatively, a growing number of firms are developing reversescenario analysis, where negative outcomes are mapped back to combinations of inputs that result in those outcomes. From there, the determined sets of inputs can be evaluated for likelihood and mitigated.

By adding these advanced analytics capabilities to their arsenals, financial firms will find themselves better prepared to take swift action in the face of future aberrant events.

Takeaway: Modeling for future events

Former US Defense Secretary, Donald Rumsfeld, once explained a situation by recourse to “known unknowns” and “unknown unknowns.” There is an element of truth in his infamous contention. “Unknown unknowns,” by their very nature of being so far removed from past experience, are only rarely anticipated much in advance. However, careful monitoring, robust analytical and decision-making capabilities and a thorough understanding of portfolio vulnerabilities can provide a trusted defense and agile response.


Anthony Mancuso is Director of Risk Modeling and Decisioning Lifecycle in the Risk Research and Quantitative Solutions Division at SAS. He has extensive experience in product development, business development, solution architecture, and consulting across all areas of financial risk. Anthony holds a Masters in Statistics and PhD in Economics (Econometrics), both from North Carolina State University.

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