Gamestop, AMC, BlackBerry, and other meme stocks have recently highlighted a new and potentially dangerous kind of market movement. Intraday changes of 50%, 100%, or more are not typically included in standard risk and scenario reporting. Risk managers need to start accounting for this level of volatility and market impact driven by social media and flash mob behavior. Robust, real-time views of risk and PnL are critical to navigating these events, especially for derivatives traders whose risk positions can change dramatically. 

Managing a new kind of risk 

Having discovered that the combination of retail investors with available funds, low liquidity assets, and heavily-shorted stocks can generate a potential squeeze scenario means that this could easily happen again. What we saw with the recent meme stocks is that the kinds of risk that asset managers have to deal with have changed drastically and become much less predictable. If market volatility on that kind of scale can come from Reddit, it can come from anywhere. 

Risk managers should start by looking at less liquid assets in their portfolio and analyzing them for the potential to be hit by this type of flash mob-driven squeeze play. That means they need to be able to identify which assets are potentially exposed and develop new scenario reports that illustrate the potential portfolio impact. They may also need to incorporate these stress scenarios into risk limits on the trading desks, to limit the downside risk. And then they will want to think about new analytics that can try to predict when the risk of this type of event has increased. 

Building risk alerts 

Risk alerts that can provide early warnings of a Gamestop-like event are a natural addition to the toolset. First off is adding or expanding categories to identify and track those assets that are at risk of these kinds of moves. Next, is an opportunity for quants and data scientists to work with the business to identify the essential quantitative and qualitative elements of an early warning system. For example, shorts as a percentage of float, liquidity holes, and social media chatter. Then risk managers can expand their awareness and scenario analyses to include this information and be better prepared for the next event. There may also be an opportunity to incentivize traders to minimize their exposure to meme-susceptible assets, by adding large movement stress scenarios into the risk limit calculations.  

These are all effective use cases for machine learning techniques, although, as this is a new risk, the data sets required for training the algorithms are still accumulating. The underlying R&D platform needs quality data sources, both structured (like price and volume history) and unstructured (such as social media feeds). The quants and data scientists need to be able to test multiple hypotheses quickly and at scale. Elastic cloud computing capabilities are especially valuable for these tests, as firms can quickly rent substantial compute capacity and pay for just what they need, instead of building and paying for hardware that is only occasionally used at full capacity. Or worse, not having the capacity to get the analysis results in time to effectively limit the risk. Then, once these new stress scenarios and warning systems have been appropriately tested, they need to be deployed for production use. And, because these are new kinds of risk, the teams need to be able to quickly iterate and evolve the analytics, while satisfying the internal controls and processes that ensure the stability and robustness of their production risk systems. 

Applying analysis across asset types

These new risks are not isolated to stocks. There was recently a similar type of squeeze play in silver futures. In general, because the current protagonists are generally retail traders, the risks may be limited to products that are accessible through retail trading platforms. For example, we might see this kind of event in assets traded on all-to-all exchanges, like equities and futures, but it would be surprising to see it happen in mostly over-the-counter markets, like interest rate derivatives or foreign exchange—for now. So this is not just a risk for equity traders, but in general for traders with positions in less liquid asset classes. 

Because the risks are inherently cross-asset, a cross-asset risk system with the ability to apply a consistent set of analytics makes it easier to keep ahead of potentially damaging market scenarios across your portfolio. 

Getting ready for the next new risk 

Beacon Platform can help investors get ready for this and other emerging types of risk and quickly react to unpredictable changes. Our development tools make it easy for our clients to design new market scenarios and quickly roll them out to risk managers. Robust workflows and controls satisfy the control requirements that modern enterprises have before changing the production environment. The platform is designed for robustness and scale, based on how we learned to build things at some of the biggest banks in the world. Our clients can trust that the machine will be running well during a crisis when they need it the most. And we include strong cross-asset support: whether it is equities, precious metals, fixed income and interest rate derivatives, energy markets, or foreign exchange, Beacon helps our clients manage risk robustly and in real time.