Avoiding the next big, short squeeze
May 2021 | Report
The market can, at times, underestimate the risks associated with short selling. We all recently witnessed the January 2021 GameStop (GME) and AMC Entertainment (AMC) frenzy, which highlights this very fact.
From a risk perspective, there is a substantial difference between being long and short. because when an investor is short, the loss is unlimited as a stock can, by definition, continue to run higher indefinitely.
As a result, investors who short generally do a lot of due diligence and are confident in their directional bet. Therefore, the short interest level—referring to the number of shares sold short but not yet repurchased or covered—provides a good indication of the negative sentiment around a stock, with a group of high conviction investors whose due diligence fuels their belief in their directional bet on a stock.
In practice: the great Volkswagen short squeeze
On October 28, 2008, a short squeeze on the Volkswagen (VWA GY) stock propelled this car maker to become the world's most valuable company for a day.
At the time, relative value trading had become a popular strategy amongst investors—strategy which identifies scenarios where two securities, which should have a relationship to each other, diverge from the mean. When these scenarios arise, opportunities appear for investors to short the security believed to be overpriced and buy the security believed to be under-priced.
This strategy played itself out through the VWA GY and Porsche (PAH3 GY) stocks. The rally was initially trigged by an unexpected announcement from Porsche. On October 26, 2008, the company issued a press release disclosing its holdings in shares and hedging positions in Volkswagen which represented a total of 74.1% of the company’s market cap. This meant that the free float had fallen to under 6% (the other 20% was held by Lower Saxony)1.
With roughly 12% of VWAGY’s outstanding stock on loan, it became mathematically impossible for every short seller to close out their positions, leaving short sellers scrambling to buy up the available stock to return the borrowed stock to the lender and close their short position2. This frenzy showed up in the stocks’ volume too. In Example A, you can see that volume around the event was significantly high, with Average Daily Volume (ADV) reaching as high as 2.75 times ADV on October 28. This short squeeze drove the price up to an intraday high of €912 on October 28 before closing at €517—a 149% gain since the close on Friday, October 25.
As can be seen from the price chart below (Example B), the market eventually corrected itself but a lot of short sellers got burned in this historic squeeze.
Source: Liquidnet Investment Analytics
While this is one of the more drastic short squeeze examples, this story showcases the benefits of having a constant in-flight view of short interest levels in a stock. Those levels, in conjunction with other data such as Days to Cover, ADV, Free Float to name a few, can help investors better understand their risk in a short trade and identify short squeeze candidates ahead of the market.
But there is more
Looking at the available short interest as a percent of free float on a dashboard isn't always going to be enough.
One significant consideration when analysing short interest is understanding the source of the short interest data and the frequency with which that data is refreshed.
Short interest data that is distributed by global exchanges, the most widely available short interest data, is a lagging indicator because it’s only refreshed bi-weekly. Looking at short interest data that is two weeks old makes it difficult to get an in-flight, updated view of the short interest risk in a stock.
While still relevant, short squeezes can happen quickly making even days old short interest data stale. It also makes it challenging to spot potential short squeezes in advance when your view is lagging.
Many also question short interest above 100% of free float. It is theoretically possible for this to happen because of the lifecycle of a stock. A stock owned by one investor taken out on loan by another who subsequently sells the stock to a third investor can again be put out on loan. This makes understanding the data sources even more essential.
What makes IA short interest data different?
Liquidnet Investment Analytics (IA) provides short interest derived analytics through IA Trader. The way the data is presented differs from one product to another but its foundations remain consistent. The short interest data showcased in both products is built around five components that makes our offering differentiated from others.
The first component is that the data we source and provide is focused on lending from institutional investors to prime brokers. The standard process for securities lending is the following:
The short interest data that we provide sources transactions between institutional investors and prime brokers. As a result, our data should be a day ahead of short interest data at the prime broker to hedge fund level because the stock has to be lent to prime brokers before it can be lent to hedge funds.
Short interest data refreshed daily
As mentioned previously, short interest data provided by exchanges is collected and made available in third-party applications with a two-week delay. In IA Trader, this data is refreshed every day at 7:30 EST. This gives users an updated and current view of short interest, helping investors grasp the pulse of the short interest sentiment in a stock.
Current short interest levels + momentum
Providing only the short interest level, particularly if it’s up to two weeks old, doesn’t provide a holistic view of the sentiment in a stock as it relates to short interest. We believe it’s important to not only compare the current level to historic levels but to also identify the momentum of the change in the short interest level.
As such, IA Trader displays the daily short interest level and plots it within a two-year range, indicating where the current level is compared to the past two years.
In addition, we show the one week change in the short interest level compared to the past two years to better understand the momentum of the change in the level.
Having both the current daily levels, a comparison against historic levels, and the speed of change or momentum in the change in the short interest level gives a more in-flight view.
Days to Cover
Other analytics we generate is Days to Cover which looks at the short interest on loans, divided by the ADV. This helps a short seller understand how many days it will take to cover their short, based on the ADV of that stock.
Generally, a short seller looking to close out a position will want to mask their activity and trade in line with the normal market volumes. In such a scenario, and where for example a stock has 20 days to cover, this short seller would take 20 days to cover their short position. In a crowded short which carries more performance risk, it’s likely that other market participants will spot the short sellers’ action and intent, which could result in a short squeeze in that stock.
Knowing the Days to Cover figure can help identify upcoming risk on an existing trade and understand potential short risk pre-trade. There is of course block trading which is another way to exit a position in a dark pool and manage market impact. Visibility on the liquidity formation like lit versus dark activity is another key analytic in IA Trader.
One of the foundational objectives of IA is to provide users with actionable intelligence—to uncover hidden insights within data and alert them to key factors that can influence their investments.
In IA Trader, we raise alerts when there is something significant taking place within a data set prompting users to dig deeper into the data and make more informed investment decisions as a result. For short interest, IA only alerts users when either the level or the speed of change is outside of the normal range/abnormal. The goal is to limit how much users need to watch a range of short interest levels across the names in their watchlist, order pad, or portfolio. Instead, alerts are triggered only when something significant is taking place.
As such, investors who have access to actionable intelligence and receive alerts about abnormality in the data, are in a far better position to act ahead of market participants who are only looking at outdated short interest data that they have to manually check and analyse. With IA, this process is automated and relies on AI tools like machine learning and natural language processing.
Bringing this into IA Trader
IA Trader includes a component, Core Summary, which summarises multi-asset factors that impact equity prices, helping focus users’ attention on the key outliers. This component flags exceptional momentum, levels, inflection points, recent signals, and unusual divergence between asset classes, highlighting where potential risks or opportunities exist.
This is helpful with single stock risk analysis or when screening large multi-day orders pre-trade. Within Core Summary, we provide the functionality to create customised views. This allows users to structure the view with analytic sets they specifically want to view instead of the pre-set views built into IA Trader.
In Example C, we have created a customised view that focuses on short interest, integrating various data sets that can be helpful in observing and analysing short interest levels and short squeeze risk.
In this view you’re able to visualize the short interest level, Days to Cover, Loan Fee, Implied Volatility, ADV, Volume, and more. The combination of these data sets together can provide a more holistic view of the short interest risk in a list of stocks. Not only are you able to analyse quicker a range of data sets that can impact short interest, but we flag (via the stamps in Section A) and trigger alerts when one of the data sets is outside of a normal range.
Within the table in Section B, each column can be filtered providing a level of customisation that allows users to only see stocks that meet the criteria set on the filter.
In Example D, we showcase how filtering the Days to Cover column to “Is greater than or equal to 20” reduces the list to one name—Visa. Having this level of customisation gives users the ability to identify particular stocks that, if short, would take them at least 20 days to cover the short—exposing them to a potential short squeeze risk.
Load IA Trader and navigate to Core
1. From Core, select the drop-down next to “Select view”
2. Scroll down to “Manage Views” and click on “Create New”
3. From this window, you can scroll through the available fields, select the data sets that you’d like to add to your new view and then select “Add” for each one.
4. As you add a data set it will populate into the Preview panel below.
5. Provide a name for you view and make it a shared view if you wish to do so.
6. Once you have finished populating your new view, you can access it from the “Select view” dropdown.
With IA, our goal is to give investors the tools to better distil actionable intelligence from all the noise around them—such as market data, news, company information, research, and more—and to distribute information in a curated and personalised way.
As highlighted above, trading short is a significantly riskier strategy than going long but long only investors also need to keep abreast of potential short squeeze. Having the right information at your fingertips with a constantly evolving in-flight view can not only help improve performance, it also allows you to better manage potential risks you’re exposed to.
To learn more about the short interest data and analytics that we provide, please reach out.
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