Understanding all the fascinating, and occasionally misconstrued, data around this phase of the GameStop saga.
By Ihor Dusaniwsky
When talking about the rollercoaster of GameStop’s stock over the past week and a half, you’ve often heard the word “float” mentioned in regard to the measure of specific stock shares that are available for trade. Here we’ll break down how to understand S3’s calculations of float as it pertains to GME’s stock.
Short interest as a percentage of float, or “SI % of Float,” is a traditional metric that attempts to measure short interest relative to the liquidity of a company’s tradable shares. Unfortunately, the traditional metric can be miscalculated and misconstrued and subsequently falls short of the bar. It doesn’t include up-to-date short interest numbers, or the “synthetic” longs that are the result of short sales.
S3’s SI % of Float metric not only meets but raises the bar by including real-time short interest as the numerator in the equation and “synthetic” longs in the denominator.
Stocks with SI % of Float over 100% highlight the difference between these two calculations. In early January, GME’s SI % of Float was 141.86%, while S3 SI % of Float was 58.65%. Just as no one can get five quarts of milk from a gallon jug, no one can short more shares of stock than exist.
While the numerators in these calculations are identical (71.19mm were the shares shorted in both calculations), the denominator for the traditional calculation was 50.19mm (the float) and 121.38mm for the S3 SI % Float (float + shares shorted). The traditional calculation misrepresented the actual tradable shares in GME. The 141.86% is a nonsensical number, while the 58.65% reveals that there are not many shares left to short in GME, and that future trading pressure will predominantly come from the long side of the market.
Using the correct metric is a useful tool in liquidity analysis and seeing how crowded a trade is on the short side. The S3 SI % of Float number checks both boxes.
S3’s analytics provide clients with the transparency and data they need to manage risk and make more informed investment decisions. Underpinning our analytics and research are multi-factor models that use private and publicly available data sets (such as exchange and contributed data) as inputs. Our models dynamically weight inputs based on security and market characteristics such as volatility and liquidity. We further refine our model output based on consensus surveys across key market participants.
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