SIVB’s stock price dropped 34% from market close on Wednesday, March 8, to market open on Thursday, March 9, with a total overall decrease of 60% before trading was halted on Friday morning, March 10.
A FactSet partner, Context Analytics generates social sentiment metrics on publicly traded securities. We use patented Natural Language Processing capabilities to analyze Twitter messages about a security and aggregate sentiment, which we believe is predictive of price movements. We also compare the trending sentiment to a security’s historical baseline to generate a standardized view of company sentiment.
We measure social sentiment with our “S-Factor metrics” that are predictive at statistically significant levels. The following graph demonstrates how social sentiment (S-Score, blue) and Twitter volume (S-Volume, gray) relate to SIVB’s price (black).
Throughout the market day on March 8, SIVB had two tweets from Context Analytics’ certified Twitter accounts, which are accounts deemed accurate and credible from historical tweet behavior. The S-Score was neutral between -1 and +1 during market hours on March 8, however plummeted below -2, which is considered extremely negative sentiment, after market close. Below is the first negative tweet after market close that caused the bank’s S-Score to fall.
That tweet was scored extremely negative and, compared to SIVB’s historical baseline S-Score, was statistically significant. At that time, SIVB’s stock was priced at $255.
Throughout after-hours trading March 8, the volume of negative SIVB tweets accelerated, and the stock price kept falling. By the end of the night, SIVB had experienced its highest-ever single-day volume on Twitter. Previously, the highest single-day number of tweets about SIVB was 42 in April 2018.
By the March 9 market open, SIVB was mentioned in over 80 tweets and was priced at $176.55. As mainstream media discussed the bank’s situation, Twitter volume accelerated further—and the stock price kept falling—before the March 10 peak of over 2,500 tweets after trading halted for the security.
To Learn More
Context Analytics' social sentiment data can help you quickly identify changes in social sentiment and Twitter volume to help inform decisions and capture crucial market movements. Visit our S-Factor Twitter Social Sentiment page on the Open:FactSet Marketplace (OFM) site to learn more.
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