US Regional Banks: The Lull Before the Storm?


Last week, we witnessed huge volatility in the US Regional Banks sector, as beautifully illustrated in the graphic below (produced by James Eagle), price action has calmed down somewhat this week. For now, attention is focused on digesting the slight fall in US Inflation and whether a deal will be done between Democrats and Republicans to avoid a Debt Ceiling Crisis.

US One-Month T-Bond Yields are now above the peak levels seen in the 2008 banking crisis with the time pressure of June expiry and a political agreement needed in before the deadline when unthinkably the US Treasury would effectively run out of cash.

Whatever happens, it is reasonable to expect an increase in market volatility in the next few weeks.


Three weeks ago, we posted a blog about how banks and investors are increasingly singling out US office commercial real estate as an area of growing concern, with property values falling and more borrowers defaulting on their loans amid rising interest rates and a slowing economy.

Since March, when the abrupt and unexpected failure of two regional U.S. banks, Silicon Valley Bank and Signature Bank, rocked U.S. markets, investors have begun voicing concerns about the balance sheet vulnerabilities of regional banks and, specifically, their exposure to commercial real estate.

Many of the smaller US banks with large commercial property loan books have continued to experience significant share price volatility.


We have plotted the daily Bitvore Sentiment Scores, and share price movements, for a sample of small US Banks whose loans books are estimated to be over 50% exposed to US commercial property. 


SVB’s and First Republic Bank’s rapid demise clearly highlighted the critical importance of real-time actionable intelligence. The video below gives a summary snapshot of our relationship mapping for SVB and First Republic Bank, offering a fascinating visualisation of their respective relationship ecosystems. It should be noted that we evaluate relationships based on the NLP and semantic understanding across our vast dataset by the number of times, or the count, of how many times we find that relationship appears in our datasets.

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