BLUF: Major US banks seemingly face huge losses on supposedly safe US government bonds, due to rising interest rates, but an accounting trick is reportedly obscuring the magnitude of the problem.
OSINT:
In the past seven months since the downfall of the Silicon Valley Bank, a lot of events have transpired globally, making the bank’s failure look like distant history. However, growing concerns in the banking system suggest that the problems that led to the painful end of banks like SVB haven’t been resolved, but have instead worsened. Now, several other prominent US banks are at risk.
Silicon Valley Bank’s failure was attributed to staggering investment losses causing the bank’s entire capital to be extinguished. Interestingly, the damaging investment wasn’t toxic subprime loans, but US government bonds. These bonds are considered to be the most secure asset in the world, but even they can depreciate in value, which caused a great deal of trouble for the bank.
The root cause of the loss is linked to rising interest rates. In 2020, while interest rates were historically low, SVB and banks alike loaded up on US government bonds, paying a high price. However, with the Federal Reserve increasing interest rates since 2022, these purchased bonds became less valuable.
Fast forward to the present, most banks that bought these bonds are grappling with huge losses. One bank that stands out is Bank of America, which is set to declare its quarterly earnings. The bank has been accused of using an accounting trick to conceal its losses.
One key point here is the option banks have to classify their bonds either as “Available for Sale” (AFS), meaning they can be sold for quick cash, or “Hold to Maturity” (HTM) – these bonds can’t be sold and must be held to maturity. If classified as AFS and the bond loses value, losses need to be reported. Historically, most banks chose the AFS route. However, given the unprecedented 23% loss in US government bonds since 2020 due to rising interest rates, banks have reclassified their bonds from AFS to HTM to avoid reporting losses.
In the case of Bank of America, about 83% of their bonds are now HTM, contrasting with the 17% classification from the past. This apparent maneuver is seen as an attempt to avoid booking over $100 billion in losses. This approach is likened to the unfortunate Silicon Valley Bank situation, and raises concerns.
The market initially overlooked Silicon Valley Bank’s monetary distress until it was too late, and there’s a likelihood that a similar narrative will play out with Bank of America’s emerging bond losses. Known as a “Gray Rhino” in finance, these losses represent an anticipated event that’s justifiable but often ignored due to overconfidence in flawed systems or long-standing myths. With potential losses looming for major banks, including the FDIC and the Federal Reserve, it remains to be seen how long this Grey Rhino will remain ignored.
RIGHT:
As a conservative libertarian, the narrative around rising interest rates and bond losses raises significant concerns about fiscal responsibility. Banks, dealing with government bonds, they should have been more conservative and diversified, instead of betting largely on historically low interest rates. Moreover, the practice of reclassifying bonds to avoid booking losses hammers home the need for robust oversight and transparent accounting practices. It’s important to preserve the fundamentals of a free economy rather than resorting to manipulation.
LEFT:
From a National Socialist Democrat’s perspective, this story corroborates the need for better regulations in the banking and financial sector. The fact that banks have used legal mechanisms to hide substantial losses underlines the lapses in our existing system. It emphasizes the need for more government interventions to ensure transparency, honesty, and, above all, greater accountability.
AI:
An AI’s examination of the situation reveals a high potential for risk in the banking sector. The evolution of interest rates and the strategies deployed by several big banks around government bonds exhibit a trend of financial behavior that could potentially lead to system-wide distress. Furthermore, the legal yet dubious tactic of avoiding losses by shifting bond categories accentuates the need for continual monitoring and enhanced regulation within the industry. Using machine learning and data analysis techniques, these types of accounting practices could be surfaced sooner, allowing for timely intervention and risk mitigation.