Understanding Bank Runs: Causes, Effects, and Historical Examples

The Essence of a Bank Run
A bank run is fundamentally a mass panic that ensues when depositors of a bank believe that the bank might be insolvent. In other words, when customers think that a bank might not have enough funds to return their deposits, they hurry to withdraw their money. The irony, however, is that this very panic, if widespread, can lead to the bank's insolvency.

Triggers and Dynamics
Usually, the trigger for a bank run is fear, not the actual financial insolvency of the institution. A bank run starts when a large portion of depositors, driven by this fear, rush to withdraw their money simultaneously. This large-scale withdrawal depletes the bank's available cash. The modern banking system operates on the fractional reserve principle, meaning banks only keep a small fraction of total deposits in the form of liquid cash. The rest is invested in longer-term assets or lent out to borrowers.

For instance, consider the recent case of the Silicon Valley Bank. After an announcement regarding financial losses, depositors raced to pull out their money. This mass withdrawal resulted in the bank reporting a negative balance, leading to its eventual closure by regulators.

Banks' Response to Mass Withdrawals
Because banks typically retain only a limited amount of cash on hand, they need to devise strategies to cater to the sudden withdrawal demands during a bank run. One common tactic is liquidating assets. However, in the face of a bank run, this sale is often rushed, leading to assets being sold at lower prices. Such sales can further deteriorate the bank's financial position and erode confidence, encouraging even more withdrawals.

To protect themselves from the implications of bank runs, many banks maintain specific reserves at the central bank. For example, the Federal Reserve's Interest on Reserve Balances (IORB) program encourages banks to hold these deposits.

Historical Context of Bank Runs
Bank runs are not a new phenomenon and have marked some of the most challenging periods in economic history. One of the most notable series of bank runs occurred during the Great Depression in the 1930s. After the 1929 stock market crash, fears about the stability of the banking system led to mass withdrawals, setting off a cascade of bank failures and deepening the economic downturn.

More recently, in addition to Silicon Valley Bank's collapse in 2023, other banks such as Washington Mutual Bank and Wachovia Bank have also experienced bank runs.

Role of Government and Insurance
In an attempt to mitigate the adverse effects of bank runs and restore depositor confidence, the Federal Deposit Insurance Corporation (FDIC) was formed in 1933. With banks insured by the FDIC, an individual depositor's money is protected up to a limit of $250,000. This insurance means that even if a bank does fail, depositors are guaranteed their money up to this limit. This assurance often prevents bank runs since depositors are confident that their money is safe, rendering the rush to withdraw unnecessary.

Bank runs, driven by fear and lack of confidence, can have catastrophic impacts on individual banks and the broader financial system. While mechanisms like the FDIC in the U.S. offer a layer of protection to depositors and can stave off bank runs to a certain extent, the importance of trust and confidence in the financial system cannot be understated. As history has shown, when that trust is eroded, the consequences can be severe.
 

Tickeron's Offerings

The fundamental premise of technical analysis lies in identifying recurring price patterns and trends, which can then be used to forecast the course of upcoming market trends. Our journey commenced with the development of AI-based Engines, such as the Pattern Search Engine, Real-Time Patterns, and the Trend Prediction Engine, which empower us to conduct a comprehensive analysis of market trends. We have delved into nearly all established methodologies, including price patterns, trend indicators, oscillators, and many more, by leveraging neural networks and deep historical backtests. As a consequence, we've been able to accumulate a suite of trading algorithms that collaboratively allow our AI Robots to effectively pinpoint pivotal moments of shifts in market trends.