The cascade effect: Are the U.S. economy and global stock markets vulnerable to the collapse of First Republic Bank?
Bibliographic record
Abstract
Following the collapse of Silicon Valley Bank and Signature Bank, First Republic Bank collapsed and is considered the second-largest bank failure in U.S. history. These bank runs can have a cascading or contagion effect on other large banks, and U.S. banking crises can flare up again. We examine the effect of the First Republic bank run on top U.S. banks, U.S. stock indices, and global stock indices using standard event study methodology. We report abnormal returns and cumulative abnormal returns for the event day (t = May 01, 2023) and the 10-day event window (t-5 to t+5), respectively, using data from the 120-day estimation window. The results indicate that on the event day, only JP Morgan Bank's returns were negative, while other banks acted as safe havens for investors. No significant change in returns on the event day is observed for U.S. sector indices (except for the healthcare sector) and global stock exchanges, except for the European and Chinese markets. During the event window, the occurrence of the event significantly affects bank returns after the event date, but no significant effect is found before the event date. Similarly, the healthcare and transportation sectors are more affected than other sectors, while the U.S. and Canadian stock markets seem to be more susceptible to the bank run. Overall, the results suggest that the U.S. government should take decisive initiatives to stop the ripple effect and protect the entire financial system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".