Financial Modernization in US Banking Markets: A Local or Global Event?
Bibliographic record
Abstract
Abstract: We test the hypothesis that the passage of the Financial Services Modernization Act (FSMA) of 1999 has spillover effects cross‐nationally, using a sample of US, non‐US transactional (Australian, Canadian, and UK), and relationship (German, Japanese, Dutch, and Swiss) banks. Our results suggest that financial modernization in the US has limited cross‐national effects. We find strong evidence that US banks were affected favorably. Although we detect some evidence of significant reactions by banks in certain countries, a closer examination reveals that the reaction is most likely attributable to events in the respective countries during the event period. We do find, however, that non‐US transactional banks have been more likely to elect financial holding company status compared to relationship banks, suggesting they are positioning themselves to exploit the expanded opportunity set created by the FSMA. Nonetheless, the majority of elections have been made by US banks. In general, the results suggest that the respective banking markets are efficient in filtering events that are largely country‐specific with only limited implications for other international banks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".