An Empirical Analysis of Regulatory Risk in the Banking Industry
Bibliographic record
Abstract
The following study investigates whether Enforcement Actions placed on banks for misbehaving, have a significant effect on their Cumulative Abnormal Returns or not. Considering a sample of 103 US Canadian banks for the period 2010-2018, I have observed that such actions levied by the Federal Deposit Insurance Corporation, the Office of the Comptroller of the Currency and the National Credit Union Administration will lead to a significant reduction in the share price of banks. Utilising a regression, I have provided evidence that the severity and type of enforcement actions will not play a major role in the abnormal returns of banks however, several banks characteristics will be the main determinants whether those banks will be affected from the enforcement actions or not. The results of this study present significant insights regarding the effects of EAs, which mostly agree with previous literature that suggests that the actions levied on banks will reduce the returns of banks leading to the formation of some issues. Finally, it can be observed that, in this study severe actions such as Cease, and Desists Orders, Prompt Corrective Actions and Formal Agreements/ Consent Orders dominate the observations number since the data range is placed just after the crisis where actions taken to prevent misconduct were much more severe and larger in number.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".