Real regulatory capital management and bank payouts: Evidence from available‐for‐sale securities
Bibliographic record
Abstract
Abstract This study examines whether payout policies create incentives for banks to engage in cherry‐picking activities using available‐for‐sale (AfS) securities, otherwise known as gains trading. Such activities are more likely to arise in situations in which capital ratios would otherwise constrain banks’ ability to distribute resources to shareholders. Using a large sample comprising 766 unique US banks, we find a significant and positive association between total payout and realized gains on AfS securities for banks with low regulatory capital. This is consistent with the conjecture that capital‐constrained banks engage in gains trading to free up resources for dividend payments or share repurchases. When partitioning our sample, we find that capital‐constrained banks realize gains and losses on AfS securities only when it is costly to decrease the payout and when the monitoring level is weaker. Further analyses reveal that banks engaging in gains trading to distribute cash to shareholders exhibit significantly higher levels of future default risk and more negative extreme bank‐specific daily returns, patterns that are consistent with risk‐shifting. Finally, with the advent of Basel III, the practice seems to continue among banks that chose to retain prudential filters.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".