Bibliographic record
Abstract
Abstract This study investigates the effects of audit committee financial expertise on the timeliness of banks' loan loss provisions. I employ two regulatory shocks that mandated audit committee expertise—the Federal Deposit Insurance Corporation Improvement Act in 1991 (FDICIA) and a modified listing standard for NYSE and NASDAQ firms in 1999—as quasi‐exogenous settings to investigate the effects of audit committee financial expertise on the timeliness of loan loss provisioning. Using a difference‐in‐differences research design, I find that the timeliness of loan loss provisions increases with audit committee financial expertise both for the FDICIA treatment group, which had larger banks than the control group, and for the NYSE and NASDAQ exchange treatment group, which had smaller banks than the control group. Further, I find that the results are stronger for banks that have lower regulatory scrutiny, are audited by Big 4 auditors, and do not have staggered boards. Finally, I find that audit committee financial expertise decreases discretionary loan loss provisions and financial restatements. Overall, these findings suggest the importance of audit committee financial expertise in loan‐related matters, which is particularly relevant in the context of the recent Current Expected Credit Losses implementation.
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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 | 0.007 | 0.079 |
| 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) | 0.008 | 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".