Regulatory consulting and banks’ financial reporting quality: evidence from the Dodd-Frank Act
Bibliographic record
Abstract
Abstract The Dodd-Frank Act expands bank managers’ reporting requirements to federal agencies, particularly relating to banks’ financial losses should common market and macroeconomic shocks occur. To comply with this regulation, bank managers have engaged in consulting arrangements (referred to as regulatory consulting). We examine the financial reporting quality implications associated with hiring external auditors for these services. We find banks with auditor-provided regulatory consulting, relative to banks without, have higher financial reporting quality as measured by loan loss provision validity. Consistent with knowledge spillover benefits accruing to financial audit teams, we find more pronounced effects in the fourth versus interim quarters and more frequent income-reducing Y9-C restatements. We also find auditor responsiveness to PCAOB inspections improves the effectiveness of regulatory consulting. Overall, our results suggest regulatory consulting improves the audits of estimates in judgmental financial statement accounts, despite regulatory concerns that these services may impair auditor independence.
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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 | 0.045 | 0.224 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".