Audit Quality and the Market Valuation of Banks’ Allowance for Loan Losses*
Bibliographic record
Abstract
Abstract The recent banking crisis has led market participants to focus on the adequacy and quality of banks’ balance sheet items such as the allowance for loan losses. Beaver and Engel (1996) document that the capital market prices the nondiscretionary component of loan loss allowance negatively and the discretionary component less negatively. Using data from the pre‐crisis period and three measures of audit quality, auditor type (i.e., Big 5 versus non–Big 5), auditor industry specialization/expertise, and audit and nonaudit fees paid to auditors, we examine the effect of audit quality on the market valuation of the discretionary component of the allowance for loan losses. We find that, relative to the nondiscretionary component, the market valuation of the discretionary component of loan loss allowance is higher for banks audited by Big 5 auditors than for banks audited by non–Big 5 auditors. We also find that the relative market valuation of the discretionary component of loan loss allowance is increasing in auditor expertise. Regarding the impact of fees paid to auditors, we find that banks paying higher audit fees have higher relative market valuation of the discretionary component of the allowance for loan losses, but banks that pay higher nonaudit fees do not.
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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 | 0.004 | 0.027 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".