Auditor Choice and the Informativeness of 10-K Reports
Bibliographic record
Abstract
This study provides new evidence on the influential role of external auditors in enhancing the informativeness of form 10-K annual reports to shareholders. Specifically, we find that the client's choice of a Big 4 auditor (PwC, EY, KPMG, and Deloitte) versus a non-Big 4 auditor contributes to cross-sectional variations in 10-K disclosure volume. We also document that the benefit of enhanced disclosures provided by Big 4 auditors is more pronounced for audit clients with poorer accrual quality and those with higher information asymmetry. Furthermore, we introduce the portion of 10-K length unexplained by operating complexity and observable client characteristics as a new proxy for audit firm effort. Specifically, we find that abnormally long disclosures are associated with higher audit fees and longer audit report lag, which implies that an incremental level of audit effort can be inferred from the discretionary component of 10-K disclosures. As audit effort is costly, a greater volume of 10-K disclosures can be expected to be associated with an improvement in the quality of financial reporting. Overall, our findings show that auditors play more than a simple attestation role in the financial reporting process, and that the quality of financial reporting in a company's 10-K annual report is a joint product of the effort and decisions of both a company's managers and its auditors.
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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 | 0.014 | 0.107 |
| 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 | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".