Does High‐Quality Auditing Mitigate or Encourage Private Information Collection?
Bibliographic record
Abstract
Abstract The finance literature offers two competing possibilities on how investors respond to the quality of public financial statements in their pricing decisions. They could collect either (i) more private information to benefit from lower information collection cost, or (ii) less private information because of lower incremental benefits. In this paper, we use the audit setting to examine which possibility prevails. Using the idiosyncratic return volatility as a proxy for firm‐specific information, we show in a sample of 51,559 firm‐year observations for 8,261 U.S. firms spanning the period of 2000–2010 that firms audited by higher‐quality auditors exhibit lower average idiosyncratic return volatility but a higher concentration of it at the time of earnings announcements. Our findings are consistent with the argument that investors reduce private information collection in response to higher audit quality. Our findings are robust to alternative measures of audit quality and idiosyncratic return volatility.
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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 | 0.017 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".