An Incomplete Audit at the Earnings Announcement: Implications for Financial Reporting Quality and the Market's Response to Earnings
Bibliographic record
Abstract
ABSTRACT There has been a substantial increase, since 2004, in the number of firms that announce annual earnings before audit completion as opposed to after audit completion. In this study, we argue that earnings announced before audit completion are associated with lower financial reporting quality and investor perceptions that earnings are more likely to be overstated. Consistent with this expectation, we document that the market places more (less) weight on good (bad) earnings news for earnings announced after audit completion relative to earnings announced before audit completion. We continue to find this differential market response when we expand the returns window to include the 10‐K filing date, suggesting that the differential response is not driven by investors' temporary concerns about earnings revisions between the earnings announcement and the 10‐K filing date or by differential GAAP disclosures in the earnings announcement, as suggested in prior research. Finally, as a direct test of financial reporting quality, we show that earnings announced with a completed audit are less likely to be restated in the future, are less likely to meet or beat expectations, and are associated with fewer income‐increasing discretionary accruals than those announced with an incomplete audit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.053 | 0.175 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".