Auditor Tenure and the Ability to Meet or Beat Earnings Forecasts*
Bibliographic record
Abstract
We examine the relation between auditor tenure and a firm's ability to use discretionary accruals to meet or beat analysts' earnings forecasts. We find evidence over the period 1988-2006 that firms with both short and long tenure are more likely to report levels of discretionary accruals that allow them to meet or beat earnings forecasts. These results suggest that while regulatory mandates for periodic auditor turnover have negative effects, sustained long-term auditor-client relationships may also be detrimental to audit quality. Further, although we observe a positive relation between tenure and the use of discretionary accruals to meet or beat earnings in the pre-Sarbanes-Oxley (SOX) period, we do not observe such a relation in the post-SOX period. This latter finding is consistent with regulatory reforms and heightened scrutiny of financial reporting in the post-SOX period resulting in less aggressive efforts at managing earnings by client firms and/or increased diligence on the part of auditors. These findings may not generalize to firms that are not covered by analysts, because these firms do not face the same public pressure to manage earnings in order to meet or beat expectations. © CAAA.
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 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 | 0.003 | 0.023 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".