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. Regulators have long expressed concern over the use of earnings management to attain earnings targets. These concerns are compounded by lingering questions over whether long-term auditor-client relationships impair an auditor's ability to independently stem such practices. The profession counter-argues that mandatory auditor rotation reduces auditors' familiarity with the client and adversely affects audit quality. Consistent with both arguments, we find that firms with both short (two to three years) and long (13-15 years or more) tenure are more likely to report levels of discretionary accruals that allow them to meet or beat earnings forecasts. The results suggest that while regulatory mandates for periodic auditor turnover have negative effects, sustained long term auditor-client relationships may be also detrimental to audit quality. The generalizability of our results may not extend to firms that are not covered by analysts, as these firms do not face the same public pressure to manage earnings in order to meet or beat expectations.
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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 | 0.003 | 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".