Is There a Relation Between Residual Audit Fees and Analysts’ Forecasts?
Bibliographic record
Abstract
We examine the relationship between residual audit fees and the ability to predict future earnings. Recent research suggests that residual audit fees contain information about accounting quality. However, residual audit fees could either represent high accounting quality or a risk premium for low accounting quality. We extend this literature by providing evidence that residual audit fees are indicative of a lower quality information environment which has a negative impact on investors’ ability to anticipate future earnings. Specifically, we first show that residual audit fees are negatively associated with the ability of current earnings to predict future earnings. Furthermore, residual audit fees are negatively associated with analyst forecast accuracy and positively associated with the dispersion in analyst forecasts. Overall, our results are consistent with the notion that residual audit fees are indicative of poor earnings quality, and that this lower quality manifests itself in a lower quality information environment for investors and analysts.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".