Investor Sentiment, Misstatements, and Auditor Behavior<sup>*</sup>
Bibliographic record
Abstract
ABSTRACT High investor sentiment has been linked with opportunistic managerial behavior in the face of more optimistic investors and analysts. We extend this line of work by documenting that the likelihood of misstatements is higher when sentiment is high. Although this would suggest elevated audit risk, we posit that a contemporaneous reduction in auditors' litigation cost could drive down audit fees and going concern opinion (GCO) reporting conservatism in order to please clientele. Consistent with this notion, we document that auditors charge lower fees and report GCOs less conservatively when sentiment is high. However, this reduction in reporting conservatism is unwarranted; results reveal that auditors are less likely to issue GCOs to clients which subsequently file for bankruptcy during high sentiment periods. We conduct additional tests to examine whether auditors' litigation costs indeed vary with sentiment and document that auditors are less likely to be sued and the market reacts less negatively to misstatement announcements when sentiment is high. Collectively, our findings suggest that, although misstatement risk is increasing with sentiment, auditors' litigation risk actually declines.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".