Auditor Responses to Shareholder Activism
Bibliographic record
Abstract
ABSTRACT In this paper, we investigate how auditors respond to shareholder activism against their clients. Our study is important because activism may be viewed by auditors as a source of increased engagement risk, thereby impacting audit outcomes. The potential relationship between shareholder activism and audit outcomes leads us to predict that activism targets will pay higher audit fees and also will be more likely to receive adverse internal control opinions (ICOs) and first‐time going concern opinions (GCOs). Our results, which support all three predictions, suggest that the public scrutiny associated with activism campaigns heightens auditors' concerns about reputational damage and litigation risk. Consistent with this notion, we find that activism targets are more likely to experience accounting‐related lawsuits. We also find that the increased likelihood of adverse ICOs documented in our baseline tests reflects higher‐quality reporting rather than increased auditor conservatism. Overall, our findings suggest that activism campaigns spur auditor diligence while also increasing the possibility of negative outcomes that may not be fully anticipated by activist investors.
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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 | 0.003 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.009 |
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".