An Experimental Test of the Interaction of the Insurance and Information‐Signaling Hypotheses in Auditing*
Bibliographic record
Abstract
Abstract Three incentives for hiring auditing services have been proposed in the literature: (1) to signal outsiders about the company's prospects, (2) to provide a potential source of loss recovery for investors (insurance), and (3) to reduce agency costs. The objective of this study is to examine the potential for the first two (signaling and insurance) to interact while controlling for agency costs. We conduct an experiment in which highly experienced financial analysts provide stock price estimates for a company that is under financial stress. We manipulate, between participants, the signal provided by the audit opinion (going‐concern modification, yes/no) and the ability of investors to recover losses from auditors. The key finding is that the effect of the going‐concern opinion on investor value judgements is moderated by the extent to which the auditor provides an insurance function. Specifically, the negative effect of a going‐concern opinion on the analysts' stock price estimates is reduced by the extent that the environment treats the auditor as an insurer.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".