Audit Quality and Investment Efficiency with Endogenous Analyst Information
Bibliographic record
Abstract
ABSTRACT We study audit quality and investment efficiency when an analyst’s information can curb overvaluation and auditors are subject to legal liability following audit failure. With the auditor’s damage payment based on price inflation after audit failure, the analyst’s information brings prices closer to fundamentals and provides a hedge to the auditor against legal liability risk. This weakens incentives for audit quality, and the analyst responds with more information production due to the penalty for mispricing. Consequently, in equilibrium, stricter legal liability leads to higher audit quality that reduces overinvestment but also less information production that increases underinvestment. Thus, stricter liability has a nonmonotonic effect on firm value; it increases the value of firms with a high valued growth option but reduces the value of firms with a low valued growth option. The results have implications for the optimal level of legal liability that maximizes the expected value of the firm. JEL Classifications: M41; M42; G14; G23; G32.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".