Auditor‐client reciprocity: Evidence from forecast‐issuing brokerage houses and forecasted companies sharing the same auditor
Bibliographic record
Abstract
Abstract We examine whether auditors share private information about some clients in their portfolio to benefit other clients (i.e., brokerage houses). This is a salient issue in China, where there are concerns about auditors leaking information to related parties, and where we observe variation in connectedness between brokerage houses and companies through shared auditors. We document that brokerage houses that share an auditor with a company issue comparatively more accurate earnings forecasts for that company. Next, cross‐sectional variation in forecast accuracy is associated with several proxies for brokerage houses' and auditors' costs and incentives to share information (e.g., investor protection, media coverage, public listing status, and the client's economic importance). Finally, auditors are more likely to secure future audits from IPO deals sponsored by brokerage house clients with higher forecast accuracy. Collectively, our evidence is suggestive of auditors sharing private information with brokerage houses in anticipation of reciprocity in the form of lucrative future engagements.
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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 | 0.009 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".