Information sharing between mutual funds and auditors
Bibliographic record
Abstract
Abstract This paper examines whether there is information sharing between mutual funds and their auditors about the auditors’ other listed firm clients. Using data from the Chinese market, we find that mutual funds earn higher profits from trading in firms that share the same auditors. The effects are more pronounced when firms have a more opaque information environment and when the audit partners for the fund and the partners for the listed firm share school ties. The evidence is consistent with information flowing from auditors to mutual funds, providing mutual funds with an information advantage in firms that share the same auditors. Our findings are robust to the use of audit‐firm mergers and acquisitions (M&As) as exogenous shocks and several other robustness checks. We further find that auditors benefit by charging higher audit fees for mutual fund clients and by improving their audit quality for listed firm clients. Our study provides evidence of bi‐directional information sharing between two important market intermediaries.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".