The Spillover Effect of SEC Comment Letters on Qualitative Corporate Disclosure: Evidence from the Risk Factor Disclosure
Bibliographic record
Abstract
ABSTRACT In this study we use the recently mandated risk factor disclosure to examine the spillover effect of the Securities and Exchange Commission (SEC) review of qualitative corporate disclosure. We find that firms not receiving any comment letter (“No‐letter Firms”) modify their subsequent year's disclosures to a larger extent if the SEC has commented on the risk factor disclosure of (i) the industry leader, (ii) a close rival, or (iii) numerous industry peers. We refer to this effect as “spillover.” Further, we find that after SEC comments on the industry leader's disclosure, No‐letter Firms also provide more firm‐specific disclosures in the subsequent year. The increased disclosure specificity reduces these firms’ likelihood of receiving SEC risk disclosure comments on their new filings. Our evidence suggests an indirect effect of the SEC review of qualitative disclosure.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".