The Determinants and Consequences of Information Acquisition via <scp>EDGAR</scp>
Bibliographic record
Abstract
Abstract Using a novel data set that tracks all web traffic on the SEC 's EDGAR servers from 2008 to 2011, we examine the determinants and capital market consequences of investor information acquisition of SEC filings. The average user employs the database very few times per quarter and most users target specific filing types such as periodic accounting reports; a small subset of users employ EDGAR almost daily and access many filings. EDGAR activity is positively related with corporate events (particularly restatements, earnings announcements, and acquisition announcements), poor stock performance, and the strength of a firm's information environment. EDGAR activity is related to, but distinct from, other proxies of investor interest such as trading volume, business press articles, and Google searches. Finally, information acquisition via EDGAR , both to obtain earnings news and to provide context for it, has a positive influence on market efficiency with respect to earnings news. Overall, our results are important because they provide a unique, user‐based perspective on investor access of mandatory disclosures and its impact on price formation.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".