SEC Filings, Regulatory Deadlines, and Capital Market Consequences
Bibliographic record
Abstract
SYNOPSIS Timely disclosure of financial statement information is a critical requirement for firms and well-functioning capital markets. Yet, every quarter or year, a non-trivial number of firms are late in filing their financial statements. This paper identifies and probes various capital market consequences for late filings of quarterly and annual financial statements. It examines the short- and long-window reaction to late filings, as well as how equity investors process statements accompanying late filing announcements, such as managers declaring intentions to file within/outside the SEC's allowed grace periods. This paper documents that delayed quarterly filings have distinctly different valuation implications than delayed annual filings over the short and long run, and that accounting problems play a unique role in signaling the seriousness of the delay. It also shows that investors do not accept management's delay-related assertions at face value, and that delayed filing announcements signal continued poor performance that is not fully reflected in stock prices at the time the announcements are made. Overall, this paper sheds new light on important capital market consequences of filing financial statements late.
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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 | 0.007 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".