Asymmetric Inefficiency in the Market Response to Non‐earnings 8‐K Information*
Bibliographic record
Abstract
ABSTRACT This paper examines the pricing efficiency of 8‐K filings for events other than earnings announcements. Since these filings provide timely information that is material to investors and explain variations in quarterly returns to a degree similar to other disclosures, understanding how the stock price absorbs their information is important for investors, regulators, and academics. By testing the statistical correlation between the immediate stock returns in response to these filings and subsequent stock returns before, during, and after the forthcoming earnings announcement, we find evidence of investor overreaction to good news but underreaction to bad news in the immediate window. Essentially, the price increases too much for good news but fails to decrease enough for bad news, resulting in overpricing for both. Most of the correction for this overpricing occurs in the period leading up to the forthcoming earnings announcement, while the rest happens during the announcement. Drawing on Miller (1977), we further illustrate that, in the presence of short‐sale constraints, increase in investor disagreement spurred by interpretation difficulty is the most likely mechanism for the observed overpricing. We fail to find sufficient evidence in support of alternative mechanisms, including managerial disclosure strategies, analyst optimistic bias, and retail investor participation. This asymmetric mispricing for non‐earnings 8‐Ks contrasts with the symmetric mispricing commonly found for other types of disclosures, where investors either systematically underreact or overreact to public information. Our results could broadly speak to the pricing of other public information that is inherently difficult to interpret.
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".