Do <scp>Firm‐Specific</scp> Stock Price Crashes Lead to a Stimulation or Distortion of Market Information Efficiency?*
Bibliographic record
Abstract
ABSTRACT Unlike prior research that focuses on determinants of firm‐specific stock price crashes (SPCs), we study the consequences of SPCs on market information efficiency. The tension underlying our research question stems from two competing explanations. As an unanticipated shock, an SPC could stimulate (distort) information efficiency by triggering investor rational attention (opinion divergence). Our identification strategy involves a difference‐in‐differences analysis in which SPC firms in the treatment sample are propensity score matched with non‐SPC firms in the industry‐peer control sample, as well as placebo tests for falsification. Consistent with the stimulation effect, we find an increase of the earnings response coefficient and a decrease in post‐earnings announcement drift, from the pre‐ to post‐SPC period, for SPC firms, but not for non‐SPC firms. Further analyses reveal that SPC firms attract increased investor attention, as reflected in greater analyst coverage and more investor access to firms' online financial filings following such an event. Using mutual fund flow redemption pressure based on hypothetical sales as an exogenous shock to SPCs, we provide evidence corroborating our causal interpretation of the main findings. Collectively, the evidence suggests that SPCs can attract increased investor attention, bringing about positive externalities by stimulating market information efficiency.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".