Stock price crash risk and unexpected earnings thresholds
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine stock price crash risk (SPCR) as a function of meeting or missing three earnings thresholds – reporting a profit (earnings level), reporting an earnings increase (earnings change) and meeting analysts’ forecasts (earnings expectation). Design/methodology/approach The authors rely upon the research design of Herrmann et al. (2011) to identify the incremental impact of the earnings level and earnings change benchmarks on SPCR, after controlling for the effects of meeting or missing analysts’ expectations. Findings The authors find that meeting analysts’ expectations is negatively associated with SPCR, and this relationship strengthens with the magnitude of the unexpected earnings. However, the authors find little evidence of incremental threshold effects to suggest that earnings level and earnings change benchmarks are critical thresholds with respect to SPCR. Our results are robust after including a number of control variables. Originality/value This study contributes to the literature that investigates determinants of SPCR while simultaneously providing new evidence to conclusions that analysts’ earnings forecast is at the top of the earnings benchmark hierarchy.
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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 | 0.024 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".