Stock investors' reaction to layoff announcements: A meta‐analysis
Bibliographic record
Abstract
Abstract Does a firm's layoff announcement elicit a negative or a positive reaction from its stock investors? The extant empirical evidence on this question is mixed. The authors' meta‐analysis of 34,594 layoff announcements taken from 126 samples featured in 78 studies reports that the average investor reaction is significantly negative (effect size of −0.549). Next, the authors use signaling theory—specifically, characteristics of the signal, the signaler, and the signaling environment—to examine variation in investor reaction. They find that investors do not react if a layoff announcement signals proactive management (e.g., cost cutting) but penalize the firm if the layoff indicates reactive management (e.g., decline in demand). The penalty is also positively associated with layoff size but unrelated to firm size. Further, investors have become less punitive over time, or if its stock is traded on an exchange in civil law (vs. common law) country. The empirical generalizations guide managers on the consequences of their layoff announcements.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.015 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".