A Cross-Cultural Multi-Country Analysis of Unfavorable News Announcements in Public Companies
Bibliographic record
Abstract
Value-relevant bad news leads to declining stockholder wealth. Various factors moderate the decline. We explore the moderating effect of culture (country), economic development levels, and insider trading laws. To this effect, we compile rich bad news announcement data from the US, Japan, China, and India. Ours is the first such study to cover comprehensive data from multiple countries. Stock markets in the US, Japan, and India experience a significant stock decline following the public announcement of bad news. In contrast, companies traded in the Chinese stock market experienced a positive stock impact. Companies in countries with high long-term orientation (Japan and China) perform better than those with low long-term orientation (the US and India). Economic development levels also play a significant mediating role. Countries with stronger trading laws do not experience stock decline before the public announcement of disruptions. Our study enriches the current state of the art by performing a multi-country analysis of stock impact from bad news announcements. The results are of interest to investors and policymakers.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".