A Pilot Study to Assess the Effects of News Coverage Articles about Security Incidents on Stock Prices in Korea
Bibliographic record
Abstract
This study was conducted to assess the effects of security incidents on the stock prices of firms in Korea. A content analysis of news coverage articles about security incidents was performed. The research questions (RQs) of the current study were as follows: RQ1: this study evaluated whether the news coverage of a security incident can influence an investor’s decision to buy or sell a stock; and RQ2: the study also analyzed whether the type of industry, the amount of damage caused by the incident, and the specific security incident itself would affect how investors assessed a stock’s value. The results of the study indicate the following: (1) news coverage articles about security incidents have a significant effect on stock prices; and (2) the degree of such an effect varies depending on the tone, theme, and category of the news coverage. A more negative tone was associated with a decrease in stock prices. Less negative and neutral tones were associated with an increase in stock prices. In particular, a neutral tone was associated with an increase in stock prices, which was commonly seen in most of the firms experiencing security incidents. Furthermore, the number of news coverage articles about security incidents had no relationship to variations in stock prices. In firms experiencing security incidents, variations in stock prices varied depending on the types of industry, the types of damages, and the type of incident. In conclusion, the current study used an event study and a content analysis of news coverage articles about security incidents to assess their effects on the stock prices of firms. Further studies are warranted to establish the feasibility of this approach in a real-world setting.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".