An Analysis of the Impact of WannaCry Cyberattack on Cybersecurity Stock Returns
Bibliographic record
Abstract
This research examines the impact of the WannaCry cyberattack, considered to be the defining event for cyber threats, on stock returns of companies operating in the cybersecurity industry on the first trading day after the event. The aim is to ascertain whether the reaction of stock prices on this day could be classified as abnormal and which direction they follow. The expectation is for a demand shift for the products and services offered by these firms and consequently stock markets should reflect this new information in equity prices. Literature has mostly focused on natural catastrophes and lately on man-made catastrophes like terror attacks. This study explores the emerging area of cyberattacks which could develop into catastrophic situations. Event-study methodology is employed for the analysis of this specific event and results clearly indicate that WannaCry had a positive effect on the equity returns of cybersecurity companies and cybersecurity investment vehicles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".