Is Prevention Better Than Cure? Effects of Cyber Risk Disclosures on Shareholder Response to Breaches
Bibliographic record
Abstract
As digitalization increases firms’ exposure to cyber risks, corporate disclosures about how these risks are managed are becoming more common and influential. This study examines 1,912 breach incidents affecting public companies to understand how shareholder reactions differ depending on the type of cyber risk strategies disclosed. We find that, although breaches generally lead to stock price declines, firms that previously disclosed preventive strategies, such as efforts to avoid breaches, experience significantly smaller losses in market value. Conversely, disclosing mitigative strategies, focused on damage control after a breach, amplifies the negative impact. These effects arise from shareholders’ loss aversion: They respond more favorably to firms perceived as trying to prevent harm rather than simply reacting to it. These findings suggest that managers should focus cyber risk disclosures on credible, prevention-oriented strategies to build investor confidence and minimize financial fallout after a breach. Additionally, our findings advise against using cyber risk disclosures as tools for impression management. Managers should ensure these disclosures accurately reflect the firm’s cyber risk management practices, as failing to do so can undermine the economic benefits of emphasizing preventive strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.034 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".