Exploring the Materiality of Data Breach Disclosures on the Australian Stock Exchange
Bibliographic record
Abstract
This study examines Australian Stock Exchange (ASX) data breach announcements to provide insights into the extent and nature of data breach disclosures, as well as the costs, particularly to stakeholder relationships. Using a dataset of all data breach‐related announcements on the ASX, we identify a lack of data breach disclosure and, where disclosures are made, a notable absence of detail. To examine how the concept of materiality is applied, given its role as a threshold for disclosure to stock markets globally, we provide an in‐depth examination of the case of Landmark White (LMW), the only company to disclose a material impact from its data breaches to the ASX. We identify an announcement paradox, where the data breach at LMW became material over time as stakeholders reacted to the announcements, pointing to a contagion effect. We recommend the creation of likely‐market‐effect models, which allow companies to calculate the likely share price impact of a data breach and use this in their decision to disclose. This approach represents a simple first step in reconceptualizing continuous disclosure regimes for the digital age, aimed at enhancing the transparency and reporting of cyber incidents to stock markets globally.
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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 | 0.009 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".