Impact of data breaches on accounting narratives : a study on S&P 500 MED firms
Bibliographic record
Abstract
In light of rising cybersecurity concerns, companies are increasingly focusing on risk management and data security. This study explores the disclosure of cybersecurity incidents as a proxy for changes in financial reporting tone. Specifically, I expect a change in tone around the publication of the annual and quarterly reports of U.S. S&P 500 healthcare and medical providers industry (MED) companies during the 2010 to 2019 time period. Building upon existing literature on narrative accounting tone and data security breaches (DSBs) and further complemented by literature on board characteristics, earnings quality, and financial performance, this research aims to fill a gap by analyzing how narrative tone shifts following DSBs. Using a quantitative approach, this study conducts a quarterly analysis of the SEC’s 10-Q and 10-K reports to assess how narrative tone shifts in the quarters preceding a DSB announcement and in the quarter a DSB is disclosed and announced. The findings underscore a noticeable impact of DSB disclosures on narrative tone. This study contributes to the existing literature by highlighting the importance of the analysis of narrative tone as a tool in risk evaluation and has implications for auditors, analysts and stakeholders of interest.
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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".