Market Reaction to Cyber Strategy Disclosure: Word Embedding Derived Approach
Bibliographic record
Abstract
In this study, we use a semi-supervised natural language processing (NLP) methodology to assess cybersecurity strategy of firms based on their 10-K filings. Adapted from the Cybersecurity Framework developed by the National Institute of Standards and Technology (NIST), five distinct cybersecurity strategies, namely identification, protection, detection, response, and recovery, are measured annually. We find evidence that cybersecurity identification strategy is positively and significantly associated with firm market value. For those firms experienced a cyberattack in the past, disclosing cybersecurity protection strategy is not positively assessed by the market. This paper makes contribution to the literature on cybersecurity by identifying the cyber strategies disclosed in 10-K reports using textual analysis, which can be used in future cyber studies. We further show empirical evidence of how market reacts to different strategies, which have valuable implications for industry as to how to better manage cyber risk.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.013 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".