Limited usefulness of firm-provided cybersecurity information in institutional investors’ investment analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this study is to examine how financial analysts deal with cybersecurity information in their investment analysis process and whether they find cybersecurity disclosures in companies’ financial reports useful. Design/methodology/approach Investment managers/financial analysts and chief information security officers (CISOs) at seven institutional investors were interviewed. Findings Not all financial analysts consider cybersecurity risk in their investment analyses. Those who do look at company strategy, how the company integrates cybersecurity into its processes and whether it has certified its cybersecurity information. The financial analysts use this qualitative information to adjust the results of their quantitative analysis. They do not find boilerplate or cursory cybersecurity information in financial reports to be useful. In fact, they view it as unreliable and prefer drawing on other information sources to assess the company’s cybersecurity risk. Practical implications The results of this study highlight to securities regulators that reported cybersecurity information is of limited usefulness. Regulators are challenged to revisit their disclosure requirements. Companies wishing to improve the usefulness of their cybersecurity information should provide more company-specific information. Originality/value To the best of the authors’ knowledge, this study is the first to look at financial analysts’ perception of cybersecurity-related information. It complements findings from prior market studies by adding new insights into the way influential market participants deal with this information in their investment analysis process.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.000 | 0.001 |
| 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 it