Limited usefulness of firm-provided cybersecurity information in institutional investors’ investment analysis
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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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 | 0.033 | 0.166 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".