Corporate Value at Risk: Why We Should Care About Climate (IFRS S2) and Cybersecurity Risks?
Bibliographic record
Abstract
This paper analyses the effects of cybersecurity risk disclosure (CyRD) and climate risk disclosure (CRD) on corporate value in rising Asian countries, in light of escalating regulatory demands and heightened stakeholder expectations for openness and non-financial disclosures.The research aims to address a knowledge gap in the literature by presenting empirical data from a previously neglected Asian environment.The study is grounded in signaling and legitimacy theories and utilises data from 2020 to 2024, encompassing a sample of multi-sector enterprises across 12 Asian nations.Multiple regression models were employed, accounting for control variables (size, leverage, profitability), to assess the net impact of disclosures.The findings indicate that cybersecurity risk disclosure is positively related to corporate value, as it enhances governance, operational resilience, and fosters investor confidence, ultimately leading to improved market valuations.Climate risk disclosure is also found to have a stronger impact, communicating a company's commitment to long-term sustainability, helping attract institutional investors, and reducing regulatory and reputational risks.The study highlights scientific value by providing new evidence from an emerging context, confirming that nonfinancial disclosures are strategic tools for value creation and risk mitigation, while offering practical recommendations for managers, investors, and policymakers to strengthen disclosure frameworks and support sustainable growth.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".