Clarity in Crisis: How UK Firms Communicated Risks during COVID-19
Bibliographic record
Abstract
This study explores the influence of risk disclosure levels and types on the readability of annual reports of non-financial firms in the UK during the COVID-19 outbreak. It further investigates how the disclosure of COVID-19-related information moderates the relationship between risk disclosure and readability. The study uses a content analysis approach and CFIE software to measure the level of risk disclosure and readability in the annual reports of non-financial firms listed on the FTSE all-share from 2019 to 2021. The results show a positive and significant effect of risk disclosure level on readability, which is stronger for firms that disclosed COVID-19 information. Different types of risk disclosure have varying effects on readability, with COVID-19 risk, credit risk, and strategic risk positively affecting readability, while operational risk negatively affects it. The study contributes to the literature on information asymmetry and institutional theory by demonstrating how risk disclosure and readability are influenced by external factors like the COVID-19 outbreak and internal factors such as firm characteristics and types of risks. It introduces a new risk definition and category specific to the COVID-19 pandemic and develops new measurements for risk disclosure, including credit, liquidity, market, operational, business, strategic, and COVID-19 risks. The study provides valuable insights for managers, investors, regulators, and standard setters on the relationship between risk disclosure and readability in annual reports. It highlights the importance of disclosing COVID-19-related information to enhance the readability and understandability of financial communication. The paper contributes to the literature and practice on risk disclosure, readability, and financial communication during crises.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".