Bibliographic record
Abstract
Following the Basel Committee’s advocacy of value-at-risk (VaR) disclosure in external reports of financial institutions, the U.S. Securities and Exchange Commission issued Financial Reporting Release No. 48 to permit VaR disclosure as one of the most important disclosure approaches for market-risk quantitative information in 1997. This study is the first to empirically examine both economic determinants and consequences of VaR disclosure informativeness in the banking industry. First, this study finds that more informative VaR disclosure is associated with more effective corporate governance characteristics, including better shareholder protection, a larger and more independent board, the presence of a separate risk committee under the board of directors, a more independent risk committee, higher institutional ownership and a better overall governance environment. These results suggest that corporate governance mechanisms are important determinants of the informativeness of VaR disclosure. Second, the evidence shows that the cost of equity capital is negatively associated with the informativeness of VaR disclosure, consistent with informative VaR disclosure effectively communicating private information to investors about a bank’s market risk exposure and its risk management system. Additional evidence during the recent crisis further suggests the importance of VaR disclosure informativeness to the capital market as a strong signal reflecting the efficacy of risk management practices and the quality of risk governance mechanisms. However, I still find that a large proportion of the sample banks choose not to disclose information with respect to some important disclosure items (e.g., quantitative stress-test results, and non-trading portfolio VaR). It is necessary for government regulators to re-consider the current regulation on VaR disclosure in the external reports of the banking industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".