Value at Risk Disclosure of Banks
Bibliographic record
Abstract
Mismanagement of risk can carry an enormous cost. In recent years, business has experienced numerous risks that have resulted in considerable financial losses, decrease in shareholder value, and damage to the banks or financial institutions reputations, dismissals of senior management and in some cases dissolution of the business. This risky environment where mismanagement of risks arrives makes it mandatory for management to adopt a more proactive perspective on risk management. In this paper we would be looking at the level of Value at Risk, Stress Tests and Enterprise Risk Management disclosure for a sample of sixteen banks where US, UK, Canadian and Japanese banks have been chosen. To measure the disclosure level of Value-at-Risk we modified an existing disclosure index; and for Stress Tests and Enterprise Risk Management we created a new disclosure index since lack of literature on ERM disclosure was found. A total score of fifteen for Value at Risk disclosure, four for Stress Tests and six for Enterprise Risk Management disclosure is assigned which captures different facets of risk disclosure where the data has been gathered from the bank’s annual reports from 2007 to 2010. We have observed that UK and Canadian banks have been consistently disclosing risk information in their annual reports, whereas, on the other hand Japanese banks and surprisingly US banks have been disclosing less information when compared to the other countries. Moreover, we have seen that few banks such as HSBC and Royal Bank of Canada have scored the highest disclosure score; and Wells Fargo and Nomura Bank have scored the least points in risk disclosure. Moreover, our results have shown that there little or no relationship between Value at Risk disclosure and the bank size and leverage and a positive relationship with banks profits.
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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".