Market risk disclosures of banks: a cross‐country study
Bibliographic record
Abstract
Purpose The purpose of this paper is to empirically investigate the disclosure practices of market risk by 30 banks in ten countries of different size and geographic distribution (USA, Canada, UK, Germany, Japan, Italy, The Netherlands, France, Greece and Cyprus). Design/methodology/approach The paper uses content analysis and other statistical techniques (regression and correlation analysis) to produce qualitative and quantitative indicators of the degree of market risk disclosure to ascertain if differences exist across countries and across banks of different size. Findings The findings validate the testing hypotheses, namely that there are still significant differences across banks in different countries, meaning that there is no harmonization in disclosure practices; that the banks in the Anglo‐Saxon countries (UK and USA) are consistently better in their overall risk reporting practices; that the banks that are “good” in reporting qualitative information are also “good” in reporting quantitative information on risk types; OLS regression analysis and correlation analysis point to a positive association between bank size (as measured by the market capitalization) and the level of risk reporting. Originality/value The study contributes to a research area that is under‐researched, especially focusing on market risk of banks across countries.
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".