PILLAR 3: MARKET DISCIPLINE OF THE KEY STAKEHOLDERS IN CEE COMMERCIAL BANK AND TURBULENT TIMES
Bibliographic record
Abstract
The study presented in the paper contributes to covering the gap in the area of sufficient information disclosure that also increases the interests of relevant stakeholders in contributing to depository market discipline and in being relevant to their interest within Pillar 3 framework. This paper is focused on an analysis of website data dedicated to Pillar 3 disclosures of commercial banks and on studying the behaviour of stakeholders in relation to the timing of serious market turbulence. The examined data consists of log files that were pre-processed using web mining techniques and from which were extracted frequent itemsets by quarters and evaluated in terms of quantity. The authors have proposed a methodology to evaluate frequent itemsets of web parts over a dedicated time period. The results show that stakeholders’ interest in disclosures is lower after turbulent times in 2009, higher in the first quarter, also higher together with annual reports (lower for Pillar 3 solo information). The paper’s results suggest that further changes in commercial banks´ information disclosure are inevitable in order to achieve an effective market discipline mechanism and meaningful disclosures according to the regulator´s expectations.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".