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Record W2289035348 · doi:10.19030/iber.v9i7.598

The Analysis Of Comments Received By The BIS On Principles For Sound Liquidity Risk Management And Supervision

2010· article· en· W2289035348 on OpenAlexaff
Jacques Préfontaine, Jean Desrochers, Lise Godbout

Bibliographic record

VenueInternational Business & Economics Research Journal (IBER) · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMarket liquidityAccountingHuman settlementLiquidity riskRisk managementBusinessFinanceEngineering

Abstract

fetched live from OpenAlex

The market turmoil that began in mid-2007 re-emphasized the importance of liquidity to the functioning of financial markets and the banking sector. In June 2008, the Basel Committee of the Bank for International Settlements (BIS) released a consultative document on Principles for Sound Liquidity Risk Management and Supervision. Interested parties were invited to provide written comments by the end of July 2008. As a result, the Committee received many comments for publication by 30 different commenters. Our analysis first indicates that comments were formulated on each of the 17 principles discussed in the consultative document. Second, comments were also made in each of the five separate defined areas of focus covered by the 17 principles. Third, the results of our analysis reveal that opinions on different principles differed the most when commenters were separated into four distinct categories: banking trade associations, regulatory supervisors, individual financial institutions, and others (consultants, academics, accounting associations, and financial information providers). Last but not least, the results of the study indicate that commenters’ opinions, both within a category and between categories, differed the most in the two following defined areas of focus: measurement and management of liquidity risk and public disclosure of quantitative information on liquidity risk management.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.656

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.073
GPT teacher head0.331
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2010
Admission routes1
Has abstractyes

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