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Record W2215974866 · doi:10.34989/sdp-2012-7

Canadian Bank Balance-Sheet Management: Breakdown by Types of Canadian Financial Institutions

2021· preprint· en· W2215974866 on OpenAlexaffabout
David Xiao Chen, H. Evren Damar, Hani Soubra, Yaz Terajima

Bibliographic record

VenueEconstor (Econstor) · 2021
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsBank of Canada
Fundersnot available
KeywordsBalance sheetEconomicsBalance (ability)Welfare economicsPolitical scienceFinancial systemFinancePsychology

Abstract

fetched live from OpenAlex

The authors document leverage, capital and liquidity ratios of banks in Canada. These ratios are important indicators of different types of risk with respect to a bank’s balance‐sheet management. Particular attention is given to the observations by different types of banks, including small banks that historically received less attention. In addition, the authors compare leverage and capital ratios for banks in Canada and the United States in the period leading up to the recent crisis. They find that in Canada, most of the risks indicated by these balance‐sheet ratios are concentrated among large banks that are more likely able to withstand shocks due to their diversified portfolios. Some smaller banks, however, reveal vulnerability against liquidity risks. Regarding a Canada - U.S. comparison, small U.S. banks show more vulnerability than their larger counterparts, as well as an increasing trend in vulnerability prior to the crisis. In contrast, the ratios for small Canadian banks show increasing resilience.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.951
Threshold uncertainty score0.354

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0150.024
Science and technology studies0.0030.001
Scholarly communication0.0040.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.016
GPT teacher head0.208
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2021
Admission routes2
Has abstractyes

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