MétaCan
Menu
Back to cohort
Record W1906959869

The Impact of the Subprime Crisis on Canadian Banks’ Stock Returns

2013· article· en· W1906959869 on OpenAlexaffabout
Jean‐Pierre Gueyié

Bibliographic record

VenueSSRN Electronic Journal · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsSubprime crisisStock (firearms)Financial systemFinancial crisisSubprime mortgage crisisBusinessFinanceEconomicsGeography
DOInot available

Abstract

fetched live from OpenAlex

This paper analyses the impact of the United States’ (US) subprime crisis on Canadian banks’ stock returns, using event study methodology. Our results suggest that despite their holdings of US toxic (subprime) mortgage-backed assets, Canadian banks have been solid in the face of the subprime crisis and the global 2007-2008 financial crisis. In spite of the huge 2007-2008 US financial turmoil, characterized by the failure of many financial institutions, Canadian banks’ stock returns were not negatively impacted by subprime events before March 14-16, 2008; more than a year after the beginning of the crisis. The fear of contagion coming from the US has been very limited. As shown by the CIBC case — the most exposed Canadian bank to subprime assets — the situation has been very well managed by banks. Moreover, the Government of Canada Insured Mortgage Purchase Program and Canadian Lenders Assurance Facility had been very helpful, and had positively impacted Canadian banks’ stock returns.

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)0.006
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 score0.050
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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

Citations2
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueSSRN Electronic JournalSame topicBanking stability, regulation, efficiencyFrench-language works237,207