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Record W3126554420 · doi:10.3390/jrfm14020061

Banks’ Foreign Claims in the Aftermath of the 2008 Crisis: Institutional Response, Financial Efficiency, and Integration of Cross-Border Banking in the Euro Area

2021· article· en· W3126554420 on OpenAlexaffvenue
Thierry Warin, Aleksandar Stojkov

Bibliographic record

VenueJournal of risk and financial management · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsFinancial crisisFinancial systemFinancial integrationBanking unionCapital (architecture)European unionBusinessCapital marketFinancial stabilityEuropean debt crisisForeign capitalMacroFinancial marketEconomicsInternational economicsEuropean integrationFinanceForeign direct investmentMacroeconomics

Abstract

fetched live from OpenAlex

Beyond financial stability as the European Banking Union’s primary objective, the European capital market integration provides an impetus for deepening bank integration and greater financial market efficiency. This article proposes an empirical framework to assess the dynamics of euro area banks’ business networking. We use banks’ foreign claims across Europe, particularly the euro area, to see how banks react to various macroeconomic signals. Banks’ foreign claims are particularly interesting due to their sensitivity. One of the main conclusions is that the euro area has seen a reallocation of capital in the aftermath of the 2008 crisis. The financial picture of Europe is different after the recent financial crisis. Although we observe a re-concentration of capital from the periphery to the core countries, we also observe some signs of recovered confidence within the European banking framework for macro-prudential reasons.

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)0.007
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.259
Teacher spread0.244 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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