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Record W2890430866 · doi:10.1002/ijfe.1457

DETERMINANTS OF FINANCIAL STRESS AND RECOVERY DURING THE GREAT RECESSION

2012· preprint· en· W2890430866 on OpenAlexaff
Joshua Aizenman, Gurnain Kaur Pasricha

Bibliographic record

VenueInternational Journal of Finance & Economics · 2012
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsBank of Canada
Fundersnot available
KeywordsFinancial crisisDeleveragingRecessionEconomicsMonetary economicsStock (firearms)International economicsFinancial systemMacroeconomicsGeography

Abstract

fetched live from OpenAlex

ABSTRACT In this article, we explore the link between stress in the domestic financial sector and the capital flight faced by countries in the 2008–2009 global crisis. Both the timing of emergence of internal financial stress in developing economies and the size of the peak–trough declines in the stock price indices were comparable with that in high‐income countries, indicating that there was no decoupling, even before Lehman Brothers’ demise. Deleveraging of Organisation for Economic Co‐operation and Development (OECD) positions seemed to dominate the patterns of capital flows during the crisis. Although high‐income countries on average saw net capital inflows and net portfolio inflows during the crisis quarters, compared with net outflows for developing economies, the indicators of banking sector stress were higher for high‐income economies on average than those for developing economies. Internal and external distress during crisis was closely interlinked with common underlying causes of both the severity of stress during the crisis and the recovery. External vulnerabilities were important in both phases, and higher international reserves did not insulate countries from stress. Copyright © 2012 John Wiley & Sons, Ltd.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.023
GPT teacher head0.256
Teacher spread0.233 · 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.

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

Citations14
Published2012
Admission routes1
Has abstractyes

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