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Record W2549146198 · doi:10.1002/ijfe.1520

Ending Over‐lending: Assessing Systemic Risk with Debt to Cash Flow

2015· article· en· W2549146198 on OpenAlexaff
Bruce Ramsay, Peter Sarlin

Bibliographic record

VenueInternational Journal of Finance & Economics · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsPacific Insight Electronics (Canada)
Fundersnot available
KeywordsSystemic riskCash flowEconomicsLeverage (statistics)DebtDebt service coverage ratioVulnerability (computing)Monetary economicsFinancial systemFinancial economicsBusinessExternal debtFinanceMacroeconomicsFinancial crisis

Abstract

fetched live from OpenAlex

Abstract This paper introduces the ratio of debt to cash flow (D/CF) of nations and their economic sectors to macroprudential analysis, particularly as an indicator of systemic risk and vulnerabilities. While leverage is oftentimes linked to the vulnerability of a nation, the stock of total debt and the flow of gross savings is a less explored measure. Cash flows certainly have a well‐known connection to corporations' ability to service debt. This paper investigates whether the D/CF provides a means for understanding systemic risks. For a panel of 33 nations, we explore historic D/CF trends and apply the same procedure to economic sectors. In terms of an early‐warning indicator, we show that the D/CF ratio provides a useful additional measure of vulnerability to systemic banking and sovereign crises, relative to more conventional indicators. As a conceptual framework, the assessment of financial stability is arranged for presentation within four vulnerability zones and exemplified with a number of illustrative case studies. Copyright © 2015 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score0.878

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 communication0.0000.001
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 head0.033
GPT teacher head0.267
Teacher spread0.234 · 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 designNot applicable
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

Citations11
Published2015
Admission routes1
Has abstractyes

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