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Record W2804056828 · doi:10.1111/acfi.12374

The relationship between operating leverage and financial leverage

2018· article· en· W2804056828 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAccounting and Finance · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsMcMaster University
Fundersnot available
KeywordsLeverage (statistics)Operating leverageBusinessMonotonic functionMonetary economicsEconomicsFinanceComputer scienceMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract We model the relationship between operating and financial leverage. When operating leverage is exogenously specified, financial leverage is a monotonically decreasing function of operating leverage. When financial leverage is exogenously specified, operating leverage is initially increasing and subsequently decreasing in financial leverage. Finally, when both operating and financial leverage are chosen by the firm, they can be positively related, negatively related or unrelated, depending on which underlying parameter is driving the changes. Thus, operating leverage and financial leverage do not always behave as substitutes, as argued in the traditional literature. The relationship is complex, possibly non‐monotonic and dependent on the circumstances; empirical tests need to take this reality into account.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.030
GPT teacher head0.229
Teacher spread0.199 · 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