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Record W2105203899 · doi:10.1287/mnsc.1090.1137

Operational Flexibility and Financial Hedging: Complements or Substitutes?

2010· article· en· W2105203899 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

VenueManagement Science · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRisk Management in Financial Firms
Canadian institutionsKellogg's (Canada)
FundersUniversity of North Carolina at Chapel HillBoston College
KeywordsFlexibility (engineering)PostponementEconomicsProfit (economics)Robustness (evolution)MicroeconomicsEconometricsOperations management

Abstract

fetched live from OpenAlex

We consider a firm that invests in capacity under demand uncertainty and thus faces two related but distinct types of risk: mismatch between capacity and demand and profit variability. Whereas mismatch risk can be mitigated with greater operational flexibility, profit variability can be reduced through financial hedging. We show that the relationship between these two risk mitigating strategies depends on the type of flexibility: Product flexibility and financial hedging tend to be complements (substitutes)—i.e., product flexibility tends to increase (decrease) the value of financial hedging, and, vice versa, financial hedging tends to increase (decrease) the value of product flexibility—when product demands are positively (negatively) correlated. In contrast to product flexibility, postponement flexibility is a substitute to financial hedging as intuitively expected. Although our analytical results assume perfect flexibility and perfect hedging and rely on a linear approximation of the value of hedging, we validate their robustness in an extensive numerical study.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.886
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.276
Teacher spread0.245 · 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