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Record W853713713

Operational Hedging and Exchange Rate Risk: A Cross-sectional Examination of Canada’s Hotel Industry

2009· article· en· W853713713 on OpenAlexaboutno aff
Charles Chang, MA Li-ya

Bibliographic record

VenueCornell Peter and Stephanie Nolan School of Hotel Administration (Cornell University) · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsCurrencyLiberian dollarForeign exchange riskOccupancyRevenueBusinessLocal currencyExchange rateMonetary economicsEconomicsFinance
DOInot available

Abstract

fetched live from OpenAlex

Rather than engage in expensive and complicated currency hedging, hotels operating in an international environment can gain similar benefits from their normal operations, including revenue management. An analysis of 1,032 Canadian hotels over a period of over two and one-half years shows that due to exhange rate interactions, ADR, occupancy, and RevPAR increase in weak currency environments, while they decrease in strong environments. As a local currency fluctuates in relation to the dollar, euro, or yen, changes in ADR, occupancy, and (thus) RevPAR offset losses from currency translation in weak environments and modify gains when the currency is strong. When a local currency loses value against the dollar, for instance, travelers consider hotels priced in that currency to be less expensive, even though the nominal price hasn’t changed. Additional travelers who are attracted by "bargain" prices increase occupancy and cause the hotel’s revenue management system to recommend higher rates. Even with higher rates, the hotel’s rates might still be favorable, and the hotel’s revenue per available room would be augmented by both higher room rates and higher occupancy. The implication is that multinational hotel chains have significantly less exposure to foreign exchange risk than implied by traditional hedging practices.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.019
GPT teacher head0.196
Teacher spread0.177 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

Explore more

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