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Record W1505671782

The Impact of Hedging on Stock Return and Firm Value: New Evidence from Canadian Oil and Gas Companies

2005· preprint· en· W1505671782 on OpenAlexaffabout
Dan Chang, Hong Gu, Kuan Xu

Bibliographic record

VenueRePEc: Research Papers in Economics · 2005
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicRisk Management in Financial Firms
Canadian institutionsDalhousie University
Fundersnot available
KeywordsDownside riskStock (firearms)Profitability indexFossil fuelFinancial economicsEconomicsLeverage (statistics)BusinessMonetary economicsPortfolioFinance
DOInot available

Abstract

fetched live from OpenAlex

This paper analyzes the impact of hedging activities of large Canadian oil and gas companies on their stock returns and firm value. Differing from the existing literature this research finds that some of these relationships are nonlinear based on the framework of nonlinear generalized additive models. The research based on this more general methodology reveals some interesting findings on oil and gas hedging activities. The large Canadian oil and gas firms are able to use hedging to protect downside risk against the unfavorable oil and gas price changes. But oil hedging appears to be more effective in protecting stock returns than gas hedging is when downside risk presents. In addition, oil and gas reserves are more likely to play a positive (negative) role when the oil and gas prices are increasing (decreasing). Finally, hedging, in particular hedging on gas, together with profitability, investment and leverage, has certain impacts on firm value.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.307
Teacher spread0.263 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2005
Admission routes2
Has abstractyes

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