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Record W2899136448 · doi:10.1002/fut.21966

Asymmetric spot‐futures price adjustments in grain markets

2018· article· en· W2899136448 on OpenAlexafffundabout
Zhige Wu, Alex Maynard, Alfons Weersink, Getu Hailu

Bibliographic record

VenueJournal of Futures Markets · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsUniversity of GuelphUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaChina Scholarship Council
KeywordsFutures contractNormal backwardationSpot contractEconomicsVolatility (finance)Spot marketForward marketFinancial economicsFutures marketMid pricePrice discoveryMonetary economicsEconometricsPrice level

Abstract

fetched live from OpenAlex

Abstract Recent volatility in food prices in the grain market has generated much interest among agricultural market participants. This study examines the nonlinear dynamic relationship between spot and futures prices in grain markets. The empirical results provide strong evidence of price asymmetries. The corn spot price adjusts faster to futures price increases than futures price decreases, whereas the soybean spot price adjusts faster to futures price decreases than futures price increases. Although this asymmetric adjustment is found for a single market in Ontario, Canada, the results may also provide insights on the spot‐futures price convergence issues in other commodity markets.

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)0.006
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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.234
Teacher spread0.221 · 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

Citations8
Published2018
Admission routes3
Has abstractyes

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