Bibliographic record
Abstract
This study is directed at predicting the determinants of oil futures prices. We evaluate commodity pricing with oil occupying a special position due to highly inelastic demand. Given the sudden fall in oil prices, there is theoretical and practical interest in identifying the determinants of falling oil prices. While the popular press dwells on oversupply in production as the principal determinant of price declines, we examine additional predictors including call option sales and put option purchases along with the Canadian dollar-US dollar exchange rate and news of future oil prices. Intraday call and put options on NYMEX oil futures were examined. Call and put option prices of 1 - 7 month-maturities, along with exchange rates, the supply of oil and news of oil prices were regressed on oil futures prices. A trading strategy was tested based on the thesis that in a period of price declines, options traders seek to profit by selling call options and purchasing put options. While oversupply of oil was the most important determinant of oil prices, trader speculation through put buying and call selling exacerbated the decline in oil prices. Call and put option prices explained oil futures prices for options of 1 - 4 month maturities. The supply of oil was significant in predicting oil futures prices in all future time periods. This was followed by the Canadian dollar-US dollar exchange rate which was significant in predicting oil prices 1, 2, 3 and 6 months into the future. Finally, news of forthcoming events affecting oil prices predicted oil futures prices 3, 4, 5, 6 and 7 months in advance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".