The dynamics of energy futures and equity sectors: evidence from the United States and Canada
Bibliographic record
Abstract
ABSTRACT This paper investigates a sector-rotation strategy that includes energy futures within a particular economic cycle in order to elucidate two congruent objectives. The first objective is to examine the dynamic relationship between various major equity sectors and energy futures, while endogenously controlling for US dollar movement and monetary policy shocks. The second objective is to assess the benefits of including energy contracts (equally weighted or optimally weighted energy futures portfolios)to enhance the performance and sturdiness of an equity-sector rotation strategy. The findings pinpoint the predictive role and the higher, positive effect of energy futures portfolios on some American and Canadian equity sectors, as well as their negative(or nonexistent) effect on other equity sectors. We find that during periods of high exchange rate, the US dollar has an additional (all direct and interaction) effect on some equity sectors: negative in the case of Canadian basic materials and American energy sectors, and positive in the Canadian financial sector. Further, evidence lends support (through a dynamic assessment of the sector-rotation strategy) to arguments in favor of diversification benefitting overall portfolio performance via the addition of energy futures, a gain that is more pronounced when using an optimally than an equally weighted (or individual futures contracts) energy futures portfolio. ;
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".