Evaluation of the Future Price of Brazilian Commodities as a Predictor of the Price of the Spot Market
Bibliographic record
Abstract
The present work seeks to bring empirical evidence on the efficiency of futures prices as predictors of spot market prices. For this, future and spot prices of live cattle, coffee, corn, soybeans, ethanol, gold and dollars traded in Brazil are considered. To compare the probability of occurrence with the event that actually happened, the score proposed by Brier in 1950 is used. It was observed that the spot and future price curves have the same trajectory and, considering the same date, have similar values. Despite this behavior, when calculating the scores, we found that the lowest was found for live cattle, 0.47, the highest for the dollar, with a value close to 1, and the other assets varied between 0.6 and 0.8. Scores of 1 denote worse predictive powers, it was noted that future prices are not good predictors for the assets considered. These results contribute to filling the gap in the financial literature that seeks to assess the efficiency of futures markets by bringing empirical evidence to Brazilian commodities and using the Brier Score. The findings are also useful for financial market agents who use these assets in their portfolios, producers and principals in the supply chain and policy makers who make decisions involving these commodities.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".