Analysis of the Impact of COVID-19 Pandemic on the Intraday Efficiency of Agricultural Futures Markets
Bibliographic record
Abstract
The investigation of the fractal nature of financial data has been growing in the literature. The purpose of this paper is to investigate the impact of the COVID-19 pandemic on the efficiency of agricultural futures markets by using multifractal detrended fluctuation analysis (MF-DFA). To better understand the relative changes in the efficiency of agriculture commodities due to the pandemic, we split the dataset into two equal periods of seven months, i.e., 1 August 2019 to 10 March 2020 and 11 March 2020 to 25 September 2020. We used the high-frequency data at 15 min intervals of cocoa, cotton, coffee, orange juice, soybean, and sugar. The findings reveal that the COVID-19 pandemic has great but varying impacts on the intraday multifractal properties of the selected agricultural future markets. In particular, the London sugar witnessed the lowest multifractality while orange juice exhibited the highest multifractality before the pandemic declaration. Cocoa became the most efficient while the cotton exhibited the minimum efficient pattern after the pandemic. Our findings show that the highest improvement is found in the market efficiency of orange juice. Furthermore, the behavior of these agriculture commodities shifted from a persistent to an antipersistent behavior after the pandemic. The information given by the detection of multifractality can be used to support investment and policy-making decisions.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".