Price Discovery in Agricultural Futures Markets: Should We Look beyond the Best Bid‐Ask Spread?
Bibliographic record
Abstract
Abstract Price discovery is the incorporation of information to prices through the actions of traders. Previous studies in financial markets have found evidence that informed traders may submit limit orders instead of market orders as part of their trading strategies. If so, the steps of limit order book (LOB) beyond the best bid and best ask spread (BAS) may contain valuable information and contribute to price discovery of the underlying asset. This is the first attempt to examine the informativeness of the LOB beyond the BAS for agricultural commodities. We reconstruct the LOB using market depth data and use three information share approaches to test to what extent the steps of LOB beyond the BAS contribute to price discovery. This is done for five major agricultural commodities, namely live cattle, lean hogs, corn, wheat, and soybeans, as well as the E‐mini Standard and Poor's 500 Index (S&P 500) futures contracts. The results show that the steps of the LOB beyond the BAS contribute by over 27% to price discovery of futures contracts. Across agricultural commodities, the steps of the LOB beyond the BAS have more information for grains than meats. Moreover, beyond the BAS, the steps closer to the top of the book contain more information for livestock and E‐mini S&P 500. For grains, the steps farther from the BAS are as informative as the steps closer to the BAS. These findings suggest that informed traders in futures electronic markets actively use limit orders with price steps beyond the BAS.
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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 | 0.007 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".