Do farmers exhibit disposition effect? Evidence from grain markets
Bibliographic record
Abstract
Purpose – The purpose of this paper is to explore the existence of disposition effect among Canadian wheat farmers when marketing their grain. This study examines the question of whether farmers wait too long to price their grain or whether they price it too soon. Design/methodology/approach – The disposition effect is a common behavior documented in financial markets, and reflects the notion that investors tend to hold losing positions too long and close winning positions too fast. This idea can also be applied to grain marketing, exploring whether farmers sell their grain more readily when prices are “high” and wait longer when prices are “low.” Based on the approach by Odean (1998), marketing strategies of 15,564 farmers between 2003/2004 and 2008/2009 are examined. Findings – Results support the existence of disposition effect in marketing decisions. Farmers seem to be eager to sell when prices offered by contracts are above their reference price and wait longer to sell when prices offered by contracts are below their reference price. There is no clear evidence that farmers might consistently benefit from this behavior. On the other hand, it is not clear whether this behavior can be costly to farmers. Originality/value – Exploring the existence of disposition effect is relevant because this behavior can affect performance. If grain is sold too early, farmers can miss opportunities to sell at higher prices later. If grain is held too long, prices can go down and farmers will end up selling at lower prices. This study uses unique data to perform the first analysis of the disposition effect in the agricultural industry, and its findings can provide new insights and move us toward a more complete understanding of decision making in this industry.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".