Regulatory Mood-congruence and Herding: Evidence from Cannabis Stocks
Bibliographic record
Abstract
Although social mood can motivate herding towards new industries, the extent to which regulators cater to social mood may affect that herding. We explore this issue in the context of the nascent cannabis industry by examining herding among the cannabis stocks listed in the US and Canada, where the regulatory treatment of cannabis varies in its congruence with the prevailing social mood on cannabis’ legalization. Canadian-listed cannabis stocks entail strong herding across all market states and sectors, alongside most capitalization-segments; conversely, herding among their US-listed counterparts is relatively limited, appearing on up-market/high-volume days, for the smallest capitalization segment, as well as for several cannabis-sectors. Herding is present (almost always absent) around cannabis’ legalization announcement-days in Canada (the US), while cross market herding between US- and Canadian-listed cannabis stocks is very weak. We attribute Canadian (US) cannabis stocks’ strong (weak) herding to cannabis’ more (less) mood-congruent regulatory treatment, which promotes (reduces) certainty and encourages investors to herd more (less) on them.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".