The Need for Weed: Role of Investor Attention in The North American Cannabis Industry
Bibliographic record
Abstract
My paper analyzes the role that investor attention plays in the North American cannabis industry by conducting an OLS regression with time fixed effects. I use the North American Marijuana Index, comprised of 44 US- and Canada-based companies operating in the industry, as well as individual company stock performance for my dependent variables as a measure of industry performance. Over a period of 221 weeks, starting in January, 2015, I utilize Google Trends data on search frequency of marijuana-related terms as a proxy for information demand and investor attention towards the marijuana industry. I find that certain search terms are significantly related to index and stock performance, correlating with increases and decreases in prices, indicating the heightened role that investor attention plays in this recently legalized and fast growing industry. Politicians, businesses, consumers, and investors alike are all gradually bound by a common trait: the need for weed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".