Bibliographic record
Abstract
ABSTRACT Purpose – The purpose of this paper is to investigate the performance of publicly-traded cannabis equities. Particularly, the goal of this research is to provide academics and practitioners with empirical evidence on how the traditional Fama-French factors, calendar effects, and social media interest explain the equity returns of the relatively new cannabis industry. Design/methodology/approach – This study uses a sample of cannabis related firms that trade on the Toronto Stock Exchange along with two indices that capture the overall cannabis industry in Canada and the U.S. Using daily data, that spans from January 2014 to April 2019, we apply both OLS and GARCH methodologies to multiple asset pricing models of equity returns. In addition to the Fama-French factors, this research also tests for calendar anomalies such as the day-of-the-week and January effect and a factor related to Twitter interest of cannabis stocks (#potstocks). Findings – First, our results show that cannabis related investments tend to have low market betas. Second, the three-, four-, and five-factor asset pricing models suggest that the size, profitability, and investment factors tend to have negative and statistically significant coefficients. Third, the coefficient on #potstocks also tends to be positive and significant, suggesting that investors can monitor investor interest via social media platforms and exploit this information to capture excess returns in the cannabis sector. Finally, the day-of-the-week effect suggests that Mondays tend to have statistically significant higher returns, while there is little evidence of the January effect. Research limitations/implications – This paper serves as a starting point for future research on cannabis investing. As the cannabis market continues to grow and evolve, within Canada and internationally, more financial capital will be required. Thus, both retail and institutional investors around the globe will need to understand the returns and risks associated with this relatively new investment opportunity. As the data sets capturing cannabis-related firms become more robust, further research surrounding the financial activities of cannabis-related firms will be required (e.g., risk management, corporate finance, financing decisions). Moreover, further research in other geographic regions will be required as regulations and legalization of cannabis continue to evolve. Finally, future studies can explore how COVID 19 lockdowns potentially impacted cannabis stock returns. Keywords Cannabis stocks, alternative investments, sin stocks, CAPM
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".