FLYING HIGH – INVESTING IN THE CANNABIS INDUSTRY
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».