CRON Stock Forecast

Outlook: CRON is assigned short-term Ba2 & long-term Ba2 estimated rating.
AUC Score : What is AUC Score?
Short-term Tactic1 :
Dominant Strategy :
Time series to forecast n: for Weeks2
ML Model Testing : Inductive Learning (ML)
Hypothesis Testing : Wilcoxon Rank-Sum Test
Surveillance : Major exchange and OTC

1Short-term revised.

2Time series is updated based on short-term trends.


Key Points

CRON is poised for significant growth driven by increasing global acceptance of cannabis products and the company's strategic expansion into new markets, particularly in Europe. This trajectory is supported by ongoing product innovation and a robust distribution network. However, substantial risks persist, including evolving regulatory landscapes that can create market uncertainty and potential challenges in achieving profitability amidst intense competition. Economic downturns could also dampen consumer spending on discretionary items like cannabis, impacting revenue.

About CRON

Cronos Group is a global cannabinoid company focused on building an enduring brand portfolio. The company engages in the development, manufacturing, and distribution of cannabis and cannabis-derived products for adult-use and medical markets. Cronos aims to differentiate itself through product innovation, strategic partnerships, and a commitment to quality and scientific advancement within the rapidly evolving cannabis industry.


Operating across various international jurisdictions, Cronos Group seeks to establish a strong presence in key markets. Its business model encompasses cultivation, extraction, and the production of a diverse range of consumer goods, including dried flower, oils, and other cannabis-infused products. The company is dedicated to advancing the understanding and application of cannabinoids, contributing to the growth and maturation of the global cannabis sector.

CRON
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ML Model Testing

F(Wilcoxon Rank-Sum Test)6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Inductive Learning (ML))3,4,5 X S(n):→ 16 Weeks R = r 1 r 2 r 3

n:Time series to forecast

p:Price signals of CRON stock

j:Nash equilibria (Neural Network)

k:Dominated move of CRON stock holders

a:Best response for CRON target price

 

For further technical information as per how our model work we invite you to visit the article below: 

How do KappaSignal algorithms actually work?

CRON Stock Forecast (Buy or Sell) Strategic Interaction Table

Strategic Interaction Table Legend:

X axis: *Likelihood% (The higher the percentage value, the more likely the event will occur.)

Y axis: *Potential Impact% (The higher the percentage value, the more likely the price will deviate.)

Z axis (Grey to Black): *Technical Analysis%

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Rating Short-Term Long-Term Senior
OutlookBa2Ba2
Income StatementCaa2Caa2
Balance SheetBaa2Caa2
Leverage RatiosBa3Baa2
Cash FlowBaa2Baa2
Rates of Return and ProfitabilityB3Baa2

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?

References

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  3. Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
  4. Wan M, Wang D, Goldman M, Taddy M, Rao J, et al. 2017. Modeling consumer preferences and price sensitiv- ities from large-scale grocery shopping transaction logs. In Proceedings of the 26th International Conference on the World Wide Web, pp. 1103–12. New York: ACM
  5. Mazumder R, Hastie T, Tibshirani R. 2010. Spectral regularization algorithms for learning large incomplete matrices. J. Mach. Learn. Res. 11:2287–322
  6. S. Bhatnagar and K. Lakshmanan. An online actor-critic algorithm with function approximation for con- strained Markov decision processes. Journal of Optimization Theory and Applications, 153(3):688–708, 2012.
  7. Belsley, D. A. (1988), "Modelling and forecast reliability," International Journal of Forecasting, 4, 427–447.

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