JBS Stock Forecast

Outlook: JBS is assigned short-term Baa2 & long-term B2 estimated rating.
AUC Score : What is AUC Score?
Short-term Tactic1 :
Dominant Strategy :
Time series to forecast n: for Weeks2
ML Model Testing : Transfer Learning (ML)
Hypothesis Testing : Lasso Regression
Surveillance : Major exchange and OTC

1Short-term revised.

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


Key Points

This exclusive content is only available to premium users.

About JBS

JBS NV, a prominent global food processing company, operates extensively across various segments of the protein industry. Its core business involves the sourcing, processing, and distribution of a wide range of meat products, including beef, pork, poultry, and lamb. The company is a significant player in the international market, with a substantial presence in North and South America, Europe, and Australia. JBS NV's operations are characterized by a vertically integrated supply chain, from farm to table, which allows for enhanced control over product quality and efficiency.


The Class A Common Shares of JBS NV represent an ownership stake in this diversified food production enterprise. The company's strategic focus includes a commitment to sustainable practices and innovation within the agricultural and food sectors. JBS NV aims to meet the growing global demand for protein while adhering to high standards of food safety and environmental responsibility. Its extensive infrastructure and broad product portfolio position it as a key contributor to the global food supply chain.

JBS
This exclusive content is only available to premium users.

ML Model Testing

F(Lasso Regression)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(Transfer Learning (ML))3,4,5 X S(n):→ 8 Weeks i = 1 n s i

n:Time series to forecast

p:Price signals of JBS stock

j:Nash equilibria (Neural Network)

k:Dominated move of JBS stock holders

a:Best response for JBS 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?

JBS 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%

This exclusive content is only available to premium users.
Rating Short-Term Long-Term Senior
OutlookBaa2B2
Income StatementB2Caa2
Balance SheetBaa2Baa2
Leverage RatiosBaa2B2
Cash FlowBa3C
Rates of Return and ProfitabilityBaa2Caa2

*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

  1. Z. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot, and N. de Freitas. Dueling network architectures for deep reinforcement learning. In Proceedings of the International Conference on Machine Learning (ICML), pages 1995–2003, 2016.
  2. J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 2137–2145, 2016.
  3. Ruiz FJ, Athey S, Blei DM. 2017. SHOPPER: a probabilistic model of consumer choice with substitutes and complements. arXiv:1711.03560 [stat.ML]
  4. Zeileis A, Hothorn T, Hornik K. 2008. Model-based recursive partitioning. J. Comput. Graph. Stat. 17:492–514 Zhou Z, Athey S, Wager S. 2018. Offline multi-action policy learning: generalization and optimization. arXiv:1810.04778 [stat.ML]
  5. R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998
  6. S. J. Russell and A. Zimdars. Q-decomposition for reinforcement learning agents. In Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), August 21-24, 2003, Washington, DC, USA, pages 656–663, 2003.
  7. Hastie T, Tibshirani R, Tibshirani RJ. 2017. Extended comparisons of best subset selection, forward stepwise selection, and the lasso. arXiv:1707.08692 [stat.ME]

This project is licensed under the license; additional terms may apply.