CEVA Stock Forecast

Outlook: CEVA is assigned short-term Ba3 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-term Tactic1 :
Dominant Strategy :
Time series to forecast n: for Weeks2
ML Model Testing : Ensemble Learning (ML)
Hypothesis Testing : Independent T-Test
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 CEVA

This exclusive content is only available to premium users.
CEVA
This exclusive content is only available to premium users.

ML Model Testing

F(Independent T-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(Ensemble Learning (ML))3,4,5 X S(n):→ 1 Year r s rs

n:Time series to forecast

p:Price signals of CEVA stock

j:Nash equilibria (Neural Network)

k:Dominated move of CEVA stock holders

a:Best response for CEVA 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?

CEVA 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
OutlookBa3B1
Income StatementBaa2B2
Balance SheetCC
Leverage RatiosBaa2Baa2
Cash FlowB3Ba1
Rates of Return and ProfitabilityBaa2C

*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. Li L, Chen S, Kleban J, Gupta A. 2014. Counterfactual estimation and optimization of click metrics for search engines: a case study. In Proceedings of the 24th International Conference on the World Wide Web, pp. 929–34. New York: ACM
  2. Bierens HJ. 1987. Kernel estimators of regression functions. In Advances in Econometrics: Fifth World Congress, Vol. 1, ed. TF Bewley, pp. 99–144. Cambridge, UK: Cambridge Univ. Press
  3. G. Theocharous and A. Hallak. Lifetime value marketing using reinforcement learning. RLDM 2013, page 19, 2013
  4. Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28
  5. Mnih A, Teh YW. 2012. A fast and simple algorithm for training neural probabilistic language models. In Proceedings of the 29th International Conference on Machine Learning, pp. 419–26. La Jolla, CA: Int. Mach. Learn. Soc.
  6. Abadie A, Cattaneo MD. 2018. Econometric methods for program evaluation. Annu. Rev. Econ. 10:465–503
  7. L. Busoniu, R. Babuska, and B. D. Schutter. A comprehensive survey of multiagent reinforcement learning. IEEE Transactions of Systems, Man, and Cybernetics Part C: Applications and Reviews, 38(2), 2008.

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