SBUX Stock Forecast

Outlook: SBUX Starbucks Corporation Common Stock is assigned short-term B3 & long-term Ba3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy :
Time series to forecast n: for Weeks2
ML Model Testing : Deductive Inference (ML)
Hypothesis Testing : Lasso Regression
Surveillance : Major exchange and OTC

1The accuracy of the model is being monitored on a regular basis.(15-minute period)

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


Key Points

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About SBUX

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SBUX
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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(Deductive Inference (ML))3,4,5 X S(n):→ 1 Year i = 1 n a i

n:Time series to forecast

p:Price signals of SBUX stock

j:Nash equilibria (Neural Network)

k:Dominated move of SBUX stock holders

a:Best response for SBUX 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?

SBUX 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
OutlookB3Ba3
Income StatementCaa2B1
Balance SheetCaa2Baa2
Leverage RatiosCCaa2
Cash FlowB2B3
Rates of Return and ProfitabilityCaa2Baa2

*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?This exclusive content is only available to premium users.This exclusive content is only available to premium users.This exclusive content is only available to premium users.This exclusive content is only available to premium users.

References

  1. Bastani H, Bayati M. 2015. Online decision-making with high-dimensional covariates. Work. Pap., Univ. Penn./ Stanford Grad. School Bus., Philadelphia/Stanford, CA
  2. M. J. Hausknecht and P. Stone. Deep recurrent Q-learning for partially observable MDPs. CoRR, abs/1507.06527, 2015
  3. LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
  4. Andrews, D. W. K. (1993), "Tests for parameter instability and structural change with unknown change point," Econometrica, 61, 821–856.
  5. Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Google's Stock Price Set to Soar in the Next 3 Months. AC Investment Research Journal, 220(44).
  6. Andrews, D. W. K. W. Ploberger (1994), "Optimal tests when a nuisance parameter is present only under the alternative," Econometrica, 62, 1383–1414.
  7. Scott SL. 2010. A modern Bayesian look at the multi-armed bandit. Appl. Stoch. Models Bus. Ind. 26:639–58

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