Carbon Revolution (CREV) Shares: Future Outlook Mixed Following Recent Trends

Outlook: Carbon Revolution is assigned short-term B1 & long-term Ba1 estimated rating.
AUC Score : What is AUC Score?
Short-term Tactic1 :
Dominant Strategy :
Time series to forecast n: for Weeks2
ML Model Testing : Modular Neural Network (Market Direction Analysis)
Hypothesis Testing : Beta
Surveillance : Major exchange and OTC

1Short-term revised.

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


Key Points

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About Carbon Revolution

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CREV
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ML Model Testing

F(Beta)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(Modular Neural Network (Market Direction Analysis))3,4,5 X S(n):→ 4 Weeks S = s 1 s 2 s 3

n:Time series to forecast

p:Price signals of Carbon Revolution stock

j:Nash equilibria (Neural Network)

k:Dominated move of Carbon Revolution stock holders

a:Best response for Carbon Revolution 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?

Carbon Revolution 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
OutlookB1Ba1
Income StatementB3B1
Balance SheetBa3C
Leverage RatiosBa3Baa2
Cash FlowBa2Baa2
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

  1. Christou, C., P. A. V. B. Swamy G. S. Tavlas (1996), "Modelling optimal strategies for the allocation of wealth in multicurrency investments," International Journal of Forecasting, 12, 483–493.
  2. Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
  3. Künzel S, Sekhon J, Bickel P, Yu B. 2017. Meta-learners for estimating heterogeneous treatment effects using machine learning. arXiv:1706.03461 [math.ST]
  4. Chen X. 2007. Large sample sieve estimation of semi-nonparametric models. In Handbook of Econometrics, Vol. 6B, ed. JJ Heckman, EE Learner, pp. 5549–632. Amsterdam: Elsevier
  5. Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
  6. Gentzkow M, Kelly BT, Taddy M. 2017. Text as data. NBER Work. Pap. 23276
  7. A. Shapiro, W. Tekaya, J. da Costa, and M. Soares. Risk neutral and risk averse stochastic dual dynamic programming method. European journal of operational research, 224(2):375–391, 2013

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