NMG Stock Forecast

Outlook: NMG is assigned short-term B2 & long-term Ba3 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 : Ridge Regression
Surveillance : Major exchange and OTC

1Short-term revised.

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


Key Points

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About NMG

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NMG
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ML Model Testing

F(Ridge 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(Inductive Learning (ML))3,4,5 X S(n):→ 1 Year r s rs

n:Time series to forecast

p:Price signals of NMG stock

j:Nash equilibria (Neural Network)

k:Dominated move of NMG stock holders

a:Best response for NMG 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?

NMG 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
OutlookB2Ba3
Income StatementBa1B1
Balance SheetCB2
Leverage RatiosCaa2Ba3
Cash FlowB2Caa2
Rates of Return and ProfitabilityBa2Baa2

*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. D. Bertsekas and J. Tsitsiklis. Neuro-dynamic programming. Athena Scientific, 1996.
  2. S. Bhatnagar, H. Prasad, and L. Prashanth. Stochastic recursive algorithms for optimization, volume 434. Springer, 2013
  3. Challen, D. W. A. J. Hagger (1983), Macroeconomic Systems: Construction, Validation and Applications. New York: St. Martin's Press.
  4. Belsley, D. A. (1988), "Modelling and forecast reliability," International Journal of Forecasting, 4, 427–447.
  5. Bessler, D. A. S. W. Fuller (1993), "Cointegration between U.S. wheat markets," Journal of Regional Science, 33, 481–501.
  6. Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:267–88
  7. Imbens G, Wooldridge J. 2009. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47:5–86

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