SGI Commodities Optimix TR Index Forecast

Outlook: SGI Commodities Optimix TR index is assigned short-term B1 & 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 : Modular Neural Network (Financial Sentiment Analysis)
Hypothesis Testing : Wilcoxon Sign-Rank Test
Surveillance : Major exchange and OTC

1Short-term revised.

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


Key Points

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About SGI Commodities Optimix TR Index

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  SGI Commodities Optimix TR
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ML Model Testing

F(Wilcoxon Sign-Rank 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(Modular Neural Network (Financial Sentiment Analysis))3,4,5 X S(n):→ 4 Weeks e x rx

n:Time series to forecast

p:Price signals of SGI Commodities Optimix TR index

j:Nash equilibria (Neural Network)

k:Dominated move of SGI Commodities Optimix TR index holders

a:Best response for SGI Commodities Optimix TR 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?

SGI Commodities Optimix TR Index Forecast 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
OutlookB1B1
Income StatementBaa2Baa2
Balance SheetCaa2B2
Leverage RatiosBaa2Caa2
Cash FlowCaa2B2
Rates of Return and ProfitabilityB2Ba2

*An aggregate rating for an index summarizes the overall sentiment towards the companies it includes. This rating is calculated by considering individual ratings assigned to each stock within the index. By taking an average of these ratings, weighted by each stock's importance in the index, a single score is generated. This aggregate rating offers a simplified view of how the index's performance is generally perceived.
How does neural network examine financial reports and understand financial state of the company?

References

  1. V. Borkar and R. Jain. Risk-constrained Markov decision processes. IEEE Transaction on Automatic Control, 2014
  2. Burgess, D. F. (1975), "Duality theory and pitfalls in the specification of technologies," Journal of Econometrics, 3, 105–121.
  3. Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
  4. Breiman L. 2001a. Random forests. Mach. Learn. 45:5–32
  5. R. Rockafellar and S. Uryasev. Optimization of conditional value-at-risk. Journal of Risk, 2:21–42, 2000.
  6. M. Sobel. The variance of discounted Markov decision processes. Applied Probability, pages 794–802, 1982
  7. Dudik M, Langford J, Li L. 2011. Doubly robust policy evaluation and learning. In Proceedings of the 28th International Conference on Machine Learning, pp. 1097–104. La Jolla, CA: Int. Mach. Learn. Soc.

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