DJ Commodity Gold Index Forecast

Outlook: DJ Commodity Gold index is assigned short-term B2 & long-term B2 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 (Emotional Trigger/Responses Analysis)
Hypothesis Testing : Logistic 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 DJ Commodity Gold Index

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DJ Commodity Gold
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ML Model Testing

F(Logistic 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(Modular Neural Network (Emotional Trigger/Responses Analysis))3,4,5 X S(n):→ 16 Weeks R = r 1 r 2 r 3

n:Time series to forecast

p:Price signals of DJ Commodity Gold index

j:Nash equilibria (Neural Network)

k:Dominated move of DJ Commodity Gold index holders

a:Best response for DJ Commodity Gold 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?

DJ Commodity Gold 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
OutlookB2B2
Income StatementCB3
Balance SheetCaa2C
Leverage RatiosB2B2
Cash FlowCaa2C
Rates of Return and ProfitabilityBaa2Baa2

*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

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  4. Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
  5. Hornik K, Stinchcombe M, White H. 1989. Multilayer feedforward networks are universal approximators. Neural Netw. 2:359–66
  6. Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94
  7. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, Newey W. 2017. Double/debiased/ Neyman machine learning of treatment effects. Am. Econ. Rev. 107:261–65

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