Dow Jones U.S. Utilities Index Forecast

Outlook: Dow Jones U.S. Utilities index is assigned short-term B2 & 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 (Speculative Sentiment Analysis)
Hypothesis Testing : Polynomial Regression
Surveillance : Major exchange and OTC

¹Short-term revised.

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

Key Points

This exclusive content is only available to premium users.

About Dow Jones U.S. Utilities Index

This exclusive content is only available to premium users.

This exclusive content is only available to premium users.

ML Model Testing

F(Polynomial Regression)^6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (Speculative Sentiment Analysis))^3,4,5 X S(n):→ 4 Weeks $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

p:Price signals of Dow Jones U.S. Utilities index

j:Nash equilibria (Neural Network)

k:Dominated move of Dow Jones U.S. Utilities index holders

a:Best response for Dow Jones U.S. Utilities 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?

Dow Jones U.S. Utilities 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%

This exclusive content is only available to premium users.

Rating	Short-Term	Long-Term Senior
Outlook	B2	B1
Income Statement	Baa2	Baa2
Balance Sheet	Ba1	B1
Leverage Ratios	C	C
Cash Flow	C	Caa2
Rates of Return and Profitability	Caa2	Ba3

*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. M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
2. Semenova V, Goldman M, Chernozhukov V, Taddy M. 2018. Orthogonal ML for demand estimation: high dimensional causal inference in dynamic panels. arXiv:1712.09988 [stat.ML]
3. C. Szepesvári. Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2010
4. Matzkin RL. 2007. Nonparametric identification. In Handbook of Econometrics, Vol. 6B, ed. J Heckman, E Learner, pp. 5307–68. Amsterdam: Elsevier
5. Pennington J, Socher R, Manning CD. 2014. GloVe: global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods on Natural Language Processing, pp. 1532–43. New York: Assoc. Comput. Linguist.
6. Athey S. 2019. The impact of machine learning on economics. In The Economics of Artificial Intelligence: An Agenda, ed. AK Agrawal, J Gans, A Goldfarb. Chicago: Univ. Chicago Press. In press
7. Morris CN. 1983. Parametric empirical Bayes inference: theory and applications. J. Am. Stat. Assoc. 78:47–55