Sempra Stock Forecast, Price & Rating (SRE)

In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders' expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. We evaluate Sempra prediction models with Modular Neural Network (Speculative Sentiment Analysis) and Spearman Correlation^1,2,3,4 and conclude that the SRE stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold SRE stock.

Keywords: SRE, Sempra, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. Is Target price a good indicator?
2. Stock Rating
3. How do you pick a stock?

SRE Target Price Prediction Modeling Methodology

Predicting the future price of financial assets has always been an important research topic in the field of quantitative finance. This paper attempts to use the latest artificial intelligence technologies to design and implement a framework for financial asset price prediction. We consider Sempra Stock Decision Process with Spearman Correlation where A is the set of discrete actions of SRE stock holders, F is the set of discrete states, P : S × F × S → R is the transition probability distribution, R : S × F → R is the reaction function, and γ ∈ [0, 1] is a move factor for expectation.^1,2,3,4

F(Spearman Correlation)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (Speculative Sentiment Analysis)) X S(n):→ (n+1 year) $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

n:Time series to forecast

p:Price signals of SRE stock

j:Nash equilibria

k:Dominated move

a:Best response for target price

 

For further technical information as per how our model work we invite you to visit the article below: 

How do AC Investment Research machine learning (predictive) algorithms actually work?

SRE Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: SRE Sempra
Time series to forecast n: 15 Oct 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold SRE stock.

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 (Yellow to Green): *Technical Analysis%

Conclusions

Sempra assigned short-term Caa2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) with Spearman Correlation^1,2,3,4 and conclude that the SRE stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold SRE stock.

Financial State Forecast for SRE Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Caa2	B1
Operational Risk	30	64
Market Risk	50	66
Technical Analysis	40	65
Fundamental Analysis	33	32
Risk Unsystematic	51	64

Prediction Confidence Score

Trust metric by Neural Network: 86 out of 100 with 455 signals.

References

1. Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
2. Bengio Y, Ducharme R, Vincent P, Janvin C. 2003. A neural probabilistic language model. J. Mach. Learn. Res. 3:1137–55
3. P. Milgrom and I. Segal. Envelope theorems for arbitrary choice sets. Econometrica, 70(2):583–601, 2002
4. N. B ̈auerle and J. Ott. Markov decision processes with average-value-at-risk criteria. Mathematical Methods of Operations Research, 74(3):361–379, 2011
5. R. Rockafellar and S. Uryasev. Conditional value-at-risk for general loss distributions. Journal of Banking and Finance, 26(7):1443 – 1471, 2002
6. Athey S, Wager S. 2017. Efficient policy learning. arXiv:1702.02896 [math.ST]
7. Bessler, D. A. T. Covey (1991), "Cointegration: Some results on U.S. cattle prices," Journal of Futures Markets, 11, 461–474.

Frequently Asked Questions

Q: What is the prediction methodology for SRE stock?
A: SRE stock prediction methodology: We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) and Spearman Correlation
Q: Is SRE stock a buy or sell?
A: The dominant strategy among neural network is to Hold SRE Stock.
Q: Is Sempra stock a good investment?
A: The consensus rating for Sempra is Hold and assigned short-term Caa2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of SRE stock?
A: The consensus rating for SRE is Hold.
Q: What is the prediction period for SRE stock?
A: The prediction period for SRE is (n+1 year)

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