SHEN Shenandoah Telecommunications Co Common Stock

Outlook: Shenandoah Telecommunications Co Common Stock assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 26 Dec 2022 for (n+3 month)
Methodology : Modular Neural Network (Emotional Trigger/Responses Analysis)

Abstract

Nowadays, the stock market's prediction is a topic that attracted researchers in the world. Stock market prediction is a process that requires a comprehensive understanding of the data stock movement and analysis it accurately. Therefore, it needs intelligent methods to deal with this task to ensure that the prediction is as correct as possible, which will return profitable benefits to investors. The main goal of this article is the employment of effective machine learning techniques to build a strong model for stock market prediction.(Hegazy, O., Soliman, O.S. and Salam, M.A., 2014. A machine learning model for stock market prediction. arXiv preprint arXiv:1402.7351.) We evaluate Shenandoah Telecommunications Co Common Stock prediction models with Modular Neural Network (Emotional Trigger/Responses Analysis) and Statistical Hypothesis Testing^1,2,3,4 and conclude that the SHEN stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Sell

Key Points

1. Investment Risk
2. What are main components of Markov decision process?
3. Reaction Function

SHEN Target Price Prediction Modeling Methodology

We consider Shenandoah Telecommunications Co Common Stock Decision Process with Modular Neural Network (Emotional Trigger/Responses Analysis) where A is the set of discrete actions of SHEN 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(Statistical Hypothesis Testing)^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 (Emotional Trigger/Responses Analysis)) X S(n):→ (n+3 month) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of SHEN stock

j:Nash equilibria (Neural Network)

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?

SHEN Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: SHEN Shenandoah Telecommunications Co Common Stock
Time series to forecast n: 26 Dec 2022 for (n+3 month)

According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Sell

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%

IFRS Reconciliation Adjustments for Shenandoah Telecommunications Co Common Stock

1. In accordance with paragraph 4.1.3(a), principal is the fair value of the financial asset at initial recognition. However that principal amount may change over the life of the financial asset (for example, if there are repayments of principal).
2. Paragraph 5.5.4 requires that lifetime expected credit losses are recognised on all financial instruments for which there has been significant increases in credit risk since initial recognition. In order to meet this objective, if an entity is not able to group financial instruments for which the credit risk is considered to have increased significantly since initial recognition based on shared credit risk characteristics, the entity should recognise lifetime expected credit losses on a portion of the financial assets for which credit risk is deemed to have increased significantly. The aggregation of financial instruments to assess whether there are changes in credit risk on a collective basis may change over time as new information becomes available on groups of, or individual, financial instruments.
3. IFRS 15, issued in May 2014, amended paragraphs 3.1.1, 4.2.1, 5.1.1, 5.2.1, 5.7.6, B3.2.13, B5.7.1, C5 and C42 and deleted paragraph C16 and its related heading. Paragraphs 5.1.3 and 5.7.1A, and a definition to Appendix A, were added. An entity shall apply those amendments when it applies IFRS 15.
4. An alternative benchmark rate designated as a non-contractually specified risk component that is not separately identifiable (see paragraphs 6.3.7(a) and B6.3.8) at the date it is designated shall be deemed to have met that requirement at that date, if, and only if, the entity reasonably expects the alternative benchmark rate will be separately identifiable within 24 months. The 24-month period applies to each alternative benchmark rate separately and starts from the date the entity designates the alternative benchmark rate as a non-contractually specified risk component for the first time (ie the 24- month period applies on a rate-by-rate basis).

*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.

Conclusions

Shenandoah Telecommunications Co Common Stock assigned short-term Ba1 & long-term Ba1 estimated rating. We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) with Statistical Hypothesis Testing^1,2,3,4 and conclude that the SHEN stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Sell

SHEN Shenandoah Telecommunications Co Common Stock Financial Analysis*

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

*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?

Prediction Confidence Score

Trust metric by Neural Network: 92 out of 100 with 747 signals.

References

1. Tibshirani R, Hastie T. 1987. Local likelihood estimation. J. Am. Stat. Assoc. 82:559–67
2. Andrews, D. W. K. W. Ploberger (1994), "Optimal tests when a nuisance parameter is present only under the alternative," Econometrica, 62, 1383–1414.
3. Mnih A, Kavukcuoglu K. 2013. Learning word embeddings efficiently with noise-contrastive estimation. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 2265–73. San Diego, CA: Neural Inf. Process. Syst. Found.
4. J. Hu and M. P. Wellman. Nash q-learning for general-sum stochastic games. Journal of Machine Learning Research, 4:1039–1069, 2003.
5. Babula, R. A. (1988), "Contemporaneous correlation and modeling Canada's imports of U.S. crops," Journal of Agricultural Economics Research, 41, 33–38.
6. C. Szepesvári. Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2010
7. uyer, S. Whiteson, B. Bakker, and N. A. Vlassis. Multiagent reinforcement learning for urban traffic control using coordination graphs. In Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I, pages 656–671, 2008.

Frequently Asked Questions

Q: What is the prediction methodology for SHEN stock?
A: SHEN stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Statistical Hypothesis Testing
Q: Is SHEN stock a buy or sell?
A: The dominant strategy among neural network is to Sell SHEN Stock.
Q: Is Shenandoah Telecommunications Co Common Stock stock a good investment?
A: The consensus rating for Shenandoah Telecommunications Co Common Stock is Sell and assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of SHEN stock?
A: The consensus rating for SHEN is Sell.
Q: What is the prediction period for SHEN stock?
A: The prediction period for SHEN is (n+3 month)

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