EHTH eHealth Inc. Common Stock (Forecast)

Outlook: eHealth Inc. Common Stock assigned short-term Caa2 & long-term B1 forecasted stock rating.
Dominant Strategy : Buy
Time series to forecast n: 16 Dec 2022 for (n+6 month)
Methodology : Modular Neural Network (Financial Sentiment Analysis)

Abstract

The nature of stock market movement has always been ambiguous for investors because of various influential factors. This study aims to significantly reduce the risk of trend prediction with machine learning and deep learning algorithms.(Devadoss, A.V. and Ligori, T.A.A., 2013. Stock prediction using artificial neural networks. International Journal of Data Mining Techniques and Applications, 2(1), pp.283-291.) We evaluate eHealth Inc. Common Stock prediction models with Modular Neural Network (Financial Sentiment Analysis) and Sign Test^1,2,3,4 and conclude that the EHTH stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Buy

Key Points

1. Can stock prices be predicted?
2. What are the most successful trading algorithms?
3. Can stock prices be predicted?

EHTH Target Price Prediction Modeling Methodology

We consider eHealth Inc. Common Stock Decision Process with Modular Neural Network (Financial Sentiment Analysis) where A is the set of discrete actions of EHTH 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(Sign Test)^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 (Financial Sentiment Analysis)) X S(n):→ (n+6 month) $\overrightarrow{R}=\left(r_1,r_2,r_3\right)$

n:Time series to forecast

p:Price signals of EHTH 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?

EHTH Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: EHTH eHealth Inc. Common Stock
Time series to forecast n: 16 Dec 2022 for (n+6 month)

According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Buy

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%

Adjusted IFRS* Prediction Methods for eHealth Inc. Common Stock

1. An entity must look through until it can identify the underlying pool of instruments that are creating (instead of passing through) the cash flows. This is the underlying pool of financial instruments.
2. It would not be acceptable to designate only some of the financial assets and financial liabilities giving rise to the inconsistency as at fair value through profit or loss if to do so would not eliminate or significantly reduce the inconsistency and would therefore not result in more relevant information. However, it would be acceptable to designate only some of a number of similar financial assets or similar financial liabilities if doing so achieves a significant reduction (and possibly a greater reduction than other allowable designations) in the inconsistency. For example, assume an entity has a number of similar financial liabilities that sum to CU100 and a number of similar financial assets that sum to CU50 but are measured on a different basis. The entity may significantly reduce the measurement inconsistency by designating at initial recognition all of the assets but only some of the liabilities (for example, individual liabilities with a combined total of CU45) as at fair value through profit or loss. However, because designation as at fair value through profit or loss can be applied only to the whole of a financial instrument, the entity in this example must designate one or more liabilities in their entirety. It could not designate either a component of a liability (eg changes in value attributable to only one risk, such as changes in a benchmark interest rate) or a proportion (ie percentage) of a liability.
3. When measuring a loss allowance for a lease receivable, the cash flows used for determining the expected credit losses should be consistent with the cash flows used in measuring the lease receivable in accordance with IFRS 16 Leases.
4. Annual Improvements to IFRSs 2010–2012 Cycle, issued in December 2013, amended paragraphs 4.2.1 and 5.7.5 as a consequential amendment derived from the amendment to IFRS 3. An entity shall apply that amendment prospectively to business combinations to which the amendment to IFRS 3 applies.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

Conclusions

eHealth Inc. Common Stock assigned short-term Caa2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with Sign Test^1,2,3,4 and conclude that the EHTH stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Buy

Financial State Forecast for EHTH eHealth Inc. Common Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Caa2	B1
Operational Risk	32	52
Market Risk	39	88
Technical Analysis	45	40
Fundamental Analysis	33	50
Risk Unsystematic	39	48

Prediction Confidence Score

Trust metric by Neural Network: 82 out of 100 with 779 signals.

References

1. Bell RM, Koren Y. 2007. Lessons from the Netflix prize challenge. ACM SIGKDD Explor. Newsl. 9:75–79
2. F. A. Oliehoek, M. T. J. Spaan, and N. A. Vlassis. Optimal and approximate q-value functions for decentralized pomdps. J. Artif. Intell. Res. (JAIR), 32:289–353, 2008
3. Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.
4. J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 2137–2145, 2016.
5. Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
6. Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, et al. 2008. Top 10 algorithms in data mining. Knowl. Inform. Syst. 14:1–37
7. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Tempur Sealy Stock Forecast & Analysis. AC Investment Research Journal, 101(3).

Frequently Asked Questions

Q: What is the prediction methodology for EHTH stock?
A: EHTH stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Sign Test
Q: Is EHTH stock a buy or sell?
A: The dominant strategy among neural network is to Buy EHTH Stock.
Q: Is eHealth Inc. Common Stock stock a good investment?
A: The consensus rating for eHealth Inc. Common Stock is Buy and assigned short-term Caa2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of EHTH stock?
A: The consensus rating for EHTH is Buy.
Q: What is the prediction period for EHTH stock?
A: The prediction period for EHTH is (n+6 month)

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