EHE Stock: A Cautionary Tale

Outlook: ESTIA HEALTH LIMITED is assigned short-term B1 & long-term Ba3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : Hold
Time series to forecast n: for Weeks²
Methodology : Inductive Learning (ML)
Hypothesis Testing : Beta
Surveillance : Major exchange and OTC

¹The accuracy of the model is being monitored on a regular basis.(15-minute period)

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

Summary

ESTIA HEALTH LIMITED prediction model is evaluated with Inductive Learning (ML) and Beta^1,2,3,4 and it is concluded that the EHE stock is predictable in the short/long term. Inductive learning is a type of machine learning in which the model learns from a set of labeled data and makes predictions about new, unlabeled data. The model is trained on the labeled data and then used to make predictions on new data. Inductive learning is a supervised learning algorithm, which means that it requires labeled data to train. The labeled data is used to train the model to make predictions about new data. There are many different types of inductive learning algorithms, including decision trees, support vector machines, and neural networks. Each type of algorithm has its own strengths and weaknesses. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Hold

Key Points

1. How do predictive algorithms actually work?
2. Market Outlook
3. What is statistical models in machine learning?

EHE Target Price Prediction Modeling Methodology

We consider ESTIA HEALTH LIMITED Decision Process with Inductive Learning (ML) where A is the set of discrete actions of EHE 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(Beta)^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(Inductive Learning (ML)) X S(n):→ 6 Month $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of EHE stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Inductive Learning (ML)

Inductive learning is a type of machine learning in which the model learns from a set of labeled data and makes predictions about new, unlabeled data. The model is trained on the labeled data and then used to make predictions on new data. Inductive learning is a supervised learning algorithm, which means that it requires labeled data to train. The labeled data is used to train the model to make predictions about new data. There are many different types of inductive learning algorithms, including decision trees, support vector machines, and neural networks. Each type of algorithm has its own strengths and weaknesses.

Beta

In statistics, beta (β) is a measure of the strength of the relationship between two variables. It is calculated as the slope of the line of best fit in a regression analysis. Beta can range from -1 to 1, with a value of 0 indicating no relationship between the two variables. A positive beta indicates that as one variable increases, the other variable also increases. A negative beta indicates that as one variable increases, the other variable decreases. For example, a study might find that there is a positive relationship between height and weight. This means that taller people tend to weigh more. The beta coefficient for this relationship would be positive.

 

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?

EHE Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: EHE ESTIA HEALTH LIMITED
Time series to forecast: 6 Month

According to price forecasts, the dominant strategy among neural network is: Hold

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%

Financial Data Adjustments for Inductive Learning (ML) based EHE Stock Prediction Model

1. Lifetime expected credit losses are not recognised on a financial instrument simply because it was considered to have low credit risk in the previous reporting period and is not considered to have low credit risk at the reporting date. In such a case, an entity shall determine whether there has been a significant increase in credit risk since initial recognition and thus whether lifetime expected credit losses are required to be recognised in accordance with paragraph 5.5.3.
2. An entity that first applies these amendments at the same time it first applies this Standard shall apply paragraphs 7.2.1–7.2.28 instead of paragraphs 7.2.31–7.2.34.
3. Lifetime expected credit losses are not recognised on a financial instrument simply because it was considered to have low credit risk in the previous reporting period and is not considered to have low credit risk at the reporting date. In such a case, an entity shall determine whether there has been a significant increase in credit risk since initial recognition and thus whether lifetime expected credit losses are required to be recognised in accordance with paragraph 5.5.3.
4. An entity shall apply Prepayment Features with Negative Compensation (Amendments to IFRS 9) retrospectively in accordance with IAS 8, except as specified in paragraphs 7.2.30–7.2.34

*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.

EHE ESTIA HEALTH LIMITED Financial Analysis*

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

Conclusions

ESTIA HEALTH LIMITED is assigned short-term B1 & long-term Ba3 estimated rating. ESTIA HEALTH LIMITED prediction model is evaluated with Inductive Learning (ML) and Beta^1,2,3,4 and it is concluded that the EHE stock is predictable in the short/long term. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Hold

Prediction Confidence Score

Trust metric by Neural Network: 88 out of 100 with 530 signals.

References

1. S. J. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs, NJ, 3nd edition, 2010
2. M. Petrik and D. Subramanian. An approximate solution method for large risk-averse Markov decision processes. In Proceedings of the 28th International Conference on Uncertainty in Artificial Intelligence, 2012.
3. H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.
4. A. Tamar and S. Mannor. Variance adjusted actor critic algorithms. arXiv preprint arXiv:1310.3697, 2013.
5. P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998
6. Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
7. Batchelor, R. P. Dua (1993), "Survey vs ARCH measures of inflation uncertainty," Oxford Bulletin of Economics Statistics, 55, 341–353.

Frequently Asked Questions

Q: What is the prediction methodology for EHE stock?
A: EHE stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Beta
Q: Is EHE stock a buy or sell?
A: The dominant strategy among neural network is to Hold EHE Stock.
Q: Is ESTIA HEALTH LIMITED stock a good investment?
A: The consensus rating for ESTIA HEALTH LIMITED is Hold and is assigned short-term B1 & long-term Ba3 estimated rating.
Q: What is the consensus rating of EHE stock?
A: The consensus rating for EHE is Hold.
Q: What is the prediction period for EHE stock?
A: The prediction period for EHE is 6 Month

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