LON:EQT EQTEC PLC (Forecast)

Outlook: EQTEC PLC is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 05 Feb 2023 for (n+1 year)
Methodology : Modular Neural Network (Market News Sentiment Analysis)

Abstract

EQTEC PLC prediction model is evaluated with Modular Neural Network (Market News Sentiment Analysis) and Polynomial Regression^1,2,3,4 and it is concluded that the LON:EQT stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell

Key Points

1. Which neural network is best for prediction?
2. What are the most successful trading algorithms?
3. How useful are statistical predictions?

LON:EQT Target Price Prediction Modeling Methodology

We consider EQTEC PLC Decision Process with Modular Neural Network (Market News Sentiment Analysis) where A is the set of discrete actions of LON:EQT 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(Polynomial Regression)^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 (Market News Sentiment Analysis)) X S(n):→ (n+1 year) $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of LON:EQT 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?

LON:EQT Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: LON:EQT EQTEC PLC
Time series to forecast n: 05 Feb 2023 for (n+1 year)

According to price forecasts for (n+1 year) 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 EQTEC PLC

1. The change in the value of the hedged item determined using a hypothetical derivative may also be used for the purpose of assessing whether a hedging relationship meets the hedge effectiveness requirements.
2. Adjusting the hedge ratio by decreasing the volume of the hedging instrument does not affect how the changes in the value of the hedged item are measured. The measurement of the changes in the fair value of the hedging instrument related to the volume that continues to be designated also remains unaffected. However, from the date of rebalancing, the volume by which the hedging instrument was decreased is no longer part of the hedging relationship. For example, if an entity originally hedged the price risk of a commodity using a derivative volume of 100 tonnes as the hedging instrument and reduces that volume by 10 tonnes on rebalancing, a nominal amount of 90 tonnes of the hedging instrument volume would remain (see paragraph B6.5.16 for the consequences for the derivative volume (ie the 10 tonnes) that is no longer a part of the hedging relationship).
3. Such designation may be used whether paragraph 4.3.3 requires the embedded derivatives to be separated from the host contract or prohibits such separation. However, paragraph 4.3.5 would not justify designating the hybrid contract as at fair value through profit or loss in the cases set out in paragraph 4.3.5(a) and (b) because doing so would not reduce complexity or increase reliability.
4. An entity shall amend a hedging relationship as required in paragraph 6.9.1 by the end of the reporting period during which a change required by interest rate benchmark reform is made to the hedged risk, hedged item or hedging instrument. For the avoidance of doubt, such an amendment to the formal designation of a hedging relationship constitutes neither the discontinuation of the hedging relationship nor the designation of a new hedging relationship.

*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

EQTEC PLC is assigned short-term Ba1 & long-term Ba1 estimated rating. EQTEC PLC prediction model is evaluated with Modular Neural Network (Market News Sentiment Analysis) and Polynomial Regression^1,2,3,4 and it is concluded that the LON:EQT stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell

LON:EQT EQTEC PLC Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Ba3	C
Balance Sheet	B3	C
Leverage Ratios	B2	Baa2
Cash Flow	Ba1	B2
Rates of Return and Profitability	Baa2	Baa2

*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: 78 out of 100 with 568 signals.

References

1. Doudchenko N, Imbens GW. 2016. Balancing, regression, difference-in-differences and synthetic control methods: a synthesis. NBER Work. Pap. 22791
2. Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
3. Ashley, R. (1983), "On the usefulness of macroeconomic forecasts as inputs to forecasting models," Journal of Forecasting, 2, 211–223.
4. Hastie T, Tibshirani R, Wainwright M. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. New York: CRC Press
5. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2018a. Double/debiased machine learning for treatment and structural parameters. Econom. J. 21:C1–68
6. Chernozhukov V, Demirer M, Duflo E, Fernandez-Val I. 2018b. Generic machine learning inference on heteroge- nous treatment effects in randomized experiments. NBER Work. Pap. 24678
7. Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press

Frequently Asked Questions

Q: What is the prediction methodology for LON:EQT stock?
A: LON:EQT stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and Polynomial Regression
Q: Is LON:EQT stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:EQT Stock.
Q: Is EQTEC PLC stock a good investment?
A: The consensus rating for EQTEC PLC is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:EQT stock?
A: The consensus rating for LON:EQT is Sell.
Q: What is the prediction period for LON:EQT stock?
A: The prediction period for LON:EQT is (n+1 year)

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