LON:NTQ ENTEQ TECHNOLOGIES PLC

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

Abstract

ENTEQ TECHNOLOGIES PLC prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Spearman Correlation^1,2,3,4 and it is concluded that the LON:NTQ 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. Can machine learning predict?
2. Which neural network is best for prediction?
3. Investment Risk

LON:NTQ Target Price Prediction Modeling Methodology

We consider ENTEQ TECHNOLOGIES PLC Decision Process with Modular Neural Network (News Feed Sentiment Analysis) where A is the set of discrete actions of LON:NTQ 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 (News Feed Sentiment Analysis)) X S(n):→ (n+1 year) $\begin{array}{l}\int e^x\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of LON:NTQ 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:NTQ Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: LON:NTQ ENTEQ TECHNOLOGIES PLC
Time series to forecast n: 05 May 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 ENTEQ TECHNOLOGIES PLC

1. For a financial guarantee contract, the entity is required to make payments only in the event of a default by the debtor in accordance with the terms of the instrument that is guaranteed. Accordingly, cash shortfalls are the expected payments to reimburse the holder for a credit loss that it incurs less any amounts that the entity expects to receive from the holder, the debtor or any other party. If the asset is fully guaranteed, the estimation of cash shortfalls for a financial guarantee contract would be consistent with the estimations of cash shortfalls for the asset subject to the guarantee
2. If the contractual cash flows on a financial asset have been renegotiated or otherwise modified, but the financial asset is not derecognised, that financial asset is not automatically considered to have lower credit risk. An entity shall assess whether there has been a significant increase in credit risk since initial recognition on the basis of all reasonable and supportable information that is available without undue cost or effort. This includes historical and forwardlooking information and an assessment of the credit risk over the expected life of the financial asset, which includes information about the circumstances that led to the modification. Evidence that the criteria for the recognition of lifetime expected credit losses are no longer met may include a history of up-to-date and timely payment performance against the modified contractual terms. Typically a customer would need to demonstrate consistently good payment behaviour over a period of time before the credit risk is considered to have decreased.
3. In accordance with the hedge effectiveness requirements, the hedge ratio of the hedging relationship must be the same as that resulting from the quantity of the hedged item that the entity actually hedges and the quantity of the hedging instrument that the entity actually uses to hedge that quantity of hedged item. Hence, if an entity hedges less than 100 per cent of the exposure on an item, such as 85 per cent, it shall designate the hedging relationship using a hedge ratio that is the same as that resulting from 85 per cent of the exposure and the quantity of the hedging instrument that the entity actually uses to hedge those 85 per cent. Similarly, if, for example, an entity hedges an exposure using a nominal amount of 40 units of a financial instrument, it shall designate the hedging relationship using a hedge ratio that is the same as that resulting from that quantity of 40 units (ie the entity must not use a hedge ratio based on a higher quantity of units that it might hold in total or a lower quantity of units) and the quantity of the hedged item that it actually hedges with those 40 units.
4. Contractual cash flows that are solely payments of principal and interest on the principal amount outstanding are consistent with a basic lending arrangement. In a basic lending arrangement, consideration for the time value of money (see paragraphs B4.1.9A–B4.1.9E) and credit risk are typically the most significant elements of interest. However, in such an arrangement, interest can also include consideration for other basic lending risks (for example, liquidity risk) and costs (for example, administrative costs) associated with holding the financial asset for a particular period of time. In addition, interest can include a profit margin that is consistent with a basic lending arrangement. In extreme economic circumstances, interest can be negative if, for example, the holder of a financial asset either explicitly or implicitly pays for the deposit of its money for a particular period of time (and that fee exceeds the consideration that the holder receives for the time value of money, credit risk and other basic lending risks and costs).

*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

ENTEQ TECHNOLOGIES PLC is assigned short-term Ba1 & long-term Ba1 estimated rating. ENTEQ TECHNOLOGIES PLC prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Spearman Correlation^1,2,3,4 and it is concluded that the LON:NTQ 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:NTQ ENTEQ TECHNOLOGIES PLC Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Baa2	B1
Balance Sheet	B2	B2
Leverage Ratios	Baa2	Baa2
Cash Flow	Baa2	Caa2
Rates of Return and Profitability	Baa2	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: 87 out of 100 with 761 signals.

References

1. Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
2. Meinshausen N. 2007. Relaxed lasso. Comput. Stat. Data Anal. 52:374–93
3. A. Shapiro, W. Tekaya, J. da Costa, and M. Soares. Risk neutral and risk averse stochastic dual dynamic programming method. European journal of operational research, 224(2):375–391, 2013
4. Alexander, J. C. Jr. (1995), "Refining the degree of earnings surprise: A comparison of statistical and analysts' forecasts," Financial Review, 30, 469–506.
5. Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
6. J. Ott. A Markov decision model for a surveillance application and risk-sensitive Markov decision processes. PhD thesis, Karlsruhe Institute of Technology, 2010.
7. Meinshausen N. 2007. Relaxed lasso. Comput. Stat. Data Anal. 52:374–93

Frequently Asked Questions

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

Premium

• Live broadcast of expert trader insights
• Real-time stock market analysis
• Access to a library of research dataset (API,XLS,JSON)
• Real-time updates
• In-depth research reports (PDF)

Login