ITE:TSX i3 Energy plc

Outlook: i3 Energy plc is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 31 May 2023 for (n+4 weeks)
Methodology : Active Learning (ML)

Abstract

i3 Energy plc prediction model is evaluated with Active Learning (ML) and Polynomial Regression^1,2,3,4 and it is concluded that the ITE:TSX stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Sell

Key Points

1. What is prediction model?
2. Can machine learning predict?
3. How do you know when a stock will go up or down?

ITE:TSX Target Price Prediction Modeling Methodology

We consider i3 Energy plc Decision Process with Active Learning (ML) where A is the set of discrete actions of ITE:TSX 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(Active Learning (ML)) X S(n):→ (n+4 weeks) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of ITE:TSX 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?

ITE:TSX Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: ITE:TSX i3 Energy plc
Time series to forecast n: 31 May 2023 for (n+4 weeks)

According to price forecasts for (n+4 weeks) 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 i3 Energy plc

1. Expected credit losses reflect an entity's own expectations of credit losses. However, when considering all reasonable and supportable information that is available without undue cost or effort in estimating expected credit losses, an entity should also consider observable market information about the credit risk of the particular financial instrument or similar financial instruments.
2. The definition of a derivative in this Standard includes contracts that are settled gross by delivery of the underlying item (eg a forward contract to purchase a fixed rate debt instrument). An entity may have a contract to buy or sell a non-financial item that can be settled net in cash or another financial instrument or by exchanging financial instruments (eg a contract to buy or sell a commodity at a fixed price at a future date). Such a contract is within the scope of this Standard unless it was entered into and continues to be held for the purpose of delivery of a non-financial item in accordance with the entity's expected purchase, sale or usage requirements. However, this Standard applies to such contracts for an entity's expected purchase, sale or usage requirements if the entity makes a designation in accordance with paragraph 2.5 (see paragraphs 2.4–2.7).
3. An entity is not required to incorporate forecasts of future conditions over the entire expected life of a financial instrument. The degree of judgement that is required to estimate expected credit losses depends on the availability of detailed information. As the forecast horizon increases, the availability of detailed information decreases and the degree of judgement required to estimate expected credit losses increases. The estimate of expected credit losses does not require a detailed estimate for periods that are far in the future—for such periods, an entity may extrapolate projections from available, detailed information.
4. When an entity first applies this Standard, it may choose as its accounting policy to continue to apply the hedge accounting requirements of IAS 39 instead of the requirements in Chapter 6 of this Standard. An entity shall apply that policy to all of its hedging relationships. An entity that chooses that policy shall also apply IFRIC 16 Hedges of a Net Investment in a Foreign Operation without the amendments that conform that Interpretation to the requirements in Chapter 6 of this Standard.

*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

i3 Energy plc is assigned short-term Ba1 & long-term Ba1 estimated rating. i3 Energy plc prediction model is evaluated with Active Learning (ML) and Polynomial Regression^1,2,3,4 and it is concluded that the ITE:TSX stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Sell

ITE:TSX i3 Energy plc Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Baa2	C
Balance Sheet	Baa2	Caa2
Leverage Ratios	Baa2	Baa2
Cash Flow	B1	B3
Rates of Return and Profitability	B3	Caa2

*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: 80 out of 100 with 523 signals.

References

1. Efron B, Hastie T, Johnstone I, Tibshirani R. 2004. Least angle regression. Ann. Stat. 32:407–99
2. ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Can Neural Networks Predict Stock Market? (No. Stock Analysis). AC Investment Research.
3. Akgiray, V. (1989), "Conditional heteroscedasticity in time series of stock returns: Evidence and forecasts," Journal of Business, 62, 55–80.
4. J. Peters, S. Vijayakumar, and S. Schaal. Natural actor-critic. In Proceedings of the Sixteenth European Conference on Machine Learning, pages 280–291, 2005.
5. Abadie A, Imbens GW. 2011. Bias-corrected matching estimators for average treatment effects. J. Bus. Econ. Stat. 29:1–11
6. Efron B, Hastie T. 2016. Computer Age Statistical Inference, Vol. 5. Cambridge, UK: Cambridge Univ. Press
7. R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972

Frequently Asked Questions

Q: What is the prediction methodology for ITE:TSX stock?
A: ITE:TSX stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Polynomial Regression
Q: Is ITE:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Sell ITE:TSX Stock.
Q: Is i3 Energy plc stock a good investment?
A: The consensus rating for i3 Energy plc is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of ITE:TSX stock?
A: The consensus rating for ITE:TSX is Sell.
Q: What is the prediction period for ITE:TSX stock?
A: The prediction period for ITE:TSX is (n+4 weeks)

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