LON:TTE TOTALENERGIES SE

Outlook: TOTALENERGIES SE is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 26 Feb 2023 for (n+4 weeks)
Methodology : Ensemble Learning (ML)

Abstract

TOTALENERGIES SE prediction model is evaluated with Ensemble Learning (ML) and Factor^1,2,3,4 and it is concluded that the LON:TTE stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Buy

Key Points

1. What is a prediction confidence?
2. Trust metric by Neural Network
3. Can neural networks predict stock market?

LON:TTE Target Price Prediction Modeling Methodology

We consider TOTALENERGIES SE Decision Process with Ensemble Learning (ML) where A is the set of discrete actions of LON:TTE 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(Factor)^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(Ensemble Learning (ML)) X S(n):→ (n+4 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of LON:TTE 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:TTE Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: LON:TTE TOTALENERGIES SE
Time series to forecast n: 26 Feb 2023 for (n+4 weeks)

According to price forecasts for (n+4 weeks) 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%

IFRS Reconciliation Adjustments for TOTALENERGIES SE

1. At the date of initial application, an entity shall use reasonable and supportable information that is available without undue cost or effort to determine the credit risk at the date that a financial instrument was initially recognised (or for loan commitments and financial guarantee contracts at the date that the entity became a party to the irrevocable commitment in accordance with paragraph 5.5.6) and compare that to the credit risk at the date of initial application of this Standard.
2. An entity's documentation of the hedging relationship includes how it will assess the hedge effectiveness requirements, including the method or methods used. The documentation of the hedging relationship shall be updated for any changes to the methods (see paragraph B6.4.17).
3. The accounting for the time value of options in accordance with paragraph 6.5.15 applies only to the extent that the time value relates to the hedged item (aligned time value). The time value of an option relates to the hedged item if the critical terms of the option (such as the nominal amount, life and underlying) are aligned with the hedged item. Hence, if the critical terms of the option and the hedged item are not fully aligned, an entity shall determine the aligned time value, ie how much of the time value included in the premium (actual time value) relates to the hedged item (and therefore should be treated in accordance with paragraph 6.5.15). An entity determines the aligned time value using the valuation of the option that would have critical terms that perfectly match the hedged item.
4. An entity can also designate only changes in the cash flows or fair value of a hedged item above or below a specified price or other variable (a 'one-sided risk'). The intrinsic value of a purchased option hedging instrument (assuming that it has the same principal terms as the designated risk), but not its time value, reflects a one-sided risk in a hedged item. For example, an entity can designate the variability of future cash flow outcomes resulting from a price increase of a forecast commodity purchase. In such a situation, the entity designates only cash flow losses that result from an increase in the price above the specified level. The hedged risk does not include the time value of a purchased option, because the time value is not a component of the forecast transaction that affects profit or loss.

*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

TOTALENERGIES SE is assigned short-term Ba1 & long-term Ba1 estimated rating. TOTALENERGIES SE prediction model is evaluated with Ensemble Learning (ML) and Factor^1,2,3,4 and it is concluded that the LON:TTE stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Buy

LON:TTE TOTALENERGIES SE Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Caa2	Baa2
Balance Sheet	Ba2	Ba3
Leverage Ratios	B1	Baa2
Cash Flow	Baa2	C
Rates of Return and Profitability	Caa2	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: 91 out of 100 with 662 signals.

References

1. M. Sobel. The variance of discounted Markov decision processes. Applied Probability, pages 794–802, 1982
2. Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
3. V. Borkar and R. Jain. Risk-constrained Markov decision processes. IEEE Transaction on Automatic Control, 2014
4. G. Shani, R. Brafman, and D. Heckerman. An MDP-based recommender system. In Proceedings of the Eigh- teenth conference on Uncertainty in artificial intelligence, pages 453–460. Morgan Kaufmann Publishers Inc., 2002
5. Athey S, Bayati M, Doudchenko N, Imbens G, Khosravi K. 2017a. Matrix completion methods for causal panel data models. arXiv:1710.10251 [math.ST]
6. Breiman L. 2001b. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16:199–231
7. Arora S, Li Y, Liang Y, Ma T. 2016. RAND-WALK: a latent variable model approach to word embeddings. Trans. Assoc. Comput. Linguist. 4:385–99

Frequently Asked Questions

Q: What is the prediction methodology for LON:TTE stock?
A: LON:TTE stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Factor
Q: Is LON:TTE stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:TTE Stock.
Q: Is TOTALENERGIES SE stock a good investment?
A: The consensus rating for TOTALENERGIES SE is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:TTE stock?
A: The consensus rating for LON:TTE is Buy.
Q: What is the prediction period for LON:TTE stock?
A: The prediction period for LON:TTE is (n+4 weeks)

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