ET^C Energy Transfer L.P. 7.375% Series C Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Unit

Outlook: Energy Transfer L.P. 7.375% Series C Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Unit is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : BuyWait until speculative trend diminishes
Time series to forecast n: 31 Jan 2023 for (n+3 month)
Methodology : Modular Neural Network (DNN Layer)

Abstract

Energy Transfer L.P. 7.375% Series C Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Unit prediction model is evaluated with Modular Neural Network (DNN Layer) and Logistic Regression^1,2,3,4 and it is concluded that the ET^C stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: BuyWait until speculative trend diminishes

Key Points

1. How do predictive algorithms actually work?
2. Understanding Buy, Sell, and Hold Ratings
3. How do predictive algorithms actually work?

ET^C Target Price Prediction Modeling Methodology

We consider Energy Transfer L.P. 7.375% Series C Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Unit Decision Process with Modular Neural Network (DNN Layer) where A is the set of discrete actions of ET^C 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(Logistic 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 (DNN Layer)) X S(n):→ (n+3 month) $\overrightarrow{R}=\left(r_1,r_2,r_3\right)$

n:Time series to forecast

p:Price signals of ET^C 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?

ET^C Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: ET^C Energy Transfer L.P. 7.375% Series C Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Unit
Time series to forecast n: 31 Jan 2023 for (n+3 month)

According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: BuyWait until speculative trend diminishes

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 Energy Transfer L.P. 7.375% Series C Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Unit

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. As noted in paragraph B4.3.1, when an entity becomes a party to a hybrid contract with a host that is not an asset within the scope of this Standard and with one or more embedded derivatives, paragraph 4.3.3 requires the entity to identify any such embedded derivative, assess whether it is required to be separated from the host contract and, for those that are required to be separated, measure the derivatives at fair value at initial recognition and subsequently. These requirements can be more complex, or result in less reliable measures, than measuring the entire instrument at fair value through profit or loss. For that reason this Standard permits the entire hybrid contract to be designated as at fair value through profit or loss.
3. For example, an entity may use this condition to designate financial liabilities as at fair value through profit or loss if it meets the principle in paragraph 4.2.2(b) and the entity has financial assets and financial liabilities that share one or more risks and those risks are managed and evaluated on a fair value basis in accordance with a documented policy of asset and liability management. An example could be an entity that has issued 'structured products' containing multiple embedded derivatives and manages the resulting risks on a fair value basis using a mix of derivative and non-derivative financial instruments
4. Historical information is an important anchor or base from which to measure expected credit losses. However, an entity shall adjust historical data, such as credit loss experience, on the basis of current observable data to reflect the effects of the current conditions and its forecasts of future conditions that did not affect the period on which the historical data is based, and to remove the effects of the conditions in the historical period that are not relevant to the future contractual cash flows. In some cases, the best reasonable and supportable information could be the unadjusted historical information, depending on the nature of the historical information and when it was calculated, compared to circumstances at the reporting date and the characteristics of the financial instrument being considered. Estimates of changes in expected credit losses should reflect, and be directionally consistent with, changes in related observable data from period to period

*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

Energy Transfer L.P. 7.375% Series C Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Unit is assigned short-term Ba1 & long-term Ba1 estimated rating. Energy Transfer L.P. 7.375% Series C Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Unit prediction model is evaluated with Modular Neural Network (DNN Layer) and Logistic Regression^1,2,3,4 and it is concluded that the ET^C stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: BuyWait until speculative trend diminishes

ET^C Energy Transfer L.P. 7.375% Series C Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Unit Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Ba3	B3
Balance Sheet	Baa2	C
Leverage Ratios	B1	C
Cash Flow	C	Caa2
Rates of Return and Profitability	C	B3

*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: 82 out of 100 with 814 signals.

References

1. Athey S, Imbens G, Wager S. 2016a. Efficient inference of average treatment effects in high dimensions via approximate residual balancing. arXiv:1604.07125 [math.ST]
2. T. Shardlow and A. Stuart. A perturbation theory for ergodic Markov chains and application to numerical approximations. SIAM journal on numerical analysis, 37(4):1120–1137, 2000
3. V. Konda and J. Tsitsiklis. Actor-Critic algorithms. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1008–1014, 2000
4. Candès EJ, Recht B. 2009. Exact matrix completion via convex optimization. Found. Comput. Math. 9:717
5. Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221
6. V. Borkar. A sensitivity formula for the risk-sensitive cost and the actor-critic algorithm. Systems & Control Letters, 44:339–346, 2001
7. Belsley, D. A. (1988), "Modelling and forecast reliability," International Journal of Forecasting, 4, 427–447.

Frequently Asked Questions

Q: What is the prediction methodology for ET^C stock?
A: ET^C stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Logistic Regression
Q: Is ET^C stock a buy or sell?
A: The dominant strategy among neural network is to BuyWait until speculative trend diminishes ET^C Stock.
Q: Is Energy Transfer L.P. 7.375% Series C Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Unit stock a good investment?
A: The consensus rating for Energy Transfer L.P. 7.375% Series C Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Unit is BuyWait until speculative trend diminishes and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of ET^C stock?
A: The consensus rating for ET^C is BuyWait until speculative trend diminishes.
Q: What is the prediction period for ET^C stock?
A: The prediction period for ET^C is (n+3 month)

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