ET^D Stock Forecast: A Sell For The Next 8 Weeks

Outlook: Energy Transfer L.P. 7.625% Series D Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Unit is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 09 Jun 2023 for 8 Weeks
Methodology : Transfer Learning (ML)

Abstract

Energy Transfer L.P. 7.625% Series D Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Unit prediction model is evaluated with Transfer Learning (ML) and Statistical Hypothesis Testing^1,2,3,4 and it is concluded that the ET^D stock is predictable in the short/long term. Transfer learning is a machine learning (ML) method where a model developed for one task is reused as the starting point for a model on a second task. This can be useful when the second task is similar to the first task, or when there is limited data available for the second task. According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Sell

Key Points

1. Trust metric by Neural Network
2. Can neural networks predict stock market?
3. What is the use of Markov decision process?

ET^D Target Price Prediction Modeling Methodology

We consider Energy Transfer L.P. 7.625% Series D Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Unit Decision Process with Transfer Learning (ML) where A is the set of discrete actions of ET^D 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(Statistical Hypothesis Testing)^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(Transfer Learning (ML)) X S(n):→ 8 Weeks $\overrightarrow{R}=\left(r_1,r_2,r_3\right)$

n:Time series to forecast

p:Price signals of ET^D stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Transfer learning is a machine learning (ML) method where a model developed for one task is reused as the starting point for a model on a second task. This can be useful when the second task is similar to the first task, or when there is limited data available for the second task.

Statistical hypothesis testing is a process used to determine whether there is enough evidence to support a claim about a population based on a sample. The process involves making two hypotheses, a null hypothesis and an alternative hypothesis, and then collecting data and using statistical tests to determine which hypothesis is more likely to be true. The null hypothesis is the statement that there is no difference between the population and the sample. The alternative hypothesis is the statement that there is a difference between the population and the sample. The statistical test is used to calculate a p-value, which is the probability of obtaining the observed data or more extreme data if the null hypothesis is true. A p-value of less than 0.05 is typically considered to be statistically significant, which means that there is less than a 5% chance of obtaining the observed data or more extreme data if the null hypothesis is true.

 

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^D Stock Forecast (Buy or Sell) for 8 Weeks

Sample Set: Neural Network
Stock/Index: ET^D Energy Transfer L.P. 7.625% Series D Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Unit
Time series to forecast n: 09 Jun 2023 for 8 Weeks

According to price forecasts for 8 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 Energy Transfer L.P. 7.625% Series D Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Unit

1. A single hedging instrument may be designated as a hedging instrument of more than one type of risk, provided that there is a specific designation of the hedging instrument and of the different risk positions as hedged items. Those hedged items can be in different hedging relationships.
2. There is a rebuttable presumption that unless inflation risk is contractually specified, it is not separately identifiable and reliably measurable and hence cannot be designated as a risk component of a financial instrument. However, in limited cases, it is possible to identify a risk component for inflation risk that is separately identifiable and reliably measurable because of the particular circumstances of the inflation environment and the relevant debt market
3. The decision of an entity to designate a financial asset or financial liability as at fair value through profit or loss is similar to an accounting policy choice (although, unlike an accounting policy choice, it is not required to be applied consistently to all similar transactions). When an entity has such a choice, paragraph 14(b) of IAS 8 requires the chosen policy to result in the financial statements providing reliable and more relevant information about the effects of transactions, other events and conditions on the entity's financial position, financial performance or cash flows. For example, in the case of designation of a financial liability as at fair value through profit or loss, paragraph 4.2.2 sets out the two circumstances when the requirement for more relevant information will be met. Accordingly, to choose such designation in accordance with paragraph 4.2.2, the entity needs to demonstrate that it falls within one (or both) of these two circumstances.
4. Rebalancing refers to the adjustments made to the designated quantities of the hedged item or the hedging instrument of an already existing hedging relationship for the purpose of maintaining a hedge ratio that complies with the hedge effectiveness requirements. Changes to designated quantities of a hedged item or of a hedging instrument for a different purpose do not constitute rebalancing for the purpose 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

Energy Transfer L.P. 7.625% Series D Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Unit is assigned short-term Ba1 & long-term Ba1 estimated rating. Energy Transfer L.P. 7.625% Series D Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Unit prediction model is evaluated with Transfer Learning (ML) and Statistical Hypothesis Testing^1,2,3,4 and it is concluded that the ET^D stock is predictable in the short/long term. According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Sell

ET^D Energy Transfer L.P. 7.625% Series D Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Unit Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	C	Caa2
Balance Sheet	Baa2	B2
Leverage Ratios	Ba2	Baa2
Cash Flow	Baa2	B2
Rates of Return and Profitability	Baa2	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: 86 out of 100 with 528 signals.

References

1. Canova, F. B. E. Hansen (1995), "Are seasonal patterns constant over time? A test for seasonal stability," Journal of Business and Economic Statistics, 13, 237–252.
2. C. Szepesvári. Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2010
3. Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
4. R. Rockafellar and S. Uryasev. Conditional value-at-risk for general loss distributions. Journal of Banking and Finance, 26(7):1443 – 1471, 2002
5. Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
6. Meinshausen N. 2007. Relaxed lasso. Comput. Stat. Data Anal. 52:374–93
7. C. Szepesvári. Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2010

Frequently Asked Questions

Q: What is the prediction methodology for ET^D stock?
A: ET^D stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Statistical Hypothesis Testing
Q: Is ET^D stock a buy or sell?
A: The dominant strategy among neural network is to Sell ET^D Stock.
Q: Is Energy Transfer L.P. 7.625% Series D Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Unit stock a good investment?
A: The consensus rating for Energy Transfer L.P. 7.625% Series D Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Unit is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of ET^D stock?
A: The consensus rating for ET^D is Sell.
Q: What is the prediction period for ET^D stock?
A: The prediction period for ET^D is 8 Weeks