Outlook: DCP Midstream LP 7.95% Series C Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Units is assigned short-term Ba3 & long-term Baa2 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Hold
Time series to forecast n: for Weeks2
Methodology : Modular Neural Network (CNN Layer)
Hypothesis Testing : Ridge Regression
Surveillance : Major exchange and OTC

1The accuracy of the model is being monitored on a regular basis.(15-minute period)

2Time series is updated based on short-term trends.

Summary

DCP Midstream LP 7.95% Series C Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Units prediction model is evaluated with Modular Neural Network (CNN Layer) and Ridge Regression1,2,3,4 and it is concluded that the DCP^C stock is predictable in the short/long term. CNN layers are a powerful tool for extracting features from images. They are able to learn to detect patterns in images that are not easily detected by humans. This makes them well-suited for a variety of MNN applications. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Hold

Key Points

1. Stock Rating
2. Can neural networks predict stock market?
3. How can neural networks improve predictions?

DCP^C Target Price Prediction Modeling Methodology

We consider DCP Midstream LP 7.95% Series C Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Units Decision Process with Modular Neural Network (CNN Layer) where A is the set of discrete actions of DCP^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(Ridge Regression)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (CNN Layer)) X S(n):→ 16 Weeks $∑ i = 1 n r i$

n:Time series to forecast

p:Price signals of DCP^C stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Modular Neural Network (CNN Layer)

CNN layers are a powerful tool for extracting features from images. They are able to learn to detect patterns in images that are not easily detected by humans. This makes them well-suited for a variety of MNN applications.

Ridge Regression

Ridge regression is a type of regression analysis that adds a penalty to the least squares objective function in order to reduce the variance of the estimates. This is done by adding a term to the objective function that is proportional to the sum of the squares of the coefficients. The penalty term is called the "ridge" penalty, and it is controlled by a parameter called the "ridge constant". Ridge regression can be used to address the problem of multicollinearity in linear regression. Multicollinearity occurs when two or more independent variables are highly correlated. This can cause the standard errors of the coefficients to be large, and it can also cause the coefficients to be unstable. Ridge regression can help to reduce the standard errors of the coefficients and to make the coefficients more stable.

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?

DCP^C Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: DCP^C DCP Midstream LP 7.95% Series C Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Units
Time series to forecast: 16 Weeks

According to price forecasts, the dominant strategy among neural network is: Hold

Strategic Interaction Table Legend:

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%

Financial Data Adjustments for Modular Neural Network (CNN Layer) based DCP^C Stock Prediction Model

1. Time value of money is the element of interest that provides consideration for only the passage of time. That is, the time value of money element does not provide consideration for other risks or costs associated with holding the financial asset. In order to assess whether the element provides consideration for only the passage of time, an entity applies judgement and considers relevant factors such as the currency in which the financial asset is denominated and the period for which the interest rate is set.
2. 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.
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. Hedging relationships that qualified for hedge accounting in accordance with IAS 39 that also qualify for hedge accounting in accordance with the criteria of this Standard (see paragraph 6.4.1), after taking into account any rebalancing of the hedging relationship on transition (see paragraph 7.2.25(b)), shall be regarded as continuing hedging relationships.

*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.

DCP^C DCP Midstream LP 7.95% Series C Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Units Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba3Baa2
Income StatementBa2Baa2
Balance SheetBaa2Ba1
Leverage RatiosCCaa2
Cash FlowB3Baa2
Rates of Return and ProfitabilityBaa2Baa2

*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?

Conclusions

DCP Midstream LP 7.95% Series C Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Units is assigned short-term Ba3 & long-term Baa2 estimated rating. DCP Midstream LP 7.95% Series C Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Units prediction model is evaluated with Modular Neural Network (CNN Layer) and Ridge Regression1,2,3,4 and it is concluded that the DCP^C stock is predictable in the short/long term. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Hold

Prediction Confidence Score

Trust metric by Neural Network: 72 out of 100 with 682 signals.

References

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3. Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Apple's Stock Price: How News Affects Volatility. AC Investment Research Journal, 220(44).
4. Bastani H, Bayati M. 2015. Online decision-making with high-dimensional covariates. Work. Pap., Univ. Penn./ Stanford Grad. School Bus., Philadelphia/Stanford, CA
5. Wan M, Wang D, Goldman M, Taddy M, Rao J, et al. 2017. Modeling consumer preferences and price sensitiv- ities from large-scale grocery shopping transaction logs. In Proceedings of the 26th International Conference on the World Wide Web, pp. 1103–12. New York: ACM
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7. Matzkin RL. 2007. Nonparametric identification. In Handbook of Econometrics, Vol. 6B, ed. J Heckman, E Learner, pp. 5307–68. Amsterdam: Elsevier
Frequently Asked QuestionsQ: What is the prediction methodology for DCP^C stock?
A: DCP^C stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Ridge Regression
Q: Is DCP^C stock a buy or sell?
A: The dominant strategy among neural network is to Hold DCP^C Stock.
Q: Is DCP Midstream LP 7.95% Series C Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Units stock a good investment?
A: The consensus rating for DCP Midstream LP 7.95% Series C Fixed-to-Floating Rate Cumulative Redeemable Perpetual Preferred Units is Hold and is assigned short-term Ba3 & long-term Baa2 estimated rating.
Q: What is the consensus rating of DCP^C stock?
A: The consensus rating for DCP^C is Hold.
Q: What is the prediction period for DCP^C stock?
A: The prediction period for DCP^C is 16 Weeks