Modelling A.I. in Economics

PPL:TSX Pembina Pipeline Corporation

Outlook: Pembina Pipeline Corporation is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 22 May 2023 for (n+4 weeks)
Methodology : Multi-Instance Learning (ML)

Abstract

Pembina Pipeline Corporation prediction model is evaluated with Multi-Instance Learning (ML) and Wilcoxon Sign-Rank Test1,2,3,4 and it is concluded that the PPL: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. Market Signals
  2. What are the most successful trading algorithms?
  3. Can neural networks predict stock market?

PPL:TSX Target Price Prediction Modeling Methodology

We consider Pembina Pipeline Corporation Decision Process with Multi-Instance Learning (ML) where A is the set of discrete actions of PPL: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(Wilcoxon Sign-Rank Test)5,6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Multi-Instance Learning (ML)) X S(n):→ (n+4 weeks) i = 1 n s i

n:Time series to forecast

p:Price signals of PPL: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?

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

Sample Set: Neural Network
Stock/Index: PPL:TSX Pembina Pipeline Corporation
Time series to forecast n: 22 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 Pembina Pipeline Corporation

  1. For the purpose of applying the requirements in paragraphs 6.4.1(c)(i) and B6.4.4–B6.4.6, an entity shall assume that the interest rate benchmark on which the hedged cash flows and/or the hedged risk (contractually or noncontractually specified) are based, or the interest rate benchmark on which the cash flows of the hedging instrument are based, is not altered as a result of interest rate benchmark reform.
  2. The underlying pool must contain one or more instruments that have contractual cash flows that are solely payments of principal and interest on the principal amount outstanding
  3. If changes are made in addition to those changes required by interest rate benchmark reform to the financial asset or financial liability designated in a hedging relationship (as described in paragraphs 5.4.6–5.4.8) or to the designation of the hedging relationship (as required by paragraph 6.9.1), an entity shall first apply the applicable requirements in this Standard to determine if those additional changes result in the discontinuation of hedge accounting. If the additional changes do not result in the discontinuation of hedge accounting, an entity shall amend the formal designation of the hedging relationship as specified in paragraph 6.9.1.
  4. The methods used to determine whether credit risk has increased significantly on a financial instrument since initial recognition should consider the characteristics of the financial instrument (or group of financial instruments) and the default patterns in the past for comparable financial instruments. Despite the requirement in paragraph 5.5.9, for financial instruments for which default patterns are not concentrated at a specific point during the expected life of the financial instrument, changes in the risk of a default occurring over the next 12 months may be a reasonable approximation of the changes in the lifetime risk of a default occurring. In such cases, an entity may use changes in the risk of a default occurring over the next 12 months to determine whether credit risk has increased significantly since initial recognition, unless circumstances indicate that a lifetime assessment is necessary

*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

Pembina Pipeline Corporation is assigned short-term Ba1 & long-term Ba1 estimated rating. Pembina Pipeline Corporation prediction model is evaluated with Multi-Instance Learning (ML) and Wilcoxon Sign-Rank Test1,2,3,4 and it is concluded that the PPL: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

PPL:TSX Pembina Pipeline Corporation Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementCaa2Caa2
Balance SheetCCaa2
Leverage RatiosB3Caa2
Cash FlowCaa2Baa2
Rates of Return and ProfitabilityCaa2Baa2

*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 509 signals.

References

  1. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Tempur Sealy Stock Forecast & Analysis. AC Investment Research Journal, 101(3).
  2. Andrews, D. W. K. W. Ploberger (1994), "Optimal tests when a nuisance parameter is present only under the alternative," Econometrica, 62, 1383–1414.
  3. D. Bertsekas. Nonlinear programming. Athena Scientific, 1999.
  4. Bickel P, Klaassen C, Ritov Y, Wellner J. 1998. Efficient and Adaptive Estimation for Semiparametric Models. Berlin: Springer
  5. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2018a. Double/debiased machine learning for treatment and structural parameters. Econom. J. 21:C1–68
  6. M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
  7. J. Ott. A Markov decision model for a surveillance application and risk-sensitive Markov decision processes. PhD thesis, Karlsruhe Institute of Technology, 2010.
Frequently Asked QuestionsQ: What is the prediction methodology for PPL:TSX stock?
A: PPL:TSX stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Wilcoxon Sign-Rank Test
Q: Is PPL:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Sell PPL:TSX Stock.
Q: Is Pembina Pipeline Corporation stock a good investment?
A: The consensus rating for Pembina Pipeline Corporation is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of PPL:TSX stock?
A: The consensus rating for PPL:TSX is Sell.
Q: What is the prediction period for PPL:TSX stock?
A: The prediction period for PPL:TSX is (n+4 weeks)



Stop Guessing, Start Winning.
Get Today's AI-Driven Picks.

Click here to see what the AI recommends.




Premium

  • Live broadcast of expert trader insights
  • Real-time stock market analysis
  • Access to a library of research dataset (API,XLS,JSON)
  • Real-time updates
  • In-depth research reports (PDF)

Login
This project is licensed under the license; additional terms may apply.