Modelling A.I. in Economics

CVE:TSX Cenovus Energy Inc.

Outlook: Cenovus Energy Inc. is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 11 Feb 2023 for (n+4 weeks)
Methodology : Supervised Machine Learning (ML)

Abstract

Cenovus Energy Inc. prediction model is evaluated with Supervised Machine Learning (ML) and Wilcoxon Sign-Rank Test1,2,3,4 and it is concluded that the CVE: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: Buy

Key Points

  1. What is neural prediction?
  2. How do predictive algorithms actually work?
  3. How do you decide buy or sell a stock?

CVE:TSX Target Price Prediction Modeling Methodology

We consider Cenovus Energy Inc. Decision Process with Supervised Machine Learning (ML) where A is the set of discrete actions of CVE: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(Supervised Machine Learning (ML)) X S(n):→ (n+4 weeks) i = 1 n a i

n:Time series to forecast

p:Price signals of CVE: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?

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

Sample Set: Neural Network
Stock/Index: CVE:TSX Cenovus Energy Inc.
Time series to forecast n: 11 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 Cenovus Energy Inc.

  1. Alternatively, the entity may base the assessment on both types of information, ie qualitative factors that are not captured through the internal ratings process and a specific internal rating category at the reporting date, taking into consideration the credit risk characteristics at initial recognition, if both types of information are relevant.
  2. An entity is not required to restate prior periods to reflect the application of these amendments. The entity may restate prior periods only if it is possible to do so without the use of hindsight. If an entity restates prior periods, the restated financial statements must reflect all the requirements in this Standard for the affected financial instruments. If an entity does not restate prior periods, the entity shall recognise any difference between the previous carrying amount and the carrying amount at the beginning of the annual reporting period that includes the date of initial application of these amendments in the opening retained earnings (or other component of equity, as appropriate) of the annual reporting period that includes the date of initial application of these amendments.
  3. If an entity originates a loan that bears an off-market interest rate (eg 5 per cent when the market rate for similar loans is 8 per cent), and receives an upfront fee as compensation, the entity recognises the loan at its fair value, ie net of the fee it receives.
  4. For the avoidance of doubt, the effects of replacing the original counterparty with a clearing counterparty and making the associated changes as described in paragraph 6.5.6 shall be reflected in the measurement of the hedging instrument and therefore in the assessment of hedge effectiveness and the measurement of hedge effectiveness

*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

Cenovus Energy Inc. is assigned short-term Ba1 & long-term Ba1 estimated rating. Cenovus Energy Inc. prediction model is evaluated with Supervised Machine Learning (ML) and Wilcoxon Sign-Rank Test1,2,3,4 and it is concluded that the CVE: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: Buy

CVE:TSX Cenovus Energy Inc. Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementBa3Baa2
Balance SheetCB3
Leverage RatiosCaa2C
Cash FlowCaa2Baa2
Rates of Return and ProfitabilityB3B1

*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: 79 out of 100 with 783 signals.

References

  1. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., MO Stock Price Prediction. AC Investment Research Journal, 101(3).
  2. V. Borkar. Q-learning for risk-sensitive control. Mathematics of Operations Research, 27:294–311, 2002.
  3. uyer, S. Whiteson, B. Bakker, and N. A. Vlassis. Multiagent reinforcement learning for urban traffic control using coordination graphs. In Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I, pages 656–671, 2008.
  4. T. Morimura, M. Sugiyama, M. Kashima, H. Hachiya, and T. Tanaka. Nonparametric return distribution ap- proximation for reinforcement learning. In Proceedings of the 27th International Conference on Machine Learning, pages 799–806, 2010
  5. Dimakopoulou M, Zhou Z, Athey S, Imbens G. 2018. Balanced linear contextual bandits. arXiv:1812.06227 [cs.LG]
  6. A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016
  7. Bera, A. M. L. Higgins (1997), "ARCH and bilinearity as competing models for nonlinear dependence," Journal of Business Economic Statistics, 15, 43–50.
Frequently Asked QuestionsQ: What is the prediction methodology for CVE:TSX stock?
A: CVE:TSX stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Wilcoxon Sign-Rank Test
Q: Is CVE:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Buy CVE:TSX Stock.
Q: Is Cenovus Energy Inc. stock a good investment?
A: The consensus rating for Cenovus Energy Inc. is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of CVE:TSX stock?
A: The consensus rating for CVE:TSX is Buy.
Q: What is the prediction period for CVE:TSX stock?
A: The prediction period for CVE:TSX is (n+4 weeks)



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