**Outlook:**Crescent Point Energy Corp. assigned short-term B2 & long-term Ba3 forecasted stock rating.

**Dominant Strategy :**Hold

**Time series to forecast n: 07 Dec 2022**for (n+8 weeks)

**Methodology :**Modular Neural Network (DNN Layer)

## Abstract

Stock market is considered chaotic, complex, volatile and dynamic. Undoubtedly, its prediction is one of the most challenging tasks in time series forecasting. Moreover existing Artificial Neural Network (ANN) approaches fail to provide encouraging results. Meanwhile advances in machine learning have presented favourable results for speech recognition, image classification and language processing.(Waqar, M., Dawood, H., Guo, P., Shahnawaz, M.B. and Ghazanfar, M.A., 2017, December. Prediction of stock market by principal component analysis. In 2017 13th International Conference on Computational Intelligence and Security (CIS) (pp. 599-602). IEEE.)** We evaluate Crescent Point Energy Corp. prediction models with Modular Neural Network (DNN Layer) and Statistical Hypothesis Testing ^{1,2,3,4} and conclude that the CPG:TSX stock is predictable in the short/long term. **

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold CPG:TSX stock.**

## Key Points

- Why do we need predictive models?
- Which neural network is best for prediction?
- How do you know when a stock will go up or down?

## CPG:TSX Target Price Prediction Modeling Methodology

We consider Crescent Point Energy Corp. Decision Process with Modular Neural Network (DNN Layer) where A is the set of discrete actions of CPG: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(Statistical Hypothesis Testing)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (DNN Layer)) X S(n):→ (n+8 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of CPG: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?

## CPG:TSX Stock Forecast (Buy or Sell) for (n+8 weeks)

**Sample Set:**Neural Network

**Stock/Index:**CPG:TSX Crescent Point Energy Corp.

**Time series to forecast n: 07 Dec 2022**for (n+8 weeks)

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold CPG:TSX stock.**

**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 (Yellow to Green): *Technical Analysis%**

## Adjusted IFRS* Prediction Methods for Crescent Point Energy Corp.

- An entity that first applies these amendments after it first applies this Standard shall apply paragraphs 7.2.32–7.2.34. The entity shall also apply the other transition requirements in this Standard necessary for applying these amendments. For that purpose, references to the date of initial application shall be read as referring to the beginning of the reporting period in which an entity first applies these amendments (date of initial application of these amendments).
- Paragraph 6.3.4 permits an entity to designate as hedged items aggregated exposures that are a combination of an exposure and a derivative. When designating such a hedged item, an entity assesses whether the aggregated exposure combines an exposure with a derivative so that it creates a different aggregated exposure that is managed as one exposure for a particular risk (or risks). In that case, the entity may designate the hedged item on the basis of the aggregated exposure
- An entity shall apply this Standard retrospectively, in accordance with IAS 8 Accounting Policies, Changes in Accounting Estimates and Errors, except as specified in paragraphs 7.2.4–7.2.26 and 7.2.28. This Standard shall not be applied to items that have already been derecognised at the date of initial application.
- When measuring hedge ineffectiveness, an entity shall consider the time value of money. Consequently, the entity determines the value of the hedged item on a present value basis and therefore the change in the value of the hedged item also includes the effect of the time value of money.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

## Conclusions

Crescent Point Energy Corp. assigned short-term B2 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (DNN Layer) with Statistical Hypothesis Testing ^{1,2,3,4} and conclude that the CPG:TSX stock is predictable in the short/long term.**

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold CPG:TSX stock.**

### Financial State Forecast for CPG:TSX Crescent Point Energy Corp. Options & Futures

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | B2 | Ba3 |

Operational Risk | 40 | 30 |

Market Risk | 54 | 71 |

Technical Analysis | 65 | 79 |

Fundamental Analysis | 67 | 79 |

Risk Unsystematic | 57 | 70 |

### Prediction Confidence Score

## References

- E. Altman, K. Avrachenkov, and R. N ́u ̃nez-Queija. Perturbation analysis for denumerable Markov chains with application to queueing models. Advances in Applied Probability, pages 839–853, 2004
- Hastie T, Tibshirani R, Tibshirani RJ. 2017. Extended comparisons of best subset selection, forward stepwise selection, and the lasso. arXiv:1707.08692 [stat.ME]
- K. Boda and J. Filar. Time consistent dynamic risk measures. Mathematical Methods of Operations Research, 63(1):169–186, 2006
- Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Tempur Sealy Stock Forecast & Analysis. AC Investment Research Journal, 101(3).
- Y. Le Tallec. Robust, risk-sensitive, and data-driven control of Markov decision processes. PhD thesis, Massachusetts Institute of Technology, 2007.
- H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.
- Athey S, Bayati M, Imbens G, Zhaonan Q. 2019. Ensemble methods for causal effects in panel data settings. NBER Work. Pap. 25675

## Frequently Asked Questions

Q: What is the prediction methodology for CPG:TSX stock?A: CPG:TSX stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Statistical Hypothesis Testing

Q: Is CPG:TSX stock a buy or sell?

A: The dominant strategy among neural network is to Hold CPG:TSX Stock.

Q: Is Crescent Point Energy Corp. stock a good investment?

A: The consensus rating for Crescent Point Energy Corp. is Hold and assigned short-term B2 & long-term Ba3 forecasted stock rating.

Q: What is the consensus rating of CPG:TSX stock?

A: The consensus rating for CPG:TSX is Hold.

Q: What is the prediction period for CPG:TSX stock?

A: The prediction period for CPG:TSX is (n+8 weeks)

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