## Summary

This paper aims to develop an innovative neural network approach to achieve better stock market predictions. Data were obtained from the live stock market for real-time and off-line analysis and results of visualizations and analytics to demonstrate Internet of Multimedia of Things for stock analysis. To study the influence of market characteristics on stock prices, traditional neural network algorithms may incorrectly predict the stock market, since the initial weight of the random selection problem can be easily prone to incorrect predictions. ** We evaluate Crescent Point Energy Corp. prediction models with Modular Neural Network (Market Volatility Analysis) and Ridge Regression ^{1,2,3,4} and conclude that the CPG:TSX stock is predictable in the short/long term. **

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold CPG:TSX stock.**

## Key Points

- Understanding Buy, Sell, and Hold Ratings
- What is prediction model?
- How useful are statistical predictions?

## CPG:TSX Target Price Prediction Modeling Methodology

We consider Crescent Point Energy Corp. Stock Decision Process with Modular Neural Network (Market Volatility Analysis) 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(Ridge Regression)

^{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 (Market Volatility Analysis)) X S(n):→ (n+6 month) $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

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+6 month)

**Sample Set:**Neural Network

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

**Time series to forecast n: 22 Nov 2022**for (n+6 month)

**According to price forecasts for (n+6 month) 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.

- 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.
- If, at the date of initial application, determining whether there has been a significant increase in credit risk since initial recognition would require undue cost or effort, an entity shall recognise a loss allowance at an amount equal to lifetime expected credit losses at each reporting date until that financial instrument is derecognised (unless that financial instrument is low credit risk at a reporting date, in which case paragraph 7.2.19(a) applies).
- For the purpose of recognising foreign exchange gains and losses under IAS 21, a financial asset measured at fair value through other comprehensive income in accordance with paragraph 4.1.2A is treated as a monetary item. Accordingly, such a financial asset is treated as an asset measured at amortised cost in the foreign currency. Exchange differences on the amortised cost are recognised in profit or loss and other changes in the carrying amount are recognised in accordance with paragraph 5.7.10.
- If a component of the cash flows of a financial or a non-financial item is designated as the hedged item, that component must be less than or equal to the total cash flows of the entire item. However, all of the cash flows of the entire item may be designated as the hedged item and hedged for only one particular risk (for example, only for those changes that are attributable to changes in LIBOR or a benchmark commodity price).

*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 Ba3 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Ridge Regression ^{1,2,3,4} and conclude that the CPG:TSX stock is predictable in the short/long term.**

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold CPG:TSX stock.**

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

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

Outlook* | Ba3 | Ba3 |

Operational Risk | 46 | 66 |

Market Risk | 65 | 68 |

Technical Analysis | 49 | 68 |

Fundamental Analysis | 83 | 52 |

Risk Unsystematic | 74 | 78 |

### Prediction Confidence Score

## References

- Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94
- Breusch, T. S. A. R. Pagan (1979), "A simple test for heteroskedasticity and random coefficient variation," Econometrica, 47, 1287–1294.
- E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999
- J. Filar, D. Krass, and K. Ross. Percentile performance criteria for limiting average Markov decision pro- cesses. IEEE Transaction of Automatic Control, 40(1):2–10, 1995.
- Zeileis A, Hothorn T, Hornik K. 2008. Model-based recursive partitioning. J. Comput. Graph. Stat. 17:492–514 Zhou Z, Athey S, Wager S. 2018. Offline multi-action policy learning: generalization and optimization. arXiv:1810.04778 [stat.ML]
- E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999
- Athey S. 2019. The impact of machine learning on economics. In The Economics of Artificial Intelligence: An Agenda, ed. AK Agrawal, J Gans, A Goldfarb. Chicago: Univ. Chicago Press. In press

## 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 (Market Volatility Analysis) and Ridge Regression

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 Ba3 & 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+6 month)