**Outlook:**Perpetua Resources Corp. assigned short-term Ba1 & long-term Ba1 estimated rating.

**Dominant Strategy :**Hold

**Time series to forecast n: 01 Jan 2023**for (n+6 month)

**Methodology :**Modular Neural Network (Market Volatility Analysis)

## Abstract

The main objective of this research is to predict the market performance on day closing using different machine learning techniques. The prediction model uses different attributes as an input and predicts market as Positive & Negative. (Vui, C.S., Soon, G.K., On, C.K., Alfred, R. and Anthony, P., 2013, November. A review of stock market prediction with Artificial neural network (ANN). In 2013 IEEE international conference on control system, computing and engineering (pp. 477-482). IEEE.)** We evaluate Perpetua Resources Corp. prediction models with Modular Neural Network (Market Volatility Analysis) and Statistical Hypothesis Testing ^{1,2,3,4} and conclude that the PPTA: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: Hold**

## Key Points

- Which neural network is best for prediction?
- Game Theory
- What is neural prediction?

## PPTA:TSX Target Price Prediction Modeling Methodology

We consider Perpetua Resources Corp. Decision Process with Modular Neural Network (Market Volatility Analysis) where A is the set of discrete actions of PPTA: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 (Market Volatility Analysis)) X S(n):→ (n+6 month) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of PPTA: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?

## PPTA:TSX Stock Forecast (Buy or Sell) for (n+6 month)

**Sample Set:**Neural Network

**Stock/Index:**PPTA:TSX Perpetua Resources Corp.

**Time series to forecast n: 01 Jan 2023**for (n+6 month)

**According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Hold**

**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 Perpetua Resources Corp.

- If a put option written by an entity prevents a transferred asset from being derecognised and the entity measures the transferred asset at fair value, the associated liability is measured at the option exercise price plus the time value of the option. The measurement of the asset at fair value is limited to the lower of the fair value and the option exercise price because the entity has no right to increases in the fair value of the transferred asset above the exercise price of the option. This ensures that the net carrying amount of the asset and the associated liability is the fair value of the put option obligation. For example, if the fair value of the underlying asset is CU120, the option exercise price is CU100 and the time value of the option is CU5, the carrying amount of the associated liability is CU105 (CU100 + CU5) and the carrying amount of the asset is CU100 (in this case the option exercise price).
- There is a rebuttable presumption that unless inflation risk is contractually specified, it is not separately identifiable and reliably measurable and hence cannot be designated as a risk component of a financial instrument. However, in limited cases, it is possible to identify a risk component for inflation risk that is separately identifiable and reliably measurable because of the particular circumstances of the inflation environment and the relevant debt market
- However, the fact that a financial asset is non-recourse does not in itself necessarily preclude the financial asset from meeting the condition in paragraphs 4.1.2(b) and 4.1.2A(b). In such situations, the creditor is required to assess ('look through to') the particular underlying assets or cash flows to determine whether the contractual cash flows of the financial asset being classified are payments of principal and interest on the principal amount outstanding. If the terms of the financial asset give rise to any other cash flows or limit the cash flows in a manner inconsistent with payments representing principal and interest, the financial asset does not meet the condition in paragraphs 4.1.2(b) and 4.1.2A(b). Whether the underlying assets are financial assets or non-financial assets does not in itself affect this assessment.
- Lifetime expected credit losses are not recognised on a financial instrument simply because it was considered to have low credit risk in the previous reporting period and is not considered to have low credit risk at the reporting date. In such a case, an entity shall determine whether there has been a significant increase in credit risk since initial recognition and thus whether lifetime expected credit losses are required to be recognised in accordance with paragraph 5.5.3.

*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

Perpetua Resources Corp. assigned short-term Ba1 & long-term Ba1 estimated rating.** We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Statistical Hypothesis Testing ^{1,2,3,4} and conclude that the PPTA: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: Hold**

### PPTA:TSX Perpetua Resources Corp. Financial Analysis*

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

Outlook* | Ba1 | Ba1 |

Income Statement | B3 | Baa2 |

Balance Sheet | Caa2 | Baa2 |

Leverage Ratios | Baa2 | C |

Cash Flow | Baa2 | C |

Rates of Return and Profitability | Baa2 | Baa2 |

*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

## References

- Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
- C. Claus and C. Boutilier. The dynamics of reinforcement learning in cooperative multiagent systems. In Proceedings of the Fifteenth National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, AAAI 98, IAAI 98, July 26-30, 1998, Madison, Wisconsin, USA., pages 746–752, 1998.
- Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2016a. Double machine learning for treatment and causal parameters. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
- Mnih A, Teh YW. 2012. A fast and simple algorithm for training neural probabilistic language models. In Proceedings of the 29th International Conference on Machine Learning, pp. 419–26. La Jolla, CA: Int. Mach. Learn. Soc.
- D. S. Bernstein, S. Zilberstein, and N. Immerman. The complexity of decentralized control of Markov Decision Processes. In UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30 - July 3, 2000, pages 32–37, 2000.
- Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
- R. Sutton, D. McAllester, S. Singh, and Y. Mansour. Policy gradient methods for reinforcement learning with function approximation. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1057–1063, 2000

## Frequently Asked Questions

Q: What is the prediction methodology for PPTA:TSX stock?A: PPTA:TSX stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Statistical Hypothesis Testing

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

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

Q: Is Perpetua Resources Corp. stock a good investment?

A: The consensus rating for Perpetua Resources Corp. is Hold and assigned short-term Ba1 & long-term Ba1 estimated rating.

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

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

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

A: The prediction period for PPTA:TSX is (n+6 month)

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