WPM:TSX Wheaton Precious Metals Corp.

Outlook: Wheaton Precious Metals Corp. is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 10 May 2023 for (n+4 weeks)
Methodology : Modular Neural Network (Social Media Sentiment Analysis)

Abstract

Wheaton Precious Metals Corp. prediction model is evaluated with Modular Neural Network (Social Media Sentiment Analysis) and Linear Regression^1,2,3,4 and it is concluded that the WPM: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: Hold

Key Points

1. Operational Risk
2. Investment Risk
3. Short/Long Term Stocks

WPM:TSX Target Price Prediction Modeling Methodology

We consider Wheaton Precious Metals Corp. Decision Process with Modular Neural Network (Social Media Sentiment Analysis) where A is the set of discrete actions of WPM: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(Linear Regression)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (Social Media Sentiment Analysis)) X S(n):→ (n+4 weeks) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of WPM: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?

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

Sample Set: Neural Network
Stock/Index: WPM:TSX Wheaton Precious Metals Corp.
Time series to forecast n: 10 May 2023 for (n+4 weeks)

According to price forecasts for (n+4 weeks) 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 Wheaton Precious Metals Corp.

1. The fact that a derivative is in or out of the money when it is designated as a hedging instrument does not in itself mean that a qualitative assessment is inappropriate. It depends on the circumstances whether hedge ineffectiveness arising from that fact could have a magnitude that a qualitative assessment would not adequately capture.
2. For some types of fair value hedges, the objective of the hedge is not primarily to offset the fair value change of the hedged item but instead to transform the cash flows of the hedged item. For example, an entity hedges the fair value interest rate risk of a fixed-rate debt instrument using an interest rate swap. The entity's hedge objective is to transform the fixed-interest cash flows into floating interest cash flows. This objective is reflected in the accounting for the hedging relationship by accruing the net interest accrual on the interest rate swap in profit or loss. In the case of a hedge of a net position (for example, a net position of a fixed-rate asset and a fixed-rate liability), this net interest accrual must be presented in a separate line item in the statement of profit or loss and other comprehensive income. This is to avoid the grossing up of a single instrument's net gains or losses into offsetting gross amounts and recognising them in different line items (for example, this avoids grossing up a net interest receipt on a single interest rate swap into gross interest revenue and gross interest expense).
3. 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.
4. If a financial asset contains a contractual term that could change the timing or amount of contractual cash flows (for example, if the asset can be prepaid before maturity or its term can be extended), the entity must determine whether the contractual cash flows that could arise over the life of the instrument due to that contractual term are solely payments of principal and interest on the principal amount outstanding. To make this determination, the entity must assess the contractual cash flows that could arise both before, and after, the change in contractual cash flows. The entity may also need to assess the nature of any contingent event (ie the trigger) that would change the timing or amount of the contractual cash flows. While the nature of the contingent event in itself is not a determinative factor in assessing whether the contractual cash flows are solely payments of principal and interest, it may be an indicator. For example, compare a financial instrument with an interest rate that is reset to a higher rate if the debtor misses a particular number of payments to a financial instrument with an interest rate that is reset to a higher rate if a specified equity index reaches a particular level. It is more likely in the former case that the contractual cash flows over the life of the instrument will be solely payments of principal and interest on the principal amount outstanding because of the relationship between missed payments and an increase in credit risk. (See also paragraph B4.1.18.)

*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

Wheaton Precious Metals Corp. is assigned short-term Ba1 & long-term Ba1 estimated rating. Wheaton Precious Metals Corp. prediction model is evaluated with Modular Neural Network (Social Media Sentiment Analysis) and Linear Regression^1,2,3,4 and it is concluded that the WPM: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: Hold

WPM:TSX Wheaton Precious Metals Corp. Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Ba3	B3
Balance Sheet	B3	Baa2
Leverage Ratios	B1	Baa2
Cash Flow	Ba3	Baa2
Rates of Return and Profitability	Baa2	Ba3

*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: 73 out of 100 with 504 signals.

References

1. J. Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki, and T. Graepel. Multi-agent Reinforcement Learning in Sequential Social Dilemmas. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), Sao Paulo, Brazil, 2017
2. O. Bardou, N. Frikha, and G. Pag`es. Computing VaR and CVaR using stochastic approximation and adaptive unconstrained importance sampling. Monte Carlo Methods and Applications, 15(3):173–210, 2009.
3. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Is FFBC Stock Buy or Sell?(Stock Forecast). AC Investment Research Journal, 101(3).
4. 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.
5. Burkov A. 2019. The Hundred-Page Machine Learning Book. Quebec City, Can.: Andriy Burkov
6. Mikolov T, Yih W, Zweig G. 2013c. Linguistic regularities in continuous space word representations. In Pro- ceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 746–51. New York: Assoc. Comput. Linguist.
7. Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer

Frequently Asked Questions

Q: What is the prediction methodology for WPM:TSX stock?
A: WPM:TSX stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Linear Regression
Q: Is WPM:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Hold WPM:TSX Stock.
Q: Is Wheaton Precious Metals Corp. stock a good investment?
A: The consensus rating for Wheaton Precious Metals Corp. is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of WPM:TSX stock?
A: The consensus rating for WPM:TSX is Hold.
Q: What is the prediction period for WPM:TSX stock?
A: The prediction period for WPM:TSX is (n+4 weeks)

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