FNV:TSX Franco-Nevada Corporation

Outlook: Franco-Nevada Corporation is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 20 May 2023 for (n+1 year)
Methodology : Modular Neural Network (DNN Layer)

Abstract

Franco-Nevada Corporation prediction model is evaluated with Modular Neural Network (DNN Layer) and Chi-Square^1,2,3,4 and it is concluded that the FNV:TSX stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell

Key Points

1. Should I buy stocks now or wait amid such uncertainty?
2. What is neural prediction?
3. What are the most successful trading algorithms?

FNV:TSX Target Price Prediction Modeling Methodology

We consider Franco-Nevada Corporation Decision Process with Modular Neural Network (DNN Layer) where A is the set of discrete actions of FNV: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(Chi-Square)^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 (DNN Layer)) X S(n):→ (n+1 year) $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of FNV: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?

FNV:TSX Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: FNV:TSX Franco-Nevada Corporation
Time series to forecast n: 20 May 2023 for (n+1 year)

According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell

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 Franco-Nevada Corporation

1. An equity method investment cannot be a hedged item in a fair value hedge. This is because the equity method recognises in profit or loss the investor's share of the investee's profit or loss, instead of changes in the investment's fair value. For a similar reason, an investment in a consolidated subsidiary cannot be a hedged item in a fair value hedge. This is because consolidation recognises in profit or loss the subsidiary's profit or loss, instead of changes in the investment's fair value. A hedge of a net investment in a foreign operation is different because it is a hedge of the foreign currency exposure, not a fair value hedge of the change in the value of the investment.
2. The accounting for the time value of options in accordance with paragraph 6.5.15 applies only to the extent that the time value relates to the hedged item (aligned time value). The time value of an option relates to the hedged item if the critical terms of the option (such as the nominal amount, life and underlying) are aligned with the hedged item. Hence, if the critical terms of the option and the hedged item are not fully aligned, an entity shall determine the aligned time value, ie how much of the time value included in the premium (actual time value) relates to the hedged item (and therefore should be treated in accordance with paragraph 6.5.15). An entity determines the aligned time value using the valuation of the option that would have critical terms that perfectly match the hedged item.
3. Unless paragraph 6.8.8 applies, for a hedge of a non-contractually specified benchmark component of interest rate risk, an entity shall apply the requirement in paragraphs 6.3.7(a) and B6.3.8—that the risk component shall be separately identifiable—only at the inception of the hedging relationship.
4. 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

Franco-Nevada Corporation is assigned short-term Ba1 & long-term Ba1 estimated rating. Franco-Nevada Corporation prediction model is evaluated with Modular Neural Network (DNN Layer) and Chi-Square^1,2,3,4 and it is concluded that the FNV:TSX stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell

FNV:TSX Franco-Nevada Corporation Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Ba3	Ba2
Balance Sheet	B1	Ba3
Leverage Ratios	Baa2	Caa2
Cash Flow	Ba3	C
Rates of Return and Profitability	Caa2	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

Trust metric by Neural Network: 77 out of 100 with 790 signals.

References

1. Semenova V, Goldman M, Chernozhukov V, Taddy M. 2018. Orthogonal ML for demand estimation: high dimensional causal inference in dynamic panels. arXiv:1712.09988 [stat.ML]
2. T. Shardlow and A. Stuart. A perturbation theory for ergodic Markov chains and application to numerical approximations. SIAM journal on numerical analysis, 37(4):1120–1137, 2000
3. Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
4. Semenova V, Goldman M, Chernozhukov V, Taddy M. 2018. Orthogonal ML for demand estimation: high dimensional causal inference in dynamic panels. arXiv:1712.09988 [stat.ML]
5. Ashley, R. (1988), "On the relative worth of recent macroeconomic forecasts," International Journal of Forecasting, 4, 363–376.
6. R. Rockafellar and S. Uryasev. Optimization of conditional value-at-risk. Journal of Risk, 2:21–42, 2000.
7. Hill JL. 2011. Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20:217–40

Frequently Asked Questions

Q: What is the prediction methodology for FNV:TSX stock?
A: FNV:TSX stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Chi-Square
Q: Is FNV:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Sell FNV:TSX Stock.
Q: Is Franco-Nevada Corporation stock a good investment?
A: The consensus rating for Franco-Nevada Corporation is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of FNV:TSX stock?
A: The consensus rating for FNV:TSX is Sell.
Q: What is the prediction period for FNV:TSX stock?
A: The prediction period for FNV:TSX is (n+1 year)

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