GOOS Canada Goose Holdings Inc. Subordinate Voting Shares

Outlook: Canada Goose Holdings Inc. Subordinate Voting Shares is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 29 Apr 2023 for (n+4 weeks)
Methodology : Inductive Learning (ML)

Abstract

Canada Goose Holdings Inc. Subordinate Voting Shares prediction model is evaluated with Inductive Learning (ML) and Polynomial Regression^1,2,3,4 and it is concluded that the GOOS 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. Dominated Move
2. What are main components of Markov decision process?
3. Nash Equilibria

GOOS Target Price Prediction Modeling Methodology

We consider Canada Goose Holdings Inc. Subordinate Voting Shares Decision Process with Inductive Learning (ML) where A is the set of discrete actions of GOOS 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(Polynomial 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(Inductive Learning (ML)) X S(n):→ (n+4 weeks) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of GOOS 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?

GOOS Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: GOOS Canada Goose Holdings Inc. Subordinate Voting Shares
Time series to forecast n: 29 Apr 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 Canada Goose Holdings Inc. Subordinate Voting Shares

1. The credit risk on a financial instrument is considered low for the purposes of paragraph 5.5.10, if the financial instrument has a low risk of default, the borrower has a strong capacity to meet its contractual cash flow obligations in the near term and adverse changes in economic and business conditions in the longer term may, but will not necessarily, reduce the ability of the borrower to fulfil its contractual cash flow obligations. Financial instruments are not considered to have low credit risk when they are regarded as having a low risk of loss simply because of the value of collateral and the financial instrument without that collateral would not be considered low credit risk. Financial instruments are also not considered to have low credit risk simply because they have a lower risk of default than the entity's other financial instruments or relative to the credit risk of the jurisdiction within which an entity operates.
2. Because the hedge accounting model is based on a general notion of offset between gains and losses on the hedging instrument and the hedged item, hedge effectiveness is determined not only by the economic relationship between those items (ie the changes in their underlyings) but also by the effect of credit risk on the value of both the hedging instrument and the hedged item. The effect of credit risk means that even if there is an economic relationship between the hedging instrument and the hedged item, the level of offset might become erratic. This can result from a change in the credit risk of either the hedging instrument or the hedged item that is of such a magnitude that the credit risk dominates the value changes that result from the economic relationship (ie the effect of the changes in the underlyings). A level of magnitude that gives rise to dominance is one that would result in the loss (or gain) from credit risk frustrating the effect of changes in the underlyings on the value of the hedging instrument or the hedged item, even if those changes were significant.
3. An entity is not required to incorporate forecasts of future conditions over the entire expected life of a financial instrument. The degree of judgement that is required to estimate expected credit losses depends on the availability of detailed information. As the forecast horizon increases, the availability of detailed information decreases and the degree of judgement required to estimate expected credit losses increases. The estimate of expected credit losses does not require a detailed estimate for periods that are far in the future—for such periods, an entity may extrapolate projections from available, detailed information.
4. When an entity, consistent with its hedge documentation, frequently resets (ie discontinues and restarts) a hedging relationship because both the hedging instrument and the hedged item frequently change (ie the entity uses a dynamic process in which both the hedged items and the hedging instruments used to manage that exposure do not remain the same for long), the entity shall apply the requirement in paragraphs 6.3.7(a) and B6.3.8—that the risk component is separately identifiable—only when it initially designates a hedged item in that hedging relationship. A hedged item that has been assessed at the time of its initial designation in the hedging relationship, whether it was at the time of the hedge inception or subsequently, is not reassessed at any subsequent redesignation in the same hedging relationship.

*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

Canada Goose Holdings Inc. Subordinate Voting Shares is assigned short-term Ba1 & long-term Ba1 estimated rating. Canada Goose Holdings Inc. Subordinate Voting Shares prediction model is evaluated with Inductive Learning (ML) and Polynomial Regression^1,2,3,4 and it is concluded that the GOOS 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

GOOS Canada Goose Holdings Inc. Subordinate Voting Shares Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Caa2	Ba1
Balance Sheet	Baa2	C
Leverage Ratios	Baa2	B3
Cash Flow	Baa2	Baa2
Rates of Return and Profitability	B1	Caa2

*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: 85 out of 100 with 646 signals.

References

1. Challen, D. W. A. J. Hagger (1983), Macroeconomic Systems: Construction, Validation and Applications. New York: St. Martin's Press.
2. Clements, M. P. D. F. Hendry (1995), "Forecasting in cointegrated systems," Journal of Applied Econometrics, 10, 127–146.
3. Imbens GW, Rubin DB. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge, UK: Cambridge Univ. Press
4. N. B ̈auerle and J. Ott. Markov decision processes with average-value-at-risk criteria. Mathematical Methods of Operations Research, 74(3):361–379, 2011
5. Bottou L. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade, ed. G Montavon, G Orr, K-R Müller, pp. 421–36. Berlin: Springer
6. J. Hu and M. P. Wellman. Nash q-learning for general-sum stochastic games. Journal of Machine Learning Research, 4:1039–1069, 2003.
7. L. Prashanth and M. Ghavamzadeh. Actor-critic algorithms for risk-sensitive MDPs. In Proceedings of Advances in Neural Information Processing Systems 26, pages 252–260, 2013.

Frequently Asked Questions

Q: What is the prediction methodology for GOOS stock?
A: GOOS stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Polynomial Regression
Q: Is GOOS stock a buy or sell?
A: The dominant strategy among neural network is to Hold GOOS Stock.
Q: Is Canada Goose Holdings Inc. Subordinate Voting Shares stock a good investment?
A: The consensus rating for Canada Goose Holdings Inc. Subordinate Voting Shares is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of GOOS stock?
A: The consensus rating for GOOS is Hold.
Q: What is the prediction period for GOOS stock?
A: The prediction period for GOOS is (n+4 weeks)

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