CIA:TSX Champion Iron Limited

Outlook: Champion Iron Limited is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 26 Mar 2023 for (n+1 year)
Methodology : Modular Neural Network (Speculative Sentiment Analysis)

Abstract

Champion Iron Limited prediction model is evaluated with Modular Neural Network (Speculative Sentiment Analysis) and Factor^1,2,3,4 and it is concluded that the CIA: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: Buy

Key Points

1. Should I buy stocks now or wait amid such uncertainty?
2. Trading Signals
3. Market Outlook

CIA:TSX Target Price Prediction Modeling Methodology

We consider Champion Iron Limited Decision Process with Modular Neural Network (Speculative Sentiment Analysis) where A is the set of discrete actions of CIA: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(Factor)^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 (Speculative Sentiment Analysis)) X S(n):→ (n+1 year) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of CIA: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?

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

Sample Set: Neural Network
Stock/Index: CIA:TSX Champion Iron Limited
Time series to forecast n: 26 Mar 2023 for (n+1 year)

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

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 Champion Iron Limited

1. The requirement that an economic relationship exists means that the hedging instrument and the hedged item have values that generally move in the opposite direction because of the same risk, which is the hedged risk. Hence, there must be an expectation that the value of the hedging instrument and the value of the hedged item will systematically change in response to movements in either the same underlying or underlyings that are economically related in such a way that they respond in a similar way to the risk that is being hedged (for example, Brent and WTI crude oil).
2. When an entity discontinues measuring the financial instrument that gives rise to the credit risk, or a proportion of that financial instrument, at fair value through profit or loss, that financial instrument's fair value at the date of discontinuation becomes its new carrying amount. Subsequently, the same measurement that was used before designating the financial instrument at fair value through profit or loss shall be applied (including amortisation that results from the new carrying amount). For example, a financial asset that had originally been classified as measured at amortised cost would revert to that measurement and its effective interest rate would be recalculated based on its new gross carrying amount on the date of discontinuing measurement at fair value through profit or loss.
3. The purpose of estimating expected credit losses is neither to estimate a worstcase scenario nor to estimate the best-case scenario. Instead, an estimate of expected credit losses shall always reflect the possibility that a credit loss occurs and the possibility that no credit loss occurs even if the most likely outcome is no credit loss.
4. At the date of initial application, an entity shall assess whether a financial asset meets the condition in paragraphs 4.1.2(a) or 4.1.2A(a) on the basis of the facts and circumstances that exist at that date. The resulting classification shall be applied retrospectively irrespective of the entity's business model in prior reporting periods.

*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

Champion Iron Limited is assigned short-term Ba1 & long-term Ba1 estimated rating. Champion Iron Limited prediction model is evaluated with Modular Neural Network (Speculative Sentiment Analysis) and Factor^1,2,3,4 and it is concluded that the CIA: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: Buy

CIA:TSX Champion Iron Limited Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	B2	Baa2
Balance Sheet	B3	Caa2
Leverage Ratios	Baa2	Baa2
Cash Flow	B1	B1
Rates of Return and Profitability	Baa2	B1

*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: 81 out of 100 with 870 signals.

References

1. Friedberg R, Tibshirani J, Athey S, Wager S. 2018. Local linear forests. arXiv:1807.11408 [stat.ML]
2. 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.
3. 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
4. White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
5. 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]
6. Friedman JH. 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38:367–78
7. Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85

Frequently Asked Questions

Q: What is the prediction methodology for CIA:TSX stock?
A: CIA:TSX stock prediction methodology: We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) and Factor
Q: Is CIA:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Buy CIA:TSX Stock.
Q: Is Champion Iron Limited stock a good investment?
A: The consensus rating for Champion Iron Limited is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of CIA:TSX stock?
A: The consensus rating for CIA:TSX is Buy.
Q: What is the prediction period for CIA:TSX stock?
A: The prediction period for CIA:TSX is (n+1 year)

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