Dominant Strategy : Hold
Time series to forecast n: 21 Jan 2023 for (n+4 weeks)
Methodology : Multi-Instance Learning (ML)
Abstract
Allkem Limited prediction model is evaluated with Multi-Instance Learning (ML) and Wilcoxon Rank-Sum Test1,2,3,4 and it is concluded that the AKE: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: HoldKey Points
- Trust metric by Neural Network
- Market Signals
- What is neural prediction?
AKE:TSX Target Price Prediction Modeling Methodology
We consider Allkem Limited Decision Process with Multi-Instance Learning (ML) where A is the set of discrete actions of AKE: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(Wilcoxon Rank-Sum Test)5,6,7= X R(Multi-Instance Learning (ML)) X S(n):→ (n+4 weeks)
n:Time series to forecast
p:Price signals of AKE: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?
AKE:TSX Stock Forecast (Buy or Sell) for (n+4 weeks)
Sample Set: Neural NetworkStock/Index: AKE:TSX Allkem Limited
Time series to forecast n: 21 Jan 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 Allkem Limited
- For the purpose of applying paragraph 6.5.11, at the point when an entity amends the description of a hedged item as required in paragraph 6.9.1(b), the amount accumulated in the cash flow hedge reserve shall be deemed to be based on the alternative benchmark rate on which the hedged future cash flows are determined.
- If the contractual cash flows on a financial asset have been renegotiated or otherwise modified, but the financial asset is not derecognised, that financial asset is not automatically considered to have lower credit risk. An entity shall assess whether there has been a significant increase in credit risk since initial recognition on the basis of all reasonable and supportable information that is available without undue cost or effort. This includes historical and forwardlooking information and an assessment of the credit risk over the expected life of the financial asset, which includes information about the circumstances that led to the modification. Evidence that the criteria for the recognition of lifetime expected credit losses are no longer met may include a history of up-to-date and timely payment performance against the modified contractual terms. Typically a customer would need to demonstrate consistently good payment behaviour over a period of time before the credit risk is considered to have decreased.
- An entity shall assess at the inception of the hedging relationship, and on an ongoing basis, whether a hedging relationship meets the hedge effectiveness requirements. At a minimum, an entity shall perform the ongoing assessment at each reporting date or upon a significant change in the circumstances affecting the hedge effectiveness requirements, whichever comes first. The assessment relates to expectations about hedge effectiveness and is therefore only forward-looking.
- For example, Entity A, whose functional currency is its local currency, has a firm commitment to pay FC150,000 for advertising expenses in nine months' time and a firm commitment to sell finished goods for FC150,000 in 15 months' time. Entity A enters into a foreign currency derivative that settles in nine months' time under which it receives FC100 and pays CU70. Entity A has no other exposures to FC. Entity A does not manage foreign currency risk on a net basis. Hence, Entity A cannot apply hedge accounting for a hedging relationship between the foreign currency derivative and a net position of FC100 (consisting of FC150,000 of the firm purchase commitment—ie advertising services—and FC149,900 (of the FC150,000) of the firm sale commitment) for a nine-month period.
*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
Allkem Limited is assigned short-term Ba1 & long-term Ba1 estimated rating. Allkem Limited prediction model is evaluated with Multi-Instance Learning (ML) and Wilcoxon Rank-Sum Test1,2,3,4 and it is concluded that the AKE: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
AKE:TSX Allkem Limited Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | Ba1 | Ba1 |
Income Statement | C | Ba3 |
Balance Sheet | C | B3 |
Leverage Ratios | Caa2 | Baa2 |
Cash Flow | B3 | Baa2 |
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
References
- A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008
- LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
- Matzkin RL. 1994. Restrictions of economic theory in nonparametric methods. In Handbook of Econometrics, Vol. 4, ed. R Engle, D McFadden, pp. 2523–58. Amsterdam: Elsevier
- 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.
- C. Wu and Y. Lin. Minimizing risk models in Markov decision processes with policies depending on target values. Journal of Mathematical Analysis and Applications, 231(1):47–67, 1999
- Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.
- Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
Frequently Asked Questions
Q: What is the prediction methodology for AKE:TSX stock?A: AKE:TSX stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Wilcoxon Rank-Sum Test
Q: Is AKE:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Hold AKE:TSX Stock.
Q: Is Allkem Limited stock a good investment?
A: The consensus rating for Allkem Limited is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of AKE:TSX stock?
A: The consensus rating for AKE:TSX is Hold.
Q: What is the prediction period for AKE:TSX stock?
A: The prediction period for AKE:TSX is (n+4 weeks)
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