CBY CANTERBURY RESOURCES LIMITED

Outlook: CANTERBURY RESOURCES LIMITED is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 22 Feb 2023 for (n+4 weeks)
Methodology : Multi-Task Learning (ML)

Abstract

CANTERBURY RESOURCES LIMITED prediction model is evaluated with Multi-Task Learning (ML) and ElasticNet Regression^1,2,3,4 and it is concluded that the CBY 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. What is neural prediction?
2. Can we predict stock market using machine learning?
3. Stock Rating

CBY Target Price Prediction Modeling Methodology

We consider CANTERBURY RESOURCES LIMITED Decision Process with Multi-Task Learning (ML) where A is the set of discrete actions of CBY 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(ElasticNet 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(Multi-Task Learning (ML)) X S(n):→ (n+4 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of CBY 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?

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

Sample Set: Neural Network
Stock/Index: CBY CANTERBURY RESOURCES LIMITED
Time series to forecast n: 22 Feb 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 CANTERBURY RESOURCES LIMITED

1. For a financial guarantee contract, the entity is required to make payments only in the event of a default by the debtor in accordance with the terms of the instrument that is guaranteed. Accordingly, cash shortfalls are the expected payments to reimburse the holder for a credit loss that it incurs less any amounts that the entity expects to receive from the holder, the debtor or any other party. If the asset is fully guaranteed, the estimation of cash shortfalls for a financial guarantee contract would be consistent with the estimations of cash shortfalls for the asset subject to the guarantee
2. Leverage is a contractual cash flow characteristic of some financial assets. Leverage increases the variability of the contractual cash flows with the result that they do not have the economic characteristics of interest. Stand-alone option, forward and swap contracts are examples of financial assets that include such leverage. Thus, such contracts do not meet the condition in paragraphs 4.1.2(b) and 4.1.2A(b) and cannot be subsequently measured at amortised cost or fair value through other comprehensive income.
3. If any instrument in the pool does not meet the conditions in either paragraph B4.1.23 or paragraph B4.1.24, the condition in paragraph B4.1.21(b) is not met. In performing this assessment, a detailed instrument-byinstrument analysis of the pool may not be necessary. However, an entity must use judgement and perform sufficient analysis to determine whether the instruments in the pool meet the conditions in paragraphs B4.1.23–B4.1.24. (See also paragraph B4.1.18 for guidance on contractual cash flow characteristics that have only a de minimis effect.)
4. There are two types of components of nominal amounts that can be designated as the hedged item in a hedging relationship: a component that is a proportion of an entire item or a layer component. The type of component changes the accounting outcome. An entity shall designate the component for accounting purposes consistently with its risk management objective.

*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

CANTERBURY RESOURCES LIMITED is assigned short-term Ba1 & long-term Ba1 estimated rating. CANTERBURY RESOURCES LIMITED prediction model is evaluated with Multi-Task Learning (ML) and ElasticNet Regression^1,2,3,4 and it is concluded that the CBY 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

CBY CANTERBURY RESOURCES LIMITED Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Ba3	C
Balance Sheet	Ba2	B2
Leverage Ratios	C	Baa2
Cash Flow	C	B2
Rates of Return and Profitability	Baa2	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: 86 out of 100 with 728 signals.

References

1. Kitagawa T, Tetenov A. 2015. Who should be treated? Empirical welfare maximization methods for treatment choice. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
2. R. Rockafellar and S. Uryasev. Optimization of conditional value-at-risk. Journal of Risk, 2:21–42, 2000.
3. V. Borkar. A sensitivity formula for the risk-sensitive cost and the actor-critic algorithm. Systems & Control Letters, 44:339–346, 2001
4. Efron B, Hastie T. 2016. Computer Age Statistical Inference, Vol. 5. Cambridge, UK: Cambridge Univ. Press
5. Hastie T, Tibshirani R, Friedman J. 2009. The Elements of Statistical Learning. Berlin: Springer
6. J. G. Schneider, W. Wong, A. W. Moore, and M. A. Riedmiller. Distributed value functions. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 371–378, 1999.
7. C. Szepesvári. Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2010

Frequently Asked Questions

Q: What is the prediction methodology for CBY stock?
A: CBY stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and ElasticNet Regression
Q: Is CBY stock a buy or sell?
A: The dominant strategy among neural network is to Hold CBY Stock.
Q: Is CANTERBURY RESOURCES LIMITED stock a good investment?
A: The consensus rating for CANTERBURY RESOURCES LIMITED is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of CBY stock?
A: The consensus rating for CBY is Hold.
Q: What is the prediction period for CBY stock?
A: The prediction period for CBY is (n+4 weeks)

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