Modelling A.I. in Economics

CST CASTILE RESOURCES LTD

Outlook: CASTILE RESOURCES LTD is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 28 Mar 2023 for (n+6 month)
Methodology : Deductive Inference (ML)

Abstract

CASTILE RESOURCES LTD prediction model is evaluated with Deductive Inference (ML) and Independent T-Test1,2,3,4 and it is concluded that the CST stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Buy

Key Points

  1. What is a prediction confidence?
  2. How do predictive algorithms actually work?
  3. What is the use of Markov decision process?

CST Target Price Prediction Modeling Methodology

We consider CASTILE RESOURCES LTD Decision Process with Deductive Inference (ML) where A is the set of discrete actions of CST 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(Independent T-Test)5,6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Deductive Inference (ML)) X S(n):→ (n+6 month) S = s 1 s 2 s 3

n:Time series to forecast

p:Price signals of CST 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?

CST Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: CST CASTILE RESOURCES LTD
Time series to forecast n: 28 Mar 2023 for (n+6 month)

According to price forecasts for (n+6 month) 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 CASTILE RESOURCES LTD

  1. To make that determination, an entity must assess whether it expects that the effects of changes in the liability's credit risk will be offset in profit or loss by a change in the fair value of another financial instrument measured at fair value through profit or loss. Such an expectation must be based on an economic relationship between the characteristics of the liability and the characteristics of the other financial instrument.
  2. When designating a hedging relationship and on an ongoing basis, an entity shall analyse the sources of hedge ineffectiveness that are expected to affect the hedging relationship during its term. This analysis (including any updates in accordance with paragraph B6.5.21 arising from rebalancing a hedging relationship) is the basis for the entity's assessment of meeting the hedge effectiveness requirements.
  3. A contractually specified inflation risk component of the cash flows of a recognised inflation-linked bond (assuming that there is no requirement to account for an embedded derivative separately) is separately identifiable and reliably measurable, as long as other cash flows of the instrument are not affected by the inflation risk component.
  4. If a put option obligation written by an entity or call option right held by an entity prevents a transferred asset from being derecognised and the entity measures the transferred asset at amortised cost, the associated liability is measured at its cost (ie the consideration received) adjusted for the amortisation of any difference between that cost and the gross carrying amount of the transferred asset at the expiration date of the option. For example, assume that the gross carrying amount of the asset on the date of the transfer is CU98 and that the consideration received is CU95. The gross carrying amount of the asset on the option exercise date will be CU100. The initial carrying amount of the associated liability is CU95 and the difference between CU95 and CU100 is recognised in profit or loss using the effective interest method. If the option is exercised, any difference between the carrying amount of the associated liability and the exercise price is recognised in profit or loss.

*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

CASTILE RESOURCES LTD is assigned short-term Ba1 & long-term Ba1 estimated rating. CASTILE RESOURCES LTD prediction model is evaluated with Deductive Inference (ML) and Independent T-Test1,2,3,4 and it is concluded that the CST stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Buy

CST CASTILE RESOURCES LTD Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementCC
Balance SheetB2Caa2
Leverage RatiosCB2
Cash FlowCC
Rates of Return and ProfitabilityB1Baa2

*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: 89 out of 100 with 585 signals.

References

  1. R. Rockafellar and S. Uryasev. Conditional value-at-risk for general loss distributions. Journal of Banking and Finance, 26(7):1443 – 1471, 2002
  2. Candès EJ, Recht B. 2009. Exact matrix completion via convex optimization. Found. Comput. Math. 9:717
  3. Mnih A, Kavukcuoglu K. 2013. Learning word embeddings efficiently with noise-contrastive estimation. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 2265–73. San Diego, CA: Neural Inf. Process. Syst. Found.
  4. S. Proper and K. Tumer. Modeling difference rewards for multiagent learning (extended abstract). In Proceedings of the Eleventh International Joint Conference on Autonomous Agents and Multiagent Systems, Valencia, Spain, June 2012
  5. Breiman L, Friedman J, Stone CJ, Olshen RA. 1984. Classification and Regression Trees. Boca Raton, FL: CRC Press
  6. Scott SL. 2010. A modern Bayesian look at the multi-armed bandit. Appl. Stoch. Models Bus. Ind. 26:639–58
  7. Athey S, Imbens GW. 2017b. The state of applied econometrics: causality and policy evaluation. J. Econ. Perspect. 31:3–32
Frequently Asked QuestionsQ: What is the prediction methodology for CST stock?
A: CST stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Independent T-Test
Q: Is CST stock a buy or sell?
A: The dominant strategy among neural network is to Buy CST Stock.
Q: Is CASTILE RESOURCES LTD stock a good investment?
A: The consensus rating for CASTILE RESOURCES LTD is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of CST stock?
A: The consensus rating for CST is Buy.
Q: What is the prediction period for CST stock?
A: The prediction period for CST is (n+6 month)

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