ALT:TSXV Alturas Minerals Corp.

Outlook: Alturas Minerals Corp. is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 13 Feb 2023 for (n+16 weeks)
Methodology : Ensemble Learning (ML)

Abstract

Alturas Minerals Corp. prediction model is evaluated with Ensemble Learning (ML) and Sign Test^1,2,3,4 and it is concluded that the ALT:TSXV stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Buy

Key Points

1. How do you know when a stock will go up or down?
2. How do you decide buy or sell a stock?
3. What is neural prediction?

ALT:TSXV Target Price Prediction Modeling Methodology

We consider Alturas Minerals Corp. Decision Process with Ensemble Learning (ML) where A is the set of discrete actions of ALT:TSXV 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(Sign Test)^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(Ensemble Learning (ML)) X S(n):→ (n+16 weeks) $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

n:Time series to forecast

p:Price signals of ALT:TSXV 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?

ALT:TSXV Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: ALT:TSXV Alturas Minerals Corp.
Time series to forecast n: 13 Feb 2023 for (n+16 weeks)

According to price forecasts for (n+16 weeks) 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 Alturas Minerals Corp.

1. If changes are made in addition to those changes required by interest rate benchmark reform to the financial asset or financial liability designated in a hedging relationship (as described in paragraphs 5.4.6–5.4.8) or to the designation of the hedging relationship (as required by paragraph 6.9.1), an entity shall first apply the applicable requirements in this Standard to determine if those additional changes result in the discontinuation of hedge accounting. If the additional changes do not result in the discontinuation of hedge accounting, an entity shall amend the formal designation of the hedging relationship as specified in paragraph 6.9.1.
2. If a component of the cash flows of a financial or a non-financial item is designated as the hedged item, that component must be less than or equal to the total cash flows of the entire item. However, all of the cash flows of the entire item may be designated as the hedged item and hedged for only one particular risk (for example, only for those changes that are attributable to changes in LIBOR or a benchmark commodity price).
3. Interest Rate Benchmark Reform, which amended IFRS 9, IAS 39 and IFRS 7, issued in September 2019, added Section 6.8 and amended paragraph 7.2.26. An entity shall apply these amendments for annual periods beginning on or after 1 January 2020. Earlier application is permitted. If an entity applies these amendments for an earlier period, it shall disclose that fact.
4. At the date of initial application, an entity shall determine whether the treatment in paragraph 5.7.7 would create or enlarge an accounting mismatch in profit or loss on the basis of the facts and circumstances that exist at the date of initial application. This Standard shall be applied retrospectively on the basis of that determination.

*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

Alturas Minerals Corp. is assigned short-term Ba1 & long-term Ba1 estimated rating. Alturas Minerals Corp. prediction model is evaluated with Ensemble Learning (ML) and Sign Test^1,2,3,4 and it is concluded that the ALT:TSXV stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Buy

ALT:TSXV Alturas Minerals Corp. Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	B1	Ba2
Balance Sheet	C	B3
Leverage Ratios	Baa2	Baa2
Cash Flow	B1	B3
Rates of Return and Profitability	B3	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: 90 out of 100 with 698 signals.

References

1. uyer, S. Whiteson, B. Bakker, and N. A. Vlassis. Multiagent reinforcement learning for urban traffic control using coordination graphs. In Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I, pages 656–671, 2008.
2. Y. Le Tallec. Robust, risk-sensitive, and data-driven control of Markov decision processes. PhD thesis, Massachusetts Institute of Technology, 2007.
3. M. Babes, E. M. de Cote, and M. L. Littman. Social reward shaping in the prisoner's dilemma. In 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), Estoril, Portugal, May 12-16, 2008, Volume 3, pages 1389–1392, 2008.
4. Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating errors. Nature 323:533–36
5. Imbens G, Wooldridge J. 2009. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47:5–86
6. Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
7. M. Babes, E. M. de Cote, and M. L. Littman. Social reward shaping in the prisoner's dilemma. In 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), Estoril, Portugal, May 12-16, 2008, Volume 3, pages 1389–1392, 2008.

Frequently Asked Questions

Q: What is the prediction methodology for ALT:TSXV stock?
A: ALT:TSXV stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Sign Test
Q: Is ALT:TSXV stock a buy or sell?
A: The dominant strategy among neural network is to Buy ALT:TSXV Stock.
Q: Is Alturas Minerals Corp. stock a good investment?
A: The consensus rating for Alturas Minerals Corp. is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of ALT:TSXV stock?
A: The consensus rating for ALT:TSXV is Buy.
Q: What is the prediction period for ALT:TSXV stock?
A: The prediction period for ALT:TSXV is (n+16 weeks)

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