AFM:TSXV Alphamin Resources Corp.

Outlook: Alphamin Resources Corp. is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 28 Feb 2023 for (n+6 month)
Methodology : Transfer Learning (ML)

Abstract

Alphamin Resources Corp. prediction model is evaluated with Transfer Learning (ML) and Paired T-Test^1,2,3,4 and it is concluded that the AFM:TSXV stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Hold

Key Points

1. Can neural networks predict stock market?
2. Is it better to buy and sell or hold?
3. Why do we need predictive models?

AFM:TSXV Target Price Prediction Modeling Methodology

We consider Alphamin Resources Corp. Decision Process with Transfer Learning (ML) where A is the set of discrete actions of AFM: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(Paired T-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(Transfer Learning (ML)) X S(n):→ (n+6 month) $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

p:Price signals of AFM: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?

AFM:TSXV Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: AFM:TSXV Alphamin Resources Corp.
Time series to forecast n: 28 Feb 2023 for (n+6 month)

According to price forecasts for (n+6 month) 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 Alphamin Resources Corp.

1. The expected credit losses on a loan commitment shall be discounted using the effective interest rate, or an approximation thereof, that will be applied when recognising the financial asset resulting from the loan commitment. This is because for the purpose of applying the impairment requirements, a financial asset that is recognised following a draw down on a loan commitment shall be treated as a continuation of that commitment instead of as a new financial instrument. The expected credit losses on the financial asset shall therefore be measured considering the initial credit risk of the loan commitment from the date that the entity became a party to the irrevocable commitment.
2. A net position is eligible for hedge accounting only if an entity hedges on a net basis for risk management purposes. Whether an entity hedges in this way is a matter of fact (not merely of assertion or documentation). Hence, an entity cannot apply hedge accounting on a net basis solely to achieve a particular accounting outcome if that would not reflect its risk management approach. Net position hedging must form part of an established risk management strategy. Normally this would be approved by key management personnel as defined in IAS 24.
3. However, in some cases, the time value of money element may be modified (ie imperfect). That would be the case, for example, if a financial asset's interest rate is periodically reset but the frequency of that reset does not match the tenor of the interest rate (for example, the interest rate resets every month to a one-year rate) or if a financial asset's interest rate is periodically reset to an average of particular short- and long-term interest rates. In such cases, an entity must assess the modification to determine whether the contractual cash flows represent solely payments of principal and interest on the principal amount outstanding. In some circumstances, the entity may be able to make that determination by performing a qualitative assessment of the time value of money element whereas, in other circumstances, it may be necessary to perform a quantitative assessment.
4. Paragraphs 6.9.7–6.9.13 provide exceptions to the requirements specified in those paragraphs only. An entity shall apply all other hedge accounting requirements in this Standard, including the qualifying criteria in paragraph 6.4.1, to hedging relationships that were directly affected by interest rate benchmark reform.

*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

Alphamin Resources Corp. is assigned short-term Ba1 & long-term Ba1 estimated rating. Alphamin Resources Corp. prediction model is evaluated with Transfer Learning (ML) and Paired T-Test^1,2,3,4 and it is concluded that the AFM:TSXV stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Hold

AFM:TSXV Alphamin Resources Corp. Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Ba3	Baa2
Balance Sheet	Baa2	Baa2
Leverage Ratios	Caa2	Caa2
Cash Flow	B1	Baa2
Rates of Return and Profitability	C	B3

*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: 80 out of 100 with 593 signals.

References

1. Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
2. Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press
3. Gentzkow M, Kelly BT, Taddy M. 2017. Text as data. NBER Work. Pap. 23276
4. Matzkin RL. 2007. Nonparametric identification. In Handbook of Econometrics, Vol. 6B, ed. J Heckman, E Learner, pp. 5307–68. Amsterdam: Elsevier
5. J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 2137–2145, 2016.
6. Bell RM, Koren Y. 2007. Lessons from the Netflix prize challenge. ACM SIGKDD Explor. Newsl. 9:75–79
7. Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM

Frequently Asked Questions

Q: What is the prediction methodology for AFM:TSXV stock?
A: AFM:TSXV stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Paired T-Test
Q: Is AFM:TSXV stock a buy or sell?
A: The dominant strategy among neural network is to Hold AFM:TSXV Stock.
Q: Is Alphamin Resources Corp. stock a good investment?
A: The consensus rating for Alphamin Resources Corp. is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of AFM:TSXV stock?
A: The consensus rating for AFM:TSXV is Hold.
Q: What is the prediction period for AFM:TSXV stock?
A: The prediction period for AFM:TSXV is (n+6 month)

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