SLMN:TSXV Solis Minerals Ltd.

Outlook: Solis Minerals Ltd. is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 09 Jan 2023 for (n+8 weeks)
Methodology : Multi-Instance Learning (ML)

Abstract

Solis Minerals Ltd. prediction model is evaluated with Multi-Instance Learning (ML) and Wilcoxon Sign-Rank Test^1,2,3,4 and it is concluded that the SLMN:TSXV stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Buy

Key Points

1. Why do we need predictive models?
2. Trust metric by Neural Network
3. Is now good time to invest?

SLMN:TSXV Target Price Prediction Modeling Methodology

We consider Solis Minerals Ltd. Decision Process with Multi-Instance Learning (ML) where A is the set of discrete actions of SLMN: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(Wilcoxon Sign-Rank 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(Multi-Instance Learning (ML)) X S(n):→ (n+8 weeks) $\overrightarrow{R}=\left(r_1,r_2,r_3\right)$

n:Time series to forecast

p:Price signals of SLMN: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?

SLMN:TSXV Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: SLMN:TSXV Solis Minerals Ltd.
Time series to forecast n: 09 Jan 2023 for (n+8 weeks)

According to price forecasts for (n+8 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 Solis Minerals Ltd.

1. Although the objective of an entity's business model may be to hold financial assets in order to collect contractual cash flows, the entity need not hold all of those instruments until maturity. Thus an entity's business model can be to hold financial assets to collect contractual cash flows even when sales of financial assets occur or are expected to occur in the future.
2. 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
3. For the purpose of applying the requirements in paragraphs 6.4.1(c)(i) and B6.4.4–B6.4.6, an entity shall assume that the interest rate benchmark on which the hedged cash flows and/or the hedged risk (contractually or noncontractually specified) are based, or the interest rate benchmark on which the cash flows of the hedging instrument are based, is not altered as a result of interest rate benchmark reform.
4. For lifetime expected credit losses, an entity shall estimate the risk of a default occurring on the financial instrument during its expected life. 12-month expected credit losses are a portion of the lifetime expected credit losses and represent the lifetime cash shortfalls that will result if a default occurs in the 12 months after the reporting date (or a shorter period if the expected life of a financial instrument is less than 12 months), weighted by the probability of that default occurring. Thus, 12-month expected credit losses are neither the lifetime expected credit losses that an entity will incur on financial instruments that it predicts will default in the next 12 months nor the cash shortfalls that are predicted over the next 12 months.

*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

Solis Minerals Ltd. is assigned short-term Ba1 & long-term Ba1 estimated rating. Solis Minerals Ltd. prediction model is evaluated with Multi-Instance Learning (ML) and Wilcoxon Sign-Rank Test^1,2,3,4 and it is concluded that the SLMN:TSXV stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Buy

SLMN:TSXV Solis Minerals Ltd. Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	C	C
Balance Sheet	Baa2	Baa2
Leverage Ratios	B2	Baa2
Cash Flow	B1	Ba1
Rates of Return and Profitability	Caa2	C

*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: 83 out of 100 with 520 signals.

References

1. P. Artzner, F. Delbaen, J. Eber, and D. Heath. Coherent measures of risk. Journal of Mathematical Finance, 9(3):203–228, 1999
2. Athey S, Imbens GW. 2017a. The econometrics of randomized experiments. In Handbook of Economic Field Experiments, Vol. 1, ed. E Duflo, A Banerjee, pp. 73–140. Amsterdam: Elsevier
3. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Trading Signals (WTS Stock Forecast). AC Investment Research Journal, 101(3).
4. A. Tamar and S. Mannor. Variance adjusted actor critic algorithms. arXiv preprint arXiv:1310.3697, 2013.
5. Arjovsky M, Bottou L. 2017. Towards principled methods for training generative adversarial networks. arXiv:1701.04862 [stat.ML]
6. Greene WH. 2000. Econometric Analysis. Upper Saddle River, N J: Prentice Hall. 4th ed.
7. Barkan O. 2016. Bayesian neural word embedding. arXiv:1603.06571 [math.ST]

Frequently Asked Questions

Q: What is the prediction methodology for SLMN:TSXV stock?
A: SLMN:TSXV stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Wilcoxon Sign-Rank Test
Q: Is SLMN:TSXV stock a buy or sell?
A: The dominant strategy among neural network is to Buy SLMN:TSXV Stock.
Q: Is Solis Minerals Ltd. stock a good investment?
A: The consensus rating for Solis Minerals Ltd. is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of SLMN:TSXV stock?
A: The consensus rating for SLMN:TSXV is Buy.
Q: What is the prediction period for SLMN:TSXV stock?
A: The prediction period for SLMN:TSXV is (n+8 weeks)

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