SSV:TSXV Southern Silver Exploration Corp.

Outlook: Southern Silver Exploration Corp. assigned short-term Ba3 & long-term B1 forecasted stock rating.
Dominant Strategy : Wait until speculative trend diminishes
Time series to forecast n: 11 Dec 2022 for (n+4 weeks)
Methodology : Modular Neural Network (Financial Sentiment Analysis)

Abstract

This study aims to predict the direction of stock prices by integrating time-varying effective transfer entropy (ETE) and various machine learning algorithms. At first, we explore that the ETE based on 3 and 6 months moving windows can be regarded as the market explanatory variable by analyzing the association between the financial crises and Granger-causal relationships among the stocks.(De Faria, E.L., Albuquerque, M.P., Gonzalez, J.L., Cavalcante, J.T.P. and Albuquerque, M.P., 2009. Predicting the Brazilian stock market through neural networks and adaptive exponential smoothing methods. Expert Systems with Applications, 36(10), pp.12506-12509.) We evaluate Southern Silver Exploration Corp. prediction models with Modular Neural Network (Financial Sentiment Analysis) and Polynomial Regression^1,2,3,4 and conclude that the SSV:TSXV stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Wait until speculative trend diminishes

Key Points

1. Operational Risk
2. What is statistical models in machine learning?
3. Can we predict stock market using machine learning?

SSV:TSXV Target Price Prediction Modeling Methodology

We consider Southern Silver Exploration Corp. Decision Process with Modular Neural Network (Financial Sentiment Analysis) where A is the set of discrete actions of SSV: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(Polynomial 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(Modular Neural Network (Financial Sentiment Analysis)) X S(n):→ (n+4 weeks) $\overrightarrow{R}=\left(r_1,r_2,r_3\right)$

n:Time series to forecast

p:Price signals of SSV: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?

SSV:TSXV Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: SSV:TSXV Southern Silver Exploration Corp.
Time series to forecast n: 11 Dec 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Wait until speculative trend diminishes

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%

Adjusted IFRS* Prediction Methods for Southern Silver Exploration Corp.

1. An entity shall assess at the inception of the hedging relationship, and on an ongoing basis, whether a hedging relationship meets the hedge effectiveness requirements. At a minimum, an entity shall perform the ongoing assessment at each reporting date or upon a significant change in the circumstances affecting the hedge effectiveness requirements, whichever comes first. The assessment relates to expectations about hedge effectiveness and is therefore only forward-looking.
2. An entity shall apply the amendments to IFRS 9 made by IFRS 17 as amended in June 2020 retrospectively in accordance with IAS 8, except as specified in paragraphs 7.2.37–7.2.42.
3. For loan commitments, an entity considers changes in the risk of a default occurring on the loan to which a loan commitment relates. For financial guarantee contracts, an entity considers the changes in the risk that the specified debtor will default on the contract.
4. The assessment of whether an economic relationship exists includes an analysis of the possible behaviour of the hedging relationship during its term to ascertain whether it can be expected to meet the risk management objective. The mere existence of a statistical correlation between two variables does not, by itself, support a valid conclusion that an economic relationship exists.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

Conclusions

Southern Silver Exploration Corp. assigned short-term Ba3 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with Polynomial Regression^1,2,3,4 and conclude that the SSV:TSXV stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Wait until speculative trend diminishes

Financial State Forecast for SSV:TSXV Southern Silver Exploration Corp. Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba3	B1
Operational Risk	72	59
Market Risk	79	75
Technical Analysis	43	78
Fundamental Analysis	75	33
Risk Unsystematic	66	35

Prediction Confidence Score

Trust metric by Neural Network: 73 out of 100 with 834 signals.

References

1. F. A. Oliehoek and C. Amato. A Concise Introduction to Decentralized POMDPs. SpringerBriefs in Intelligent Systems. Springer, 2016
2. Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, Vol. 25, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 1097–105. San Diego, CA: Neural Inf. Process. Syst. Found.
3. Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
4. M. Ono, M. Pavone, Y. Kuwata, and J. Balaram. Chance-constrained dynamic programming with application to risk-aware robotic space exploration. Autonomous Robots, 39(4):555–571, 2015
5. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., How do you decide buy or sell a stock?(SAIC Stock Forecast). AC Investment Research Journal, 101(3).
6. D. Bertsekas. Min common/max crossing duality: A geometric view of conjugacy in convex optimization. Lab. for Information and Decision Systems, MIT, Tech. Rep. Report LIDS-P-2796, 2009
7. A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015

Frequently Asked Questions

Q: What is the prediction methodology for SSV:TSXV stock?
A: SSV:TSXV stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Polynomial Regression
Q: Is SSV:TSXV stock a buy or sell?
A: The dominant strategy among neural network is to Wait until speculative trend diminishes SSV:TSXV Stock.
Q: Is Southern Silver Exploration Corp. stock a good investment?
A: The consensus rating for Southern Silver Exploration Corp. is Wait until speculative trend diminishes and assigned short-term Ba3 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of SSV:TSXV stock?
A: The consensus rating for SSV:TSXV is Wait until speculative trend diminishes.
Q: What is the prediction period for SSV:TSXV stock?
A: The prediction period for SSV:TSXV is (n+4 weeks)

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