ALT:TSXV Alturas Minerals Corp.

Alturas Minerals Corp. Research Report

Summary

With the advent of machine learning, numerous approaches have been proposed to forecast stock prices. Various models have been developed to date such as Recurrent Neural Networks, Long Short-Term Memory, Convolutional Neural Network sliding window, etc., but were not accurate enough. Here, the aim is to predict the price of a stock and compare the results obtained using three major algorithms namely Kalman filters, XGBoost and ARIMA. We evaluate Alturas Minerals Corp. prediction models with Supervised Machine Learning (ML) and Pearson Correlation^1,2,3,4 and conclude that the ALT: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 to Sell ALT:TSXV stock.

Key Points

1. Dominated Move
2. Can statistics predict the future?
3. How do you know when a stock will go up or down?

ALT:TSXV Target Price Prediction Modeling Methodology

We consider Alturas Minerals Corp. Decision Process with Supervised Machine 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(Pearson Correlation)^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(Supervised Machine 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 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+6 month)

Sample Set: Neural Network
Stock/Index: ALT:TSXV Alturas Minerals Corp.
Time series to forecast n: 30 Nov 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Sell ALT:TSXV stock.

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 (Yellow to Green): *Technical Analysis%

Adjusted IFRS* Prediction Methods for Alturas Minerals Corp.

1. 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.
2. Alternatively, the entity may base the assessment on both types of information, ie qualitative factors that are not captured through the internal ratings process and a specific internal rating category at the reporting date, taking into consideration the credit risk characteristics at initial recognition, if both types of information are relevant.
3. 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.
4. In some cases, the qualitative and non-statistical quantitative information available may be sufficient to determine that a financial instrument has met the criterion for the recognition of a loss allowance at an amount equal to lifetime expected credit losses. That is, the information does not need to flow through a statistical model or credit ratings process in order to determine whether there has been a significant increase in the credit risk of the financial instrument. In other cases, an entity may need to consider other information, including information from its statistical models or credit ratings processes.

*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

Alturas Minerals Corp. assigned short-term B3 & long-term B2 forecasted stock rating. We evaluate the prediction models Supervised Machine Learning (ML) with Pearson Correlation^1,2,3,4 and conclude that the ALT: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 to Sell ALT:TSXV stock.

Financial State Forecast for ALT:TSXV Alturas Minerals Corp. Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B3	B2
Operational Risk	43	40
Market Risk	32	81
Technical Analysis	59	35
Fundamental Analysis	80	48
Risk Unsystematic	32	49

Prediction Confidence Score

Trust metric by Neural Network: 86 out of 100 with 711 signals.

References

1. Candès E, Tao T. 2007. The Dantzig selector: statistical estimation when p is much larger than n. Ann. Stat. 35:2313–51
2. Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
3. Abadie A, Imbens GW. 2011. Bias-corrected matching estimators for average treatment effects. J. Bus. Econ. Stat. 29:1–11
4. Z. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot, and N. de Freitas. Dueling network architectures for deep reinforcement learning. In Proceedings of the International Conference on Machine Learning (ICML), pages 1995–2003, 2016.
5. N. B ̈auerle and J. Ott. Markov decision processes with average-value-at-risk criteria. Mathematical Methods of Operations Research, 74(3):361–379, 2011
6. Byron, R. P. O. Ashenfelter (1995), "Predicting the quality of an unborn grange," Economic Record, 71, 40–53.
7. Zeileis A, Hothorn T, Hornik K. 2008. Model-based recursive partitioning. J. Comput. Graph. Stat. 17:492–514 Zhou Z, Athey S, Wager S. 2018. Offline multi-action policy learning: generalization and optimization. arXiv:1810.04778 [stat.ML]

Frequently Asked Questions

Q: What is the prediction methodology for ALT:TSXV stock?
A: ALT:TSXV stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Pearson Correlation
Q: Is ALT:TSXV stock a buy or sell?
A: The dominant strategy among neural network is to Sell ALT:TSXV Stock.
Q: Is Alturas Minerals Corp. stock a good investment?
A: The consensus rating for Alturas Minerals Corp. is Sell and assigned short-term B3 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of ALT:TSXV stock?
A: The consensus rating for ALT:TSXV is Sell.
Q: What is the prediction period for ALT:TSXV stock?
A: The prediction period for ALT:TSXV is (n+6 month)

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