**Outlook:**Alturas Minerals Corp. assigned short-term Ba2 & long-term B2 forecasted stock rating.

**Dominant Strategy :**Buy

**Time series to forecast n: 07 Dec 2022**for (n+8 weeks)

**Methodology :**Modular Neural Network (CNN Layer)

## Abstract

In this paper we investigate ways to use prior knowledge and neural networks to improve multivariate prediction ability. Daily stock prices are predicted as a complicated real-world problem, taking non-numerical factors such as political and international events are into account. We have studied types of prior knowledge which are difficult to insert into initial network structures or to represent in the form of error measurements. (Goel, S.K., Poovathingal, B. and Kumari, N., 2016. Applications of neural networks to stock market prediction. Int Res J Eng Technol: IRJET, 3(05), pp.2192-2197.)** We evaluate Alturas Minerals Corp. prediction models with Modular Neural Network (CNN Layer) and ElasticNet Regression ^{1,2,3,4} and conclude that the ALT: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 to Buy ALT:TSXV stock.**

## Key Points

- What is the best way to predict stock prices?
- Stock Rating
- Investment Risk

## ALT:TSXV Target Price Prediction Modeling Methodology

We consider Alturas Minerals Corp. Decision Process with Modular Neural Network (CNN Layer) 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(ElasticNet Regression)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (CNN Layer)) X S(n):→ (n+8 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

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+8 weeks)

**Sample Set:**Neural Network

**Stock/Index:**ALT:TSXV Alturas Minerals Corp.

**Time series to forecast n: 07 Dec 2022**for (n+8 weeks)

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Buy 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.

- As with all fair value measurements, an entity's measurement method for determining the portion of the change in the liability's fair value that is attributable to changes in its credit risk must make maximum use of relevant observable inputs and minimum use of unobservable inputs.
- Amounts presented in other comprehensive income shall not be subsequently transferred to profit or loss. However, the entity may transfer the cumulative gain or loss within equity.
- If an entity measures a hybrid contract at fair value in accordance with paragraphs 4.1.2A, 4.1.4 or 4.1.5 but the fair value of the hybrid contract had not been measured in comparative reporting periods, the fair value of the hybrid contract in the comparative reporting periods shall be the sum of the fair values of the components (ie the non-derivative host and the embedded derivative) at the end of each comparative reporting period if the entity restates prior periods (see paragraph 7.2.15).
- An entity's estimate of expected credit losses on loan commitments shall be consistent with its expectations of drawdowns on that loan commitment, ie it shall consider the expected portion of the loan commitment that will be drawn down within 12 months of the reporting date when estimating 12-month expected credit losses, and the expected portion of the loan commitment that will be drawn down over the expected life of the loan commitment when estimating lifetime expected credit losses.

*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 Ba2 & long-term B2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (CNN Layer) with ElasticNet Regression ^{1,2,3,4} and conclude that the ALT: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 to Buy ALT:TSXV stock.**

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

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | Ba2 | B2 |

Operational Risk | 85 | 30 |

Market Risk | 32 | 57 |

Technical Analysis | 88 | 35 |

Fundamental Analysis | 45 | 57 |

Risk Unsystematic | 88 | 89 |

### Prediction Confidence Score

## References

- L. Prashanth and M. Ghavamzadeh. Actor-critic algorithms for risk-sensitive MDPs. In Proceedings of Advances in Neural Information Processing Systems 26, pages 252–260, 2013.
- Robins J, Rotnitzky A. 1995. Semiparametric efficiency in multivariate regression models with missing data. J. Am. Stat. Assoc. 90:122–29
- Breiman L. 2001a. Random forests. Mach. Learn. 45:5–32
- P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998
- S. Bhatnagar, H. Prasad, and L. Prashanth. Stochastic recursive algorithms for optimization, volume 434. Springer, 2013
- M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
- R. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Ma- chine learning, 8(3-4):229–256, 1992

## Frequently Asked Questions

Q: What is the prediction methodology for ALT:TSXV stock?A: ALT:TSXV stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and ElasticNet Regression

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 assigned short-term Ba2 & long-term B2 forecasted stock 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+8 weeks)