## Summary

In this paper, we propose a hybrid machine learning system based on Genetic Algor ithm (GA) and Support Vector Machines (SVM) for stock market prediction. A variety of indicators from the technical analysis field of study are used as input features. We also make use of the correlation between stock prices of different companies to forecast the price of a stock, making use of technical indicators of highly correlated stocks, not only the stock to be predicted. The genetic algorithm is used to select the set of most informative input features from among all the technical indicators.** We evaluate Almonty Industries Inc. prediction models with Modular Neural Network (CNN Layer) and Logistic Regression ^{1,2,3,4} and conclude that the AII:TSX stock is predictable in the short/long term. **

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold AII:TSX stock.**

## Key Points

- Trading Interaction
- How can neural networks improve predictions?
- Short/Long Term Stocks

## AII:TSX Target Price Prediction Modeling Methodology

We consider Almonty Industries Inc. Stock Decision Process with Modular Neural Network (CNN Layer) where A is the set of discrete actions of AII:TSX 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(Logistic 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+16 weeks) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of AII:TSX 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?

## AII:TSX Stock Forecast (Buy or Sell) for (n+16 weeks)

**Sample Set:**Neural Network

**Stock/Index:**AII:TSX Almonty Industries Inc.

**Time series to forecast n: 22 Nov 2022**for (n+16 weeks)

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold AII:TSX 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 Almonty Industries Inc.

- For the purposes of the transition provisions in paragraphs 7.2.1, 7.2.3–7.2.28 and 7.3.2, the date of initial application is the date when an entity first applies those requirements of this Standard and must be the beginning of a reporting period after the issue of this Standard. Depending on the entity's chosen approach to applying IFRS 9, the transition can involve one or more than one date of initial application for different requirements.
- 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.
- IFRS 15, issued in May 2014, amended paragraphs 3.1.1, 4.2.1, 5.1.1, 5.2.1, 5.7.6, B3.2.13, B5.7.1, C5 and C42 and deleted paragraph C16 and its related heading. Paragraphs 5.1.3 and 5.7.1A, and a definition to Appendix A, were added. An entity shall apply those amendments when it applies IFRS 15.
- Rebalancing is accounted for as a continuation of the hedging relationship in accordance with paragraphs B6.5.9–B6.5.21. On rebalancing, the hedge ineffectiveness of the hedging relationship is determined and recognised immediately before adjusting the hedging relationship.

*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

Almonty Industries Inc. assigned short-term Caa2 & long-term B2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (CNN Layer) with Logistic Regression ^{1,2,3,4} and conclude that the AII:TSX stock is predictable in the short/long term.**

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold AII:TSX stock.**

### Financial State Forecast for AII:TSX Almonty Industries Inc. Stock Options & Futures

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

Outlook* | Caa2 | B2 |

Operational Risk | 43 | 35 |

Market Risk | 35 | 67 |

Technical Analysis | 55 | 44 |

Fundamental Analysis | 37 | 77 |

Risk Unsystematic | 46 | 52 |

### Prediction Confidence Score

## References

- Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60
- Efron B, Hastie T. 2016. Computer Age Statistical Inference, Vol. 5. Cambridge, UK: Cambridge Univ. Press
- D. Bertsekas. Dynamic programming and optimal control. Athena Scientific, 1995.
- Breusch, T. S. A. R. Pagan (1979), "A simple test for heteroskedasticity and random coefficient variation," Econometrica, 47, 1287–1294.
- Candès EJ, Recht B. 2009. Exact matrix completion via convex optimization. Found. Comput. Math. 9:717
- Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
- Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, et al. 2008. Top 10 algorithms in data mining. Knowl. Inform. Syst. 14:1–37

## Frequently Asked Questions

Q: What is the prediction methodology for AII:TSX stock?A: AII:TSX stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Logistic Regression

Q: Is AII:TSX stock a buy or sell?

A: The dominant strategy among neural network is to Hold AII:TSX Stock.

Q: Is Almonty Industries Inc. stock a good investment?

A: The consensus rating for Almonty Industries Inc. is Hold and assigned short-term Caa2 & long-term B2 forecasted stock rating.

Q: What is the consensus rating of AII:TSX stock?

A: The consensus rating for AII:TSX is Hold.

Q: What is the prediction period for AII:TSX stock?

A: The prediction period for AII:TSX is (n+16 weeks)

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