## Abstract

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**Outlook:**AUSTRALIAN SILICA QUARTZ GROUP LTD assigned short-term B2 & long-term Baa2 forecasted stock rating.

**Signal:**Buy

**Time series to forecast n: 06 Dec 2022**for (n+4 weeks)

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Prediction of future movement of stock prices has been a subject matter of many research work. In this work, we propose a hybrid approach for stock price prediction using machine learning and deep learning-based methods.(Patil, P., Wu, C.S.M., Potika, K. and Orang, M., 2020, January. Stock market prediction using ensemble of graph theory, machine learning and deep learning models. In Proceedings of the 3rd International Conference on Software Engineering and Information Management (pp. 85-92).)** We evaluate AUSTRALIAN SILICA QUARTZ GROUP LTD prediction models with Modular Neural Network (Emotional Trigger/Responses Analysis) and Independent T-Test ^{1,2,3,4} and conclude that the ASQ stock is predictable in the short/long term. **

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Buy ASQ stock.**

## Key Points

- Understanding Buy, Sell, and Hold Ratings
- Market Risk
- Prediction Modeling

## ASQ Target Price Prediction Modeling Methodology

We consider AUSTRALIAN SILICA QUARTZ GROUP LTD Decision Process with Modular Neural Network (Emotional Trigger/Responses Analysis) where A is the set of discrete actions of ASQ 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(Independent T-Test)

^{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 (Emotional Trigger/Responses Analysis)) X S(n):→ (n+4 weeks) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of ASQ 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?

## ASQ Stock Forecast (Buy or Sell) for (n+4 weeks)

**Sample Set:**Neural Network

**Stock/Index:**ASQ AUSTRALIAN SILICA QUARTZ GROUP LTD

**Time series to forecast n: 06 Dec 2022**for (n+4 weeks)

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Buy ASQ 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 AUSTRALIAN SILICA QUARTZ GROUP LTD

- When measuring a loss allowance for a lease receivable, the cash flows used for determining the expected credit losses should be consistent with the cash flows used in measuring the lease receivable in accordance with IFRS 16 Leases.
- When measuring the fair values of the part that continues to be recognised and the part that is derecognised for the purposes of applying paragraph 3.2.13, an entity applies the fair value measurement requirements in IFRS 13 Fair Value Measurement in addition to paragraph 3.2.14.
- For the purposes of applying the requirement in paragraph 5.7.7(a), credit risk is different from asset-specific performance risk. Asset-specific performance risk is not related to the risk that an entity will fail to discharge a particular obligation but instead it is related to the risk that a single asset or a group of assets will perform poorly (or not at all).
- An entity is not required to restate prior periods to reflect the application of these amendments. The entity may restate prior periods if, and only if, it is possible without the use of hindsight. If an entity does not restate prior periods, the entity shall recognise any difference between the previous carrying amount and the carrying amount at the beginning of the annual reporting period that includes the date of initial application of these amendments in the opening retained earnings (or other component of equity, as appropriate) of the annual reporting period that includes the date of initial application of these amendments.

*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

AUSTRALIAN SILICA QUARTZ GROUP LTD assigned short-term B2 & long-term Baa2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) with Independent T-Test ^{1,2,3,4} and conclude that the ASQ stock is predictable in the short/long term.**

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Buy ASQ stock.**

### Financial State Forecast for ASQ AUSTRALIAN SILICA QUARTZ GROUP LTD Options & Futures

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

Outlook* | B2 | Baa2 |

Operational Risk | 77 | 81 |

Market Risk | 30 | 64 |

Technical Analysis | 30 | 53 |

Fundamental Analysis | 82 | 90 |

Risk Unsystematic | 61 | 87 |

### Prediction Confidence Score

## References

- A. Tamar, D. Di Castro, and S. Mannor. Policy gradients with variance related risk criteria. In Proceedings of the Twenty-Ninth International Conference on Machine Learning, pages 387–396, 2012.
- R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998
- Breiman L. 2001b. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16:199–231
- S. Bhatnagar and K. Lakshmanan. An online actor-critic algorithm with function approximation for con- strained Markov decision processes. Journal of Optimization Theory and Applications, 153(3):688–708, 2012.
- Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2016a. Double machine learning for treatment and causal parameters. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
- Candès EJ, Recht B. 2009. Exact matrix completion via convex optimization. Found. Comput. Math. 9:717
- Hill JL. 2011. Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20:217–40

## Frequently Asked Questions

Q: What is the prediction methodology for ASQ stock?A: ASQ stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Independent T-Test

Q: Is ASQ stock a buy or sell?

A: The dominant strategy among neural network is to Buy ASQ Stock.

Q: Is AUSTRALIAN SILICA QUARTZ GROUP LTD stock a good investment?

A: The consensus rating for AUSTRALIAN SILICA QUARTZ GROUP LTD is Buy and assigned short-term B2 & long-term Baa2 forecasted stock rating.

Q: What is the consensus rating of ASQ stock?

A: The consensus rating for ASQ is Buy.

Q: What is the prediction period for ASQ stock?

A: The prediction period for ASQ is (n+4 weeks)

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