**Outlook:**AUSTRAL RESOURCES AUSTRALIA LTD assigned short-term B2 & long-term B2 forecasted stock rating.

**Dominant Strategy :**Hold

**Time series to forecast n: 11 Dec 2022**for (n+8 weeks)

**Methodology :**Modular Neural Network (Speculative Sentiment Analysis)

## Abstract

How to predict stock price movements based on quantitative market data modeling is an attractive topic. In front of the market news and stock prices that are commonly believed as two important market data sources, how to extract and exploit the hidden information within the raw data and make both accurate and fast predictions simultaneously becomes a challenging problem. In this paper, we present the design and architecture of our trading signal mining platform that employs extreme learning machine (ELM) to make stock price prediction based on those two data sources concurrently.(Rouf, N., Malik, M.B., Arif, T., Sharma, S., Singh, S., Aich, S. and Kim, H.C., 2021. Stock market prediction using machine learning techniques: a decade survey on methodologies, recent developments, and future directions. Electronics, 10(21), p.2717.)** We evaluate AUSTRAL RESOURCES AUSTRALIA LTD prediction models with Modular Neural Network (Speculative Sentiment Analysis) and Pearson Correlation ^{1,2,3,4} and conclude that the AR1 stock is predictable in the short/long term. **

**According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Hold**

## Key Points

- What are buy sell or hold recommendations?
- Trust metric by Neural Network
- Investment Risk

## AR1 Target Price Prediction Modeling Methodology

We consider AUSTRAL RESOURCES AUSTRALIA LTD Decision Process with Modular Neural Network (Speculative Sentiment Analysis) where A is the set of discrete actions of AR1 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}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Speculative Sentiment Analysis)) X S(n):→ (n+8 weeks) $\sum _{i=1}^{n}\left({r}_{i}\right)$

n:Time series to forecast

p:Price signals of AR1 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?

## AR1 Stock Forecast (Buy or Sell) for (n+8 weeks)

**Sample Set:**Neural Network

**Stock/Index:**AR1 AUSTRAL RESOURCES AUSTRALIA LTD

**Time series to forecast n: 11 Dec 2022**for (n+8 weeks)

**According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Hold**

**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 AUSTRAL RESOURCES AUSTRALIA LTD

- For the purpose of recognising foreign exchange gains and losses under IAS 21, a financial asset measured at fair value through other comprehensive income in accordance with paragraph 4.1.2A is treated as a monetary item. Accordingly, such a financial asset is treated as an asset measured at amortised cost in the foreign currency. Exchange differences on the amortised cost are recognised in profit or loss and other changes in the carrying amount are recognised in accordance with paragraph 5.7.10.
- An entity is not required to incorporate forecasts of future conditions over the entire expected life of a financial instrument. The degree of judgement that is required to estimate expected credit losses depends on the availability of detailed information. As the forecast horizon increases, the availability of detailed information decreases and the degree of judgement required to estimate expected credit losses increases. The estimate of expected credit losses does not require a detailed estimate for periods that are far in the future—for such periods, an entity may extrapolate projections from available, detailed information.
- Unless paragraph 6.8.8 applies, for a hedge of a non-contractually specified benchmark component of interest rate risk, an entity shall apply the requirement in paragraphs 6.3.7(a) and B6.3.8—that the risk component shall be separately identifiable—only at the inception of the hedging relationship.
- An entity may use practical expedients when measuring expected credit losses if they are consistent with the principles in paragraph 5.5.17. An example of a practical expedient is the calculation of the expected credit losses on trade receivables using a provision matrix. The entity would use its historical credit loss experience (adjusted as appropriate in accordance with paragraphs B5.5.51–B5.5.52) for trade receivables to estimate the 12-month expected credit losses or the lifetime expected credit losses on the financial assets as relevant. A provision matrix might, for example, specify fixed provision rates depending on the number of days that a trade receivable is past due (for example, 1 per cent if not past due, 2 per cent if less than 30 days past due, 3 per cent if more than 30 days but less than 90 days past due, 20 per cent if 90–180 days past due etc). Depending on the diversity of its customer base, the entity would use appropriate groupings if its historical credit loss experience shows significantly different loss patterns for different customer segments. Examples of criteria that might be used to group assets include geographical region, product type, customer rating, collateral or trade credit insurance and type of customer (such as wholesale or retail)

*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

AUSTRAL RESOURCES AUSTRALIA LTD assigned short-term B2 & long-term B2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) with Pearson Correlation ^{1,2,3,4} and conclude that the AR1 stock is predictable in the short/long term.**

**According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Hold**

### Financial State Forecast for AR1 AUSTRAL RESOURCES AUSTRALIA LTD Options & Futures

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

Outlook* | B2 | B2 |

Operational Risk | 33 | 35 |

Market Risk | 71 | 64 |

Technical Analysis | 32 | 40 |

Fundamental Analysis | 70 | 69 |

Risk Unsystematic | 70 | 60 |

### Prediction Confidence Score

## References

- Hastie T, Tibshirani R, Wainwright M. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. New York: CRC Press
- 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.
- Breiman L. 1993. Better subset selection using the non-negative garotte. Tech. Rep., Univ. Calif., Berkeley
- S. Bhatnagar. An actor-critic algorithm with function approximation for discounted cost constrained Markov decision processes. Systems & Control Letters, 59(12):760–766, 2010
- Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
- Allen, P. G. (1994), "Economic forecasting in agriculture," International Journal of Forecasting, 10, 81–135.
- Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]

## Frequently Asked Questions

Q: What is the prediction methodology for AR1 stock?A: AR1 stock prediction methodology: We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) and Pearson Correlation

Q: Is AR1 stock a buy or sell?

A: The dominant strategy among neural network is to Hold AR1 Stock.

Q: Is AUSTRAL RESOURCES AUSTRALIA LTD stock a good investment?

A: The consensus rating for AUSTRAL RESOURCES AUSTRALIA LTD is Hold and assigned short-term B2 & long-term B2 forecasted stock rating.

Q: What is the consensus rating of AR1 stock?

A: The consensus rating for AR1 is Hold.

Q: What is the prediction period for AR1 stock?

A: The prediction period for AR1 is (n+8 weeks)