Outlook: Austral Gold Limited assigned short-term Ba3 & long-term B1 forecasted stock rating.
Dominant Strategy : Hold
Time series to forecast n: 18 Dec 2022 for (n+1 year)
Methodology : Reinforcement Machine Learning (ML)

## Abstract

Recently, numerous investigations for stock price prediction and portfolio management using machine learning have been trying to develop efficient mechanical trading systems. But these systems have a limitation in that they are mainly based on the supervised learning which is not so adequate for learning problems with long-term goals and delayed rewards. This paper proposes a method of applying reinforcement learning, suitable for modeling and learning various kinds of interactions in real situations, to the problem of stock price prediction.(Mokhtari, S., Yen, K.K. and Liu, J., 2021. Effectiveness of artificial intelligence in stock market prediction based on machine learning. arXiv preprint arXiv:2107.01031.) We evaluate Austral Gold Limited prediction models with Reinforcement Machine Learning (ML) and Wilcoxon Sign-Rank Test1,2,3,4 and conclude that the AGLD:TSXV stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Hold

## Key Points

1. Is it better to buy and sell or hold?
2. What are main components of Markov decision process?
3. What is prediction model?

## AGLD:TSXV Target Price Prediction Modeling Methodology

We consider Austral Gold Limited Decision Process with Reinforcement Machine Learning (ML) where A is the set of discrete actions of AGLD: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(Wilcoxon Sign-Rank Test)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Reinforcement Machine Learning (ML)) X S(n):→ (n+1 year) $∑ i = 1 n r i$

n:Time series to forecast

p:Price signals of AGLD: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?

## AGLD:TSXV Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: AGLD:TSXV Austral Gold Limited
Time series to forecast n: 18 Dec 2022 for (n+1 year)

According to price forecasts for (n+1 year) 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 Gold Limited

1. Time value of money is the element of interest that provides consideration for only the passage of time. That is, the time value of money element does not provide consideration for other risks or costs associated with holding the financial asset. In order to assess whether the element provides consideration for only the passage of time, an entity applies judgement and considers relevant factors such as the currency in which the financial asset is denominated and the period for which the interest rate is set.
2. The definition of a derivative in this Standard includes contracts that are settled gross by delivery of the underlying item (eg a forward contract to purchase a fixed rate debt instrument). An entity may have a contract to buy or sell a non-financial item that can be settled net in cash or another financial instrument or by exchanging financial instruments (eg a contract to buy or sell a commodity at a fixed price at a future date). Such a contract is within the scope of this Standard unless it was entered into and continues to be held for the purpose of delivery of a non-financial item in accordance with the entity's expected purchase, sale or usage requirements. However, this Standard applies to such contracts for an entity's expected purchase, sale or usage requirements if the entity makes a designation in accordance with paragraph 2.5 (see paragraphs 2.4–2.7).
3. An entity shall apply Prepayment Features with Negative Compensation (Amendments to IFRS 9) retrospectively in accordance with IAS 8, except as specified in paragraphs 7.2.30–7.2.34
4. 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.

*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 Gold Limited assigned short-term Ba3 & long-term B1 forecasted stock rating. We evaluate the prediction models Reinforcement Machine Learning (ML) with Wilcoxon Sign-Rank Test1,2,3,4 and conclude that the AGLD:TSXV stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Hold

### Financial State Forecast for AGLD:TSXV Austral Gold Limited Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba3B1
Operational Risk 6431
Market Risk7489
Technical Analysis8842
Fundamental Analysis3254
Risk Unsystematic6870

### Prediction Confidence Score

Trust metric by Neural Network: 74 out of 100 with 661 signals.

## References

1. R. Rockafellar and S. Uryasev. Conditional value-at-risk for general loss distributions. Journal of Banking and Finance, 26(7):1443 – 1471, 2002
2. Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:267–88
3. K. Boda and J. Filar. Time consistent dynamic risk measures. Mathematical Methods of Operations Research, 63(1):169–186, 2006
4. Candès E, Tao T. 2007. The Dantzig selector: statistical estimation when p is much larger than n. Ann. Stat. 35:2313–51
5. Allen, P. G. (1994), "Economic forecasting in agriculture," International Journal of Forecasting, 10, 81–135.
6. Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, Vol. 25, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 1097–105. San Diego, CA: Neural Inf. Process. Syst. Found.
7. M. L. Littman. Friend-or-foe q-learning in general-sum games. In Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 2001, pages 322–328, 2001
Frequently Asked QuestionsQ: What is the prediction methodology for AGLD:TSXV stock?
A: AGLD:TSXV stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Wilcoxon Sign-Rank Test
Q: Is AGLD:TSXV stock a buy or sell?
A: The dominant strategy among neural network is to Hold AGLD:TSXV Stock.
Q: Is Austral Gold Limited stock a good investment?
A: The consensus rating for Austral Gold Limited is Hold and assigned short-term Ba3 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of AGLD:TSXV stock?
A: The consensus rating for AGLD:TSXV is Hold.
Q: What is the prediction period for AGLD:TSXV stock?
A: The prediction period for AGLD:TSXV is (n+1 year)