**Outlook:**Sarama Resources Ltd. assigned short-term B1 & long-term B1 forecasted stock rating.

**Dominant Strategy :**Sell

**Time series to forecast n: 11 Dec 2022**for (n+4 weeks)

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

## Abstract

Market systems are so complex that they overwhelm the ability of any individual to predict. But it is crucial for the investors to predict stock market price to generate notable profit. We have taken into factors such as Commodity Prices (crude oil, gold, silver), Market History, and Foreign exchange rate (FEX) that influence the stock trend.(Shah, V.H., 2007. Machine learning techniques for stock prediction. Foundations of Machine Learning| Spring, 1(1), pp.6-12.)** We evaluate Sarama Resources Ltd. prediction models with Modular Neural Network (DNN Layer) and Polynomial Regression ^{1,2,3,4} and conclude that the SWA:TSXV stock is predictable in the short/long term. **

**According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Sell**

## Key Points

- What is the use of Markov decision process?
- What is a prediction confidence?
- What is neural prediction?

## SWA:TSXV Target Price Prediction Modeling Methodology

We consider Sarama Resources Ltd. Decision Process with Modular Neural Network (DNN Layer) where A is the set of discrete actions of SWA: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(Polynomial 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 (DNN Layer)) X S(n):→ (n+4 weeks) $\sum _{i=1}^{n}\left({r}_{i}\right)$

n:Time series to forecast

p:Price signals of SWA: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?

## SWA:TSXV Stock Forecast (Buy or Sell) for (n+4 weeks)

**Sample Set:**Neural Network

**Stock/Index:**SWA:TSXV Sarama Resources Ltd.

**Time series to forecast n: 11 Dec 2022**for (n+4 weeks)

**According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Sell**

**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 Sarama Resources Ltd.

- For the avoidance of doubt, the effects of replacing the original counterparty with a clearing counterparty and making the associated changes as described in paragraph 6.5.6 shall be reflected in the measurement of the hedging instrument and therefore in the assessment of hedge effectiveness and the measurement of hedge effectiveness
- When defining default for the purposes of determining the risk of a default occurring, an entity shall apply a default definition that is consistent with the definition used for internal credit risk management purposes for the relevant financial instrument and consider qualitative indicators (for example, financial covenants) when appropriate. However, there is a rebuttable presumption that default does not occur later than when a financial asset is 90 days past due unless an entity has reasonable and supportable information to demonstrate that a more lagging default criterion is more appropriate. The definition of default used for these purposes shall be applied consistently to all financial instruments unless information becomes available that demonstrates that another default definition is more appropriate for a particular financial instrument.
- 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.
- An entity shall apply the impairment requirements in Section 5.5 retrospectively in accordance with IAS 8 subject to paragraphs 7.2.15 and 7.2.18–7.2.20.

*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

Sarama Resources Ltd. assigned short-term B1 & long-term B1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (DNN Layer) with Polynomial Regression ^{1,2,3,4} and conclude that the SWA:TSXV stock is predictable in the short/long term.**

**According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Sell**

### Financial State Forecast for SWA:TSXV Sarama Resources Ltd. Options & Futures

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

Outlook* | B1 | B1 |

Operational Risk | 49 | 38 |

Market Risk | 69 | 54 |

Technical Analysis | 44 | 61 |

Fundamental Analysis | 59 | 73 |

Risk Unsystematic | 89 | 52 |

### Prediction Confidence Score

## References

- J. G. Schneider, W. Wong, A. W. Moore, and M. A. Riedmiller. Distributed value functions. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 371–378, 1999.
- Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60
- K. Boda and J. Filar. Time consistent dynamic risk measures. Mathematical Methods of Operations Research, 63(1):169–186, 2006
- L. Busoniu, R. Babuska, and B. D. Schutter. A comprehensive survey of multiagent reinforcement learning. IEEE Transactions of Systems, Man, and Cybernetics Part C: Applications and Reviews, 38(2), 2008.
- Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Is FFBC Stock Buy or Sell?(Stock Forecast). AC Investment Research Journal, 101(3).
- H. Khalil and J. Grizzle. Nonlinear systems, volume 3. Prentice hall Upper Saddle River, 2002.
- Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM

## Frequently Asked Questions

Q: What is the prediction methodology for SWA:TSXV stock?A: SWA:TSXV stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Polynomial Regression

Q: Is SWA:TSXV stock a buy or sell?

A: The dominant strategy among neural network is to Sell SWA:TSXV Stock.

Q: Is Sarama Resources Ltd. stock a good investment?

A: The consensus rating for Sarama Resources Ltd. is Sell and assigned short-term B1 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of SWA:TSXV stock?

A: The consensus rating for SWA:TSXV is Sell.

Q: What is the prediction period for SWA:TSXV stock?

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