SLI:TSXV Standard Lithium Ltd.

Outlook: Standard Lithium Ltd. assigned short-term Ba3 & long-term B2 forecasted stock rating.
Dominant Strategy : Buy
Time series to forecast n: 11 Dec 2022 for (n+6 month)
Methodology : Modular Neural Network (Market Direction Analysis)

Abstract

Recently, a lot of interesting work has been done in the area of applying Machine Learning Algorithms for analyzing price patterns and predicting stock prices and index changes. Most stock traders nowadays depend on Intelligent Trading Systems which help them in predicting prices based on various situations and conditions, thereby helping them in making instantaneous investment decisions.(Chen, S. and He, H., 2018, October. Stock prediction using convolutional neural network. In IOP Conference series: materials science and engineering (Vol. 435, No. 1, p. 012026). IOP Publishing.) We evaluate Standard Lithium Ltd. prediction models with Modular Neural Network (Market Direction Analysis) and Chi-Square^1,2,3,4 and conclude that the SLI:TSXV stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Buy

Key Points

1. What is the use of Markov decision process?
2. What is the use of Markov decision process?
3. Which neural network is best for prediction?

SLI:TSXV Target Price Prediction Modeling Methodology

We consider Standard Lithium Ltd. Decision Process with Modular Neural Network (Market Direction Analysis) where A is the set of discrete actions of SLI: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(Chi-Square)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (Market Direction Analysis)) X S(n):→ (n+6 month) $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

n:Time series to forecast

p:Price signals of SLI: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?

SLI:TSXV Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: SLI:TSXV Standard Lithium Ltd.
Time series to forecast n: 11 Dec 2022 for (n+6 month)

According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Buy

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 Standard Lithium Ltd.

1. To the extent that a transfer of a financial asset does not qualify for derecognition, the transferor's contractual rights or obligations related to the transfer are not accounted for separately as derivatives if recognising both the derivative and either the transferred asset or the liability arising from the transfer would result in recognising the same rights or obligations twice. For example, a call option retained by the transferor may prevent a transfer of financial assets from being accounted for as a sale. In that case, the call option is not separately recognised as a derivative asset.
2. If an entity prepares interim financial reports in accordance with IAS 34 Interim Financial Reporting the entity need not apply the requirements in this Standard to interim periods prior to the date of initial application if it is impracticable (as defined in IAS 8).
3. The following are examples of when the objective of the entity's business model may be achieved by both collecting contractual cash flows and selling financial assets. This list of examples is not exhaustive. Furthermore, the examples are not intended to describe all the factors that may be relevant to the assessment of the entity's business model nor specify the relative importance of the factors.
4. When an entity designates a financial liability as at fair value through profit or loss, it must determine whether presenting in other comprehensive income the effects of changes in the liability's credit risk would create or enlarge an accounting mismatch in profit or loss. An accounting mismatch would be created or enlarged if presenting the effects of changes in the liability's credit risk in other comprehensive income would result in a greater mismatch in profit or loss than if those amounts were presented in profit or loss

*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

Standard Lithium Ltd. assigned short-term Ba3 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Chi-Square^1,2,3,4 and conclude that the SLI:TSXV stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Buy

Financial State Forecast for SLI:TSXV Standard Lithium Ltd. Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba3	B2
Operational Risk	52	68
Market Risk	84	42
Technical Analysis	70	49
Fundamental Analysis	64	53
Risk Unsystematic	46	31

Prediction Confidence Score

Trust metric by Neural Network: 88 out of 100 with 748 signals.

References

1. Keane MP. 2013. Panel data discrete choice models of consumer demand. In The Oxford Handbook of Panel Data, ed. BH Baltagi, pp. 54–102. Oxford, UK: Oxford Univ. Press
2. Arora S, Li Y, Liang Y, Ma T. 2016. RAND-WALK: a latent variable model approach to word embeddings. Trans. Assoc. Comput. Linguist. 4:385–99
3. Abadie A, Diamond A, Hainmueller J. 2015. Comparative politics and the synthetic control method. Am. J. Political Sci. 59:495–510
4. G. Konidaris, S. Osentoski, and P. Thomas. Value function approximation in reinforcement learning using the Fourier basis. In AAAI, 2011
5. Clements, M. P. D. F. Hendry (1997), "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, 13, 341–355.
6. S. J. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs, NJ, 3nd edition, 2010
7. Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]

Frequently Asked Questions

Q: What is the prediction methodology for SLI:TSXV stock?
A: SLI:TSXV stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Chi-Square
Q: Is SLI:TSXV stock a buy or sell?
A: The dominant strategy among neural network is to Buy SLI:TSXV Stock.
Q: Is Standard Lithium Ltd. stock a good investment?
A: The consensus rating for Standard Lithium Ltd. is Buy and assigned short-term Ba3 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of SLI:TSXV stock?
A: The consensus rating for SLI:TSXV is Buy.
Q: What is the prediction period for SLI:TSXV stock?
A: The prediction period for SLI:TSXV is (n+6 month)

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