LON:ARS Stock: Set a stop-loss order

Outlook: ASIAMET RESOURCES LIMITED is assigned short-term B3 & long-term B2 estimated rating.
Dominant Strategy : Speculative Trend
Time series to forecast n: 23 Jun 2023 for 6 Month
Methodology : Modular Neural Network (Market Volatility Analysis)

Abstract

ASIAMET RESOURCES LIMITED prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and ElasticNet Regression^1,2,3,4 and it is concluded that the LON:ARS stock is predictable in the short/long term. Modular neural networks (MNNs) are a type of artificial neural network that can be used for market volatility analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying patterns in data or predicting future price movements. The modules are then combined to form a single neural network that can perform multiple tasks.In the context of market volatility analysis, MNNs can be used to identify patterns in market data that suggest that the market is becoming more or less volatile. This information can then be used to make predictions about future price movements. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Speculative Trend

Key Points

1. What is a prediction confidence?
2. Prediction Modeling
3. Can machine learning predict?

LON:ARS Target Price Prediction Modeling Methodology

We consider ASIAMET RESOURCES LIMITED Decision Process with Modular Neural Network (Market Volatility Analysis) where A is the set of discrete actions of LON:ARS 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(ElasticNet Regression)^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 Volatility Analysis)) X S(n):→ 6 Month $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

p:Price signals of LON:ARS stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Modular Neural Network (Market Volatility Analysis)

Modular neural networks (MNNs) are a type of artificial neural network that can be used for market volatility analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying patterns in data or predicting future price movements. The modules are then combined to form a single neural network that can perform multiple tasks.In the context of market volatility analysis, MNNs can be used to identify patterns in market data that suggest that the market is becoming more or less volatile. This information can then be used to make predictions about future price movements.

ElasticNet Regression

Elastic net regression is a type of regression analysis that combines the benefits of ridge regression and lasso regression. It is a regularized regression method that adds a penalty to the least squares objective function in order to reduce the variance of the estimates, induce sparsity in the model, and reduce overfitting. This is done by adding a term to the objective function that is proportional to the sum of the squares of the coefficients and the sum of the absolute values of the coefficients. The penalty terms are controlled by two parameters, called the ridge constant and the lasso constant. Elastic net regression can be used to address the problems of multicollinearity, overfitting, and sensitivity to outliers. It is a more flexible method than ridge regression or lasso regression, and it can often achieve better results.

 

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?

LON:ARS Stock Forecast (Buy or Sell) for 6 Month

Sample Set: Neural Network
Stock/Index: LON:ARS ASIAMET RESOURCES LIMITED
Time series to forecast n: 23 Jun 2023 for 6 Month

According to price forecasts for 6 Month period, the dominant strategy among neural network is: Speculative Trend

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%

IFRS Reconciliation Adjustments for ASIAMET RESOURCES LIMITED

1. The assessment of whether lifetime expected credit losses should be recognised is based on significant increases in the likelihood or risk of a default occurring since initial recognition (irrespective of whether a financial instrument has been repriced to reflect an increase in credit risk) instead of on evidence of a financial asset being credit-impaired at the reporting date or an actual default occurring. Generally, there will be a significant increase in credit risk before a financial asset becomes credit-impaired or an actual default occurs.
2. For loan commitments, an entity considers changes in the risk of a default occurring on the loan to which a loan commitment relates. For financial guarantee contracts, an entity considers the changes in the risk that the specified debtor will default on the contract.
3. An entity's documentation of the hedging relationship includes how it will assess the hedge effectiveness requirements, including the method or methods used. The documentation of the hedging relationship shall be updated for any changes to the methods (see paragraph B6.4.17).
4. An entity shall assess at the inception of the hedging relationship, and on an ongoing basis, whether a hedging relationship meets the hedge effectiveness requirements. At a minimum, an entity shall perform the ongoing assessment at each reporting date or upon a significant change in the circumstances affecting the hedge effectiveness requirements, whichever comes first. The assessment relates to expectations about hedge effectiveness and is therefore only forward-looking.

*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.

Conclusions

ASIAMET RESOURCES LIMITED is assigned short-term B3 & long-term B2 estimated rating. ASIAMET RESOURCES LIMITED prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and ElasticNet Regression^1,2,3,4 and it is concluded that the LON:ARS stock is predictable in the short/long term. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Speculative Trend

LON:ARS ASIAMET RESOURCES LIMITED Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	B3	B2
Income Statement	C	C
Balance Sheet	C	Ba2
Leverage Ratios	B3	B3
Cash Flow	Baa2	Caa2
Rates of Return and Profitability	Caa2	B3

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?

Prediction Confidence Score

Trust metric by Neural Network: 93 out of 100 with 833 signals.

References

1. Athey S, Mobius MM, Pál J. 2017c. The impact of aggregators on internet news consumption. Unpublished manuscript, Grad. School Bus., Stanford Univ., Stanford, CA
2. Mikolov T, Chen K, Corrado GS, Dean J. 2013a. Efficient estimation of word representations in vector space. arXiv:1301.3781 [cs.CL]
3. Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
4. Mikolov T, Yih W, Zweig G. 2013c. Linguistic regularities in continuous space word representations. In Pro- ceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 746–51. New York: Assoc. Comput. Linguist.
5. Breiman L. 1996. Bagging predictors. Mach. Learn. 24:123–40
6. Rosenbaum PR, Rubin DB. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55
7. Chamberlain G. 2000. Econometrics and decision theory. J. Econom. 95:255–83

Frequently Asked Questions

Q: What is the prediction methodology for LON:ARS stock?
A: LON:ARS stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and ElasticNet Regression
Q: Is LON:ARS stock a buy or sell?
A: The dominant strategy among neural network is to Speculative Trend LON:ARS Stock.
Q: Is ASIAMET RESOURCES LIMITED stock a good investment?
A: The consensus rating for ASIAMET RESOURCES LIMITED is Speculative Trend and is assigned short-term B3 & long-term B2 estimated rating.
Q: What is the consensus rating of LON:ARS stock?
A: The consensus rating for LON:ARS is Speculative Trend.
Q: What is the prediction period for LON:ARS stock?
A: The prediction period for LON:ARS is 6 Month

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