AUC Score :
Short-Term Revised1 :
Dominant Strategy : Sell
Time series to forecast n:
Methodology : Modular Neural Network (Market Direction Analysis)
Hypothesis Testing : Paired T-Test
Surveillance : Major exchange and OTC
1The accuracy of the model is being monitored on a regular basis.(15-minute period)
2Time series is updated based on short-term trends.
Abstract
LYKOS METALS LIMITED prediction model is evaluated with Modular Neural Network (Market Direction Analysis) and Paired T-Test1,2,3,4 and it is concluded that the LYK 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 direction 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 direction analysis, MNNs can be used to identify patterns in market data that suggest that the market is likely to move in a particular direction. This information can then be used to make predictions about future price movements. According to price forecasts for 3 Month period, the dominant strategy among neural network is: Sell
Key Points
- What is neural prediction?
- Market Signals
- Nash Equilibria
LYK Target Price Prediction Modeling Methodology
We consider LYKOS METALS LIMITED Decision Process with Modular Neural Network (Market Direction Analysis) where A is the set of discrete actions of LYK 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(Paired T-Test)5,6,7= X R(Modular Neural Network (Market Direction Analysis)) X S(n):→ 3 Month
n:Time series to forecast
p:Price signals of LYK stock
j:Nash equilibria (Neural Network)
k:Dominated move
a:Best response for target price
Modular Neural Network (Market Direction Analysis)
Modular neural networks (MNNs) are a type of artificial neural network that can be used for market direction 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 direction analysis, MNNs can be used to identify patterns in market data that suggest that the market is likely to move in a particular direction. This information can then be used to make predictions about future price movements.Paired T-Test
A paired t-test is a statistical test that compares the means of two paired samples. In a paired t-test, each data point in one sample is paired with a data point in the other sample. The pairs are typically related in some way, such as before and after measurements, or measurements from the same subject under different conditions. The paired t-test is a parametric test, which means that it assumes that the data is normally distributed. The paired t-test is also a dependent samples test, which means that the data points in each pair are correlated.
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?
LYK Stock Forecast (Buy or Sell)
Sample Set: Neural NetworkStock/Index: LYK LYKOS METALS LIMITED
Time series to forecast: 3 Month
According to price forecasts, the dominant strategy among neural network is: Sell
Strategic Interaction Table Legend:
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%
Financial Data Adjustments for Modular Neural Network (Market Direction Analysis) based LYK Stock Prediction Model
- 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.
- For floating-rate financial assets and floating-rate financial liabilities, periodic re-estimation of cash flows to reflect the movements in the market rates of interest alters the effective interest rate. If a floating-rate financial asset or a floating-rate financial liability is recognised initially at an amount equal to the principal receivable or payable on maturity, re-estimating the future interest payments normally has no significant effect on the carrying amount of the asset or the liability.
- An entity need not undertake an exhaustive search for information but shall consider all reasonable and supportable information that is available without undue cost or effort and that is relevant to the estimate of expected credit losses, including the effect of expected prepayments. The information used shall include factors that are specific to the borrower, general economic conditions and an assessment of both the current as well as the forecast direction of conditions at the reporting date. An entity may use various sources of data, that may be both internal (entity-specific) and external. Possible data sources include internal historical credit loss experience, internal ratings, credit loss experience of other entities and external ratings, reports and statistics. Entities that have no, or insufficient, sources of entityspecific data may use peer group experience for the comparable financial instrument (or groups of financial instruments).
- Fluctuation around a constant hedge ratio (and hence the related hedge ineffectiveness) cannot be reduced by adjusting the hedge ratio in response to each particular outcome. Hence, in such circumstances, the change in the extent of offset is a matter of measuring and recognising hedge ineffectiveness but does not require rebalancing.
*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.
LYK LYKOS METALS LIMITED Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | B2 | B1 |
Income Statement | Caa2 | Caa2 |
Balance Sheet | C | Baa2 |
Leverage Ratios | Baa2 | Caa2 |
Cash Flow | B3 | Caa2 |
Rates of Return and Profitability | Baa2 | Baa2 |
*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?
Conclusions
LYKOS METALS LIMITED is assigned short-term B2 & long-term B1 estimated rating. LYKOS METALS LIMITED prediction model is evaluated with Modular Neural Network (Market Direction Analysis) and Paired T-Test1,2,3,4 and it is concluded that the LYK stock is predictable in the short/long term. According to price forecasts for 3 Month period, the dominant strategy among neural network is: Sell
Prediction Confidence Score
References
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- J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.
- O. Bardou, N. Frikha, and G. Pag`es. Computing VaR and CVaR using stochastic approximation and adaptive unconstrained importance sampling. Monte Carlo Methods and Applications, 15(3):173–210, 2009.
- 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.
- Bottou L. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade, ed. G Montavon, G Orr, K-R Müller, pp. 421–36. Berlin: Springer
- Challen, D. W. A. J. Hagger (1983), Macroeconomic Systems: Construction, Validation and Applications. New York: St. Martin's Press.
- Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
Frequently Asked Questions
Q: What is the prediction methodology for LYK stock?A: LYK stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Paired T-Test
Q: Is LYK stock a buy or sell?
A: The dominant strategy among neural network is to Sell LYK Stock.
Q: Is LYKOS METALS LIMITED stock a good investment?
A: The consensus rating for LYKOS METALS LIMITED is Sell and is assigned short-term B2 & long-term B1 estimated rating.
Q: What is the consensus rating of LYK stock?
A: The consensus rating for LYK is Sell.
Q: What is the prediction period for LYK stock?
A: The prediction period for LYK is 3 Month
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