Dominant Strategy : Wait until speculative trend diminishes
Time series to forecast n: 25 Feb 2023 for (n+6 month)
Methodology : Modular Neural Network (CNN Layer)
Abstract
OMNIA METALS GROUP LTD prediction model is evaluated with Modular Neural Network (CNN Layer) and Factor1,2,3,4 and it is concluded that the OM1 stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Wait until speculative trend diminishesKey Points
- Market Risk
- Investment Risk
- What are buy sell or hold recommendations?
OM1 Target Price Prediction Modeling Methodology
We consider OMNIA METALS GROUP LTD Decision Process with Modular Neural Network (CNN Layer) where A is the set of discrete actions of OM1 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(Factor)5,6,7= X R(Modular Neural Network (CNN Layer)) X S(n):→ (n+6 month)
n:Time series to forecast
p:Price signals of OM1 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?
OM1 Stock Forecast (Buy or Sell) for (n+6 month)
Sample Set: Neural NetworkStock/Index: OM1 OMNIA METALS GROUP LTD
Time series to forecast n: 25 Feb 2023 for (n+6 month)
According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Wait until speculative trend diminishes
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 OMNIA METALS GROUP LTD
- The fair value of a financial instrument at initial recognition is normally the transaction price (ie the fair value of the consideration given or received, see also paragraph B5.1.2A and IFRS 13). However, if part of the consideration given or received is for something other than the financial instrument, an entity shall measure the fair value of the financial instrument. For example, the fair value of a long-term loan or receivable that carries no interest can be measured as the present value of all future cash receipts discounted using the prevailing market rate(s) of interest for a similar instrument (similar as to currency, term, type of interest rate and other factors) with a similar credit rating. Any additional amount lent is an expense or a reduction of income unless it qualifies for recognition as some other type of asset.
- For the purposes of measuring expected credit losses, the estimate of expected cash shortfalls shall reflect the cash flows expected from collateral and other credit enhancements that are part of the contractual terms and are not recognised separately by the entity. The estimate of expected cash shortfalls on a collateralised financial instrument reflects the amount and timing of cash flows that are expected from foreclosure on the collateral less the costs of obtaining and selling the collateral, irrespective of whether foreclosure is probable (ie the estimate of expected cash flows considers the probability of a foreclosure and the cash flows that would result from it). Consequently, any cash flows that are expected from the realisation of the collateral beyond the contractual maturity of the contract should be included in this analysis. Any collateral obtained as a result of foreclosure is not recognised as an asset that is separate from the collateralised financial instrument unless it meets the relevant recognition criteria for an asset in this or other Standards.
- For lifetime expected credit losses, an entity shall estimate the risk of a default occurring on the financial instrument during its expected life. 12-month expected credit losses are a portion of the lifetime expected credit losses and represent the lifetime cash shortfalls that will result if a default occurs in the 12 months after the reporting date (or a shorter period if the expected life of a financial instrument is less than 12 months), weighted by the probability of that default occurring. Thus, 12-month expected credit losses are neither the lifetime expected credit losses that an entity will incur on financial instruments that it predicts will default in the next 12 months nor the cash shortfalls that are predicted over the next 12 months.
- 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) 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
OMNIA METALS GROUP LTD is assigned short-term Ba1 & long-term Ba1 estimated rating. OMNIA METALS GROUP LTD prediction model is evaluated with Modular Neural Network (CNN Layer) and Factor1,2,3,4 and it is concluded that the OM1 stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Wait until speculative trend diminishes
OM1 OMNIA METALS GROUP LTD Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | Ba1 | Ba1 |
Income Statement | B3 | C |
Balance Sheet | Baa2 | Baa2 |
Leverage Ratios | Ba3 | B2 |
Cash Flow | Ba3 | Ba1 |
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?
Prediction Confidence Score

References
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- Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28
- Candès EJ, Recht B. 2009. Exact matrix completion via convex optimization. Found. Comput. Math. 9:717
- Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
- Candès E, Tao T. 2007. The Dantzig selector: statistical estimation when p is much larger than n. Ann. Stat. 35:2313–51
- L. Prashanth and M. Ghavamzadeh. Actor-critic algorithms for risk-sensitive MDPs. In Proceedings of Advances in Neural Information Processing Systems 26, pages 252–260, 2013.
- A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008
Frequently Asked Questions
Q: What is the prediction methodology for OM1 stock?A: OM1 stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Factor
Q: Is OM1 stock a buy or sell?
A: The dominant strategy among neural network is to Wait until speculative trend diminishes OM1 Stock.
Q: Is OMNIA METALS GROUP LTD stock a good investment?
A: The consensus rating for OMNIA METALS GROUP LTD is Wait until speculative trend diminishes and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of OM1 stock?
A: The consensus rating for OM1 is Wait until speculative trend diminishes.
Q: What is the prediction period for OM1 stock?
A: The prediction period for OM1 is (n+6 month)
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