Dominant Strategy : Buy
Time series to forecast n: 18 Jan 2023 for (n+8 weeks)
Methodology : Multi-Task Learning (ML)
Abstract
Horizonte Minerals Plc prediction model is evaluated with Multi-Task Learning (ML) and Ridge Regression1,2,3,4 and it is concluded that the HZM:TSX stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: BuyKey Points
- Buy, Sell and Hold Signals
- What are main components of Markov decision process?
- Game Theory
HZM:TSX Target Price Prediction Modeling Methodology
We consider Horizonte Minerals Plc Decision Process with Multi-Task Learning (ML) where A is the set of discrete actions of HZM:TSX 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(Ridge Regression)5,6,7= X R(Multi-Task Learning (ML)) X S(n):→ (n+8 weeks)
n:Time series to forecast
p:Price signals of HZM:TSX 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?
HZM:TSX Stock Forecast (Buy or Sell) for (n+8 weeks)
Sample Set: Neural NetworkStock/Index: HZM:TSX Horizonte Minerals Plc
Time series to forecast n: 18 Jan 2023 for (n+8 weeks)
According to price forecasts for (n+8 weeks) 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%
IFRS Reconciliation Adjustments for Horizonte Minerals Plc
- An entity can also designate only changes in the cash flows or fair value of a hedged item above or below a specified price or other variable (a 'one-sided risk'). The intrinsic value of a purchased option hedging instrument (assuming that it has the same principal terms as the designated risk), but not its time value, reflects a one-sided risk in a hedged item. For example, an entity can designate the variability of future cash flow outcomes resulting from a price increase of a forecast commodity purchase. In such a situation, the entity designates only cash flow losses that result from an increase in the price above the specified level. The hedged risk does not include the time value of a purchased option, because the time value is not a component of the forecast transaction that affects profit or loss.
- Expected credit losses reflect an entity's own expectations of credit losses. However, when considering all reasonable and supportable information that is available without undue cost or effort in estimating expected credit losses, an entity should also consider observable market information about the credit risk of the particular financial instrument or similar financial instruments.
- To make that determination, an entity must assess whether it expects that the effects of changes in the liability's credit risk will be offset in profit or loss by a change in the fair value of another financial instrument measured at fair value through profit or loss. Such an expectation must be based on an economic relationship between the characteristics of the liability and the characteristics of the other financial instrument.
- An entity is not required to restate prior periods to reflect the application of these amendments. The entity may restate prior periods if, and only if, it is possible without the use of hindsight and the restated financial statements reflect all the requirements in this Standard. If an entity does not restate prior periods, the entity shall recognise any difference between the previous carrying amount and the carrying amount at the beginning of the annual reporting period that includes the date of initial application of these amendments in the opening retained earnings (or other component of equity, as appropriate) of the annual reporting period that includes the date of initial application of these amendments.
*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
Horizonte Minerals Plc is assigned short-term Ba1 & long-term Ba1 estimated rating. Horizonte Minerals Plc prediction model is evaluated with Multi-Task Learning (ML) and Ridge Regression1,2,3,4 and it is concluded that the HZM:TSX stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Buy
HZM:TSX Horizonte Minerals Plc Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | Ba1 | Ba1 |
Income Statement | Caa2 | Baa2 |
Balance Sheet | B2 | Ba1 |
Leverage Ratios | Baa2 | Baa2 |
Cash Flow | Baa2 | Baa2 |
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
- Chernozhukov V, Demirer M, Duflo E, Fernandez-Val I. 2018b. Generic machine learning inference on heteroge- nous treatment effects in randomized experiments. NBER Work. Pap. 24678
- Semenova V, Goldman M, Chernozhukov V, Taddy M. 2018. Orthogonal ML for demand estimation: high dimensional causal inference in dynamic panels. arXiv:1712.09988 [stat.ML]
- Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]
- S. Proper and K. Tumer. Modeling difference rewards for multiagent learning (extended abstract). In Proceedings of the Eleventh International Joint Conference on Autonomous Agents and Multiagent Systems, Valencia, Spain, June 2012
- Breiman L. 2001b. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16:199–231
- Akgiray, V. (1989), "Conditional heteroscedasticity in time series of stock returns: Evidence and forecasts," Journal of Business, 62, 55–80.
- K. Boda, J. Filar, Y. Lin, and L. Spanjers. Stochastic target hitting time and the problem of early retirement. Automatic Control, IEEE Transactions on, 49(3):409–419, 2004
Frequently Asked Questions
Q: What is the prediction methodology for HZM:TSX stock?A: HZM:TSX stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Ridge Regression
Q: Is HZM:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Buy HZM:TSX Stock.
Q: Is Horizonte Minerals Plc stock a good investment?
A: The consensus rating for Horizonte Minerals Plc is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of HZM:TSX stock?
A: The consensus rating for HZM:TSX is Buy.
Q: What is the prediction period for HZM:TSX stock?
A: The prediction period for HZM:TSX is (n+8 weeks)
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