Outlook: SCOTGOLD RESOURCES LIMITED is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 13 Mar 2023 for (n+3 month)
Methodology : Ensemble Learning (ML)

Abstract

SCOTGOLD RESOURCES LIMITED prediction model is evaluated with Ensemble Learning (ML) and ElasticNet Regression1,2,3,4 and it is concluded that the LON:SGZ stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Hold

Key Points

1. What are the most successful trading algorithms?
2. Prediction Modeling
3. Market Signals

LON:SGZ Target Price Prediction Modeling Methodology

We consider SCOTGOLD RESOURCES LIMITED Decision Process with Ensemble Learning (ML) where A is the set of discrete actions of LON:SGZ 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= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Ensemble Learning (ML)) X S(n):→ (n+3 month) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of LON:SGZ 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?

LON:SGZ Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: LON:SGZ SCOTGOLD RESOURCES LIMITED
Time series to forecast n: 13 Mar 2023 for (n+3 month)

According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Hold

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 SCOTGOLD RESOURCES LIMITED

1. It would not be acceptable to designate only some of the financial assets and financial liabilities giving rise to the inconsistency as at fair value through profit or loss if to do so would not eliminate or significantly reduce the inconsistency and would therefore not result in more relevant information. However, it would be acceptable to designate only some of a number of similar financial assets or similar financial liabilities if doing so achieves a significant reduction (and possibly a greater reduction than other allowable designations) in the inconsistency. For example, assume an entity has a number of similar financial liabilities that sum to CU100 and a number of similar financial assets that sum to CU50 but are measured on a different basis. The entity may significantly reduce the measurement inconsistency by designating at initial recognition all of the assets but only some of the liabilities (for example, individual liabilities with a combined total of CU45) as at fair value through profit or loss. However, because designation as at fair value through profit or loss can be applied only to the whole of a financial instrument, the entity in this example must designate one or more liabilities in their entirety. It could not designate either a component of a liability (eg changes in value attributable to only one risk, such as changes in a benchmark interest rate) or a proportion (ie percentage) of a liability.
2. Adjusting the hedge ratio by increasing the volume of the hedging instrument does not affect how the changes in the value of the hedged item are measured. The measurement of the changes in the fair value of the hedging instrument related to the previously designated volume also remains unaffected. However, from the date of rebalancing, the changes in the fair value of the hedging instrument also include the changes in the value of the additional volume of the hedging instrument. The changes are measured starting from, and by reference to, the date of rebalancing instead of the date on which the hedging relationship was designated. For example, if an entity originally hedged the price risk of a commodity using a derivative volume of 100 tonnes as the hedging instrument and added a volume of 10 tonnes on rebalancing, the hedging instrument after rebalancing would comprise a total derivative volume of 110 tonnes. The change in the fair value of the hedging instrument is the total change in the fair value of the derivatives that make up the total volume of 110 tonnes. These derivatives could (and probably would) have different critical terms, such as their forward rates, because they were entered into at different points in time (including the possibility of designating derivatives into hedging relationships after their initial recognition).
3. When measuring a loss allowance for a lease receivable, the cash flows used for determining the expected credit losses should be consistent with the cash flows used in measuring the lease receivable in accordance with IFRS 16 Leases.
4. An entity's business model is determined at a level that reflects how groups of financial assets are managed together to achieve a particular business objective. The entity's business model does not depend on management's intentions for an individual instrument. Accordingly, this condition is not an instrument-by-instrument approach to classification and should be determined on a higher level of aggregation. However, a single entity may have more than one business model for managing its financial instruments. Consequently, classification need not be determined at the reporting entity level. For example, an entity may hold a portfolio of investments that it manages in order to collect contractual cash flows and another portfolio of investments that it manages in order to trade to realise fair value changes. Similarly, in some circumstances, it may be appropriate to separate a portfolio of financial assets into subportfolios in order to reflect the level at which an entity manages those financial assets. For example, that may be the case if an entity originates or purchases a portfolio of mortgage loans and manages some of the loans with an objective of collecting contractual cash flows and manages the other loans with an objective of selling them.

*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

SCOTGOLD RESOURCES LIMITED is assigned short-term Ba1 & long-term Ba1 estimated rating. SCOTGOLD RESOURCES LIMITED prediction model is evaluated with Ensemble Learning (ML) and ElasticNet Regression1,2,3,4 and it is concluded that the LON:SGZ stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Hold

LON:SGZ SCOTGOLD RESOURCES LIMITED Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementCB1
Balance SheetCaa2Caa2
Leverage RatiosB1Baa2
Cash FlowCaa2B3
Rates of Return and ProfitabilityBaa2B2

*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: 73 out of 100 with 747 signals.

References

1. Jacobs B, Donkers B, Fok D. 2014. Product Recommendations Based on Latent Purchase Motivations. Rotterdam, Neth.: ERIM
2. Belsley, D. A. (1988), "Modelling and forecast reliability," International Journal of Forecasting, 4, 427–447.
3. J. Filar, L. Kallenberg, and H. Lee. Variance-penalized Markov decision processes. Mathematics of Opera- tions Research, 14(1):147–161, 1989
4. Bottomley, P. R. Fildes (1998), "The role of prices in models of innovation diffusion," Journal of Forecasting, 17, 539–555.
5. Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, et al. 2008. Top 10 algorithms in data mining. Knowl. Inform. Syst. 14:1–37
6. Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.
7. A. Y. Ng, D. Harada, and S. J. Russell. Policy invariance under reward transformations: Theory and application to reward shaping. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 278–287, 1999.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:SGZ stock?
A: LON:SGZ stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and ElasticNet Regression
Q: Is LON:SGZ stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:SGZ Stock.
Q: Is SCOTGOLD RESOURCES LIMITED stock a good investment?
A: The consensus rating for SCOTGOLD RESOURCES LIMITED is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:SGZ stock?
A: The consensus rating for LON:SGZ is Hold.
Q: What is the prediction period for LON:SGZ stock?
A: The prediction period for LON:SGZ is (n+3 month)