PRM PROMINENCE ENERGY LIMITED

PROMINENCE ENERGY LIMITED Research Report

Summary

The aim of this study is to evaluate the effectiveness of using external indicators, such as commodity prices and currency exchange rates, in predicting movements. The performance of each technique is evaluated using different domain specific metrics. A comprehensive evaluation procedure is described, involving the use of trading simulations to assess the practical value of predictive models, and comparison with simple benchmarks that respond to underlying market growth. We evaluate PROMINENCE ENERGY LIMITED prediction models with Transductive Learning (ML) and Independent T-Test^1,2,3,4 and conclude that the PRM stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold PRM stock.

Key Points

1. Can machine learning predict?
2. Operational Risk
3. How do predictive algorithms actually work?

PRM Target Price Prediction Modeling Methodology

We consider PROMINENCE ENERGY LIMITED Decision Process with Transductive Learning (ML) where A is the set of discrete actions of PRM 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(Independent T-Test)^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(Transductive Learning (ML)) X S(n):→ (n+16 weeks) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of PRM 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?

PRM Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: PRM PROMINENCE ENERGY LIMITED
Time series to forecast n: 30 Nov 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold PRM stock.

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 (Yellow to Green): *Technical Analysis%

Adjusted IFRS* Prediction Methods for PROMINENCE ENERGY LIMITED

1. When using historical credit loss experience in estimating expected credit losses, it is important that information about historical credit loss rates is applied to groups that are defined in a manner that is consistent with the groups for which the historical credit loss rates were observed. Consequently, the method used shall enable each group of financial assets to be associated with information about past credit loss experience in groups of financial assets with similar risk characteristics and with relevant observable data that reflects current conditions.
2. An entity shall apply Prepayment Features with Negative Compensation (Amendments to IFRS 9) retrospectively in accordance with IAS 8, except as specified in paragraphs 7.2.30–7.2.34
3. 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.
4. An entity shall amend a hedging relationship as required in paragraph 6.9.1 by the end of the reporting period during which a change required by interest rate benchmark reform is made to the hedged risk, hedged item or hedging instrument. For the avoidance of doubt, such an amendment to the formal designation of a hedging relationship constitutes neither the discontinuation of the hedging relationship nor the designation of a new hedging relationship.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

Conclusions

PROMINENCE ENERGY LIMITED assigned short-term B2 & long-term B3 forecasted stock rating. We evaluate the prediction models Transductive Learning (ML) with Independent T-Test^1,2,3,4 and conclude that the PRM stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold PRM stock.

Financial State Forecast for PRM PROMINENCE ENERGY LIMITED Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B2	B3
Operational Risk	33	32
Market Risk	39	32
Technical Analysis	62	50
Fundamental Analysis	81	70
Risk Unsystematic	48	46

Prediction Confidence Score

Trust metric by Neural Network: 72 out of 100 with 723 signals.

References

1. Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
2. Mikolov T, Chen K, Corrado GS, Dean J. 2013a. Efficient estimation of word representations in vector space. arXiv:1301.3781 [cs.CL]
3. Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28
4. R. Rockafellar and S. Uryasev. Conditional value-at-risk for general loss distributions. Journal of Banking and Finance, 26(7):1443 – 1471, 2002
5. Candès E, Tao T. 2007. The Dantzig selector: statistical estimation when p is much larger than n. Ann. Stat. 35:2313–51
6. Athey S, Imbens GW. 2017b. The state of applied econometrics: causality and policy evaluation. J. Econ. Perspect. 31:3–32
7. Batchelor, R. P. Dua (1993), "Survey vs ARCH measures of inflation uncertainty," Oxford Bulletin of Economics Statistics, 55, 341–353.

Frequently Asked Questions

Q: What is the prediction methodology for PRM stock?
A: PRM stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Independent T-Test
Q: Is PRM stock a buy or sell?
A: The dominant strategy among neural network is to Hold PRM Stock.
Q: Is PROMINENCE ENERGY LIMITED stock a good investment?
A: The consensus rating for PROMINENCE ENERGY LIMITED is Hold and assigned short-term B2 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of PRM stock?
A: The consensus rating for PRM is Hold.
Q: What is the prediction period for PRM stock?
A: The prediction period for PRM is (n+16 weeks)

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