LON:MSMN MOSMAN OIL AND GAS LIMITED

Outlook: MOSMAN OIL AND GAS LIMITED assigned short-term B1 & long-term Ba1 forecasted stock rating.
Dominant Strategy : Sell
Time series to forecast n: 11 Dec 2022 for (n+8 weeks)
Methodology : Ensemble Learning (ML)

Abstract

With the advent of technological marvels like global digitization, the prediction of the stock market has entered a technologically advanced era, revamping the old model of trading. With the ceaseless increase in market capitalization, stock trading has become a center of investment for many financial investors. Many analysts and researchers have developed tools and techniques that predict stock price movements and help investors in proper decision-making.(Hushani, P., 2019. Using autoregressive modelling and machine learning for stock market prediction and trading. In Third International Congress on Information and Communication Technology (pp. 767-774). Springer, Singapore.) We evaluate MOSMAN OIL AND GAS LIMITED prediction models with Ensemble Learning (ML) and ElasticNet Regression^1,2,3,4 and conclude that the LON:MSMN stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Sell

Key Points

1. Market Signals
2. How useful are statistical predictions?
3. Reaction Function

LON:MSMN Target Price Prediction Modeling Methodology

We consider MOSMAN OIL AND GAS LIMITED Decision Process with Ensemble Learning (ML) where A is the set of discrete actions of LON:MSMN 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= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Ensemble Learning (ML)) X S(n):→ (n+8 weeks) $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of LON:MSMN 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:MSMN Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: LON:MSMN MOSMAN OIL AND GAS LIMITED
Time series to forecast n: 11 Dec 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Sell

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%

Adjusted IFRS* Prediction Methods for MOSMAN OIL AND GAS LIMITED

1. 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.
2. Sales that occur for other reasons, such as sales made to manage credit concentration risk (without an increase in the assets' credit risk), may also be consistent with a business model whose objective is to hold financial assets in order to collect contractual cash flows. In particular, such sales may be consistent with a business model whose objective is to hold financial assets in order to collect contractual cash flows if those sales are infrequent (even if significant in value) or insignificant in value both individually and in aggregate (even if frequent). If more than an infrequent number of such sales are made out of a portfolio and those sales are more than insignificant in value (either individually or in aggregate), the entity needs to assess whether and how such sales are consistent with an objective of collecting contractual cash flows. Whether a third party imposes the requirement to sell the financial assets, or that activity is at the entity's discretion, is not relevant to this assessment. An increase in the frequency or value of sales in a particular period is not necessarily inconsistent with an objective to hold financial assets in order to collect contractual cash flows, if an entity can explain the reasons for those sales and demonstrate why those sales do not reflect a change in the entity's business model. In addition, sales may be consistent with the objective of holding financial assets in order to collect contractual cash flows if the sales are made close to the maturity of the financial assets and the proceeds from the sales approximate the collection of the remaining contractual cash flows.
3. A net position is eligible for hedge accounting only if an entity hedges on a net basis for risk management purposes. Whether an entity hedges in this way is a matter of fact (not merely of assertion or documentation). Hence, an entity cannot apply hedge accounting on a net basis solely to achieve a particular accounting outcome if that would not reflect its risk management approach. Net position hedging must form part of an established risk management strategy. Normally this would be approved by key management personnel as defined in IAS 24.
4. For a discontinued hedging relationship, when the interest rate benchmark on which the hedged future cash flows had been based is changed as required by interest rate benchmark reform, for the purpose of applying paragraph 6.5.12 in order to determine whether the hedged future cash flows are expected to occur, the amount accumulated in the cash flow hedge reserve for that hedging relationship shall be deemed to be based on the alternative benchmark rate on which the hedged future cash flows will be based.

*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

MOSMAN OIL AND GAS LIMITED assigned short-term B1 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Ensemble Learning (ML) with ElasticNet Regression^1,2,3,4 and conclude that the LON:MSMN stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Sell

Financial State Forecast for LON:MSMN MOSMAN OIL AND GAS LIMITED Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B1	Ba1
Operational Risk	46	86
Market Risk	33	76
Technical Analysis	88	64
Fundamental Analysis	86	83
Risk Unsystematic	41	39

Prediction Confidence Score

Trust metric by Neural Network: 77 out of 100 with 463 signals.

References

1. Bengio Y, Ducharme R, Vincent P, Janvin C. 2003. A neural probabilistic language model. J. Mach. Learn. Res. 3:1137–55
2. Alexander, J. C. Jr. (1995), "Refining the degree of earnings surprise: A comparison of statistical and analysts' forecasts," Financial Review, 30, 469–506.
3. M. L. Littman. Friend-or-foe q-learning in general-sum games. In Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 2001, pages 322–328, 2001
4. Jacobs B, Donkers B, Fok D. 2014. Product Recommendations Based on Latent Purchase Motivations. Rotterdam, Neth.: ERIM
5. K. Tumer and D. Wolpert. A survey of collectives. In K. Tumer and D. Wolpert, editors, Collectives and the Design of Complex Systems, pages 1–42. Springer, 2004.
6. Candès EJ, Recht B. 2009. Exact matrix completion via convex optimization. Found. Comput. Math. 9:717
7. D. Bertsekas and J. Tsitsiklis. Neuro-dynamic programming. Athena Scientific, 1996.

Frequently Asked Questions

Q: What is the prediction methodology for LON:MSMN stock?
A: LON:MSMN stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and ElasticNet Regression
Q: Is LON:MSMN stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:MSMN Stock.
Q: Is MOSMAN OIL AND GAS LIMITED stock a good investment?
A: The consensus rating for MOSMAN OIL AND GAS LIMITED is Sell and assigned short-term B1 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of LON:MSMN stock?
A: The consensus rating for LON:MSMN is Sell.
Q: What is the prediction period for LON:MSMN stock?
A: The prediction period for LON:MSMN is (n+8 weeks)

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