LON:GSEO VH GLOBAL SUSTAINABLE ENERGY OPPORTUNITIES PLC

Outlook: VH GLOBAL SUSTAINABLE ENERGY OPPORTUNITIES PLC assigned short-term B2 & long-term B3 forecasted stock rating.
Dominant Strategy : Sell
Time series to forecast n: 06 Dec 2022 for (n+8 weeks)
Methodology : Active Learning (ML)

Abstract

Accurate prediction of stock market returns is a very challenging task due to volatile and non-linear nature of the financial stock markets. With the introduction of artificial intelligence and increased computational capabilities, programmed methods of prediction have proved to be more efficient in predicting stock prices.(Madeeh, O.D. and Abdullah, H.S., 2021, February. An efficient prediction model based on machine learning techniques for prediction of the stock market. In Journal of Physics: Conference Series (Vol. 1804, No. 1, p. 012008). IOP Publishing.) We evaluate VH GLOBAL SUSTAINABLE ENERGY OPPORTUNITIES PLC prediction models with Active Learning (ML) and ElasticNet Regression^1,2,3,4 and conclude that the LON:GSEO stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Sell LON:GSEO stock.

Key Points

1. Game Theory
2. Stock Rating
3. What is a prediction confidence?

LON:GSEO Target Price Prediction Modeling Methodology

We consider VH GLOBAL SUSTAINABLE ENERGY OPPORTUNITIES PLC Decision Process with Active Learning (ML) where A is the set of discrete actions of LON:GSEO 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(Active Learning (ML)) X S(n):→ (n+8 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: LON:GSEO VH GLOBAL SUSTAINABLE ENERGY OPPORTUNITIES PLC
Time series to forecast n: 06 Dec 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Sell LON:GSEO 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 VH GLOBAL SUSTAINABLE ENERGY OPPORTUNITIES PLC

1. Interest Rate Benchmark Reform—Phase 2, which amended IFRS 9, IAS 39, IFRS 7, IFRS 4 and IFRS 16, issued in August 2020, added paragraphs 5.4.5–5.4.9, 6.8.13, Section 6.9 and paragraphs 7.2.43–7.2.46. An entity shall apply these amendments for annual periods beginning on or after 1 January 2021. Earlier application is permitted. If an entity applies these amendments for an earlier period, it shall disclose that fact.
2. To the extent that a transfer of a financial asset does not qualify for derecognition, the transferee does not recognise the transferred asset as its asset. The transferee derecognises the cash or other consideration paid and recognises a receivable from the transferor. If the transferor has both a right and an obligation to reacquire control of the entire transferred asset for a fixed amount (such as under a repurchase agreement), the transferee may measure its receivable at amortised cost if it meets the criteria in paragraph 4.1.2.
3. An entity shall assess whether contractual cash flows are solely payments of principal and interest on the principal amount outstanding for the currency in which the financial asset is denominated.
4. If such a mismatch would be created or enlarged, the entity is required to present all changes in fair value (including the effects of changes in the credit risk of the liability) in profit or loss. If such a mismatch would not be created or enlarged, the entity is required to present the effects of changes in the liability's credit risk in other comprehensive income.

*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

VH GLOBAL SUSTAINABLE ENERGY OPPORTUNITIES PLC assigned short-term B2 & long-term B3 forecasted stock rating. We evaluate the prediction models Active Learning (ML) with ElasticNet Regression^1,2,3,4 and conclude that the LON:GSEO stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Sell LON:GSEO stock.

Financial State Forecast for LON:GSEO VH GLOBAL SUSTAINABLE ENERGY OPPORTUNITIES PLC Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B2	B3
Operational Risk	53	32
Market Risk	46	30
Technical Analysis	87	34
Fundamental Analysis	45	75
Risk Unsystematic	37	62

Prediction Confidence Score

Trust metric by Neural Network: 89 out of 100 with 650 signals.

References

1. Bessler, D. A. S. W. Fuller (1993), "Cointegration between U.S. wheat markets," Journal of Regional Science, 33, 481–501.
2. G. Theocharous and A. Hallak. Lifetime value marketing using reinforcement learning. RLDM 2013, page 19, 2013
3. Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221
4. P. Milgrom and I. Segal. Envelope theorems for arbitrary choice sets. Econometrica, 70(2):583–601, 2002
5. Efron B, Hastie T, Johnstone I, Tibshirani R. 2004. Least angle regression. Ann. Stat. 32:407–99
6. Thomas P, Brunskill E. 2016. Data-efficient off-policy policy evaluation for reinforcement learning. In Pro- ceedings of the International Conference on Machine Learning, pp. 2139–48. La Jolla, CA: Int. Mach. Learn. Soc.
7. E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999

Frequently Asked Questions

Q: What is the prediction methodology for LON:GSEO stock?
A: LON:GSEO stock prediction methodology: We evaluate the prediction models Active Learning (ML) and ElasticNet Regression
Q: Is LON:GSEO stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:GSEO Stock.
Q: Is VH GLOBAL SUSTAINABLE ENERGY OPPORTUNITIES PLC stock a good investment?
A: The consensus rating for VH GLOBAL SUSTAINABLE ENERGY OPPORTUNITIES PLC is Sell and assigned short-term B2 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of LON:GSEO stock?
A: The consensus rating for LON:GSEO is Sell.
Q: What is the prediction period for LON:GSEO stock?
A: The prediction period for LON:GSEO is (n+8 weeks)

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