LON:CNE CAPRICORN ENERGY PLC

Outlook: CAPRICORN ENERGY PLC assigned short-term Baa2 & long-term Ba3 forecasted stock rating.
Dominant Strategy : Sell
Time series to forecast n: 09 Dec 2022 for (n+8 weeks)
Methodology : Inductive Learning (ML)

Abstract

Efficient Market Hypothesis (EMH) is the cornerstone of the modern financial theory and it states that it is impossible to predict the price of any stock using any trend, fundamental or technical analysis. Stock trading is one of the most important activities in the world of finance. Stock price prediction has been an age-old problem and many researchers from academia and business have tried to solve it using many techniques ranging from basic statistics to machine learning using relevant information such as news sentiment and historical prices.(Samarawickrama, A.J.P. and Fernando, T.G.I., 2017, December. A recurrent neural network approach in predicting daily stock prices an application to the Sri Lankan stock market. In 2017 IEEE International Conference on Industrial and Information Systems (ICIIS) (pp. 1-6). IEEE.) We evaluate CAPRICORN ENERGY PLC prediction models with Inductive Learning (ML) and Wilcoxon Rank-Sum Test^1,2,3,4 and conclude that the LON:CNE 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. Which neural network is best for prediction?
2. Buy, Sell and Hold Signals
3. Trust metric by Neural Network

LON:CNE Target Price Prediction Modeling Methodology

We consider CAPRICORN ENERGY PLC Decision Process with Inductive Learning (ML) where A is the set of discrete actions of LON:CNE 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(Wilcoxon Rank-Sum 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(Inductive Learning (ML)) X S(n):→ (n+8 weeks) $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: LON:CNE CAPRICORN ENERGY PLC
Time series to forecast n: 09 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 CAPRICORN ENERGY PLC

1. For example, when the critical terms (such as the nominal amount, maturity and underlying) of the hedging instrument and the hedged item match or are closely aligned, it might be possible for an entity to conclude on the basis of a qualitative assessment of those critical terms that the hedging instrument and the hedged item have values that will generally move in the opposite direction because of the same risk and hence that an economic relationship exists between the hedged item and the hedging instrument (see paragraphs B6.4.4–B6.4.6).
2. An entity that first applies IFRS 17 as amended in June 2020 at the same time it first applies this Standard shall apply paragraphs 7.2.1–7.2.28 instead of paragraphs 7.2.38–7.2.42.
3. IFRS 16, issued in January 2016, amended paragraphs 2.1, 5.5.15, B4.3.8, B5.5.34 and B5.5.46. An entity shall apply those amendments when it applies IFRS 16.
4. IFRS 15, issued in May 2014, amended paragraphs 3.1.1, 4.2.1, 5.1.1, 5.2.1, 5.7.6, B3.2.13, B5.7.1, C5 and C42 and deleted paragraph C16 and its related heading. Paragraphs 5.1.3 and 5.7.1A, and a definition to Appendix A, were added. An entity shall apply those amendments when it applies IFRS 15.

*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

CAPRICORN ENERGY PLC assigned short-term Baa2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Inductive Learning (ML) with Wilcoxon Rank-Sum Test^1,2,3,4 and conclude that the LON:CNE 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:CNE CAPRICORN ENERGY PLC Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Baa2	Ba3
Operational Risk	77	50
Market Risk	68	47
Technical Analysis	75	86
Fundamental Analysis	68	66
Risk Unsystematic	85	66

Prediction Confidence Score

Trust metric by Neural Network: 85 out of 100 with 629 signals.

References

1. L. Busoniu, R. Babuska, and B. D. Schutter. A comprehensive survey of multiagent reinforcement learning. IEEE Transactions of Systems, Man, and Cybernetics Part C: Applications and Reviews, 38(2), 2008.
2. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Short/Long Term Stocks: FOX Stock Forecast. AC Investment Research Journal, 101(3).
3. Bengio Y, Schwenk H, Senécal JS, Morin F, Gauvain JL. 2006. Neural probabilistic language models. In Innovations in Machine Learning: Theory and Applications, ed. DE Holmes, pp. 137–86. Berlin: Springer
4. Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]
5. Wan M, Wang D, Goldman M, Taddy M, Rao J, et al. 2017. Modeling consumer preferences and price sensitiv- ities from large-scale grocery shopping transaction logs. In Proceedings of the 26th International Conference on the World Wide Web, pp. 1103–12. New York: ACM
6. Schapire RE, Freund Y. 2012. Boosting: Foundations and Algorithms. Cambridge, MA: MIT Press
7. E. van der Pol and F. A. Oliehoek. Coordinated deep reinforcement learners for traffic light control. NIPS Workshop on Learning, Inference and Control of Multi-Agent Systems, 2016.

Frequently Asked Questions

Q: What is the prediction methodology for LON:CNE stock?
A: LON:CNE stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Wilcoxon Rank-Sum Test
Q: Is LON:CNE stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:CNE Stock.
Q: Is CAPRICORN ENERGY PLC stock a good investment?
A: The consensus rating for CAPRICORN ENERGY PLC is Sell and assigned short-term Baa2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of LON:CNE stock?
A: The consensus rating for LON:CNE is Sell.
Q: What is the prediction period for LON:CNE stock?
A: The prediction period for LON:CNE is (n+8 weeks)

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