GLOBAL PETROLEUM LIMITED assigned short-term B1 & long-term Ba2 forecasted stock rating.

A speculator on a Stock Market, aside from having money to spare, needs at least one other thing — a means of producing accurate and understandable predictions ahead of others in the Market, so that a tactical and price advantage can be gained. This work demonstrates that it is possible to predict one such Market to a high degree of accuracy. We evaluate GLOBAL PETROLEUM LIMITED prediction models with Deductive Inference (ML) and Spearman Correlation^1,2,3,4 and conclude that the LON:GBP 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 LON:GBP stock.

Keywords: LON:GBP, GLOBAL PETROLEUM LIMITED, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. Operational Risk
2. What are main components of Markov decision process?
3. Should I buy stocks now or wait amid such uncertainty?

LON:GBP Target Price Prediction Modeling Methodology

The main objective of this research is to predict the market performance on day closing using different machine learning techniques. The prediction model uses different attributes as an input and predicts market as Positive & Negative. We consider GLOBAL PETROLEUM LIMITED Stock Decision Process with Spearman Correlation where A is the set of discrete actions of LON:GBP 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(Spearman Correlation)^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(Deductive Inference (ML)) X S(n):→ (n+16 weeks) $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

p:Price signals of LON:GBP stock

j:Nash equilibria

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

Sample Set: Neural Network
Stock/Index: LON:GBP GLOBAL PETROLEUM LIMITED
Time series to forecast n: 16 Oct 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold LON:GBP 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%

Conclusions

GLOBAL PETROLEUM LIMITED assigned short-term B1 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Deductive Inference (ML) with Spearman Correlation^1,2,3,4 and conclude that the LON:GBP 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 LON:GBP stock.

Financial State Forecast for LON:GBP Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B1	Ba2
Operational Risk	33	85
Market Risk	67	54
Technical Analysis	67	84
Fundamental Analysis	58	82
Risk Unsystematic	66	32

Prediction Confidence Score

Trust metric by Neural Network: 77 out of 100 with 859 signals.

References

1. Abadie A, Diamond A, Hainmueller J. 2015. Comparative politics and the synthetic control method. Am. J. Political Sci. 59:495–510
2. E. Altman, K. Avrachenkov, and R. N ́u ̃nez-Queija. Perturbation analysis for denumerable Markov chains with application to queueing models. Advances in Applied Probability, pages 839–853, 2004
3. S. Bhatnagar, R. Sutton, M. Ghavamzadeh, and M. Lee. Natural actor-critic algorithms. Automatica, 45(11): 2471–2482, 2009
4. Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60
5. Cortes C, Vapnik V. 1995. Support-vector networks. Mach. Learn. 20:273–97
6. Christou, C., P. A. V. B. Swamy G. S. Tavlas (1996), "Modelling optimal strategies for the allocation of wealth in multicurrency investments," International Journal of Forecasting, 12, 483–493.
7. Clements, M. P. D. F. Hendry (1996), "Intercept corrections and structural change," Journal of Applied Econometrics, 11, 475–494.

Frequently Asked Questions

Q: What is the prediction methodology for LON:GBP stock?
A: LON:GBP stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Spearman Correlation
Q: Is LON:GBP stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:GBP Stock.
Q: Is GLOBAL PETROLEUM LIMITED stock a good investment?
A: The consensus rating for GLOBAL PETROLEUM LIMITED is Hold and assigned short-term B1 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of LON:GBP stock?
A: The consensus rating for LON:GBP is Hold.
Q: What is the prediction period for LON:GBP stock?
A: The prediction period for LON:GBP is (n+16 weeks)

Premium

• Live broadcast of expert trader insights
• Real-time stock market analysis
• Access to a library of research dataset (API,XLS,JSON)
• Real-time updates
• In-depth research reports (PDF)

Login