This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies. The stock market prediction can be modeled based on two principal analyses called technical and fundamental. In the technical analysis approach, the regression machine learning (ML) algorithms are employed to predict the stock price trend at the end of a business day based on the historical price data. In contrast, in the fundamental analysis, the classification ML algorithms are applied to classify the public sentiment based on news and social media.** We evaluate GLOBAL PETROLEUM LIMITED prediction models with Supervised Machine Learning (ML) and Beta ^{1,2,3,4} and conclude that the LON:GBP 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 Hold LON:GBP stock.**

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

*Keywords:*## Key Points

- Trust metric by Neural Network
- What is prediction model?
- What are the most successful trading algorithms?

## LON:GBP Target Price Prediction Modeling Methodology

Stock market forecasting is considered to be a challenging topic among time series forecasting. This study proposes a novel two-stage ensemble machine learning model named SVR-ENANFIS for stock price prediction by combining features of support vector regression (SVR) and ensemble adaptive neuro fuzzy inference system (ENANFIS). We consider GLOBAL PETROLEUM LIMITED Stock Decision Process with Beta 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(Beta)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Supervised Machine Learning (ML)) X S(n):→ (n+8 weeks) $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

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+8 weeks)

**Sample Set:**Neural Network

**Stock/Index:**LON:GBP GLOBAL PETROLEUM LIMITED

**Time series to forecast n: 21 Oct 2022**for (n+8 weeks)

**According to price forecasts for (n+8 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 Ba3 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Supervised Machine Learning (ML) with Beta ^{1,2,3,4} and conclude that the LON:GBP 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 Hold LON:GBP stock.**

### Financial State Forecast for LON:GBP Stock Options & Futures

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | Ba3 | Ba3 |

Operational Risk | 45 | 86 |

Market Risk | 88 | 31 |

Technical Analysis | 68 | 83 |

Fundamental Analysis | 83 | 84 |

Risk Unsystematic | 44 | 38 |

### Prediction Confidence Score

## References

- Barrett, C. B. (1997), "Heteroscedastic price forecasting for food security management in developing countries," Oxford Development Studies, 25, 225–236.
- Brailsford, T.J. R.W. Faff (1996), "An evaluation of volatility forecasting techniques," Journal of Banking Finance, 20, 419–438.
- J. Filar, L. Kallenberg, and H. Lee. Variance-penalized Markov decision processes. Mathematics of Opera- tions Research, 14(1):147–161, 1989
- Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:267–88
- Brailsford, T.J. R.W. Faff (1996), "An evaluation of volatility forecasting techniques," Journal of Banking Finance, 20, 419–438.
- Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.
- Künzel S, Sekhon J, Bickel P, Yu B. 2017. Meta-learners for estimating heterogeneous treatment effects using machine learning. arXiv:1706.03461 [math.ST]

## Frequently Asked Questions

Q: What is the prediction methodology for LON:GBP stock?A: LON:GBP stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Beta

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 Ba3 & long-term Ba3 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+8 weeks)

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