Predicting stock index with traditional time series analysis has proven to be difficult an Artificial Neural network may be suitable for the task. A Neural Network has the ability to extract useful information from large set of data. This paper presents a review of literature application of Artificial Neural Network for stock market predictions and from this literature found that Artificial Neural Network is very useful for predicting world stock markets.** We evaluate UK OIL & GAS PLC prediction models with Transfer Learning (ML) and Paired T-Test ^{1,2,3,4} and conclude that the LON:UKOG 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:UKOG stock.**

**LON:UKOG, UK OIL & GAS PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Trading Interaction
- What are main components of Markov decision process?
- What are the most successful trading algorithms?

## LON:UKOG Target Price Prediction Modeling Methodology

We present an Artificial Neural Network (ANN) approach to predict stock market indices, particularly with respect to the forecast of their trend movements up or down. Exploiting different Neural Networks architectures, we provide numerical analysis of concrete financial time series. In particular, after a brief r ́esum ́e of the existing literature on the subject, we consider the Multi-layer Perceptron (MLP), the Convolutional Neural Net- works (CNN), and the Long Short-Term Memory (LSTM) recurrent neural networks techniques. We consider UK OIL & GAS PLC Stock Decision Process with Paired T-Test where A is the set of discrete actions of LON:UKOG 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(Paired T-Test)

^{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(Transfer Learning (ML)) X S(n):→ (n+16 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:UKOG stock

j:Nash equilibria

k:Dominated move

a:Best response for target price

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## LON:UKOG Stock Forecast (Buy or Sell) for (n+16 weeks)

**Sample Set:**Neural Network

**Stock/Index:**LON:UKOG UK OIL & GAS PLC

**Time series to forecast n: 17 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:UKOG 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

UK OIL & GAS PLC assigned short-term Ba3 & long-term B1 forecasted stock rating.** We evaluate the prediction models Transfer Learning (ML) with Paired T-Test ^{1,2,3,4} and conclude that the LON:UKOG 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:UKOG stock.**

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

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

Outlook* | Ba3 | B1 |

Operational Risk | 67 | 57 |

Market Risk | 81 | 57 |

Technical Analysis | 30 | 47 |

Fundamental Analysis | 65 | 37 |

Risk Unsystematic | 72 | 85 |

### Prediction Confidence Score

## References

- A. Tamar, D. Di Castro, and S. Mannor. Policy gradients with variance related risk criteria. In Proceedings of the Twenty-Ninth International Conference on Machine Learning, pages 387–396, 2012.
- Breiman L, Friedman J, Stone CJ, Olshen RA. 1984. Classification and Regression Trees. Boca Raton, FL: CRC Press
- Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
- 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.
- LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
- Chamberlain G. 2000. Econometrics and decision theory. J. Econom. 95:255–83
- Chen X. 2007. Large sample sieve estimation of semi-nonparametric models. In Handbook of Econometrics, Vol. 6B, ed. JJ Heckman, EE Learner, pp. 5549–632. Amsterdam: Elsevier

## Frequently Asked Questions

Q: What is the prediction methodology for LON:UKOG stock?A: LON:UKOG stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Paired T-Test

Q: Is LON:UKOG stock a buy or sell?

A: The dominant strategy among neural network is to Hold LON:UKOG Stock.

Q: Is UK OIL & GAS PLC stock a good investment?

A: The consensus rating for UK OIL & GAS PLC is Hold and assigned short-term Ba3 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of LON:UKOG stock?

A: The consensus rating for LON:UKOG is Hold.

Q: What is the prediction period for LON:UKOG stock?

A: The prediction period for LON:UKOG is (n+16 weeks)

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