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 PHAROS ENERGY PLC prediction models with Supervised Machine Learning (ML) and Sign Test ^{1,2,3,4} and conclude that the LON:PHAR 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 Buy LON:PHAR stock.**

**LON:PHAR, PHAROS ENERGY PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Can we predict stock market using machine learning?
- Buy, Sell and Hold Signals
- How can neural networks improve predictions?

## LON:PHAR Target Price Prediction Modeling Methodology

Investors raise profit from stock market by maximising gains and minimising loses. The profit is difficult to raise because of the volatile nature of stock market prices. Predictive modelling allows investors to make informed decisions. We consider PHAROS ENERGY PLC Stock Decision Process with Sign Test where A is the set of discrete actions of LON:PHAR 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(Sign 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(Supervised Machine Learning (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:PHAR 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:PHAR Stock Forecast (Buy or Sell) for (n+16 weeks)

**Sample Set:**Neural Network

**Stock/Index:**LON:PHAR PHAROS ENERGY PLC

**Time series to forecast n: 21 Sep 2022**for (n+16 weeks)

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Buy LON:PHAR 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

PHAROS ENERGY PLC assigned short-term B2 & long-term B1 forecasted stock rating.** We evaluate the prediction models Supervised Machine Learning (ML) with Sign Test ^{1,2,3,4} and conclude that the LON:PHAR 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 Buy LON:PHAR stock.**

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

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

Outlook* | B2 | B1 |

Operational Risk | 38 | 58 |

Market Risk | 85 | 57 |

Technical Analysis | 47 | 80 |

Fundamental Analysis | 48 | 39 |

Risk Unsystematic | 47 | 61 |

### Prediction Confidence Score

## References

- Matzkin RL. 2007. Nonparametric identification. In Handbook of Econometrics, Vol. 6B, ed. J Heckman, E Learner, pp. 5307–68. Amsterdam: Elsevier
- Bai J, Ng S. 2017. Principal components and regularized estimation of factor models. arXiv:1708.08137 [stat.ME]
- Miller A. 2002. Subset Selection in Regression. New York: CRC Press
- Nie X, Wager S. 2019. Quasi-oracle estimation of heterogeneous treatment effects. arXiv:1712.04912 [stat.ML]
- Breiman L. 1993. Better subset selection using the non-negative garotte. Tech. Rep., Univ. Calif., Berkeley
- K. Tuyls and G. Weiss. Multiagent learning: Basics, challenges, and prospects. AI Magazine, 33(3): 41–52, 2012
- 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.

## Frequently Asked Questions

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

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

A: The dominant strategy among neural network is to Buy LON:PHAR Stock.

Q: Is PHAROS ENERGY PLC stock a good investment?

A: The consensus rating for PHAROS ENERGY PLC is Buy and assigned short-term B2 & long-term B1 forecasted stock rating.

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

A: The consensus rating for LON:PHAR is Buy.

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

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