Stock prediction is a very hot topic in our life. However, in the early time, because of some reasons and the limitation of the device, only a few people had the access to the study. Thanks to the rapid development of science and technology, in recent years more and more people are devoted to the study of the prediction and it becomes easier and easier for us to make stock prediction by using different ways now, including machine learning, deep learning and so on. ** We evaluate GRAND VISION MEDIA HOLDINGS PLC prediction models with Reinforcement Machine Learning (ML) and Pearson Correlation ^{1,2,3,4} and conclude that the LON:GVMH 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:GVMH stock.**

**LON:GVMH, GRAND VISION MEDIA HOLDINGS PLC, 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
- Game Theory
- Operational Risk

## LON:GVMH Target Price Prediction Modeling Methodology

Predicting the future price of financial assets has always been an important research topic in the field of quantitative finance. This paper attempts to use the latest artificial intelligence technologies to design and implement a framework for financial asset price prediction. We consider GRAND VISION MEDIA HOLDINGS PLC Stock Decision Process with Pearson Correlation where A is the set of discrete actions of LON:GVMH 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(Pearson Correlation)

^{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(Reinforcement Machine Learning (ML)) X S(n):→ (n+16 weeks) $\sum _{i=1}^{n}\left({s}_{i}\right)$

n:Time series to forecast

p:Price signals of LON:GVMH 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:GVMH Stock Forecast (Buy or Sell) for (n+16 weeks)

**Sample Set:**Neural Network

**Stock/Index:**LON:GVMH GRAND VISION MEDIA HOLDINGS PLC

**Time series to forecast n: 24 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:GVMH 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

GRAND VISION MEDIA HOLDINGS PLC assigned short-term Ba3 & long-term Ba1 forecasted stock rating.** We evaluate the prediction models Reinforcement Machine Learning (ML) with Pearson Correlation ^{1,2,3,4} and conclude that the LON:GVMH 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:GVMH stock.**

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

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

Outlook* | Ba3 | Ba1 |

Operational Risk | 73 | 87 |

Market Risk | 38 | 72 |

Technical Analysis | 54 | 33 |

Fundamental Analysis | 77 | 77 |

Risk Unsystematic | 74 | 88 |

### Prediction Confidence Score

## References

- Bai J, Ng S. 2017. Principal components and regularized estimation of factor models. arXiv:1708.08137 [stat.ME]
- V. Konda and J. Tsitsiklis. Actor-Critic algorithms. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1008–1014, 2000
- Mnih A, Kavukcuoglu K. 2013. Learning word embeddings efficiently with noise-contrastive estimation. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 2265–73. San Diego, CA: Neural Inf. Process. Syst. Found.
- N. B ̈auerle and A. Mundt. Dynamic mean-risk optimization in a binomial model. Mathematical Methods of Operations Research, 70(2):219–239, 2009.
- Canova, F. B. E. Hansen (1995), "Are seasonal patterns constant over time? A test for seasonal stability," Journal of Business and Economic Statistics, 13, 237–252.
- Brailsford, T.J. R.W. Faff (1996), "An evaluation of volatility forecasting techniques," Journal of Banking Finance, 20, 419–438.
- Bera, A. M. L. Higgins (1997), "ARCH and bilinearity as competing models for nonlinear dependence," Journal of Business Economic Statistics, 15, 43–50.

## Frequently Asked Questions

Q: What is the prediction methodology for LON:GVMH stock?A: LON:GVMH stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Pearson Correlation

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

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

Q: Is GRAND VISION MEDIA HOLDINGS PLC stock a good investment?

A: The consensus rating for GRAND VISION MEDIA HOLDINGS PLC is Hold and assigned short-term Ba3 & long-term Ba1 forecasted stock rating.

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

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

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

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