This paper examines the theory and practice of regression techniques for prediction of stock price trend by using a transformed data set in ordinal data format. The original pretransformed data source contains data of heterogeneous data types used for handling of currency values and financial ratios. The data formats in currency values and financial ratios provide a process for computation of stock prices. The transformed data set contains only a standardized ordinal data type which provides a process to measure rankings of stock price trends.** 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+4 weeks) period: The dominant strategy among neural network is to Buy 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

- Can we predict stock market using machine learning?
- What statistical methods are used to analyze data?
- Operational Risk

## LON:GVMH Target Price Prediction Modeling Methodology

As part of this research, different techniques have been studied for data extraction and analysis. After having reviewed the work related to the initial idea of the research, it is shown the development carried out, together with the data extraction and the machine learning algorithms for prediction used. The calculation of technical analysis metrics is also included. The development of a visualization platform has been proposed for high-level interaction between the user and the recommendation system. 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+4 weeks) $\sum _{i=1}^{n}\left({r}_{i}\right)$

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+4 weeks)

**Sample Set:**Neural Network

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

**Time series to forecast n: 07 Oct 2022**for (n+4 weeks)

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Buy 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 Baa2 & long-term B1 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+4 weeks) period: The dominant strategy among neural network is to Buy LON:GVMH stock.**

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

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

Outlook* | Baa2 | B1 |

Operational Risk | 65 | 80 |

Market Risk | 88 | 70 |

Technical Analysis | 90 | 41 |

Fundamental Analysis | 66 | 46 |

Risk Unsystematic | 63 | 53 |

### Prediction Confidence Score

## References

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- E. van der Pol and F. A. Oliehoek. Coordinated deep reinforcement learners for traffic light control. NIPS Workshop on Learning, Inference and Control of Multi-Agent Systems, 2016.
- Armstrong, J. S. M. C. Grohman (1972), "A comparative study of methods for long-range market forecasting," Management Science, 19, 211–221.
- Clements, M. P. D. F. Hendry (1995), "Forecasting in cointegrated systems," Journal of Applied Econometrics, 10, 127–146.
- Blei DM, Lafferty JD. 2009. Topic models. In Text Mining: Classification, Clustering, and Applications, ed. A Srivastava, M Sahami, pp. 101–24. Boca Raton, FL: CRC Press
- Athey S. 2019. The impact of machine learning on economics. In The Economics of Artificial Intelligence: An Agenda, ed. AK Agrawal, J Gans, A Goldfarb. Chicago: Univ. Chicago Press. In press
- Candès EJ, Recht B. 2009. Exact matrix completion via convex optimization. Found. Comput. Math. 9:717

## 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 Buy 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 Buy and assigned short-term Baa2 & long-term B1 forecasted stock rating.

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

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

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

A: The prediction period for LON:GVMH is (n+4 weeks)