Neural networks, as an intelligent data mining method, have been used in many different challenging pattern recognition problems such as stock market prediction. However, there is no formal method to determine the optimal neural network for prediction purpose in the literature. In this paper, two kinds of neural networks, a feed forward multi layer Perceptron (MLP) and an Elman recurrent network, are used to predict a company's stock value based on its stock share value history.** We evaluate VERICI DX PLC prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Lasso Regression ^{1,2,3,4} and conclude that the LON:VRCI stock is predictable in the short/long term. **

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:VRCI stock.**

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

*Keywords:*## Key Points

- Reaction Function
- How do you decide buy or sell a stock?
- What are the most successful trading algorithms?

## LON:VRCI Target Price Prediction Modeling Methodology

The study of financial markets has been addressed in many works during the last years. Different methods have been used in order to capture the non-linear behavior which is characteristic of these complex systems. The development of profitable strategies has been associated with the predictive character of the market movement, and special attention has been devoted to forecast the trends of financial markets. We consider VERICI DX PLC Stock Decision Process with Lasso Regression where A is the set of discrete actions of LON:VRCI 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(Lasso Regression)

^{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(Modular Neural Network (Social Media Sentiment Analysis)) X S(n):→ (n+6 month) $\sum _{i=1}^{n}\left({s}_{i}\right)$

n:Time series to forecast

p:Price signals of LON:VRCI 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:VRCI Stock Forecast (Buy or Sell) for (n+6 month)

**Sample Set:**Neural Network

**Stock/Index:**LON:VRCI VERICI DX PLC

**Time series to forecast n: 28 Sep 2022**for (n+6 month)

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:VRCI 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

VERICI DX PLC assigned short-term Ba2 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Lasso Regression ^{1,2,3,4} and conclude that the LON:VRCI stock is predictable in the short/long term.**

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:VRCI stock.**

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

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

Outlook* | Ba2 | Ba3 |

Operational Risk | 33 | 87 |

Market Risk | 87 | 48 |

Technical Analysis | 68 | 89 |

Fundamental Analysis | 74 | 33 |

Risk Unsystematic | 83 | 61 |

### Prediction Confidence Score

## References

- Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.
- Thomas P, Brunskill E. 2016. Data-efficient off-policy policy evaluation for reinforcement learning. In Pro- ceedings of the International Conference on Machine Learning, pp. 2139–48. La Jolla, CA: Int. Mach. Learn. Soc.
- Clements, M. P. D. F. Hendry (1996), "Intercept corrections and structural change," Journal of Applied Econometrics, 11, 475–494.
- Akgiray, V. (1989), "Conditional heteroscedasticity in time series of stock returns: Evidence and forecasts," Journal of Business, 62, 55–80.
- Imbens GW, Lemieux T. 2008. Regression discontinuity designs: a guide to practice. J. Econom. 142:615–35
- Andrews, D. W. K. (1993), "Tests for parameter instability and structural change with unknown change point," Econometrica, 61, 821–856.
- C. Szepesvári. Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2010

## Frequently Asked Questions

Q: What is the prediction methodology for LON:VRCI stock?A: LON:VRCI stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Lasso Regression

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

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

Q: Is VERICI DX PLC stock a good investment?

A: The consensus rating for VERICI DX PLC is Hold and assigned short-term Ba2 & long-term Ba3 forecasted stock rating.

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

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

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

A: The prediction period for LON:VRCI is (n+6 month)