Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators.** We evaluate EAGLE EYE SOLUTIONS GROUP PLC prediction models with Modular Neural Network (DNN Layer) and Chi-Square ^{1,2,3,4} and conclude that the LON:EYE 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 Hold LON:EYE stock.**

**LON:EYE, EAGLE EYE SOLUTIONS GROUP PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Is it better to buy and sell or hold?
- Buy, Sell and Hold Signals
- How do predictive algorithms actually work?

## LON:EYE Target Price Prediction Modeling Methodology

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 consider EAGLE EYE SOLUTIONS GROUP PLC Stock Decision Process with Chi-Square where A is the set of discrete actions of LON:EYE 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(Chi-Square)

^{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 (DNN Layer)) X S(n):→ (n+4 weeks) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of LON:EYE 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:EYE Stock Forecast (Buy or Sell) for (n+4 weeks)

**Sample Set:**Neural Network

**Stock/Index:**LON:EYE EAGLE EYE SOLUTIONS GROUP PLC

**Time series to forecast n: 03 Oct 2022**for (n+4 weeks)

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:EYE 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

EAGLE EYE SOLUTIONS GROUP PLC assigned short-term Baa2 & long-term B2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (DNN Layer) with Chi-Square ^{1,2,3,4} and conclude that the LON:EYE 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 Hold LON:EYE stock.**

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

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

Outlook* | Baa2 | B2 |

Operational Risk | 87 | 36 |

Market Risk | 32 | 58 |

Technical Analysis | 84 | 35 |

Fundamental Analysis | 81 | 58 |

Risk Unsystematic | 78 | 81 |

### Prediction Confidence Score

## References

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- Efron B, Hastie T, Johnstone I, Tibshirani R. 2004. Least angle regression. Ann. Stat. 32:407–99
- D. S. Bernstein, S. Zilberstein, and N. Immerman. The complexity of decentralized control of Markov Decision Processes. In UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30 - July 3, 2000, pages 32–37, 2000.
- J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.
- S. Bhatnagar, R. Sutton, M. Ghavamzadeh, and M. Lee. Natural actor-critic algorithms. Automatica, 45(11): 2471–2482, 2009
- V. Borkar. Q-learning for risk-sensitive control. Mathematics of Operations Research, 27:294–311, 2002.
- Alexander, J. C. Jr. (1995), "Refining the degree of earnings surprise: A comparison of statistical and analysts' forecasts," Financial Review, 30, 469–506.

## Frequently Asked Questions

Q: What is the prediction methodology for LON:EYE stock?A: LON:EYE stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Chi-Square

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

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

Q: Is EAGLE EYE SOLUTIONS GROUP PLC stock a good investment?

A: The consensus rating for EAGLE EYE SOLUTIONS GROUP PLC is Hold and assigned short-term Baa2 & long-term B2 forecasted stock rating.

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

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

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

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

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