This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies. The stock market prediction can be modeled based on two principal analyses called technical and fundamental. In the technical analysis approach, the regression machine learning (ML) algorithms are employed to predict the stock price trend at the end of a business day based on the historical price data. In contrast, in the fundamental analysis, the classification ML algorithms are applied to classify the public sentiment based on news and social media.** We evaluate EPE SPECIAL OPPORTUNITIES LIMITED prediction models with Modular Neural Network (CNN Layer) and Chi-Square ^{1,2,3,4} and conclude that the LON:ESOZ stock is predictable in the short/long term. **

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell LON:ESOZ stock.**

**LON:ESOZ, EPE SPECIAL OPPORTUNITIES LIMITED, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- What is prediction in deep learning?
- How accurate is machine learning in stock market?
- How useful are statistical predictions?

## LON:ESOZ Target Price Prediction Modeling Methodology

This paper surveys machine learning techniques for stock market prediction. The prediction of stock markets is regarded as a challenging task of financial time series prediction. We consider EPE SPECIAL OPPORTUNITIES LIMITED Stock Decision Process with Chi-Square where A is the set of discrete actions of LON:ESOZ 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 (CNN Layer)) X S(n):→ (n+1 year) $\sum _{i=1}^{n}\left({a}_{i}\right)$

n:Time series to forecast

p:Price signals of LON:ESOZ 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:ESOZ Stock Forecast (Buy or Sell) for (n+1 year)

**Sample Set:**Neural Network

**Stock/Index:**LON:ESOZ EPE SPECIAL OPPORTUNITIES LIMITED

**Time series to forecast n: 22 Oct 2022**for (n+1 year)

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell LON:ESOZ 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

EPE SPECIAL OPPORTUNITIES LIMITED assigned short-term B1 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (CNN Layer) with Chi-Square ^{1,2,3,4} and conclude that the LON:ESOZ stock is predictable in the short/long term.**

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell LON:ESOZ stock.**

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

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

Outlook* | B1 | Ba3 |

Operational Risk | 56 | 45 |

Market Risk | 74 | 36 |

Technical Analysis | 61 | 89 |

Fundamental Analysis | 81 | 87 |

Risk Unsystematic | 31 | 62 |

### Prediction Confidence Score

## References

- Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
- V. Borkar. Stochastic approximation: a dynamical systems viewpoint. Cambridge University Press, 2008
- D. Bertsekas. Dynamic programming and optimal control. Athena Scientific, 1995.
- uyer, S. Whiteson, B. Bakker, and N. A. Vlassis. Multiagent reinforcement learning for urban traffic control using coordination graphs. In Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I, pages 656–671, 2008.
- Dimakopoulou M, Zhou Z, Athey S, Imbens G. 2018. Balanced linear contextual bandits. arXiv:1812.06227 [cs.LG]
- Harris ZS. 1954. Distributional structure. Word 10:146–62
- Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley

## Frequently Asked Questions

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

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

A: The dominant strategy among neural network is to Sell LON:ESOZ Stock.

Q: Is EPE SPECIAL OPPORTUNITIES LIMITED stock a good investment?

A: The consensus rating for EPE SPECIAL OPPORTUNITIES LIMITED is Sell and assigned short-term B1 & long-term Ba3 forecasted stock rating.

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

A: The consensus rating for LON:ESOZ is Sell.

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

A: The prediction period for LON:ESOZ is (n+1 year)