Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most challenge in this area of research. We propose a hybrid approach for stock price movement prediction using machine learning, deep learning, and natural language processing. ** We evaluate ZOO DIGITAL GROUP PLC prediction models with Statistical Inference (ML) and Wilcoxon Rank-Sum Test ^{1,2,3,4} and conclude that the LON:ZOO stock is predictable in the short/long term. **

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold LON:ZOO stock.**

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

*Keywords:*## Key Points

- Fundemental Analysis with Algorithmic Trading
- Market Risk
- Operational Risk

## LON:ZOO Target Price Prediction Modeling Methodology

Investors raise profit from stock market by maximising gains and minimising loses. The profit is difficult to raise because of the volatile nature of stock market prices. Predictive modelling allows investors to make informed decisions. We consider ZOO DIGITAL GROUP PLC Stock Decision Process with Wilcoxon Rank-Sum Test where A is the set of discrete actions of LON:ZOO 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(Wilcoxon Rank-Sum Test)

^{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(Statistical Inference (ML)) X S(n):→ (n+3 month) $\sum _{i=1}^{n}\left({r}_{i}\right)$

n:Time series to forecast

p:Price signals of LON:ZOO 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:ZOO Stock Forecast (Buy or Sell) for (n+3 month)

**Sample Set:**Neural Network

**Stock/Index:**LON:ZOO ZOO DIGITAL GROUP PLC

**Time series to forecast n: 26 Sep 2022**for (n+3 month)

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold LON:ZOO 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

ZOO DIGITAL GROUP PLC assigned short-term B1 & long-term Baa2 forecasted stock rating.** We evaluate the prediction models Statistical Inference (ML) with Wilcoxon Rank-Sum Test ^{1,2,3,4} and conclude that the LON:ZOO stock is predictable in the short/long term.**

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold LON:ZOO stock.**

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

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

Outlook* | B1 | Baa2 |

Operational Risk | 64 | 74 |

Market Risk | 37 | 82 |

Technical Analysis | 58 | 87 |

Fundamental Analysis | 66 | 69 |

Risk Unsystematic | 76 | 87 |

### Prediction Confidence Score

## References

- Barkan O. 2016. Bayesian neural word embedding. arXiv:1603.06571 [math.ST]
- N. B ̈auerle and A. Mundt. Dynamic mean-risk optimization in a binomial model. Mathematical Methods of Operations Research, 70(2):219–239, 2009.
- Dudik M, Langford J, Li L. 2011. Doubly robust policy evaluation and learning. In Proceedings of the 28th International Conference on Machine Learning, pp. 1097–104. La Jolla, CA: Int. Mach. Learn. Soc.
- Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
- Ashley, R. (1988), "On the relative worth of recent macroeconomic forecasts," International Journal of Forecasting, 4, 363–376.
- Farrell MH, Liang T, Misra S. 2018. Deep neural networks for estimation and inference: application to causal effects and other semiparametric estimands. arXiv:1809.09953 [econ.EM]
- Dietterich TG. 2000. Ensemble methods in machine learning. In Multiple Classifier Systems: First International Workshop, Cagliari, Italy, June 21–23, pp. 1–15. Berlin: Springer

## Frequently Asked Questions

Q: What is the prediction methodology for LON:ZOO stock?A: LON:ZOO stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Wilcoxon Rank-Sum Test

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

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

Q: Is ZOO DIGITAL GROUP PLC stock a good investment?

A: The consensus rating for ZOO DIGITAL GROUP PLC is Hold and assigned short-term B1 & long-term Baa2 forecasted stock rating.

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

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

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

A: The prediction period for LON:ZOO is (n+3 month)

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