One decision in Stock Market can make huge impact on an investor's life. The stock market is a complex system and often covered in mystery, it is therefore, very difficult to analyze all the impacting factors before making a decision. In this research, we have tried to design a stock market prediction model which is based on different factors. We evaluate ZAMBEEF PRODUCTS PLC prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Multiple Regression1,2,3,4 and conclude that the LON:ZAM 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:ZAM stock.

Keywords: LON:ZAM, ZAMBEEF PRODUCTS PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. What is neural prediction?
2. Decision Making
3. How do predictive algorithms actually work? ## LON:ZAM Target Price Prediction Modeling Methodology

Accurate prediction of stock price movements is highly challenging and significant topic for investors. Investors need to understand that stock price data is the most essential information which is highly volatile, non-linear, and non-parametric and are affected by many uncertainties and interrelated economic and political factors across the globe. Artificial Neural Networks (ANN) have been found to be an efficient tool in modeling stock prices and quite a large number of studies have been done on it. We consider ZAMBEEF PRODUCTS PLC Stock Decision Process with Multiple Regression where A is the set of discrete actions of LON:ZAM 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(Multiple Regression)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Social Media Sentiment Analysis)) X S(n):→ (n+4 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: LON:ZAM ZAMBEEF PRODUCTS PLC
Time series to forecast n: 24 Sep 2022 for (n+4 weeks)

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

ZAMBEEF PRODUCTS PLC assigned short-term Ba1 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Multiple Regression1,2,3,4 and conclude that the LON:ZAM 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:ZAM stock.

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

Rating Short-Term Long-Term Senior
Outlook*Ba1B2
Operational Risk 7257
Market Risk8842
Technical Analysis6974
Fundamental Analysis6168
Risk Unsystematic7133

### Prediction Confidence Score

Trust metric by Neural Network: 86 out of 100 with 649 signals.

## References

1. L. Busoniu, R. Babuska, and B. D. Schutter. A comprehensive survey of multiagent reinforcement learning. IEEE Transactions of Systems, Man, and Cybernetics Part C: Applications and Reviews, 38(2), 2008.
2. Hastie T, Tibshirani R, Friedman J. 2009. The Elements of Statistical Learning. Berlin: Springer
3. Arjovsky M, Bottou L. 2017. Towards principled methods for training generative adversarial networks. arXiv:1701.04862 [stat.ML]
4. Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221
5. Friedberg R, Tibshirani J, Athey S, Wager S. 2018. Local linear forests. arXiv:1807.11408 [stat.ML]
6. Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
7. Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]
Frequently Asked QuestionsQ: What is the prediction methodology for LON:ZAM stock?
A: LON:ZAM stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Multiple Regression
Q: Is LON:ZAM stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:ZAM Stock.
Q: Is ZAMBEEF PRODUCTS PLC stock a good investment?
A: The consensus rating for ZAMBEEF PRODUCTS PLC is Buy and assigned short-term Ba1 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:ZAM stock?
A: The consensus rating for LON:ZAM is Buy.
Q: What is the prediction period for LON:ZAM stock?
A: The prediction period for LON:ZAM is (n+4 weeks)