Several intelligent data mining approaches, including neural networks, have been widely employed by academics during the last decade. In today's rapidly evolving economy, stock market data prediction and analysis play a significant role. Several non-linear models like neural network, generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive conditional heteroscedasticity (ARCH) as well as linear models like Auto- Regressive Integrated Moving Average (ARIMA), Moving Average (MA) and Auto Regressive (AR) may be used for stock forecasting. We evaluate FEVERTREE DRINKS PLC prediction models with Deductive Inference (ML) and Beta1,2,3,4 and conclude that the LON:FEVR stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Sell LON:FEVR stock.

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

## Key Points

1. What are buy sell or hold recommendations?
2. How accurate is machine learning in stock market?
3. Can we predict stock market using machine learning?

## LON:FEVR Target Price Prediction Modeling Methodology

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 consider FEVERTREE DRINKS PLC Stock Decision Process with Beta where A is the set of discrete actions of LON:FEVR 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(Beta)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(Deductive Inference (ML)) X S(n):→ (n+16 weeks) $∑ i = 1 n a i$

n:Time series to forecast

p:Price signals of LON:FEVR 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:FEVR Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: LON:FEVR FEVERTREE DRINKS PLC
Time series to forecast n: 14 Oct 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Sell LON:FEVR 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

FEVERTREE DRINKS PLC assigned short-term B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Deductive Inference (ML) with Beta1,2,3,4 and conclude that the LON:FEVR stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Sell LON:FEVR stock.

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

Rating Short-Term Long-Term Senior
Outlook*B1B1
Operational Risk 4931
Market Risk4385
Technical Analysis7660
Fundamental Analysis4938
Risk Unsystematic7875

### Prediction Confidence Score

Trust metric by Neural Network: 82 out of 100 with 641 signals.

## References

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2. G. Theocharous and A. Hallak. Lifetime value marketing using reinforcement learning. RLDM 2013, page 19, 2013
3. Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
4. M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Ma- chine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994, pages 157–163, 1994
5. D. Bertsekas. Dynamic programming and optimal control. Athena Scientific, 1995.
6. Armstrong, J. S. M. C. Grohman (1972), "A comparative study of methods for long-range market forecasting," Management Science, 19, 211–221.
7. Athey S. 2019. The impact of machine learning on economics. In The Economics of Artificial Intelligence: An Agenda, ed. AK Agrawal, J Gans, A Goldfarb. Chicago: Univ. Chicago Press. In press
Frequently Asked QuestionsQ: What is the prediction methodology for LON:FEVR stock?
A: LON:FEVR stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Beta
Q: Is LON:FEVR stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:FEVR Stock.
Q: Is FEVERTREE DRINKS PLC stock a good investment?
A: The consensus rating for FEVERTREE DRINKS PLC is Sell and assigned short-term B1 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:FEVR stock?
A: The consensus rating for LON:FEVR is Sell.
Q: What is the prediction period for LON:FEVR stock?
A: The prediction period for LON:FEVR is (n+16 weeks)