LON:ZTF Target Price Prediction

Stock index price prediction is prevalent in both academic and economic fields. The index price is hard to forecast due to its uncertain noise. With the development of computer science, neural networks are applied in kinds of industrial fields. In this paper, we introduce four different methods in machine learning including three typical machine learning models: Multilayer Perceptron (MLP), Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) and one attention-based neural network. We evaluate ZOTEFOAMS PLC prediction models with Inductive Learning (ML) and Logistic Regression1,2,3,4 and conclude that the LON:ZTF stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:ZTF stock.


Keywords: LON:ZTF, ZOTEFOAMS 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 the use of Markov decision process?
  2. Can machine learning predict?
  3. Market Signals

LON:ZTF Target Price Prediction Modeling Methodology

How to predict stock price movements based on quantitative market data modeling is an attractive topic. In front of the market news and stock prices that are commonly believed as two important market data sources, how to extract and exploit the hidden information within the raw data and make both accurate and fast predictions simultaneously becomes a challenging problem. In this paper, we present the design and architecture of our trading signal mining platform that employs extreme learning machine (ELM) to make stock price prediction based on those two data sources concurrently. We consider ZOTEFOAMS PLC Stock Decision Process with Logistic Regression where A is the set of discrete actions of LON:ZTF 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(Logistic Regression)5,6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Inductive Learning (ML)) X S(n):→ (n+6 month) R = 1 0 0 0 1 0 0 0 1

n:Time series to forecast

p:Price signals of LON:ZTF 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:ZTF Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: LON:ZTF ZOTEFOAMS PLC
Time series to forecast n: 11 Oct 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:ZTF 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

ZOTEFOAMS PLC assigned short-term B1 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Inductive Learning (ML) with Logistic Regression1,2,3,4 and conclude that the LON:ZTF stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:ZTF stock.

Financial State Forecast for LON:ZTF Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1Ba3
Operational Risk 4788
Market Risk3385
Technical Analysis8733
Fundamental Analysis7670
Risk Unsystematic7038

Prediction Confidence Score

Trust metric by Neural Network: 82 out of 100 with 834 signals.

References

  1. Athey S, Tibshirani J, Wager S. 2016b. Generalized random forests. arXiv:1610.01271 [stat.ME]
  2. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2016a. Double machine learning for treatment and causal parameters. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
  3. Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]
  4. Arjovsky M, Bottou L. 2017. Towards principled methods for training generative adversarial networks. arXiv:1701.04862 [stat.ML]
  5. G. Konidaris, S. Osentoski, and P. Thomas. Value function approximation in reinforcement learning using the Fourier basis. In AAAI, 2011
  6. Nie X, Wager S. 2019. Quasi-oracle estimation of heterogeneous treatment effects. arXiv:1712.04912 [stat.ML]
  7. Arora S, Li Y, Liang Y, Ma T. 2016. RAND-WALK: a latent variable model approach to word embeddings. Trans. Assoc. Comput. Linguist. 4:385–99
Frequently Asked QuestionsQ: What is the prediction methodology for LON:ZTF stock?
A: LON:ZTF stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Logistic Regression
Q: Is LON:ZTF stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:ZTF Stock.
Q: Is ZOTEFOAMS PLC stock a good investment?
A: The consensus rating for ZOTEFOAMS PLC is Hold and assigned short-term B1 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of LON:ZTF stock?
A: The consensus rating for LON:ZTF is Hold.
Q: What is the prediction period for LON:ZTF stock?
A: The prediction period for LON:ZTF is (n+6 month)

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