Is LON:JUST Stock Buy or Sell? (Stock Forecast)

With the advent of machine learning, numerous approaches have been proposed to forecast stock prices. Various models have been developed to date such as Recurrent Neural Networks, Long Short-Term Memory, Convolutional Neural Network sliding window, etc., but were not accurate enough. Here, the aim is to predict the price of a stock and compare the results obtained using three major algorithms namely Kalman filters, XGBoost and ARIMA. We evaluate JUST GROUP PLC prediction models with Multi-Instance Learning (ML) and Beta^1,2,3,4 and conclude that the LON:JUST 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 Hold LON:JUST stock.

Keywords: LON:JUST, JUST GROUP 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 best way to predict stock prices?
2. Market Risk
3. How do predictive algorithms actually work?

LON:JUST Target Price Prediction Modeling Methodology

This study aims to predict the direction of stock prices by integrating time-varying effective transfer entropy (ETE) and various machine learning algorithms. At first, we explore that the ETE based on 3 and 6 months moving windows can be regarded as the market explanatory variable by analyzing the association between the financial crises and Granger-causal relationships among the stocks. We consider JUST GROUP PLC Stock Decision Process with Beta where A is the set of discrete actions of LON:JUST 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= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Multi-Instance Learning (ML)) X S(n):→ (n+16 weeks) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: LON:JUST JUST GROUP PLC
Time series to forecast n: 12 Sep 2022 for (n+16 weeks)

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

JUST GROUP PLC assigned short-term B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Multi-Instance Learning (ML) with Beta^1,2,3,4 and conclude that the LON:JUST 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 Hold LON:JUST stock.

Financial State Forecast for LON:JUST Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B2	B1
Operational Risk	73	46
Market Risk	33	36
Technical Analysis	41	54
Fundamental Analysis	79	78
Risk Unsystematic	62	89

Prediction Confidence Score

Trust metric by Neural Network: 74 out of 100 with 452 signals.

References

1. S. Bhatnagar, R. Sutton, M. Ghavamzadeh, and M. Lee. Natural actor-critic algorithms. Automatica, 45(11): 2471–2482, 2009
2. Scott SL. 2010. A modern Bayesian look at the multi-armed bandit. Appl. Stoch. Models Bus. Ind. 26:639–58
3. E. Altman, K. Avrachenkov, and R. N ́u ̃nez-Queija. Perturbation analysis for denumerable Markov chains with application to queueing models. Advances in Applied Probability, pages 839–853, 2004
4. Clements, M. P. D. F. Hendry (1995), "Forecasting in cointegrated systems," Journal of Applied Econometrics, 10, 127–146.
5. Chow, G. C. (1960), "Tests of equality between sets of coefficients in two linear regressions," Econometrica, 28, 591–605.
6. Athey S, Imbens GW. 2017a. The econometrics of randomized experiments. In Handbook of Economic Field Experiments, Vol. 1, ed. E Duflo, A Banerjee, pp. 73–140. Amsterdam: Elsevier
7. M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016

Frequently Asked Questions

Q: What is the prediction methodology for LON:JUST stock?
A: LON:JUST stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Beta
Q: Is LON:JUST stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:JUST Stock.
Q: Is JUST GROUP PLC stock a good investment?
A: The consensus rating for JUST GROUP PLC is Hold and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:JUST stock?
A: The consensus rating for LON:JUST is Hold.
Q: What is the prediction period for LON:JUST stock?
A: The prediction period for LON:JUST is (n+16 weeks)

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