LON:BMTO Stock Price Prediction

Prediction of stock market is a long-time attractive topic to researchers from different fields. In particular, numerous studies have been conducted to predict the movement of stock market using machine learning algorithms such as support vector machine (SVM) and reinforcement learning. In this project, we propose a new prediction algorithm that exploits the temporal correlation among global stock markets and various financial products to predict the next-day stock trend. We evaluate BRAIME GROUP PLC prediction models with Transfer Learning (ML) and Factor^1,2,3,4 and conclude that the LON:BMTO 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:BMTO stock.

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

Key Points

1. Trading Signals
2. Stock Forecast Based On a Predictive Algorithm
3. Game Theory

LON:BMTO Target Price Prediction Modeling Methodology

Stock market is considered chaotic, complex, volatile and dynamic. Undoubtedly, its prediction is one of the most challenging tasks in time series forecasting. Moreover existing Artificial Neural Network (ANN) approaches fail to provide encouraging results. Meanwhile advances in machine learning have presented favourable results for speech recognition, image classification and language processing. We consider BRAIME GROUP PLC Stock Decision Process with Factor where A is the set of discrete actions of LON:BMTO 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(Factor)^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(Transfer Learning (ML)) X S(n):→ (n+16 weeks) $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: LON:BMTO BRAIME GROUP PLC
Time series to forecast n: 22 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:BMTO 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

BRAIME GROUP PLC assigned short-term Baa2 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Transfer Learning (ML) with Factor^1,2,3,4 and conclude that the LON:BMTO 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:BMTO stock.

Financial State Forecast for LON:BMTO Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Baa2	Ba2
Operational Risk	87	71
Market Risk	69	62
Technical Analysis	80	73
Fundamental Analysis	79	64
Risk Unsystematic	85	70

Prediction Confidence Score

Trust metric by Neural Network: 90 out of 100 with 590 signals.

References

1. Zeileis A, Hothorn T, Hornik K. 2008. Model-based recursive partitioning. J. Comput. Graph. Stat. 17:492–514 Zhou Z, Athey S, Wager S. 2018. Offline multi-action policy learning: generalization and optimization. arXiv:1810.04778 [stat.ML]
2. M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
3. Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
4. A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016
5. Andrews, D. W. K. (1993), "Tests for parameter instability and structural change with unknown change point," Econometrica, 61, 821–856.
6. V. Borkar. Stochastic approximation: a dynamical systems viewpoint. Cambridge University Press, 2008
7. Barrett, C. B. (1997), "Heteroscedastic price forecasting for food security management in developing countries," Oxford Development Studies, 25, 225–236.

Frequently Asked Questions

Q: What is the prediction methodology for LON:BMTO stock?
A: LON:BMTO stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Factor
Q: Is LON:BMTO stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:BMTO Stock.
Q: Is BRAIME GROUP PLC stock a good investment?
A: The consensus rating for BRAIME GROUP PLC is Hold and assigned short-term Baa2 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of LON:BMTO stock?
A: The consensus rating for LON:BMTO is Hold.
Q: What is the prediction period for LON:BMTO stock?
A: The prediction period for LON:BMTO is (n+16 weeks)

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