Fuzzy rough theory can describe real-world situations in a mathematically effective and interpretable way, while evolutionary neural networks can be utilized to solve complex problems. Combining them with these complementary capabilities may lead to evolutionary fuzzy rough neural network with the interpretability and prediction capability. In this article, we propose modifications to the existing models of fuzzy rough neural network and then develop a powerful evolutionary framework for fuzzy rough neural networks by inheriting the merits of both the aforementioned systems. We evaluate CAMBRIA AFRICA PLC prediction models with Reinforcement Machine Learning (ML) and Spearman Correlation1,2,3,4 and conclude that the LON:CMB 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 Buy LON:CMB stock.
Keywords: LON:CMB, CAMBRIA AFRICA PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- How accurate is machine learning in stock market?
- Nash Equilibria
- How can neural networks improve predictions?
LON:CMB Target Price Prediction Modeling Methodology
Stock market is a promising financial investment that can generate great wealth. However, the volatile nature of the stock market makes it a very high risk investment. Thus, a lot of researchers have contributed their efforts to forecast the stock market pricing and average movement. Researchers have used various methods in computer science and economics in their quests to gain a piece of this volatile information and make great fortune out of the stock market investment. This paper investigates various techniques for the stock market prediction using artificial neural network (ANN). We consider CAMBRIA AFRICA PLC Stock Decision Process with Spearman Correlation where A is the set of discrete actions of LON:CMB 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(Spearman Correlation)5,6,7= X R(Reinforcement Machine Learning (ML)) X S(n):→ (n+6 month)
n:Time series to forecast
p:Price signals of LON:CMB 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:CMB Stock Forecast (Buy or Sell) for (n+6 month)
Sample Set: Neural NetworkStock/Index: LON:CMB CAMBRIA AFRICA 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 Buy LON:CMB 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
CAMBRIA AFRICA PLC assigned short-term B2 & long-term B2 forecasted stock rating. We evaluate the prediction models Reinforcement Machine Learning (ML) with Spearman Correlation1,2,3,4 and conclude that the LON:CMB 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 Buy LON:CMB stock.
Financial State Forecast for LON:CMB Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B2 | B2 |
| Operational Risk | 84 | 77 |
| Market Risk | 51 | 32 |
| Technical Analysis | 63 | 82 |
| Fundamental Analysis | 36 | 44 |
| Risk Unsystematic | 36 | 41 |
Prediction Confidence Score
References
- H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.
- R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998
- D. Bertsekas. Min common/max crossing duality: A geometric view of conjugacy in convex optimization. Lab. for Information and Decision Systems, MIT, Tech. Rep. Report LIDS-P-2796, 2009
- Künzel S, Sekhon J, Bickel P, Yu B. 2017. Meta-learners for estimating heterogeneous treatment effects using machine learning. arXiv:1706.03461 [math.ST]
- Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
- Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, Vol. 25, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 1097–105. San Diego, CA: Neural Inf. Process. Syst. Found.
- Li L, Chen S, Kleban J, Gupta A. 2014. Counterfactual estimation and optimization of click metrics for search engines: a case study. In Proceedings of the 24th International Conference on the World Wide Web, pp. 929–34. New York: ACM
Frequently Asked Questions
Q: What is the prediction methodology for LON:CMB stock?A: LON:CMB stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Spearman Correlation
Q: Is LON:CMB stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:CMB Stock.
Q: Is CAMBRIA AFRICA PLC stock a good investment?
A: The consensus rating for CAMBRIA AFRICA PLC is Buy and assigned short-term B2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of LON:CMB stock?
A: The consensus rating for LON:CMB is Buy.
Q: What is the prediction period for LON:CMB stock?
A: The prediction period for LON:CMB is (n+6 month)
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