Can neural networks predict stock market? (LON:EUA Stock Forecast)

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 EURASIA MINING PLC prediction models with Modular Neural Network (Financial Sentiment Analysis) and Stepwise Regression1,2,3,4 and conclude that the LON:EUA stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Buy LON:EUA stock.


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

Key Points

  1. Stock Forecast Based On a Predictive Algorithm
  2. Operational Risk
  3. Trading Signals

LON:EUA Target Price Prediction Modeling Methodology

The classical linear multi-factor stock selection model is widely used for long-term stock price trend prediction. However, the stock market is chaotic, complex, and dynamic, for which reasons the linear model assumption may be unreasonable, and it is more meaningful to construct a better-integrated stock selection model based on different feature selection and nonlinear stock price trend prediction methods. We consider EURASIA MINING PLC Stock Decision Process with Stepwise Regression where A is the set of discrete actions of LON:EUA 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(Stepwise 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(Modular Neural Network (Financial Sentiment Analysis)) X S(n):→ (n+4 weeks) R = r 1 r 2 r 3

n:Time series to forecast

p:Price signals of LON:EUA 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:EUA Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: LON:EUA EURASIA MINING PLC
Time series to forecast n: 12 Oct 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Buy LON:EUA 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

EURASIA MINING PLC assigned short-term B2 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with Stepwise Regression1,2,3,4 and conclude that the LON:EUA stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Buy LON:EUA stock.

Financial State Forecast for LON:EUA Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B2Baa2
Operational Risk 5576
Market Risk8362
Technical Analysis4487
Fundamental Analysis5290
Risk Unsystematic4584

Prediction Confidence Score

Trust metric by Neural Network: 90 out of 100 with 604 signals.

References

  1. Athey S, Imbens GW. 2017b. The state of applied econometrics: causality and policy evaluation. J. Econ. Perspect. 31:3–32
  2. Abadie A, Diamond A, Hainmueller J. 2010. Synthetic control methods for comparative case studies: estimat- ing the effect of California's tobacco control program. J. Am. Stat. Assoc. 105:493–505
  3. Abadie A, Imbens GW. 2011. Bias-corrected matching estimators for average treatment effects. J. Bus. Econ. Stat. 29:1–11
  4. G. J. Laurent, L. Matignon, and N. L. Fort-Piat. The world of independent learners is not Markovian. Int. J. Know.-Based Intell. Eng. Syst., 15(1):55–64, 2011
  5. 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
  6. Dimakopoulou M, Zhou Z, Athey S, Imbens G. 2018. Balanced linear contextual bandits. arXiv:1812.06227 [cs.LG]
  7. S. Bhatnagar, R. Sutton, M. Ghavamzadeh, and M. Lee. Natural actor-critic algorithms. Automatica, 45(11): 2471–2482, 2009
Frequently Asked QuestionsQ: What is the prediction methodology for LON:EUA stock?
A: LON:EUA stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Stepwise Regression
Q: Is LON:EUA stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:EUA Stock.
Q: Is EURASIA MINING PLC stock a good investment?
A: The consensus rating for EURASIA MINING PLC is Buy and assigned short-term B2 & long-term Baa2 forecasted stock rating.
Q: What is the consensus rating of LON:EUA stock?
A: The consensus rating for LON:EUA is Buy.
Q: What is the prediction period for LON:EUA stock?
A: The prediction period for LON:EUA is (n+4 weeks)

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