How accurate is machine learning in stock market? (LON:FUM Stock Forecast)


Recurrent Neural Networks (RNNs) is a sub type of neural networks that use feedback connections. Several types of RNN models are used in predicting financial time series. This study was conducted to develop models to predict daily stock prices based on Recurrent Neural Network (RNN) Approach and to measure the accuracy of the models developed and identify the shortcomings of the models if present. We evaluate FUTURA MEDICAL PLC prediction models with Deductive Inference (ML) and Linear Regression1,2,3,4 and conclude that the LON:FUM 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 Hold LON:FUM stock.


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

Key Points

  1. How accurate is machine learning in stock market?
  2. What are main components of Markov decision process?
  3. Technical Analysis with Algorithmic Trading

LON:FUM Target Price Prediction Modeling Methodology

Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks' historical data. Most of these existing approaches have focused on short term prediction using stocks' historical price and technical indicators. We consider FUTURA MEDICAL PLC Stock Decision Process with Linear Regression where A is the set of discrete actions of LON:FUM 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(Linear 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(Deductive Inference (ML)) X S(n):→ (n+4 weeks) r s rs

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: LON:FUM FUTURA MEDICAL PLC
Time series to forecast n: 14 Sep 2022 for (n+4 weeks)

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

FUTURA MEDICAL PLC assigned short-term Ba3 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Deductive Inference (ML) with Linear Regression1,2,3,4 and conclude that the LON:FUM 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 Hold LON:FUM stock.

Financial State Forecast for LON:FUM Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba3Ba3
Operational Risk 6758
Market Risk6760
Technical Analysis6561
Fundamental Analysis4846
Risk Unsystematic6785

Prediction Confidence Score

Trust metric by Neural Network: 86 out of 100 with 603 signals.

References

  1. Bierens HJ. 1987. Kernel estimators of regression functions. In Advances in Econometrics: Fifth World Congress, Vol. 1, ed. TF Bewley, pp. 99–144. Cambridge, UK: Cambridge Univ. Press
  2. M. Babes, E. M. de Cote, and M. L. Littman. Social reward shaping in the prisoner's dilemma. In 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), Estoril, Portugal, May 12-16, 2008, Volume 3, pages 1389–1392, 2008.
  3. B. Derfer, N. Goodyear, K. Hung, C. Matthews, G. Paoni, K. Rollins, R. Rose, M. Seaman, and J. Wiles. Online marketing platform, August 17 2007. US Patent App. 11/893,765
  4. Künzel S, Sekhon J, Bickel P, Yu B. 2017. Meta-learners for estimating heterogeneous treatment effects using machine learning. arXiv:1706.03461 [math.ST]
  5. J. Filar, L. Kallenberg, and H. Lee. Variance-penalized Markov decision processes. Mathematics of Opera- tions Research, 14(1):147–161, 1989
  6. Harris ZS. 1954. Distributional structure. Word 10:146–62
  7. Chen X. 2007. Large sample sieve estimation of semi-nonparametric models. In Handbook of Econometrics, Vol. 6B, ed. JJ Heckman, EE Learner, pp. 5549–632. Amsterdam: Elsevier
Frequently Asked QuestionsQ: What is the prediction methodology for LON:FUM stock?
A: LON:FUM stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Linear Regression
Q: Is LON:FUM stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:FUM Stock.
Q: Is FUTURA MEDICAL PLC stock a good investment?
A: The consensus rating for FUTURA MEDICAL PLC is Hold and assigned short-term Ba3 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of LON:FUM stock?
A: The consensus rating for LON:FUM is Hold.
Q: What is the prediction period for LON:FUM stock?
A: The prediction period for LON:FUM is (n+4 weeks)

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