What is LON:HLCL stock prediction?


Stock prediction with data mining techniques is one of the most important issues in finance being investigated by researchers across the globe. Data mining techniques can be used extensively in the financial markets to help investors make qualitative decision. One of the techniques is artificial neural network (ANN). However, in the application of ANN for predicting the financial market the use of technical analysis variables for stock prediction is predominant. In this paper, we present a hybridized approach which combines the use of the variables of technical and fundamental analysis of stock market indicators for prediction of future price of stock in order to improve on the existing approaches. We evaluate HELICAL PLC prediction models with Supervised Machine Learning (ML) and Wilcoxon Sign-Rank Test1,2,3,4 and conclude that the LON:HLCL stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:HLCL stock.


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

Key Points

  1. Prediction Modeling
  2. How accurate is machine learning in stock market?
  3. Is it better to buy and sell or hold?

LON:HLCL Target Price Prediction Modeling Methodology

The research reported in the paper focuses on the stock market prediction problem, the main aim being the development of a methodology to forecast the stock closing price. The methodology is based on some novel variable selection methods and an analysis of neural network and support vector machines based prediction models. Also, a hybrid approach which combines the use of the variables derived from technical and fundamental analysis of stock market indicators in order to improve prediction results of the proposed approaches is reported in this paper. We consider HELICAL PLC Stock Decision Process with Wilcoxon Sign-Rank Test where A is the set of discrete actions of LON:HLCL 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(Wilcoxon Sign-Rank Test)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(Supervised Machine Learning (ML)) X S(n):→ (n+1 year) i = 1 n r i

n:Time series to forecast

p:Price signals of LON:HLCL stock

j:Nash equilibria

k:Dominated move

a:Best response for target price

 

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LON:HLCL Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: LON:HLCL HELICAL PLC
Time series to forecast n: 19 Sep 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:HLCL 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

HELICAL PLC assigned short-term B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Supervised Machine Learning (ML) with Wilcoxon Sign-Rank Test1,2,3,4 and conclude that the LON:HLCL stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold LON:HLCL stock.

Financial State Forecast for LON:HLCL Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1B1
Operational Risk 7180
Market Risk7755
Technical Analysis7338
Fundamental Analysis4269
Risk Unsystematic3854

Prediction Confidence Score

Trust metric by Neural Network: 81 out of 100 with 496 signals.

References

  1. 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
  2. A. Y. Ng, D. Harada, and S. J. Russell. Policy invariance under reward transformations: Theory and application to reward shaping. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 278–287, 1999.
  3. F. A. Oliehoek and C. Amato. A Concise Introduction to Decentralized POMDPs. SpringerBriefs in Intelligent Systems. Springer, 2016
  4. M. J. Hausknecht and P. Stone. Deep recurrent Q-learning for partially observable MDPs. CoRR, abs/1507.06527, 2015
  5. uyer, S. Whiteson, B. Bakker, and N. A. Vlassis. Multiagent reinforcement learning for urban traffic control using coordination graphs. In Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I, pages 656–671, 2008.
  6. Harris ZS. 1954. Distributional structure. Word 10:146–62
  7. Semenova V, Goldman M, Chernozhukov V, Taddy M. 2018. Orthogonal ML for demand estimation: high dimensional causal inference in dynamic panels. arXiv:1712.09988 [stat.ML]
Frequently Asked QuestionsQ: What is the prediction methodology for LON:HLCL stock?
A: LON:HLCL stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Wilcoxon Sign-Rank Test
Q: Is LON:HLCL stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:HLCL Stock.
Q: Is HELICAL PLC stock a good investment?
A: The consensus rating for HELICAL PLC is Hold and assigned short-term B1 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:HLCL stock?
A: The consensus rating for LON:HLCL is Hold.
Q: What is the prediction period for LON:HLCL stock?
A: The prediction period for LON:HLCL is (n+1 year)

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