LON:CTL Stock Price Prediction


Recently, numerous investigations for stock price prediction and portfolio management using machine learning have been trying to develop efficient mechanical trading systems. But these systems have a limitation in that they are mainly based on the supervised learning which is not so adequate for learning problems with long-term goals and delayed rewards. This paper proposes a method of applying reinforcement learning, suitable for modeling and learning various kinds of interactions in real situations, to the problem of stock price prediction. We evaluate CLEANTECH LITHIUM PLC prediction models with Supervised Machine Learning (ML) and Chi-Square1,2,3,4 and conclude that the LON:CTL 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 Hold LON:CTL stock.


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

Key Points

  1. What is prediction model?
  2. Fundemental Analysis with Algorithmic Trading
  3. What is the best way to predict stock prices?

LON:CTL Target Price Prediction Modeling Methodology

Short-term trading is a difficult task due to fluctuating demand and supply in the stock market. These demands and supply are reflected in stock prices. The stock prices may be predicted using technical indicators. Most of the existing literature considered the limited technical indicators to measure short-term prices. We have considered 82 different combinations of technical indicators to predict the stock prices. We consider CLEANTECH LITHIUM PLC Stock Decision Process with Chi-Square where A is the set of discrete actions of LON:CTL 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(Chi-Square)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+6 month) i = 1 n a i

n:Time series to forecast

p:Price signals of LON:CTL 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:CTL Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: LON:CTL CLEANTECH LITHIUM PLC
Time series to forecast n: 16 Sep 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:CTL 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

CLEANTECH LITHIUM PLC assigned short-term Baa2 & long-term B1 forecasted stock rating. We evaluate the prediction models Supervised Machine Learning (ML) with Chi-Square1,2,3,4 and conclude that the LON:CTL 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 Hold LON:CTL stock.

Financial State Forecast for LON:CTL Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Baa2B1
Operational Risk 7257
Market Risk8749
Technical Analysis8540
Fundamental Analysis7573
Risk Unsystematic6570

Prediction Confidence Score

Trust metric by Neural Network: 92 out of 100 with 813 signals.

References

  1. R. Rockafellar and S. Uryasev. Conditional value-at-risk for general loss distributions. Journal of Banking and Finance, 26(7):1443 – 1471, 2002
  2. Ashley, R. (1988), "On the relative worth of recent macroeconomic forecasts," International Journal of Forecasting, 4, 363–376.
  3. 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.
  4. Bengio Y, Schwenk H, Senécal JS, Morin F, Gauvain JL. 2006. Neural probabilistic language models. In Innovations in Machine Learning: Theory and Applications, ed. DE Holmes, pp. 137–86. Berlin: Springer
  5. J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 2137–2145, 2016.
  6. Dietterich TG. 2000. Ensemble methods in machine learning. In Multiple Classifier Systems: First International Workshop, Cagliari, Italy, June 21–23, pp. 1–15. Berlin: Springer
  7. Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:CTL stock?
A: LON:CTL stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Chi-Square
Q: Is LON:CTL stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:CTL Stock.
Q: Is CLEANTECH LITHIUM PLC stock a good investment?
A: The consensus rating for CLEANTECH LITHIUM PLC is Hold and assigned short-term Baa2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:CTL stock?
A: The consensus rating for LON:CTL is Hold.
Q: What is the prediction period for LON:CTL stock?
A: The prediction period for LON:CTL is (n+6 month)

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