Modelling A.I. in Economics

Should You Buy Now or Wait? (LON:CMRS Stock Forecast)

Accurate prediction of stock price movements is highly challenging and significant topic for investors. Investors need to understand that stock price data is the most essential information which is highly volatile, non-linear, and non-parametric and are affected by many uncertainties and interrelated economic and political factors across the globe. Artificial Neural Networks (ANN) have been found to be an efficient tool in modeling stock prices and quite a large number of studies have been done on it. We evaluate CAERUS MINERAL RESOURCES PLC prediction models with Supervised Machine Learning (ML) and Paired T-Test1,2,3,4 and conclude that the LON:CMRS 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:CMRS stock.


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

Key Points

  1. Is it better to buy and sell or hold?
  2. Can we predict stock market using machine learning?
  3. Stock Forecast Based On a Predictive Algorithm

LON:CMRS 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 CAERUS MINERAL RESOURCES PLC Stock Decision Process with Paired T-Test where A is the set of discrete actions of LON:CMRS 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(Paired T-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) R = r 1 r 2 r 3

n:Time series to forecast

p:Price signals of LON:CMRS 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:CMRS Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: LON:CMRS CAERUS MINERAL RESOURCES PLC
Time series to forecast n: 04 Oct 2022 for (n+1 year)

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

CAERUS MINERAL RESOURCES PLC assigned short-term Baa2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Supervised Machine Learning (ML) with Paired T-Test1,2,3,4 and conclude that the LON:CMRS 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:CMRS stock.

Financial State Forecast for LON:CMRS Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Baa2Ba3
Operational Risk 5546
Market Risk7090
Technical Analysis8839
Fundamental Analysis7575
Risk Unsystematic8969

Prediction Confidence Score

Trust metric by Neural Network: 88 out of 100 with 793 signals.

References

  1. Gentzkow M, Kelly BT, Taddy M. 2017. Text as data. NBER Work. Pap. 23276
  2. Armstrong, J. S. M. C. Grohman (1972), "A comparative study of methods for long-range market forecasting," Management Science, 19, 211–221.
  3. Künzel S, Sekhon J, Bickel P, Yu B. 2017. Meta-learners for estimating heterogeneous treatment effects using machine learning. arXiv:1706.03461 [math.ST]
  4. Athey S, Blei D, Donnelly R, Ruiz F. 2017b. Counterfactual inference for consumer choice across many prod- uct categories. AEA Pap. Proc. 108:64–67
  5. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. 2013b. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 3111–19. San Diego, CA: Neural Inf. Process. Syst. Found.
  6. R. Rockafellar and S. Uryasev. Optimization of conditional value-at-risk. Journal of Risk, 2:21–42, 2000.
  7. Angrist JD, Pischke JS. 2008. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton, NJ: Princeton Univ. Press
Frequently Asked QuestionsQ: What is the prediction methodology for LON:CMRS stock?
A: LON:CMRS stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Paired T-Test
Q: Is LON:CMRS stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:CMRS Stock.
Q: Is CAERUS MINERAL RESOURCES PLC stock a good investment?
A: The consensus rating for CAERUS MINERAL RESOURCES PLC is Hold and assigned short-term Baa2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of LON:CMRS stock?
A: The consensus rating for LON:CMRS is Hold.
Q: What is the prediction period for LON:CMRS stock?
A: The prediction period for LON:CMRS is (n+1 year)

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