Modelling A.I. in Economics

How do you predict if a stock will go up or down? (LON:CRH Stock Prediction) (Forecast)

Stock market is a promising financial investment that can generate great wealth. However, the volatile nature of the stock market makes it a very high risk investment. Thus, a lot of researchers have contributed their efforts to forecast the stock market pricing and average movement. Researchers have used various methods in computer science and economics in their quests to gain a piece of this volatile information and make great fortune out of the stock market investment. This paper investigates various techniques for the stock market prediction using artificial neural network (ANN). We evaluate CRH PLC prediction models with Modular Neural Network (Speculative Sentiment Analysis) and Stepwise Regression1,2,3,4 and conclude that the LON:CRH 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:CRH stock.


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

Key Points

  1. What are buy sell or hold recommendations?
  2. Prediction Modeling
  3. What is the use of Markov decision process?

LON:CRH Target Price Prediction Modeling Methodology

Prediction of stock market is a long-time attractive topic to researchers from different fields. In particular, numerous studies have been conducted to predict the movement of stock market using machine learning algorithms such as support vector machine (SVM) and reinforcement learning. In this project, we propose a new prediction algorithm that exploits the temporal correlation among global stock markets and various financial products to predict the next-day stock trend. We consider CRH PLC Stock Decision Process with Stepwise Regression where A is the set of discrete actions of LON:CRH 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 (Speculative 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:CRH 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:CRH Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: LON:CRH CRH PLC
Time series to forecast n: 19 Oct 2022 for (n+4 weeks)

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

CRH PLC assigned short-term B1 & long-term B3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) with Stepwise Regression1,2,3,4 and conclude that the LON:CRH 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:CRH stock.

Financial State Forecast for LON:CRH Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1B3
Operational Risk 4731
Market Risk7341
Technical Analysis5831
Fundamental Analysis6954
Risk Unsystematic6163

Prediction Confidence Score

Trust metric by Neural Network: 82 out of 100 with 676 signals.

References

  1. Barkan O. 2016. Bayesian neural word embedding. arXiv:1603.06571 [math.ST]
  2. J. G. Schneider, W. Wong, A. W. Moore, and M. A. Riedmiller. Distributed value functions. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 371–378, 1999.
  3. 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.
  4. V. Konda and J. Tsitsiklis. Actor-Critic algorithms. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1008–1014, 2000
  5. J. Ott. A Markov decision model for a surveillance application and risk-sensitive Markov decision processes. PhD thesis, Karlsruhe Institute of Technology, 2010.
  6. Efron B, Hastie T. 2016. Computer Age Statistical Inference, Vol. 5. Cambridge, UK: Cambridge Univ. Press
  7. Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]
Frequently Asked QuestionsQ: What is the prediction methodology for LON:CRH stock?
A: LON:CRH stock prediction methodology: We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) and Stepwise Regression
Q: Is LON:CRH stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:CRH Stock.
Q: Is CRH PLC stock a good investment?
A: The consensus rating for CRH PLC is Hold and assigned short-term B1 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of LON:CRH stock?
A: The consensus rating for LON:CRH is Hold.
Q: What is the prediction period for LON:CRH stock?
A: The prediction period for LON:CRH is (n+4 weeks)

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