Modelling A.I. in Economics

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

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 INTEGRATED DIAGNOSTICS HOLDINGS PLC prediction models with Transductive Learning (ML) and Paired T-Test1,2,3,4 and conclude that the LON:IDHC stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell LON:IDHC stock.


Keywords: LON:IDHC, INTEGRATED DIAGNOSTICS HOLDINGS 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 the use of Markov decision process?
  2. Trading Signals
  3. Trust metric by Neural Network

LON:IDHC Target Price Prediction Modeling Methodology

Development of linguistic technologies and penetration of social media provide powerful possibilities to investigate users' moods and psychological states of people. In this paper we discussed possibility to improve accuracy of stock market indicators predictions by using data about psychological states of Twitter users. For analysis of psychological states we used lexicon-based approach. We consider INTEGRATED DIAGNOSTICS HOLDINGS PLC Stock Decision Process with Paired T-Test where A is the set of discrete actions of LON:IDHC 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(Transductive Learning (ML)) X S(n):→ (n+3 month) i = 1 n a i

n:Time series to forecast

p:Price signals of LON:IDHC 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:IDHC Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: LON:IDHC INTEGRATED DIAGNOSTICS HOLDINGS PLC
Time series to forecast n: 04 Oct 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell LON:IDHC 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

INTEGRATED DIAGNOSTICS HOLDINGS PLC assigned short-term B2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Transductive Learning (ML) with Paired T-Test1,2,3,4 and conclude that the LON:IDHC stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell LON:IDHC stock.

Financial State Forecast for LON:IDHC Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B2Ba3
Operational Risk 5883
Market Risk3958
Technical Analysis6039
Fundamental Analysis7874
Risk Unsystematic4766

Prediction Confidence Score

Trust metric by Neural Network: 91 out of 100 with 587 signals.

References

  1. 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.
  2. J. Hu and M. P. Wellman. Nash q-learning for general-sum stochastic games. Journal of Machine Learning Research, 4:1039–1069, 2003.
  3. A. Tamar, D. Di Castro, and S. Mannor. Policy gradients with variance related risk criteria. In Proceedings of the Twenty-Ninth International Conference on Machine Learning, pages 387–396, 2012.
  4. Mnih A, Kavukcuoglu K. 2013. Learning word embeddings efficiently with noise-contrastive estimation. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 2265–73. San Diego, CA: Neural Inf. Process. Syst. Found.
  5. Kitagawa T, Tetenov A. 2015. Who should be treated? Empirical welfare maximization methods for treatment choice. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
  6. D. Bertsekas and J. Tsitsiklis. Neuro-dynamic programming. Athena Scientific, 1996.
  7. Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, et al. 2008. Top 10 algorithms in data mining. Knowl. Inform. Syst. 14:1–37
Frequently Asked QuestionsQ: What is the prediction methodology for LON:IDHC stock?
A: LON:IDHC stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Paired T-Test
Q: Is LON:IDHC stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:IDHC Stock.
Q: Is INTEGRATED DIAGNOSTICS HOLDINGS PLC stock a good investment?
A: The consensus rating for INTEGRATED DIAGNOSTICS HOLDINGS PLC is Sell and assigned short-term B2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of LON:IDHC stock?
A: The consensus rating for LON:IDHC is Sell.
Q: What is the prediction period for LON:IDHC stock?
A: The prediction period for LON:IDHC is (n+3 month)

Premium

  • Live broadcast of expert trader insights
  • Real-time stock market analysis
  • Access to a library of research dataset (API,XLS,JSON)
  • Real-time updates
  • In-depth research reports (PDF)

Login
This project is licensed under the license; additional terms may apply.