Modelling A.I. in Economics

Machine Learning stock prediction: ICE Stock Prediction (Forecast)

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 Intercontinental Exchange prediction models with Ensemble Learning (ML) and Spearman Correlation1,2,3,4 and conclude that the ICE 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 ICE stock.


Keywords: ICE, Intercontinental Exchange, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

  1. What are main components of Markov decision process?
  2. What are the most successful trading algorithms?
  3. Market Risk

ICE Target Price Prediction Modeling Methodology

Prediction of stock market movement is extremely difficult due to its high mutable nature. The rapid ups and downs occur in stock market because of impact from foreign commodities like emotional behavior of investors, political, psychological and economical factors. Continuous unsettlement in the stock market is major reason why investors sell out at the wrong time and often fail to gain the benefit. While investing in stock market investors must not forget the risk of reward rule and expose their holdings to greater risks. Although it is not possible predict stock market movement with full accuracy, losses from selling stocks at wrong time and its impacts can be reduce to greater extent using prediction of stock market movement based on analysis of historical data. We consider Intercontinental Exchange Stock Decision Process with Spearman Correlation where A is the set of discrete actions of ICE 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(Spearman Correlation)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(Ensemble Learning (ML)) X S(n):→ (n+1 year) S = s 1 s 2 s 3

n:Time series to forecast

p:Price signals of ICE 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?

ICE Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: ICE Intercontinental Exchange
Time series to forecast n: 09 Oct 2022 for (n+1 year)

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

Intercontinental Exchange assigned short-term Baa2 & long-term B3 forecasted stock rating. We evaluate the prediction models Ensemble Learning (ML) with Spearman Correlation1,2,3,4 and conclude that the ICE 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 ICE stock.

Financial State Forecast for ICE Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Baa2B3
Operational Risk 6831
Market Risk6939
Technical Analysis8759
Fundamental Analysis8245
Risk Unsystematic7750

Prediction Confidence Score

Trust metric by Neural Network: 74 out of 100 with 743 signals.

References

  1. Firth JR. 1957. A synopsis of linguistic theory 1930–1955. In Studies in Linguistic Analysis (Special Volume of the Philological Society), ed. JR Firth, pp. 1–32. Oxford, UK: Blackwell
  2. Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
  3. Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
  4. K. Tumer and D. Wolpert. A survey of collectives. In K. Tumer and D. Wolpert, editors, Collectives and the Design of Complex Systems, pages 1–42. Springer, 2004.
  5. S. Bhatnagar, H. Prasad, and L. Prashanth. Stochastic recursive algorithms for optimization, volume 434. Springer, 2013
  6. Clements, M. P. D. F. Hendry (1996), "Intercept corrections and structural change," Journal of Applied Econometrics, 11, 475–494.
  7. Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20
Frequently Asked QuestionsQ: What is the prediction methodology for ICE stock?
A: ICE stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Spearman Correlation
Q: Is ICE stock a buy or sell?
A: The dominant strategy among neural network is to Hold ICE Stock.
Q: Is Intercontinental Exchange stock a good investment?
A: The consensus rating for Intercontinental Exchange is Hold and assigned short-term Baa2 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of ICE stock?
A: The consensus rating for ICE is Hold.
Q: What is the prediction period for ICE stock?
A: The prediction period for ICE is (n+1 year)

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