Stock market is considered chaotic, complex, volatile and dynamic. Undoubtedly, its prediction is one of the most challenging tasks in time series forecasting. Moreover existing Artificial Neural Network (ANN) approaches fail to provide encouraging results. Meanwhile advances in machine learning have presented favourable results for speech recognition, image classification and language processing. We evaluate OMX Copenhagen 25 Index prediction models with Deductive Inference (ML) and Polynomial Regression1,2,3,4 and conclude that the OMX Copenhagen 25 Index 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 Hold OMX Copenhagen 25 Index stock.

Keywords: OMX Copenhagen 25 Index, OMX Copenhagen 25 Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. How do you pick a stock?
2. What is neural prediction?
3. Dominated Move

OMX Copenhagen 25 Index Target Price Prediction Modeling Methodology

Stock prediction is a very hot topic in our life. However, in the early time, because of some reasons and the limitation of the device, only a few people had the access to the study. Thanks to the rapid development of science and technology, in recent years more and more people are devoted to the study of the prediction and it becomes easier and easier for us to make stock prediction by using different ways now, including machine learning, deep learning and so on. We consider OMX Copenhagen 25 Index Stock Decision Process with Polynomial Regression where A is the set of discrete actions of OMX Copenhagen 25 Index 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(Polynomial Regression)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Deductive Inference (ML)) X S(n):→ (n+3 month) $∑ i = 1 n r i$

n:Time series to forecast

p:Price signals of OMX Copenhagen 25 Index 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?

OMX Copenhagen 25 Index Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: OMX Copenhagen 25 Index OMX Copenhagen 25 Index
Time series to forecast n: 21 Sep 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold OMX Copenhagen 25 Index 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

OMX Copenhagen 25 Index assigned short-term B3 & long-term B2 forecasted stock rating. We evaluate the prediction models Deductive Inference (ML) with Polynomial Regression1,2,3,4 and conclude that the OMX Copenhagen 25 Index 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 Hold OMX Copenhagen 25 Index stock.

Financial State Forecast for OMX Copenhagen 25 Index Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B3B2
Operational Risk 3935
Market Risk3980
Technical Analysis6041
Fundamental Analysis8049
Risk Unsystematic3966

Prediction Confidence Score

Trust metric by Neural Network: 81 out of 100 with 838 signals.

References

1. Sutton RS, Barto AG. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press
2. Mikolov T, Chen K, Corrado GS, Dean J. 2013a. Efficient estimation of word representations in vector space. arXiv:1301.3781 [cs.CL]
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. J. Peters, S. Vijayakumar, and S. Schaal. Natural actor-critic. In Proceedings of the Sixteenth European Conference on Machine Learning, pages 280–291, 2005.
5. Hoerl AE, Kennard RW. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67
6. 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.
7. M. Ono, M. Pavone, Y. Kuwata, and J. Balaram. Chance-constrained dynamic programming with application to risk-aware robotic space exploration. Autonomous Robots, 39(4):555–571, 2015
Frequently Asked QuestionsQ: What is the prediction methodology for OMX Copenhagen 25 Index stock?
A: OMX Copenhagen 25 Index stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Polynomial Regression
Q: Is OMX Copenhagen 25 Index stock a buy or sell?
A: The dominant strategy among neural network is to Hold OMX Copenhagen 25 Index Stock.
Q: Is OMX Copenhagen 25 Index stock a good investment?
A: The consensus rating for OMX Copenhagen 25 Index is Hold and assigned short-term B3 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of OMX Copenhagen 25 Index stock?
A: The consensus rating for OMX Copenhagen 25 Index is Hold.
Q: What is the prediction period for OMX Copenhagen 25 Index stock?
A: The prediction period for OMX Copenhagen 25 Index is (n+3 month)