OMX Copenhagen 25 Index Research Report

## Summary

The categorization of high dimensional data present a fascinating challenge to machine learning models as frequent number of highly correlated dimensions or attributes can affect the accuracy of classification model. In this paper, the problem of high dimensionality of stock exchange is investigated to predict the market trends by applying the principal component analysis (PCA) with linear regression. PCA can help to improve the predictive performance of machine learning methods while reducing the redundancy among the data. We evaluate OMX Copenhagen 25 Index prediction models with Modular Neural Network (CNN Layer) and Sign Test1,2,3,4 and conclude that the OMX Copenhagen 25 Index stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold OMX Copenhagen 25 Index stock.

## Key Points

1. Stock Rating
2. Investment Risk
3. What is neural prediction?

## OMX Copenhagen 25 Index Target Price Prediction Modeling Methodology

We consider OMX Copenhagen 25 Index Stock Decision Process with Modular Neural Network (CNN Layer) 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(Sign Test)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(Modular Neural Network (CNN Layer)) X S(n):→ (n+4 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of OMX Copenhagen 25 Index stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

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## OMX Copenhagen 25 Index Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: OMX Copenhagen 25 Index OMX Copenhagen 25 Index
Time series to forecast n: 23 Nov 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) 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%

## Adjusted IFRS* Prediction Methods for OMX Copenhagen 25 Index

1. The business model may be to hold assets to collect contractual cash flows even if the entity sells financial assets when there is an increase in the assets' credit risk. To determine whether there has been an increase in the assets' credit risk, the entity considers reasonable and supportable information, including forward looking information. Irrespective of their frequency and value, sales due to an increase in the assets' credit risk are not inconsistent with a business model whose objective is to hold financial assets to collect contractual cash flows because the credit quality of financial assets is relevant to the entity's ability to collect contractual cash flows. Credit risk management activities that are aimed at minimising potential credit losses due to credit deterioration are integral to such a business model. Selling a financial asset because it no longer meets the credit criteria specified in the entity's documented investment policy is an example of a sale that has occurred due to an increase in credit risk. However, in the absence of such a policy, the entity may demonstrate in other ways that the sale occurred due to an increase in credit risk.
2. For purchased or originated credit-impaired financial assets, expected credit losses shall be discounted using the credit-adjusted effective interest rate determined at initial recognition.
3. When designating a hedging relationship and on an ongoing basis, an entity shall analyse the sources of hedge ineffectiveness that are expected to affect the hedging relationship during its term. This analysis (including any updates in accordance with paragraph B6.5.21 arising from rebalancing a hedging relationship) is the basis for the entity's assessment of meeting the hedge effectiveness requirements.
4. Hedging relationships that qualified for hedge accounting in accordance with IAS 39 that also qualify for hedge accounting in accordance with the criteria of this Standard (see paragraph 6.4.1), after taking into account any rebalancing of the hedging relationship on transition (see paragraph 7.2.25(b)), shall be regarded as continuing hedging relationships.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

## Conclusions

OMX Copenhagen 25 Index assigned short-term Baa2 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (CNN Layer) with Sign Test1,2,3,4 and conclude that the OMX Copenhagen 25 Index stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold OMX Copenhagen 25 Index stock.

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

Rating Short-Term Long-Term Senior
Outlook*Baa2B2
Operational Risk 8650
Market Risk4160
Technical Analysis9060
Fundamental Analysis7634
Risk Unsystematic8547

### Prediction Confidence Score

Trust metric by Neural Network: 86 out of 100 with 484 signals.

## References

1. J. Filar, L. Kallenberg, and H. Lee. Variance-penalized Markov decision processes. Mathematics of Opera- tions Research, 14(1):147–161, 1989
2. Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
3. Friedberg R, Tibshirani J, Athey S, Wager S. 2018. Local linear forests. arXiv:1807.11408 [stat.ML]
4. O. Bardou, N. Frikha, and G. Pag`es. Computing VaR and CVaR using stochastic approximation and adaptive unconstrained importance sampling. Monte Carlo Methods and Applications, 15(3):173–210, 2009.
5. Mikolov T, Chen K, Corrado GS, Dean J. 2013a. Efficient estimation of word representations in vector space. arXiv:1301.3781 [cs.CL]
6. Hastie T, Tibshirani R, Tibshirani RJ. 2017. Extended comparisons of best subset selection, forward stepwise selection, and the lasso. arXiv:1707.08692 [stat.ME]
7. 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.
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 Modular Neural Network (CNN Layer) and Sign Test
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 Baa2 & 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+4 weeks)

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