CDRO Codere Online Luxembourg S.A. Ordinary Shares

Outlook: Codere Online Luxembourg S.A. Ordinary Shares assigned short-term B2 & long-term Ba2 forecasted stock rating.
Dominant Strategy : Buy
Time series to forecast n: 18 Dec 2022 for (n+6 month)
Methodology : Modular Neural Network (Market Direction Analysis)

Abstract

Stock market prediction is a crucial and challenging task due to its nonlinear, evolutionary, complex, and dynamic nature. Research on the stock market has been an important issue for researchers in recent years. Companies invest in trading the stock market. Predicting the stock market trend accurately will minimize the risk and bring a maximum amount of profit for all the stakeholders. During the last several years, a lot of studies have been done to predict stock market trends using Traditional, Machine learning and deep learning techniques. (Umer, M., Awais, M. and Muzammul, M., 2019. Stock market prediction using machine learning (ML) algorithms. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 8(4), pp.97-116.) We evaluate Codere Online Luxembourg S.A. Ordinary Shares prediction models with Modular Neural Network (Market Direction Analysis) and ElasticNet Regression^1,2,3,4 and conclude that the CDRO stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Buy

Key Points

1. How do you pick a stock?
2. Can stock prices be predicted?
3. Nash Equilibria

CDRO Target Price Prediction Modeling Methodology

We consider Codere Online Luxembourg S.A. Ordinary Shares Decision Process with Modular Neural Network (Market Direction Analysis) where A is the set of discrete actions of CDRO 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(ElasticNet Regression)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (Market Direction Analysis)) X S(n):→ (n+6 month) $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

n:Time series to forecast

p:Price signals of CDRO stock

j:Nash equilibria (Neural Network)

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?

CDRO Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: CDRO Codere Online Luxembourg S.A. Ordinary Shares
Time series to forecast n: 18 Dec 2022 for (n+6 month)

According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Buy

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 (Grey to Black): *Technical Analysis%

Adjusted IFRS* Prediction Methods for Codere Online Luxembourg S.A. Ordinary Shares

1. An entity may manage and evaluate the performance of a group of financial liabilities or financial assets and financial liabilities in such a way that measuring that group at fair value through profit or loss results in more relevant information. The focus in this instance is on the way the entity manages and evaluates performance, instead of on the nature of its financial instruments.
2. The expected credit losses on a loan commitment shall be discounted using the effective interest rate, or an approximation thereof, that will be applied when recognising the financial asset resulting from the loan commitment. This is because for the purpose of applying the impairment requirements, a financial asset that is recognised following a draw down on a loan commitment shall be treated as a continuation of that commitment instead of as a new financial instrument. The expected credit losses on the financial asset shall therefore be measured considering the initial credit risk of the loan commitment from the date that the entity became a party to the irrevocable commitment.
3. When applying the effective interest method, an entity generally amortises any fees, points paid or received, transaction costs and other premiums or discounts that are included in the calculation of the effective interest rate over the expected life of the financial instrument. However, a shorter period is used if this is the period to which the fees, points paid or received, transaction costs, premiums or discounts relate. This will be the case when the variable to which the fees, points paid or received, transaction costs, premiums or discounts relate is repriced to market rates before the expected maturity of the financial instrument. In such a case, the appropriate amortisation period is the period to the next such repricing date. For example, if a premium or discount on a floating-rate financial instrument reflects the interest that has accrued on that financial instrument since the interest was last paid, or changes in the market rates since the floating interest rate was reset to the market rates, it will be amortised to the next date when the floating interest is reset to market rates. This is because the premium or discount relates to the period to the next interest reset date because, at that date, the variable to which the premium or discount relates (ie interest rates) is reset to the market rates. If, however, the premium or discount results from a change in the credit spread over the floating rate specified in the financial instrument, or other variables that are not reset to the market rates, it is amortised over the expected life of the financial instrument.
4. When measuring hedge ineffectiveness, an entity shall consider the time value of money. Consequently, the entity determines the value of the hedged item on a present value basis and therefore the change in the value of the hedged item also includes the effect of the time value of money.

*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

Codere Online Luxembourg S.A. Ordinary Shares assigned short-term B2 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with ElasticNet Regression^1,2,3,4 and conclude that the CDRO stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Buy

Financial State Forecast for CDRO Codere Online Luxembourg S.A. Ordinary Shares Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B2	Ba2
Operational Risk	90	87
Market Risk	31	59
Technical Analysis	40	70
Fundamental Analysis	50	69
Risk Unsystematic	60	48

Prediction Confidence Score

Trust metric by Neural Network: 80 out of 100 with 613 signals.

References

1. A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015
2. Y. Le Tallec. Robust, risk-sensitive, and data-driven control of Markov decision processes. PhD thesis, Massachusetts Institute of Technology, 2007.
3. Swaminathan A, Joachims T. 2015. Batch learning from logged bandit feedback through counterfactual risk minimization. J. Mach. Learn. Res. 16:1731–55
4. Athey S, Imbens GW. 2017b. The state of applied econometrics: causality and policy evaluation. J. Econ. Perspect. 31:3–32
5. Mnih A, Hinton GE. 2007. Three new graphical models for statistical language modelling. In International Conference on Machine Learning, pp. 641–48. La Jolla, CA: Int. Mach. Learn. Soc.
6. M. Petrik and D. Subramanian. An approximate solution method for large risk-averse Markov decision processes. In Proceedings of the 28th International Conference on Uncertainty in Artificial Intelligence, 2012.
7. R. Sutton, D. McAllester, S. Singh, and Y. Mansour. Policy gradient methods for reinforcement learning with function approximation. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1057–1063, 2000

Frequently Asked Questions

Q: What is the prediction methodology for CDRO stock?
A: CDRO stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and ElasticNet Regression
Q: Is CDRO stock a buy or sell?
A: The dominant strategy among neural network is to Buy CDRO Stock.
Q: Is Codere Online Luxembourg S.A. Ordinary Shares stock a good investment?
A: The consensus rating for Codere Online Luxembourg S.A. Ordinary Shares is Buy and assigned short-term B2 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of CDRO stock?
A: The consensus rating for CDRO is Buy.
Q: What is the prediction period for CDRO stock?
A: The prediction period for CDRO is (n+6 month)

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