Dominant Strategy : Hold
Time series to forecast n: 07 Jan 2023 for (n+6 month)
Methodology : Modular Neural Network (Market News Sentiment Analysis)
Abstract
Grifols S.A. American Depositary Shares prediction model is evaluated with Modular Neural Network (Market News Sentiment Analysis) and Wilcoxon Rank-Sum Test1,2,3,4 and it is concluded that the GRFS stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: HoldKey Points
- Trading Signals
- How do predictive algorithms actually work?
- What is prediction in deep learning?
GRFS Target Price Prediction Modeling Methodology
We consider Grifols S.A. American Depositary Shares Decision Process with Modular Neural Network (Market News Sentiment Analysis) where A is the set of discrete actions of GRFS 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(Wilcoxon Rank-Sum Test)5,6,7= X R(Modular Neural Network (Market News Sentiment Analysis)) X S(n):→ (n+6 month)
n:Time series to forecast
p:Price signals of GRFS 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?
GRFS Stock Forecast (Buy or Sell) for (n+6 month)
Sample Set: Neural NetworkStock/Index: GRFS Grifols S.A. American Depositary Shares
Time series to forecast n: 07 Jan 2023 for (n+6 month)
According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Hold
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%
IFRS Reconciliation Adjustments for Grifols S.A. American Depositary Shares
- Paragraph 6.3.4 permits an entity to designate as hedged items aggregated exposures that are a combination of an exposure and a derivative. When designating such a hedged item, an entity assesses whether the aggregated exposure combines an exposure with a derivative so that it creates a different aggregated exposure that is managed as one exposure for a particular risk (or risks). In that case, the entity may designate the hedged item on the basis of the aggregated exposure
- Lifetime expected credit losses are not recognised on a financial instrument simply because it was considered to have low credit risk in the previous reporting period and is not considered to have low credit risk at the reporting date. In such a case, an entity shall determine whether there has been a significant increase in credit risk since initial recognition and thus whether lifetime expected credit losses are required to be recognised in accordance with paragraph 5.5.3.
- Expected credit losses reflect an entity's own expectations of credit losses. However, when considering all reasonable and supportable information that is available without undue cost or effort in estimating expected credit losses, an entity should also consider observable market information about the credit risk of the particular financial instrument or similar financial instruments.
- An entity can also designate only changes in the cash flows or fair value of a hedged item above or below a specified price or other variable (a 'one-sided risk'). The intrinsic value of a purchased option hedging instrument (assuming that it has the same principal terms as the designated risk), but not its time value, reflects a one-sided risk in a hedged item. For example, an entity can designate the variability of future cash flow outcomes resulting from a price increase of a forecast commodity purchase. In such a situation, the entity designates only cash flow losses that result from an increase in the price above the specified level. The hedged risk does not include the time value of a purchased option, because the time value is not a component of the forecast transaction that affects profit or loss.
*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.
Conclusions
Grifols S.A. American Depositary Shares is assigned short-term Ba1 & long-term Ba1 estimated rating. Grifols S.A. American Depositary Shares prediction model is evaluated with Modular Neural Network (Market News Sentiment Analysis) and Wilcoxon Rank-Sum Test1,2,3,4 and it is concluded that the GRFS stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Hold
GRFS Grifols S.A. American Depositary Shares Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | Ba1 | Ba1 |
Income Statement | C | B3 |
Balance Sheet | Baa2 | Baa2 |
Leverage Ratios | B2 | C |
Cash Flow | C | B1 |
Rates of Return and Profitability | Caa2 | Caa2 |
*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?
Prediction Confidence Score
References
- Alexander, J. C. Jr. (1995), "Refining the degree of earnings surprise: A comparison of statistical and analysts' forecasts," Financial Review, 30, 469–506.
- Bottou L. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade, ed. G Montavon, G Orr, K-R Müller, pp. 421–36. Berlin: Springer
- Thomas P, Brunskill E. 2016. Data-efficient off-policy policy evaluation for reinforcement learning. In Pro- ceedings of the International Conference on Machine Learning, pp. 2139–48. La Jolla, CA: Int. Mach. Learn. Soc.
- 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.
- V. Borkar. An actor-critic algorithm for constrained Markov decision processes. Systems & Control Letters, 54(3):207–213, 2005.
- Y. Chow and M. Ghavamzadeh. Algorithms for CVaR optimization in MDPs. In Advances in Neural Infor- mation Processing Systems, pages 3509–3517, 2014.
- Tibshirani R, Hastie T. 1987. Local likelihood estimation. J. Am. Stat. Assoc. 82:559–67
Frequently Asked Questions
Q: What is the prediction methodology for GRFS stock?A: GRFS stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and Wilcoxon Rank-Sum Test
Q: Is GRFS stock a buy or sell?
A: The dominant strategy among neural network is to Hold GRFS Stock.
Q: Is Grifols S.A. American Depositary Shares stock a good investment?
A: The consensus rating for Grifols S.A. American Depositary Shares is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of GRFS stock?
A: The consensus rating for GRFS is Hold.
Q: What is the prediction period for GRFS stock?
A: The prediction period for GRFS is (n+6 month)
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