AUC Score :
Short-Term Revised1 :
Dominant Strategy : Buy
Time series to forecast n:
Methodology : Modular Neural Network (CNN Layer)
Hypothesis Testing : Beta
Surveillance : Major exchange and OTC
1The accuracy of the model is being monitored on a regular basis.(15-minute period)
2Time series is updated based on short-term trends.
Summary
Global Net Lease Inc. 6.875% Series B Cumulative Redeemable Perpetual Preferred Stock prediction model is evaluated with Modular Neural Network (CNN Layer) and Beta1,2,3,4 and it is concluded that the GNL^B stock is predictable in the short/long term. CNN layers are a powerful tool for extracting features from images. They are able to learn to detect patterns in images that are not easily detected by humans. This makes them well-suited for a variety of MNN applications. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Buy
Key Points
- What statistical methods are used to analyze data?
- How accurate is machine learning in stock market?
- Operational Risk
GNL^B Target Price Prediction Modeling Methodology
We consider Global Net Lease Inc. 6.875% Series B Cumulative Redeemable Perpetual Preferred Stock Decision Process with Modular Neural Network (CNN Layer) where A is the set of discrete actions of GNL^B 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(Beta)5,6,7= X R(Modular Neural Network (CNN Layer)) X S(n):→ 1 Year
n:Time series to forecast
p:Price signals of GNL^B stock
j:Nash equilibria (Neural Network)
k:Dominated move
a:Best response for target price
Modular Neural Network (CNN Layer)
CNN layers are a powerful tool for extracting features from images. They are able to learn to detect patterns in images that are not easily detected by humans. This makes them well-suited for a variety of MNN applications.Beta
In statistics, beta (β) is a measure of the strength of the relationship between two variables. It is calculated as the slope of the line of best fit in a regression analysis. Beta can range from -1 to 1, with a value of 0 indicating no relationship between the two variables. A positive beta indicates that as one variable increases, the other variable also increases. A negative beta indicates that as one variable increases, the other variable decreases. For example, a study might find that there is a positive relationship between height and weight. This means that taller people tend to weigh more. The beta coefficient for this relationship would be positive.
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?
GNL^B Stock Forecast (Buy or Sell)
Sample Set: Neural NetworkStock/Index: GNL^B Global Net Lease Inc. 6.875% Series B Cumulative Redeemable Perpetual Preferred Stock
Time series to forecast: 1 Year
According to price forecasts, the dominant strategy among neural network is: Buy
Strategic Interaction Table Legend:
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%
Financial Data Adjustments for Modular Neural Network (CNN Layer) based GNL^B Stock Prediction Model
- An entity's business model refers to how an entity manages its financial assets in order to generate cash flows. That is, the entity's business model determines whether cash flows will result from collecting contractual cash flows, selling financial assets or both. Consequently, this assessment is not performed on the basis of scenarios that the entity does not reasonably expect to occur, such as so-called 'worst case' or 'stress case' scenarios. For example, if an entity expects that it will sell a particular portfolio of financial assets only in a stress case scenario, that scenario would not affect the entity's assessment of the business model for those assets if the entity reasonably expects that such a scenario will not occur. If cash flows are realised in a way that is different from the entity's expectations at the date that the entity assessed the business model (for example, if the entity sells more or fewer financial assets than it expected when it classified the assets), that does not give rise to a prior period error in the entity's financial statements (see IAS 8 Accounting Policies, Changes in Accounting Estimates and Errors) nor does it change the classification of the remaining financial assets held in that business model (ie those assets that the entity recognised in prior periods and still holds) as long as the entity considered all relevant information that was available at the time that it made the business model assessment.
- Paragraph 5.5.4 requires that lifetime expected credit losses are recognised on all financial instruments for which there has been significant increases in credit risk since initial recognition. In order to meet this objective, if an entity is not able to group financial instruments for which the credit risk is considered to have increased significantly since initial recognition based on shared credit risk characteristics, the entity should recognise lifetime expected credit losses on a portion of the financial assets for which credit risk is deemed to have increased significantly. The aggregation of financial instruments to assess whether there are changes in credit risk on a collective basis may change over time as new information becomes available on groups of, or individual, financial instruments.
- A hedge of a firm commitment (for example, a hedge of the change in fuel price relating to an unrecognised contractual commitment by an electric utility to purchase fuel at a fixed price) is a hedge of an exposure to a change in fair value. Accordingly, such a hedge is a fair value hedge. However, in accordance with paragraph 6.5.4, a hedge of the foreign currency risk of a firm commitment could alternatively be accounted for as a cash flow hedge.
- An entity shall apply the impairment requirements in Section 5.5 retrospectively in accordance with IAS 8 subject to paragraphs 7.2.15 and 7.2.18–7.2.20.
*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.
GNL^B Global Net Lease Inc. 6.875% Series B Cumulative Redeemable Perpetual Preferred Stock Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | B2 | Ba3 |
Income Statement | Caa2 | Baa2 |
Balance Sheet | B2 | Caa2 |
Leverage Ratios | Ba3 | Ba2 |
Cash Flow | Ba3 | Baa2 |
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?
Conclusions
Global Net Lease Inc. 6.875% Series B Cumulative Redeemable Perpetual Preferred Stock is assigned short-term B2 & long-term Ba3 estimated rating. Global Net Lease Inc. 6.875% Series B Cumulative Redeemable Perpetual Preferred Stock prediction model is evaluated with Modular Neural Network (CNN Layer) and Beta1,2,3,4 and it is concluded that the GNL^B stock is predictable in the short/long term. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Buy
Prediction Confidence Score
References
- P. Milgrom and I. Segal. Envelope theorems for arbitrary choice sets. Econometrica, 70(2):583–601, 2002
- V. Mnih, K. Kavukcuoglu, D. Silver, A. Rusu, J. Veness, M. Bellemare, A. Graves, M. Riedmiller, A. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 02 2015.
- Matzkin RL. 2007. Nonparametric identification. In Handbook of Econometrics, Vol. 6B, ed. J Heckman, E Learner, pp. 5307–68. Amsterdam: Elsevier
- 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
- Bessler, D. A. T. Covey (1991), "Cointegration: Some results on U.S. cattle prices," Journal of Futures Markets, 11, 461–474.
- Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
- D. Bertsekas. Nonlinear programming. Athena Scientific, 1999.
Frequently Asked Questions
Q: What is the prediction methodology for GNL^B stock?A: GNL^B stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Beta
Q: Is GNL^B stock a buy or sell?
A: The dominant strategy among neural network is to Buy GNL^B Stock.
Q: Is Global Net Lease Inc. 6.875% Series B Cumulative Redeemable Perpetual Preferred Stock stock a good investment?
A: The consensus rating for Global Net Lease Inc. 6.875% Series B Cumulative Redeemable Perpetual Preferred Stock is Buy and is assigned short-term B2 & long-term Ba3 estimated rating.
Q: What is the consensus rating of GNL^B stock?
A: The consensus rating for GNL^B is Buy.
Q: What is the prediction period for GNL^B stock?
A: The prediction period for GNL^B is 1 Year
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