DLR^K Stock Forecast (Forecast)

Digital Realty Trust Inc. 5.850% Series K Cumulative Redeemable Preferred Stock par value $0.01 per share Research Report

Summary

Security indices are the main tools for evaluation of the status of financial markets. Moreover, a main part of the economy of any country is constituted of investment in stock markets. Therefore, investors could maximize the return of investment if it becomes possible to predict the future trend of stock market with appropriate methods. The nonlinearity and nonstationarity of financial series make their prediction complicated. This study seeks to evaluate the prediction power of machine-learning models in a stock market. We evaluate Digital Realty Trust Inc. 5.850% Series K Cumulative Redeemable Preferred Stock par value $0.01 per share prediction models with Modular Neural Network (Market Volatility Analysis) and Spearman Correlation^1,2,3,4 and conclude that the DLR^K stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold DLR^K stock.

Key Points

1. What statistical methods are used to analyze data?
2. Stock Forecast Based On a Predictive Algorithm
3. Dominated Move

DLR^K Target Price Prediction Modeling Methodology

We consider Digital Realty Trust Inc. 5.850% Series K Cumulative Redeemable Preferred Stock par value $0.01 per share Stock Decision Process with Modular Neural Network (Market Volatility Analysis) where A is the set of discrete actions of DLR^K 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(Spearman Correlation)^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 Volatility Analysis)) X S(n):→ (n+8 weeks) $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

p:Price signals of DLR^K 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?

DLR^K Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: DLR^K Digital Realty Trust Inc. 5.850% Series K Cumulative Redeemable Preferred Stock par value $0.01 per share
Time series to forecast n: 27 Nov 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold DLR^K 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 Digital Realty Trust Inc. 5.850% Series K Cumulative Redeemable Preferred Stock par value $0.01 per share

1. However, the designation of the hedging relationship using the same hedge ratio as that resulting from the quantities of the hedged item and the hedging instrument that the entity actually uses shall not reflect an imbalance between the weightings of the hedged item and the hedging instrument that would in turn create hedge ineffectiveness (irrespective of whether recognised or not) that could result in an accounting outcome that would be inconsistent with the purpose of hedge accounting. Hence, for the purpose of designating a hedging relationship, an entity must adjust the hedge ratio that results from the quantities of the hedged item and the hedging instrument that the entity actually uses if that is needed to avoid such an imbalance
2. For example, Entity A, whose functional currency is its local currency, has a firm commitment to pay FC150,000 for advertising expenses in nine months' time and a firm commitment to sell finished goods for FC150,000 in 15 months' time. Entity A enters into a foreign currency derivative that settles in nine months' time under which it receives FC100 and pays CU70. Entity A has no other exposures to FC. Entity A does not manage foreign currency risk on a net basis. Hence, Entity A cannot apply hedge accounting for a hedging relationship between the foreign currency derivative and a net position of FC100 (consisting of FC150,000 of the firm purchase commitment—ie advertising services—and FC149,900 (of the FC150,000) of the firm sale commitment) for a nine-month period.
3. When a group of items that constitute a net position is designated as a hedged item, an entity shall designate the overall group of items that includes the items that can make up the net position. An entity is not permitted to designate a non-specific abstract amount of a net position. For example, an entity has a group of firm sale commitments in nine months' time for FC100 and a group of firm purchase commitments in 18 months' time for FC120. The entity cannot designate an abstract amount of a net position up to FC20. Instead, it must designate a gross amount of purchases and a gross amount of sales that together give rise to the hedged net position. An entity shall designate gross positions that give rise to the net position so that the entity is able to comply with the requirements for the accounting for qualifying hedging relationships.
4. Although the objective of an entity's business model may be to hold financial assets in order to collect contractual cash flows, the entity need not hold all of those instruments until maturity. Thus an entity's business model can be to hold financial assets to collect contractual cash flows even when sales of financial assets occur or are expected to occur in the future.

*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

Digital Realty Trust Inc. 5.850% Series K Cumulative Redeemable Preferred Stock par value $0.01 per share assigned short-term B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Spearman Correlation^1,2,3,4 and conclude that the DLR^K stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold DLR^K stock.

Financial State Forecast for DLR^K Digital Realty Trust Inc. 5.850% Series K Cumulative Redeemable Preferred Stock par value $0.01 per share Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B2	B1
Operational Risk	48	52
Market Risk	36	47
Technical Analysis	74	81
Fundamental Analysis	71	50
Risk Unsystematic	37	64

Prediction Confidence Score

Trust metric by Neural Network: 90 out of 100 with 676 signals.

References

1. Efron B, Hastie T, Johnstone I, Tibshirani R. 2004. Least angle regression. Ann. Stat. 32:407–99
2. J. Hu and M. P. Wellman. Nash q-learning for general-sum stochastic games. Journal of Machine Learning Research, 4:1039–1069, 2003.
3. Bengio Y, Ducharme R, Vincent P, Janvin C. 2003. A neural probabilistic language model. J. Mach. Learn. Res. 3:1137–55
4. M. J. Hausknecht and P. Stone. Deep recurrent Q-learning for partially observable MDPs. CoRR, abs/1507.06527, 2015
5. J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.
6. E. van der Pol and F. A. Oliehoek. Coordinated deep reinforcement learners for traffic light control. NIPS Workshop on Learning, Inference and Control of Multi-Agent Systems, 2016.
7. S. J. Russell and A. Zimdars. Q-decomposition for reinforcement learning agents. In Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), August 21-24, 2003, Washington, DC, USA, pages 656–663, 2003.

Frequently Asked Questions

Q: What is the prediction methodology for DLR^K stock?
A: DLR^K stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Spearman Correlation
Q: Is DLR^K stock a buy or sell?
A: The dominant strategy among neural network is to Hold DLR^K Stock.
Q: Is Digital Realty Trust Inc. 5.850% Series K Cumulative Redeemable Preferred Stock par value $0.01 per share stock a good investment?
A: The consensus rating for Digital Realty Trust Inc. 5.850% Series K Cumulative Redeemable Preferred Stock par value $0.01 per share is Hold and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of DLR^K stock?
A: The consensus rating for DLR^K is Hold.
Q: What is the prediction period for DLR^K stock?
A: The prediction period for DLR^K is (n+8 weeks)

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