HPP^C Hudson Pacific Properties Inc. 4.750% Series C Cumulative Redeemable Preferred Stock

Outlook: Hudson Pacific Properties Inc. 4.750% Series C Cumulative Redeemable Preferred Stock is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Wait until speculative trend diminishes
Time series to forecast n: 25 May 2023 for (n+6 month)
Methodology : Supervised Machine Learning (ML)

Abstract

Hudson Pacific Properties Inc. 4.750% Series C Cumulative Redeemable Preferred Stock prediction model is evaluated with Supervised Machine Learning (ML) and Ridge Regression^1,2,3,4 and it is concluded that the HPP^C stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Wait until speculative trend diminishes

Key Points

1. Operational Risk
2. Understanding Buy, Sell, and Hold Ratings
3. Is Target price a good indicator?

HPP^C Target Price Prediction Modeling Methodology

We consider Hudson Pacific Properties Inc. 4.750% Series C Cumulative Redeemable Preferred Stock Decision Process with Supervised Machine Learning (ML) where A is the set of discrete actions of HPP^C 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(Ridge 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(Supervised Machine Learning (ML)) X S(n):→ (n+6 month) $\begin{array}{l}\int e^x\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of HPP^C 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?

HPP^C Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: HPP^C Hudson Pacific Properties Inc. 4.750% Series C Cumulative Redeemable Preferred Stock
Time series to forecast n: 25 May 2023 for (n+6 month)

According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Wait until speculative trend diminishes

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 Hudson Pacific Properties Inc. 4.750% Series C Cumulative Redeemable Preferred Stock

1. 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.
2. It would not be acceptable to designate only some of the financial assets and financial liabilities giving rise to the inconsistency as at fair value through profit or loss if to do so would not eliminate or significantly reduce the inconsistency and would therefore not result in more relevant information. However, it would be acceptable to designate only some of a number of similar financial assets or similar financial liabilities if doing so achieves a significant reduction (and possibly a greater reduction than other allowable designations) in the inconsistency. For example, assume an entity has a number of similar financial liabilities that sum to CU100 and a number of similar financial assets that sum to CU50 but are measured on a different basis. The entity may significantly reduce the measurement inconsistency by designating at initial recognition all of the assets but only some of the liabilities (for example, individual liabilities with a combined total of CU45) as at fair value through profit or loss. However, because designation as at fair value through profit or loss can be applied only to the whole of a financial instrument, the entity in this example must designate one or more liabilities in their entirety. It could not designate either a component of a liability (eg changes in value attributable to only one risk, such as changes in a benchmark interest rate) or a proportion (ie percentage) of a liability.
3. An entity can rebut this presumption. However, it can do so only when it has reasonable and supportable information available that demonstrates that even if contractual payments become more than 30 days past due, this does not represent a significant increase in the credit risk of a financial instrument. For example when non-payment was an administrative oversight, instead of resulting from financial difficulty of the borrower, or the entity has access to historical evidence that demonstrates that there is no correlation between significant increases in the risk of a default occurring and financial assets on which payments are more than 30 days past due, but that evidence does identify such a correlation when payments are more than 60 days past due.
4. An entity need not undertake an exhaustive search for information but shall consider all reasonable and supportable information that is available without undue cost or effort and that is relevant to the estimate of expected credit losses, including the effect of expected prepayments. The information used shall include factors that are specific to the borrower, general economic conditions and an assessment of both the current as well as the forecast direction of conditions at the reporting date. An entity may use various sources of data, that may be both internal (entity-specific) and external. Possible data sources include internal historical credit loss experience, internal ratings, credit loss experience of other entities and external ratings, reports and statistics. Entities that have no, or insufficient, sources of entityspecific data may use peer group experience for the comparable financial instrument (or groups of financial instruments).

*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

Hudson Pacific Properties Inc. 4.750% Series C Cumulative Redeemable Preferred Stock is assigned short-term Ba1 & long-term Ba1 estimated rating. Hudson Pacific Properties Inc. 4.750% Series C Cumulative Redeemable Preferred Stock prediction model is evaluated with Supervised Machine Learning (ML) and Ridge Regression^1,2,3,4 and it is concluded that the HPP^C stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Wait until speculative trend diminishes

HPP^C Hudson Pacific Properties Inc. 4.750% Series C Cumulative Redeemable Preferred Stock Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Baa2	Baa2
Balance Sheet	B2	Baa2
Leverage Ratios	Caa2	Baa2
Cash Flow	Caa2	Ba2
Rates of Return and Profitability	Caa2	Baa2

*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

Trust metric by Neural Network: 76 out of 100 with 826 signals.

References

1. J. Filar, D. Krass, and K. Ross. Percentile performance criteria for limiting average Markov decision pro- cesses. IEEE Transaction of Automatic Control, 40(1):2–10, 1995.
2. Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
3. Pennington J, Socher R, Manning CD. 2014. GloVe: global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods on Natural Language Processing, pp. 1532–43. New York: Assoc. Comput. Linguist.
4. Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, et al. 2008. Top 10 algorithms in data mining. Knowl. Inform. Syst. 14:1–37
5. Athey S, Bayati M, Doudchenko N, Imbens G, Khosravi K. 2017a. Matrix completion methods for causal panel data models. arXiv:1710.10251 [math.ST]
6. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, Newey W. 2017. Double/debiased/ Neyman machine learning of treatment effects. Am. Econ. Rev. 107:261–65
7. Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.

Frequently Asked Questions

Q: What is the prediction methodology for HPP^C stock?
A: HPP^C stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Ridge Regression
Q: Is HPP^C stock a buy or sell?
A: The dominant strategy among neural network is to Wait until speculative trend diminishes HPP^C Stock.
Q: Is Hudson Pacific Properties Inc. 4.750% Series C Cumulative Redeemable Preferred Stock stock a good investment?
A: The consensus rating for Hudson Pacific Properties Inc. 4.750% Series C Cumulative Redeemable Preferred Stock is Wait until speculative trend diminishes and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of HPP^C stock?
A: The consensus rating for HPP^C is Wait until speculative trend diminishes.
Q: What is the prediction period for HPP^C stock?
A: The prediction period for HPP^C is (n+6 month)

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