HPP Hudson Pacific Properties Inc. Common Stock

Outlook: Hudson Pacific Properties Inc. Common Stock assigned short-term B3 & long-term B3 forecasted stock rating.
Dominant Strategy : Sell
Time series to forecast n: 18 Dec 2022 for (n+4 weeks)
Methodology : Transductive Learning (ML)

Abstract

This paper addresses problem of predicting direction of movement of stock and stock price index. The study compares four prediction models, Artificial Neural Network (ANN), Support Vector Machine (SVM), random forest and naive-Bayes with two approaches for input to these models.(Kanade, P.A., Singh, S., Rajoria, S., Veer, P. and Wandile, N., 2020. Machine learning model for stock market prediction. International Journal for Research in Applied Science and Engineering Technology, 8(6), pp.209-216.) We evaluate Hudson Pacific Properties Inc. Common Stock prediction models with Transductive Learning (ML) and Logistic Regression^1,2,3,4 and conclude that the HPP stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Sell

Key Points

1. Investment Risk
2. What are main components of Markov decision process?
3. Is Target price a good indicator?

HPP Target Price Prediction Modeling Methodology

We consider Hudson Pacific Properties Inc. Common Stock Decision Process with Transductive Learning (ML) where A is the set of discrete actions of HPP 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(Logistic 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(Transductive Learning (ML)) X S(n):→ (n+4 weeks) $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of HPP 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 Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: HPP Hudson Pacific Properties Inc. Common Stock
Time series to forecast n: 18 Dec 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Sell

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 Hudson Pacific Properties Inc. Common Stock

1. An entity that first applies these amendments after it first applies this Standard shall apply paragraphs 7.2.32–7.2.34. The entity shall also apply the other transition requirements in this Standard necessary for applying these amendments. For that purpose, references to the date of initial application shall be read as referring to the beginning of the reporting period in which an entity first applies these amendments (date of initial application of these amendments).
2. If a component of the cash flows of a financial or a non-financial item is designated as the hedged item, that component must be less than or equal to the total cash flows of the entire item. However, all of the cash flows of the entire item may be designated as the hedged item and hedged for only one particular risk (for example, only for those changes that are attributable to changes in LIBOR or a benchmark commodity price).
3. An entity that first applies IFRS 17 as amended in June 2020 after it first applies this Standard shall apply paragraphs 7.2.39–7.2.42. The entity shall also apply the other transition requirements in this Standard necessary for applying these amendments. For that purpose, references to the date of initial application shall be read as referring to the beginning of the reporting period in which an entity first applies these amendments (date of initial application of these amendments).
4. If a put option written by an entity prevents a transferred asset from being derecognised and the entity measures the transferred asset at fair value, the associated liability is measured at the option exercise price plus the time value of the option. The measurement of the asset at fair value is limited to the lower of the fair value and the option exercise price because the entity has no right to increases in the fair value of the transferred asset above the exercise price of the option. This ensures that the net carrying amount of the asset and the associated liability is the fair value of the put option obligation. For example, if the fair value of the underlying asset is CU120, the option exercise price is CU100 and the time value of the option is CU5, the carrying amount of the associated liability is CU105 (CU100 + CU5) and the carrying amount of the asset is CU100 (in this case the option exercise price).

*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

Hudson Pacific Properties Inc. Common Stock assigned short-term B3 & long-term B3 forecasted stock rating. We evaluate the prediction models Transductive Learning (ML) with Logistic Regression^1,2,3,4 and conclude that the HPP stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Sell

Financial State Forecast for HPP Hudson Pacific Properties Inc. Common Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B3	B3
Operational Risk	30	41
Market Risk	38	47
Technical Analysis	33	33
Fundamental Analysis	70	51
Risk Unsystematic	77	46

Prediction Confidence Score

Trust metric by Neural Network: 72 out of 100 with 770 signals.

References

1. Robins J, Rotnitzky A. 1995. Semiparametric efficiency in multivariate regression models with missing data. J. Am. Stat. Assoc. 90:122–29
2. Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94
3. 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
4. J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 2137–2145, 2016.
5. Abadie A, Diamond A, Hainmueller J. 2010. Synthetic control methods for comparative case studies: estimat- ing the effect of California's tobacco control program. J. Am. Stat. Assoc. 105:493–505
6. Andrews, D. W. K. W. Ploberger (1994), "Optimal tests when a nuisance parameter is present only under the alternative," Econometrica, 62, 1383–1414.
7. Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:267–88

Frequently Asked Questions

Q: What is the prediction methodology for HPP stock?
A: HPP stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Logistic Regression
Q: Is HPP stock a buy or sell?
A: The dominant strategy among neural network is to Sell HPP Stock.
Q: Is Hudson Pacific Properties Inc. Common Stock stock a good investment?
A: The consensus rating for Hudson Pacific Properties Inc. Common Stock is Sell and assigned short-term B3 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of HPP stock?
A: The consensus rating for HPP is Sell.
Q: What is the prediction period for HPP stock?
A: The prediction period for HPP is (n+4 weeks)

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