Outlook: PS Business Parks Inc. Depositary Shares Each Representing 1/1000 of a Share of 4.875% Cumulative Preferred Stock Series Z par value \$0.01 per share is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 28 May 2023 for (n+16 weeks)
Methodology : Ensemble Learning (ML)

## Abstract

PS Business Parks Inc. Depositary Shares Each Representing 1/1000 of a Share of 4.875% Cumulative Preferred Stock Series Z par value \$0.01 per share prediction model is evaluated with Ensemble Learning (ML) and Lasso Regression1,2,3,4 and it is concluded that the PSB^Z stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Hold

## Key Points

1. How accurate is machine learning in stock market?
2. What is statistical models in machine learning?
3. Prediction Modeling

## PSB^Z Target Price Prediction Modeling Methodology

We consider PS Business Parks Inc. Depositary Shares Each Representing 1/1000 of a Share of 4.875% Cumulative Preferred Stock Series Z par value \$0.01 per share Decision Process with Ensemble Learning (ML) where A is the set of discrete actions of PSB^Z 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(Lasso Regression)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Ensemble Learning (ML)) X S(n):→ (n+16 weeks) $∑ i = 1 n a i$

n:Time series to forecast

p:Price signals of PSB^Z 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?

## PSB^Z Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: PSB^Z PS Business Parks Inc. Depositary Shares Each Representing 1/1000 of a Share of 4.875% Cumulative Preferred Stock Series Z par value \$0.01 per share
Time series to forecast n: 28 May 2023 for (n+16 weeks)

According to price forecasts for (n+16 weeks) 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 PS Business Parks Inc. Depositary Shares Each Representing 1/1000 of a Share of 4.875% Cumulative Preferred Stock Series Z par value \$0.01 per share

2. Despite the requirement in paragraph 7.2.1, an entity that adopts the classification and measurement requirements of this Standard (which include the requirements related to amortised cost measurement for financial assets and impairment in Sections 5.4 and 5.5) shall provide the disclosures set out in paragraphs 42L–42O of IFRS 7 but need not restate prior periods. The entity may restate prior periods if, and only if, it is possible without the use of hindsight. If an entity does not restate prior periods, the entity shall recognise any difference between the previous carrying amount and the carrying amount at the beginning of the annual reporting period that includes the date of initial application in the opening retained earnings (or other component of equity, as appropriate) of the annual reporting period that includes the date of initial application. However, if an entity restates prior periods, the restated financial statements must reflect all of the requirements in this Standard. If an entity's chosen approach to applying IFRS 9 results in more than one date of initial application for different requirements, this paragraph applies at each date of initial application (see paragraph 7.2.2). This would be the case, for example, if an entity elects to early apply only the requirements for the presentation of gains and losses on financial liabilities designated as at fair value through profit or loss in accordance with paragraph 7.1.2 before applying the other requirements in this Standard.
3. Interest Rate Benchmark Reform, which amended IFRS 9, IAS 39 and IFRS 7, issued in September 2019, added Section 6.8 and amended paragraph 7.2.26. An entity shall apply these amendments for annual periods beginning on or after 1 January 2020. Earlier application is permitted. If an entity applies these amendments for an earlier period, it shall disclose that fact.
4. Historical information is an important anchor or base from which to measure expected credit losses. However, an entity shall adjust historical data, such as credit loss experience, on the basis of current observable data to reflect the effects of the current conditions and its forecasts of future conditions that did not affect the period on which the historical data is based, and to remove the effects of the conditions in the historical period that are not relevant to the future contractual cash flows. In some cases, the best reasonable and supportable information could be the unadjusted historical information, depending on the nature of the historical information and when it was calculated, compared to circumstances at the reporting date and the characteristics of the financial instrument being considered. Estimates of changes in expected credit losses should reflect, and be directionally consistent with, changes in related observable data from period to period

*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

PS Business Parks Inc. Depositary Shares Each Representing 1/1000 of a Share of 4.875% Cumulative Preferred Stock Series Z par value \$0.01 per share is assigned short-term Ba1 & long-term Ba1 estimated rating. PS Business Parks Inc. Depositary Shares Each Representing 1/1000 of a Share of 4.875% Cumulative Preferred Stock Series Z par value \$0.01 per share prediction model is evaluated with Ensemble Learning (ML) and Lasso Regression1,2,3,4 and it is concluded that the PSB^Z stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Hold

### PSB^Z PS Business Parks Inc. Depositary Shares Each Representing 1/1000 of a Share of 4.875% Cumulative Preferred Stock Series Z par value \$0.01 per share Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementBaa2Caa2
Balance SheetBa3B1
Leverage RatiosBa1C
Cash FlowCBaa2
Rates of Return and ProfitabilityB3B1

*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: 84 out of 100 with 624 signals.

## References

1. R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972
2. C. Claus and C. Boutilier. The dynamics of reinforcement learning in cooperative multiagent systems. In Proceedings of the Fifteenth National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, AAAI 98, IAAI 98, July 26-30, 1998, Madison, Wisconsin, USA., pages 746–752, 1998.
3. J. Peters, S. Vijayakumar, and S. Schaal. Natural actor-critic. In Proceedings of the Sixteenth European Conference on Machine Learning, pages 280–291, 2005.
4. Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
5. J. Ott. A Markov decision model for a surveillance application and risk-sensitive Markov decision processes. PhD thesis, Karlsruhe Institute of Technology, 2010.
6. M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
7. Dimakopoulou M, Athey S, Imbens G. 2017. Estimation considerations in contextual bandits. arXiv:1711.07077 [stat.ML]
Frequently Asked QuestionsQ: What is the prediction methodology for PSB^Z stock?
A: PSB^Z stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Lasso Regression
Q: Is PSB^Z stock a buy or sell?
A: The dominant strategy among neural network is to Hold PSB^Z Stock.
Q: Is PS Business Parks Inc. Depositary Shares Each Representing 1/1000 of a Share of 4.875% Cumulative Preferred Stock Series Z par value \$0.01 per share stock a good investment?
A: The consensus rating for PS Business Parks Inc. Depositary Shares Each Representing 1/1000 of a Share of 4.875% Cumulative Preferred Stock Series Z par value \$0.01 per share is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of PSB^Z stock?
A: The consensus rating for PSB^Z is Hold.
Q: What is the prediction period for PSB^Z stock?
A: The prediction period for PSB^Z is (n+16 weeks)

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