Outlook: PS Business Parks Inc. 5.20% Cumulative Preferred Stock Series Y is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 07 Jun 2023 for 3 Month
Methodology : Supervised Machine Learning (ML)

## Abstract

PS Business Parks Inc. 5.20% Cumulative Preferred Stock Series Y prediction model is evaluated with Supervised Machine Learning (ML) and Paired T-Test1,2,3,4 and it is concluded that the PSB^Y stock is predictable in the short/long term. Supervised machine learning (ML) is a type of machine learning where a model is trained on labeled data. This means that the data has been tagged with the correct output for the input data. The model learns to predict the output for new input data based on the labeled data. Supervised ML is a powerful tool that can be used for a variety of tasks, including classification, regression, and forecasting. Classification tasks involve predicting the category of an input data, such as whether an email is spam or not. Regression tasks involve predicting a numerical value for an input data, such as the price of a house. Forecasting tasks involve predicting future values for a time series, such as the sales of a product. According to price forecasts for 3 Month period, the dominant strategy among neural network is: Sell

## Key Points

1. Can stock prices be predicted?
2. Should I buy stocks now or wait amid such uncertainty?
3. What are the most successful trading algorithms?

## PSB^Y Target Price Prediction Modeling Methodology

We consider PS Business Parks Inc. 5.20% Cumulative Preferred Stock Series Y Decision Process with Supervised Machine Learning (ML) where A is the set of discrete actions of PSB^Y 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(Paired T-Test)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(Supervised Machine Learning (ML)) X S(n):→ 3 Month $∑ i = 1 n r i$

n:Time series to forecast

p:Price signals of PSB^Y stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Supervised machine learning (ML) is a type of machine learning where a model is trained on labeled data. This means that the data has been tagged with the correct output for the input data. The model learns to predict the output for new input data based on the labeled data. Supervised ML is a powerful tool that can be used for a variety of tasks, including classification, regression, and forecasting. Classification tasks involve predicting the category of an input data, such as whether an email is spam or not. Regression tasks involve predicting a numerical value for an input data, such as the price of a house. Forecasting tasks involve predicting future values for a time series, such as the sales of a product.

A paired t-test is a statistical test that compares the means of two paired samples. In a paired t-test, each data point in one sample is paired with a data point in the other sample. The pairs are typically related in some way, such as before and after measurements, or measurements from the same subject under different conditions. The paired t-test is a parametric test, which means that it assumes that the data is normally distributed. The paired t-test is also a dependent samples test, which means that the data points in each pair are correlated.

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^Y Stock Forecast (Buy or Sell) for 3 Month

Sample Set: Neural Network
Stock/Index: PSB^Y PS Business Parks Inc. 5.20% Cumulative Preferred Stock Series Y
Time series to forecast n: 07 Jun 2023 for 3 Month

According to price forecasts for 3 Month 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%

## IFRS Reconciliation Adjustments for PS Business Parks Inc. 5.20% Cumulative Preferred Stock Series Y

1. 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).
2. In almost every lending transaction the creditor's instrument is ranked relative to the instruments of the debtor's other creditors. An instrument that is subordinated to other instruments may have contractual cash flows that are payments of principal and interest on the principal amount outstanding if the debtor's non-payment is a breach of contract and the holder has a contractual right to unpaid amounts of principal and interest on the principal amount outstanding even in the event of the debtor's bankruptcy. For example, a trade receivable that ranks its creditor as a general creditor would qualify as having payments of principal and interest on the principal amount outstanding. This is the case even if the debtor issued loans that are collateralised, which in the event of bankruptcy would give that loan holder priority over the claims of the general creditor in respect of the collateral but does not affect the contractual right of the general creditor to unpaid principal and other amounts due.
3. An entity that first applies IFRS 17 as amended in June 2020 at the same time it first applies this Standard shall apply paragraphs 7.2.1–7.2.28 instead of paragraphs 7.2.38–7.2.42.
4. The assessment of whether lifetime expected credit losses should be recognised is based on significant increases in the likelihood or risk of a default occurring since initial recognition (irrespective of whether a financial instrument has been repriced to reflect an increase in credit risk) instead of on evidence of a financial asset being credit-impaired at the reporting date or an actual default occurring. Generally, there will be a significant increase in credit risk before a financial asset becomes credit-impaired or an actual default occurs.

*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. 5.20% Cumulative Preferred Stock Series Y is assigned short-term Ba1 & long-term Ba1 estimated rating. PS Business Parks Inc. 5.20% Cumulative Preferred Stock Series Y prediction model is evaluated with Supervised Machine Learning (ML) and Paired T-Test1,2,3,4 and it is concluded that the PSB^Y stock is predictable in the short/long term. According to price forecasts for 3 Month period, the dominant strategy among neural network is: Sell

### PSB^Y PS Business Parks Inc. 5.20% Cumulative Preferred Stock Series Y Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementB1B2
Balance SheetCaa2Baa2
Leverage RatiosB2C
Cash FlowBa1C
Rates of Return and ProfitabilityBaa2Ba1

*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: 89 out of 100 with 858 signals.

## References

1. C. Wu and Y. Lin. Minimizing risk models in Markov decision processes with policies depending on target values. Journal of Mathematical Analysis and Applications, 231(1):47–67, 1999
2. Dimakopoulou M, Athey S, Imbens G. 2017. Estimation considerations in contextual bandits. arXiv:1711.07077 [stat.ML]
3. Hornik K, Stinchcombe M, White H. 1989. Multilayer feedforward networks are universal approximators. Neural Netw. 2:359–66
4. Hornik K, Stinchcombe M, White H. 1989. Multilayer feedforward networks are universal approximators. Neural Netw. 2:359–66
5. Dimakopoulou M, Zhou Z, Athey S, Imbens G. 2018. Balanced linear contextual bandits. arXiv:1812.06227 [cs.LG]
6. Y. Chow and M. Ghavamzadeh. Algorithms for CVaR optimization in MDPs. In Advances in Neural Infor- mation Processing Systems, pages 3509–3517, 2014.
7. Abadir, K. M., K. Hadri E. Tzavalis (1999), "The influence of VAR dimensions on estimator biases," Econometrica, 67, 163–181.
Frequently Asked QuestionsQ: What is the prediction methodology for PSB^Y stock?
A: PSB^Y stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Paired T-Test
Q: Is PSB^Y stock a buy or sell?
A: The dominant strategy among neural network is to Sell PSB^Y Stock.
Q: Is PS Business Parks Inc. 5.20% Cumulative Preferred Stock Series Y stock a good investment?
A: The consensus rating for PS Business Parks Inc. 5.20% Cumulative Preferred Stock Series Y is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of PSB^Y stock?
A: The consensus rating for PSB^Y is Sell.
Q: What is the prediction period for PSB^Y stock?
A: The prediction period for PSB^Y is 3 Month