Outlook: Vornado Realty Trust 5.25% Series M Cumulative Redeemable Preferred Shares of Beneficial Interest liquidation preference \$25.00 per share no par value per share is assigned short-term Ba1 & long-term Ba1 estimated rating.
Time series to forecast n: 10 Apr 2023 for (n+1 year)
Methodology : Modular Neural Network (News Feed Sentiment Analysis)

## Abstract

Vornado Realty Trust 5.25% Series M Cumulative Redeemable Preferred Shares of Beneficial Interest liquidation preference \$25.00 per share no par value per share prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Wilcoxon Rank-Sum Test1,2,3,4 and it is concluded that the VNO^M stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy

## Key Points

1. How do you pick a stock?
2. Dominated Move
3. Market Signals

## VNO^M Target Price Prediction Modeling Methodology

We consider Vornado Realty Trust 5.25% Series M Cumulative Redeemable Preferred Shares of Beneficial Interest liquidation preference \$25.00 per share no par value per share Decision Process with Modular Neural Network (News Feed Sentiment Analysis) where A is the set of discrete actions of VNO^M 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(Wilcoxon Rank-Sum 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(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ (n+1 year) $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of VNO^M 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?

## VNO^M Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: VNO^M Vornado Realty Trust 5.25% Series M Cumulative Redeemable Preferred Shares of Beneficial Interest liquidation preference \$25.00 per share no par value per share
Time series to forecast n: 10 Apr 2023 for (n+1 year)

According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy

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 Vornado Realty Trust 5.25% Series M Cumulative Redeemable Preferred Shares of Beneficial Interest liquidation preference \$25.00 per share no par value per share

1. Sales that occur for other reasons, such as sales made to manage credit concentration risk (without an increase in the assets' credit risk), may also be consistent with a business model whose objective is to hold financial assets in order to collect contractual cash flows. In particular, such sales may be consistent with a business model whose objective is to hold financial assets in order to collect contractual cash flows if those sales are infrequent (even if significant in value) or insignificant in value both individually and in aggregate (even if frequent). If more than an infrequent number of such sales are made out of a portfolio and those sales are more than insignificant in value (either individually or in aggregate), the entity needs to assess whether and how such sales are consistent with an objective of collecting contractual cash flows. Whether a third party imposes the requirement to sell the financial assets, or that activity is at the entity's discretion, is not relevant to this assessment. An increase in the frequency or value of sales in a particular period is not necessarily inconsistent with an objective to hold financial assets in order to collect contractual cash flows, if an entity can explain the reasons for those sales and demonstrate why those sales do not reflect a change in the entity's business model. In addition, sales may be consistent with the objective of holding financial assets in order to collect contractual cash flows if the sales are made close to the maturity of the financial assets and the proceeds from the sales approximate the collection of the remaining contractual cash flows.
2. For example, an entity may use this condition to designate financial liabilities as at fair value through profit or loss if it meets the principle in paragraph 4.2.2(b) and the entity has financial assets and financial liabilities that share one or more risks and those risks are managed and evaluated on a fair value basis in accordance with a documented policy of asset and liability management. An example could be an entity that has issued 'structured products' containing multiple embedded derivatives and manages the resulting risks on a fair value basis using a mix of derivative and non-derivative financial instruments
3. When measuring the fair values of the part that continues to be recognised and the part that is derecognised for the purposes of applying paragraph 3.2.13, an entity applies the fair value measurement requirements in IFRS 13 Fair Value Measurement in addition to paragraph 3.2.14.
4. If a financial instrument that was previously recognised as a financial asset is measured at fair value through profit or loss and its fair value decreases below zero, it is a financial liability measured in accordance with paragraph 4.2.1. However, hybrid contracts with hosts that are assets within the scope of this Standard are always measured in accordance with paragraph 4.3.2.

*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

Vornado Realty Trust 5.25% Series M Cumulative Redeemable Preferred Shares of Beneficial Interest liquidation preference \$25.00 per share no par value per share is assigned short-term Ba1 & long-term Ba1 estimated rating. Vornado Realty Trust 5.25% Series M Cumulative Redeemable Preferred Shares of Beneficial Interest liquidation preference \$25.00 per share no par value per share prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Wilcoxon Rank-Sum Test1,2,3,4 and it is concluded that the VNO^M stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy

### VNO^M Vornado Realty Trust 5.25% Series M Cumulative Redeemable Preferred Shares of Beneficial Interest liquidation preference \$25.00 per share no par value per share Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementCC
Balance SheetBaa2Ba2
Leverage RatiosB1Ba3
Cash FlowBaa2B3
Rates of Return and ProfitabilityBaa2B1

*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: 80 out of 100 with 794 signals.

## References

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3. 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.
4. Rosenbaum PR, Rubin DB. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55
5. L. Panait and S. Luke. Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems, 11(3):387–434, 2005.
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7. Breusch, T. S. A. R. Pagan (1979), "A simple test for heteroskedasticity and random coefficient variation," Econometrica, 47, 1287–1294.
Frequently Asked QuestionsQ: What is the prediction methodology for VNO^M stock?
A: VNO^M stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Wilcoxon Rank-Sum Test
Q: Is VNO^M stock a buy or sell?
A: The dominant strategy among neural network is to Buy VNO^M Stock.
Q: Is Vornado Realty Trust 5.25% Series M Cumulative Redeemable Preferred Shares of Beneficial Interest liquidation preference \$25.00 per share no par value per share stock a good investment?
A: The consensus rating for Vornado Realty Trust 5.25% Series M Cumulative Redeemable Preferred Shares of Beneficial Interest liquidation preference \$25.00 per share no par value per share is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of VNO^M stock?
A: The consensus rating for VNO^M is Buy.
Q: What is the prediction period for VNO^M stock?
A: The prediction period for VNO^M is (n+1 year)