AUC Score :
Short-Term Revised1 :
Dominant Strategy : Hold
Time series to forecast n:
Methodology : Modular Neural Network (Market Volatility Analysis)
Hypothesis Testing : Independent T-Test
Surveillance : Major exchange and OTC
1The accuracy of the model is being monitored on a regular basis.(15-minute period)
2Time series is updated based on short-term trends.
Urstadt Biddle Properties Inc. 5.875% Series K Cumulative Redeemable Preferred Stock prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and Independent T-Test1,2,3,4 and it is concluded that the UBP^K stock is predictable in the short/long term. Modular neural networks (MNNs) are a type of artificial neural network that can be used for market volatility analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying patterns in data or predicting future price movements. The modules are then combined to form a single neural network that can perform multiple tasks.In the context of market volatility analysis, MNNs can be used to identify patterns in market data that suggest that the market is becoming more or less volatile. This information can then be used to make predictions about future price movements.5 According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Hold

Key Points
- Can machine learning predict?
- How accurate is machine learning in stock market?
- Trust metric by Neural Network
UBP^K Stock Price Forecast
We consider Urstadt Biddle Properties Inc. 5.875% Series K Cumulative Redeemable Preferred Stock Decision Process with Modular Neural Network (Market Volatility Analysis) where A is the set of discrete actions of UBP^K 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
Sample Set: Neural Network
Stock/Index: UBP^K Urstadt Biddle Properties Inc. 5.875% Series K Cumulative Redeemable Preferred Stock
Time series to forecast: 4 Weeks
According to price forecasts, the dominant strategy among neural network is: Hold
n:Time series to forecast
p:Price signals of UBP^K stock
j:Nash equilibria (Neural Network)
k:Dominated move of UBP^K stock holders
a:Best response for UBP^K target price
Modular neural networks (MNNs) are a type of artificial neural network that can be used for market volatility analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying patterns in data or predicting future price movements. The modules are then combined to form a single neural network that can perform multiple tasks.In the context of market volatility analysis, MNNs can be used to identify patterns in market data that suggest that the market is becoming more or less volatile. This information can then be used to make predictions about future price movements.5 An independent t-test is a statistical test that compares the means of two independent samples. In an independent t-test, the data points in each sample are not related to each other. The independent t-test is a parametric test, which means that it assumes that the data is normally distributed. The independent t-test is also a two-sample test, which means that it compares the means of two independent samples.6,7
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?
UBP^K Stock Forecast (Buy or Sell) Strategic Interaction Table
Strategic Interaction Table Legend:
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%
Financial Data Adjustments for Modular Neural Network (Market Volatility Analysis) based UBP^K Stock Prediction Model
- If an entity originates a loan that bears an off-market interest rate (eg 5 per cent when the market rate for similar loans is 8 per cent), and receives an upfront fee as compensation, the entity recognises the loan at its fair value, ie net of the fee it receives.
- An embedded prepayment option in an interest-only or principal-only strip is closely related to the host contract provided the host contract (i) initially resulted from separating the right to receive contractual cash flows of a financial instrument that, in and of itself, did not contain an embedded derivative, and (ii) does not contain any terms not present in the original host debt contract.
- For the purposes of applying the requirements in paragraphs 5.7.7 and 5.7.8, an accounting mismatch is not caused solely by the measurement method that an entity uses to determine the effects of changes in a liability's credit risk. An accounting mismatch in profit or loss would arise only when the effects of changes in the liability's credit risk (as defined in IFRS 7) are expected to be offset by changes in the fair value of another financial instrument. A mismatch that arises solely as a result of the measurement method (ie because an entity does not isolate changes in a liability's credit risk from some other changes in its fair value) does not affect the determination required by paragraphs 5.7.7 and 5.7.8. For example, an entity may not isolate changes in a liability's credit risk from changes in liquidity risk. If the entity presents the combined effect of both factors in other comprehensive income, a mismatch may occur because changes in liquidity risk may be included in the fair value measurement of the entity's financial assets and the entire fair value change of those assets is presented in profit or loss. However, such a mismatch is caused by measurement imprecision, not the offsetting relationship described in paragraph B5.7.6 and, therefore, does not affect the determination required by paragraphs 5.7.7 and 5.7.8.
- An entity applies IAS 21 to financial assets and financial liabilities that are monetary items in accordance with IAS 21 and denominated in a foreign currency. IAS 21 requires any foreign exchange gains and losses on monetary assets and monetary liabilities to be recognised in profit or loss. An exception is a monetary item that is designated as a hedging instrument in a cash flow hedge (see paragraph 6.5.11), a hedge of a net investment (see paragraph 6.5.13) or a fair value hedge of an equity instrument for which an entity has elected to present changes in fair value in other comprehensive income in accordance with paragraph 5.7.5 (see paragraph 6.5.8).
*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.
UBP^K Urstadt Biddle Properties Inc. 5.875% Series K Cumulative Redeemable Preferred Stock Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | Caa2 | B3 |
Income Statement | C | Caa2 |
Balance Sheet | B3 | B1 |
Leverage Ratios | B3 | C |
Cash Flow | Caa2 | Caa2 |
Rates of Return and Profitability | C | Caa2 |
*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?
References
- L. Prashanth and M. Ghavamzadeh. Actor-critic algorithms for risk-sensitive MDPs. In Proceedings of Advances in Neural Information Processing Systems 26, pages 252–260, 2013.
- Bertsimas D, King A, Mazumder R. 2016. Best subset selection via a modern optimization lens. Ann. Stat. 44:813–52
- uyer, S. Whiteson, B. Bakker, and N. A. Vlassis. Multiagent reinforcement learning for urban traffic control using coordination graphs. In Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I, pages 656–671, 2008.
- Abadir, K. M., K. Hadri E. Tzavalis (1999), "The influence of VAR dimensions on estimator biases," Econometrica, 67, 163–181.
- K. Tuyls and G. Weiss. Multiagent learning: Basics, challenges, and prospects. AI Magazine, 33(3): 41–52, 2012
- Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
- Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]
Frequently Asked Questions
Q: What is the prediction methodology for UBP^K stock?A: UBP^K stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Independent T-Test
Q: Is UBP^K stock a buy or sell?
A: The dominant strategy among neural network is to Hold UBP^K Stock.
Q: Is Urstadt Biddle Properties Inc. 5.875% Series K Cumulative Redeemable Preferred Stock stock a good investment?
A: The consensus rating for Urstadt Biddle Properties Inc. 5.875% Series K Cumulative Redeemable Preferred Stock is Hold and is assigned short-term Caa2 & long-term B3 estimated rating.
Q: What is the consensus rating of UBP^K stock?
A: The consensus rating for UBP^K is Hold.
Q: What is the prediction period for UBP^K stock?
A: The prediction period for UBP^K is 4 Weeks
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