AUC Score :
Short-Term Revised1 :
Dominant Strategy : Sell
Time series to forecast n:
Methodology : Deductive Inference (ML)
Hypothesis Testing : Paired 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.
Abstract
Wheeler Real Estate Investment Trust Inc. Series D Cumulative Preferred Stock prediction model is evaluated with Deductive Inference (ML) and Paired T-Test1,2,3,4 and it is concluded that the WHLRD stock is predictable in the short/long term. Deductive inference is a type of reasoning in which a conclusion is drawn based on a set of premises that are assumed to be true. In machine learning (ML), deductive inference can be used to create models that can make predictions about new data based on a set of known rules. Deductive inference is a supervised learning algorithm, which means that it requires labeled data to train. The labeled data is used to train the model to make predictions about new data. There are many different types of deductive inference algorithms, including decision trees, rule-based systems, and expert systems. Each type of algorithm has its own strengths and weaknesses. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Sell
Key Points
- Prediction Modeling
- Decision Making
- What are buy sell or hold recommendations?
WHLRD Target Price Prediction Modeling Methodology
We consider Wheeler Real Estate Investment Trust Inc. Series D Cumulative Preferred Stock Decision Process with Deductive Inference (ML) where A is the set of discrete actions of WHLRD 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= X R(Deductive Inference (ML)) X S(n):→ 6 Month
n:Time series to forecast
p:Price signals of WHLRD stock
j:Nash equilibria (Neural Network)
k:Dominated move
a:Best response for target price
Deductive Inference (ML)
Deductive inference is a type of reasoning in which a conclusion is drawn based on a set of premises that are assumed to be true. In machine learning (ML), deductive inference can be used to create models that can make predictions about new data based on a set of known rules. Deductive inference is a supervised learning algorithm, which means that it requires labeled data to train. The labeled data is used to train the model to make predictions about new data. There are many different types of deductive inference algorithms, including decision trees, rule-based systems, and expert systems. Each type of algorithm has its own strengths and weaknesses.Paired T-Test
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?
WHLRD Stock Forecast (Buy or Sell)
Sample Set: Neural NetworkStock/Index: WHLRD Wheeler Real Estate Investment Trust Inc. Series D Cumulative Preferred Stock
Time series to forecast: 6 Month
According to price forecasts, the dominant strategy among neural network is: Sell
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 Deductive Inference (ML) based WHLRD Stock Prediction Model
- Leverage is a contractual cash flow characteristic of some financial assets. Leverage increases the variability of the contractual cash flows with the result that they do not have the economic characteristics of interest. Stand-alone option, forward and swap contracts are examples of financial assets that include such leverage. Thus, such contracts do not meet the condition in paragraphs 4.1.2(b) and 4.1.2A(b) and cannot be subsequently measured at amortised cost or fair value through other comprehensive income.
- Expected credit losses shall be discounted to the reporting date, not to the expected default or some other date, using the effective interest rate determined at initial recognition or an approximation thereof. If a financial instrument has a variable interest rate, expected credit losses shall be discounted using the current effective interest rate determined in accordance with paragraph B5.4.5.
- Hedge effectiveness is the extent to which changes in the fair value or the cash flows of the hedging instrument offset changes in the fair value or the cash flows of the hedged item (for example, when the hedged item is a risk component, the relevant change in fair value or cash flows of an item is the one that is attributable to the hedged risk). Hedge ineffectiveness is the extent to which the changes in the fair value or the cash flows of the hedging instrument are greater or less than those on the hedged item.
- 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.
WHLRD Wheeler Real Estate Investment Trust Inc. Series D Cumulative Preferred Stock Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | Ba2 | B1 |
Income Statement | B3 | Ba1 |
Balance Sheet | Baa2 | B2 |
Leverage Ratios | Caa2 | B1 |
Cash Flow | Baa2 | Caa2 |
Rates of Return and Profitability | Baa2 | B2 |
*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?
Conclusions
Wheeler Real Estate Investment Trust Inc. Series D Cumulative Preferred Stock is assigned short-term Ba2 & long-term B1 estimated rating. Wheeler Real Estate Investment Trust Inc. Series D Cumulative Preferred Stock prediction model is evaluated with Deductive Inference (ML) and Paired T-Test1,2,3,4 and it is concluded that the WHLRD stock is predictable in the short/long term. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Sell
Prediction Confidence Score
References
- Angrist JD, Pischke JS. 2008. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton, NJ: Princeton Univ. Press
- Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press
- G. Konidaris, S. Osentoski, and P. Thomas. Value function approximation in reinforcement learning using the Fourier basis. In AAAI, 2011
- Ruiz FJ, Athey S, Blei DM. 2017. SHOPPER: a probabilistic model of consumer choice with substitutes and complements. arXiv:1711.03560 [stat.ML]
- A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015
- Cheung, Y. M.D. Chinn (1997), "Further investigation of the uncertain unit root in GNP," Journal of Business and Economic Statistics, 15, 68–73.
- Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Apple's Stock Price: How News Affects Volatility. AC Investment Research Journal, 220(44).
Frequently Asked Questions
Q: What is the prediction methodology for WHLRD stock?A: WHLRD stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Paired T-Test
Q: Is WHLRD stock a buy or sell?
A: The dominant strategy among neural network is to Sell WHLRD Stock.
Q: Is Wheeler Real Estate Investment Trust Inc. Series D Cumulative Preferred Stock stock a good investment?
A: The consensus rating for Wheeler Real Estate Investment Trust Inc. Series D Cumulative Preferred Stock is Sell and is assigned short-term Ba2 & long-term B1 estimated rating.
Q: What is the consensus rating of WHLRD stock?
A: The consensus rating for WHLRD is Sell.
Q: What is the prediction period for WHLRD stock?
A: The prediction period for WHLRD is 6 Month
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