WHLRD Stock: Future is Bright, but The Road Ahead is Bumpy

Outlook: Wheeler Real Estate Investment Trust Inc. Series D Cumulative Preferred Stock is assigned short-term B1 & long-term B3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : Buy
Time series to forecast n: for Weeks²
Methodology : Modular Neural Network (CNN Layer)
Hypothesis Testing : Pearson Correlation
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 Modular Neural Network (CNN Layer) and Pearson Correlation^1,2,3,4 and it is concluded that the WHLRD stock is predictable in the short/long term. CNN layers are a powerful tool for extracting features from images. They are able to learn to detect patterns in images that are not easily detected by humans. This makes them well-suited for a variety of MNN applications. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Buy

Key Points

1. What are the most successful trading algorithms?
2. Decision Making
3. What is prediction in deep learning?

WHLRD Target Price Prediction Modeling Methodology

We consider Wheeler Real Estate Investment Trust Inc. Series D Cumulative Preferred Stock Decision Process with Modular Neural Network (CNN Layer) 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(Pearson Correlation)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (CNN Layer)) X S(n):→ 16 Weeks $\overrightarrow{R}=\left(r_1,r_2,r_3\right)$

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

Modular Neural Network (CNN Layer)

CNN layers are a powerful tool for extracting features from images. They are able to learn to detect patterns in images that are not easily detected by humans. This makes them well-suited for a variety of MNN applications.

Pearson Correlation

Pearson correlation, also known as Pearson's product-moment correlation, is a measure of the linear relationship between two variables. It is a statistical measure that assesses the strength and direction of a linear relationship between two variables. The sign of the correlation coefficient indicates the direction of the relationship, while the magnitude of the correlation coefficient indicates the strength of the relationship. A correlation coefficient of 0.9 indicates a strong positive correlation, while a correlation coefficient of 0.2 indicates a weak positive correlation.

 

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 Network
Stock/Index: WHLRD Wheeler Real Estate Investment Trust Inc. Series D Cumulative Preferred Stock
Time series to forecast: 16 Weeks

According to price forecasts, the dominant strategy among neural network is: Buy

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 (CNN Layer) based WHLRD Stock Prediction Model

1. A portfolio of financial assets that is managed and whose performance is evaluated on a fair value basis (as described in paragraph 4.2.2(b)) is neither held to collect contractual cash flows nor held both to collect contractual cash flows and to sell financial assets. The entity is primarily focused on fair value information and uses that information to assess the assets' performance and to make decisions. In addition, a portfolio of financial assets that meets the definition of held for trading is not held to collect contractual cash flows or held both to collect contractual cash flows and to sell financial assets. For such portfolios, the collection of contractual cash flows is only incidental to achieving the business model's objective. Consequently, such portfolios of financial assets must be measured at fair value through profit or loss.
2. An entity's estimate of expected credit losses on loan commitments shall be consistent with its expectations of drawdowns on that loan commitment, ie it shall consider the expected portion of the loan commitment that will be drawn down within 12 months of the reporting date when estimating 12-month expected credit losses, and the expected portion of the loan commitment that will be drawn down over the expected life of the loan commitment when estimating lifetime expected credit losses.
3. 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.
4. When an entity separates the foreign currency basis spread from a financial instrument and excludes it from the designation of that financial instrument as the hedging instrument (see paragraph 6.2.4(b)), the application guidance in paragraphs B6.5.34–B6.5.38 applies to the foreign currency basis spread in the same manner as it is applied to the forward element of a forward contract.

*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*	B1	B3
Income Statement	Caa2	Caa2
Balance Sheet	Ba2	Caa2
Leverage Ratios	Ba3	C
Cash Flow	Baa2	C
Rates of Return and Profitability	Caa2	B1

*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 B1 & long-term B3 estimated rating. Wheeler Real Estate Investment Trust Inc. Series D Cumulative Preferred Stock prediction model is evaluated with Modular Neural Network (CNN Layer) and Pearson Correlation^1,2,3,4 and it is concluded that the WHLRD stock is predictable in the short/long term. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Buy

Prediction Confidence Score

Trust metric by Neural Network: 82 out of 100 with 769 signals.

References

1. Hastie T, Tibshirani R, Friedman J. 2009. The Elements of Statistical Learning. Berlin: Springer
2. Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20
3. A. Tamar, D. Di Castro, and S. Mannor. Policy gradients with variance related risk criteria. In Proceedings of the Twenty-Ninth International Conference on Machine Learning, pages 387–396, 2012.
4. Brailsford, T.J. R.W. Faff (1996), "An evaluation of volatility forecasting techniques," Journal of Banking Finance, 20, 419–438.
5. Christou, C., P. A. V. B. Swamy G. S. Tavlas (1996), "Modelling optimal strategies for the allocation of wealth in multicurrency investments," International Journal of Forecasting, 12, 483–493.
6. A. Shapiro, W. Tekaya, J. da Costa, and M. Soares. Risk neutral and risk averse stochastic dual dynamic programming method. European journal of operational research, 224(2):375–391, 2013
7. Dietterich TG. 2000. Ensemble methods in machine learning. In Multiple Classifier Systems: First International Workshop, Cagliari, Italy, June 21–23, pp. 1–15. Berlin: Springer

Frequently Asked Questions

Q: What is the prediction methodology for WHLRD stock?
A: WHLRD stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Pearson Correlation
Q: Is WHLRD stock a buy or sell?
A: The dominant strategy among neural network is to Buy 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 Buy and is assigned short-term B1 & long-term B3 estimated rating.
Q: What is the consensus rating of WHLRD stock?
A: The consensus rating for WHLRD is Buy.
Q: What is the prediction period for WHLRD stock?
A: The prediction period for WHLRD is 16 Weeks

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