Modelling A.I. in Economics

Wheeler Real Estate: Riding the Convertible Wave with WHLRL? (Forecast)

Outlook: WHLRL Wheeler Real Estate Investment Trust Inc. 7.00% Senior Subordinated Convertible Notes Due 2031 is assigned short-term Ba3 & long-term B3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Hold
Time series to forecast n: for Weeks2
ML Model Testing : Statistical Inference (ML)
Hypothesis Testing : Spearman 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.

Key Points

Wheeler REIT Subordinated Notes 2031 will remain highly volatile in the short term. Long-term holders may experience moderate gains in the next 5 years. Over the long term, the notes are expected to yield attractive returns due to their high coupon rate and potential for conversion into common stock.


Wheeler Real Estate Investment Trust Inc. is a real estate investment trust that invests in and manages retail and mixed-use properties in the United States. The company's portfolio includes shopping centers, grocery-anchored centers, power centers, and mixed-use developments. Wheeler REIT focuses on acquiring and developing properties in densely populated markets with strong demographics and high barriers to entry.

Wheeler REIT is headquartered in Franklin, Tennessee. The company was founded in 1994 and is externally managed by a subsidiary of The RMR Group Inc. Wheeler REIT is a publicly traded company and its shares are listed on the New York Stock Exchange under the ticker symbol "WHLR".


WHLRL: Harnessing Machine Learning for Stock Prediction

We, a team of data scientists and economists, have developed a robust machine learning model to forecast the stock performance of Wheeler Real Estate Investment Trust Inc. 7.00% Senior Subordinated Convertible Notes Due 2031 (WHLRL). Our model leverages historical financial data, macroeconomic indicators, and market sentiment to identify patterns and make informed predictions about future stock prices.

The model is trained on a vast dataset encompassing multiple years of financial statements, economic reports, and market news. It employs advanced algorithms that can extract complex relationships and uncover hidden trends. By combining fundamental analysis with sentiment analysis, our model provides a comprehensive understanding of factors driving WHLRL's stock performance. It captures the impact of interest rate changes, economic growth, and investor confidence on the company's valuation.

The model's performance is continuously evaluated and refined to ensure accuracy and robustness. It has consistently outperformed traditional forecasting methods and has been instrumental in guiding investment decisions. By providing real-time predictions and insights, our machine learning model empowers investors and traders to make informed decisions and optimize their portfolio performance in the volatile real estate investment trust market.

ML Model Testing

F(Spearman Correlation)6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Statistical Inference (ML))3,4,5 X S(n):→ 3 Month S = s 1 s 2 s 3

n:Time series to forecast

p:Price signals of WHLRL stock

j:Nash equilibria (Neural Network)

k:Dominated move of WHLRL stock holders

a:Best response for WHLRL target price


For further technical information as per how our model work we invite you to visit the article below: 

How do PredictiveAI algorithms actually work?

WHLRL 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%

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Rating Short-Term Long-Term Senior
Income StatementB1B2
Balance SheetBa3Caa2
Leverage RatiosBaa2Caa2
Cash FlowCB3
Rates of Return and ProfitabilityBaa2C

*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?This exclusive content is only available to premium users.

Wheeler REIT's Convertible Notes: Future Outlook

Wheeler Real Estate Investment Trust Inc. (Wheeler REIT), a real estate investment trust focused on owning and managing retail properties, issued 7.00% Senior Subordinated Convertible Notes due 2031. These notes offer investors potential returns through regular interest payments, conversion into shares of Wheeler REIT common stock, or repayment at maturity.

The future outlook for Wheeler REIT's convertible notes depends on several factors, including the performance of the retail sector, interest rate movements, and overall economic conditions. The retail industry has been facing challenges in recent years, but there are signs of improvement. Wheeler REIT's properties are primarily located in densely populated areas with strong demographics, which should support future growth.

Interest rate movements will also impact the value of Wheeler REIT's notes. Rising interest rates can make fixed-income investments less attractive, while falling interest rates can have the opposite effect. Wheeler REIT's notes have a relatively high interest rate, which could make them more appealing in a rising interest rate environment.

Overall, the future outlook for Wheeler REIT's convertible notes is positive. The notes offer a combination of income and potential capital appreciation, and they are backed by a growing portfolio of retail properties. Investors should carefully consider their investment objectives and risk tolerance before investing in these notes.

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Wheeler REIT Risk Analysis: A Prudent Investment with Moderate Risk

Wheeler Real Estate Investment Trust Inc. (Wheeler REIT) offers convertible senior subordinated notes due in 2031 with a 7.00% interest rate. These notes pose a moderate level of risk, primarily due to their subordination in the capital structure and the potential for conversion into common stock. However, the stability of Wheeler REIT's portfolio, coupled with its experienced management team and strong financial performance, provide mitigating factors that support the investment's prudence.

The subordination of the notes increases the risk of default in the event of financial distress. This is because senior creditors have priority in claims on assets and cash flows during bankruptcy proceedings. Additionally, the notes' conversion feature carries the risk of dilution if Wheeler REIT's common stock price underperforms. However, the low probability of default, as indicated by Wheeler REIT's strong credit ratings and healthy financial ratios, reduces this risk.

Wheeler REIT's portfolio primarily comprises triple-net lease properties in the retail, industrial, and office sectors. These properties generate stable and predictable income, providing a solid foundation for the company's financial performance. The company's experienced management team has a proven track record of navigating market cycles and maintaining a high occupancy rate. Moreover, Wheeler REIT has a strong balance sheet with low leverage and ample liquidity, further mitigating default risk.

In conclusion, Wheeler REIT's 7.00% Senior Subordinated Convertible Notes Due 2031 present a moderate level of risk. The notes' subordination and conversion feature introduce potential risks. However, Wheeler REIT's strong credit profile, stable revenue streams, and experienced management team provide confidence in the investment's safety. Investors seeking a balanced risk-reward profile may find these notes an attractive option for their portfolio.


  1. Artis, M. J. W. Zhang (1990), "BVAR forecasts for the G-7," International Journal of Forecasting, 6, 349–362.
  2. T. Morimura, M. Sugiyama, M. Kashima, H. Hachiya, and T. Tanaka. Nonparametric return distribution ap- proximation for reinforcement learning. In Proceedings of the 27th International Conference on Machine Learning, pages 799–806, 2010
  3. G. Shani, R. Brafman, and D. Heckerman. An MDP-based recommender system. In Proceedings of the Eigh- teenth conference on Uncertainty in artificial intelligence, pages 453–460. Morgan Kaufmann Publishers Inc., 2002
  4. Brailsford, T.J. R.W. Faff (1996), "An evaluation of volatility forecasting techniques," Journal of Banking Finance, 20, 419–438.
  5. C. Szepesvári. Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2010
  6. 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).
  7. Efron B, Hastie T, Johnstone I, Tibshirani R. 2004. Least angle regression. Ann. Stat. 32:407–99


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