Modelling A.I. in Economics

CDR^B Stock: A High-Risk, High-Reward Investment

Outlook: Cedar Realty Trust Inc. 7.25% Series B Cumulative Redeemable Preferred Stock is assigned short-term Ba3 & long-term Ba2 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Buy
Time series to forecast n: for Weeks2
Methodology : Inductive Learning (ML)
Hypothesis Testing : Stepwise Regression
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.


Cedar Realty Trust Inc. 7.25% Series B Cumulative Redeemable Preferred Stock prediction model is evaluated with Inductive Learning (ML) and Stepwise Regression1,2,3,4 and it is concluded that the CDR^B stock is predictable in the short/long term. Inductive learning is a type of machine learning in which the model learns from a set of labeled data and makes predictions about new, unlabeled data. The model is trained on the labeled data and then used to make predictions on new data. Inductive learning 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 inductive learning algorithms, including decision trees, support vector machines, and neural networks. Each type of algorithm has its own strengths and weaknesses.6 According to price forecasts for 1 Year period, the dominant strategy among neural network is: Buy

Graph 49

Key Points

  1. Market Risk
  2. Can stock prices be predicted?
  3. Stock Rating

CDR^B Stock Price Forecast

We consider Cedar Realty Trust Inc. 7.25% Series B Cumulative Redeemable Preferred Stock Decision Process with Inductive Learning (ML) where A is the set of discrete actions of CDR^B 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: CDR^B Cedar Realty Trust Inc. 7.25% Series B Cumulative Redeemable Preferred Stock
Time series to forecast: 1 Year

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


F(Stepwise Regression)5,6,7= 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(Inductive Learning (ML)) X S(n):→ 1 Year i = 1 n a i

n:Time series to forecast

p:Price signals of CDR^B stock

j:Nash equilibria (Neural Network)

k:Dominated move of CDR^B stock holders

a:Best response for CDR^B target price


Inductive learning is a type of machine learning in which the model learns from a set of labeled data and makes predictions about new, unlabeled data. The model is trained on the labeled data and then used to make predictions on new data. Inductive learning 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 inductive learning algorithms, including decision trees, support vector machines, and neural networks. Each type of algorithm has its own strengths and weaknesses.6 Stepwise regression is a method of variable selection in which variables are added or removed from a model one at a time, based on their statistical significance. There are two main types of stepwise regression: forward selection and backward elimination. In forward selection, variables are added to the model one at a time, starting with the variable with the highest F-statistic. The F-statistic is a measure of how much improvement in the model is gained by adding the variable. Variables are added to the model until no variable adds a statistically significant improvement to the model.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?

CDR^B 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 Inductive Learning (ML) based CDR^B Stock Prediction Model

  1. If any instrument in the pool does not meet the conditions in either paragraph B4.1.23 or paragraph B4.1.24, the condition in paragraph B4.1.21(b) is not met. In performing this assessment, a detailed instrument-byinstrument analysis of the pool may not be necessary. However, an entity must use judgement and perform sufficient analysis to determine whether the instruments in the pool meet the conditions in paragraphs B4.1.23–B4.1.24. (See also paragraph B4.1.18 for guidance on contractual cash flow characteristics that have only a de minimis effect.)
  2. For the purpose of applying paragraphs B4.1.11(b) and B4.1.12(b), irrespective of the event or circumstance that causes the early termination of the contract, a party may pay or receive reasonable compensation for that early termination. For example, a party may pay or receive reasonable compensation when it chooses to terminate the contract early (or otherwise causes the early termination to occur).
  3. There are two types of components of nominal amounts that can be designated as the hedged item in a hedging relationship: a component that is a proportion of an entire item or a layer component. The type of component changes the accounting outcome. An entity shall designate the component for accounting purposes consistently with its risk management objective.
  4. For hedges other than hedges of foreign currency risk, when an entity designates a non-derivative financial asset or a non-derivative financial liability measured at fair value through profit or loss as a hedging instrument, it may only designate the non-derivative financial instrument in its entirety or a proportion of it.

*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.

CDR^B Cedar Realty Trust Inc. 7.25% Series B Cumulative Redeemable Preferred Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba3Ba2
Income StatementCaa2Baa2
Balance SheetBaa2C
Leverage RatiosBaa2Baa2
Cash FlowB2Baa2
Rates of Return and ProfitabilityB1B3

*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

  1. Matzkin RL. 1994. Restrictions of economic theory in nonparametric methods. In Handbook of Econometrics, Vol. 4, ed. R Engle, D McFadden, pp. 2523–58. Amsterdam: Elsevier
  2. Abadie A, Diamond A, Hainmueller J. 2015. Comparative politics and the synthetic control method. Am. J. Political Sci. 59:495–510
  3. Rosenbaum PR, Rubin DB. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55
  4. S. Bhatnagar. An actor-critic algorithm with function approximation for discounted cost constrained Markov decision processes. Systems & Control Letters, 59(12):760–766, 2010
  5. S. Devlin, L. Yliniemi, D. Kudenko, and K. Tumer. Potential-based difference rewards for multiagent reinforcement learning. In Proceedings of the Thirteenth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2014
  6. S. Bhatnagar, R. Sutton, M. Ghavamzadeh, and M. Lee. Natural actor-critic algorithms. Automatica, 45(11): 2471–2482, 2009
  7. Bengio Y, Schwenk H, Senécal JS, Morin F, Gauvain JL. 2006. Neural probabilistic language models. In Innovations in Machine Learning: Theory and Applications, ed. DE Holmes, pp. 137–86. Berlin: Springer
Frequently Asked QuestionsQ: What is the prediction methodology for CDR^B stock?
A: CDR^B stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Stepwise Regression
Q: Is CDR^B stock a buy or sell?
A: The dominant strategy among neural network is to Buy CDR^B Stock.
Q: Is Cedar Realty Trust Inc. 7.25% Series B Cumulative Redeemable Preferred Stock stock a good investment?
A: The consensus rating for Cedar Realty Trust Inc. 7.25% Series B Cumulative Redeemable Preferred Stock is Buy and is assigned short-term Ba3 & long-term Ba2 estimated rating.
Q: What is the consensus rating of CDR^B stock?
A: The consensus rating for CDR^B is Buy.
Q: What is the prediction period for CDR^B stock?
A: The prediction period for CDR^B is 1 Year



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