KIM^L Kimco Realty Corporation Class L Depositary Shares each of which represents a one-one thousandth fractional interest in a share of 5.125% Class L Cumulative Redeemable Preferred Stock liquidation preference $25000.00 per share

Outlook: Kimco Realty Corporation Class L Depositary Shares each of which represents a one-one thousandth fractional interest in a share of 5.125% Class L Cumulative Redeemable Preferred Stock liquidation preference $25000.00 per share is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 09 Mar 2023 for (n+3 month)
Methodology : Reinforcement Machine Learning (ML)

Abstract

Kimco Realty Corporation Class L Depositary Shares each of which represents a one-one thousandth fractional interest in a share of 5.125% Class L Cumulative Redeemable Preferred Stock liquidation preference $25000.00 per share prediction model is evaluated with Reinforcement Machine Learning (ML) and Sign Test^1,2,3,4 and it is concluded that the KIM^L stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Hold

Key Points

1. Trading Interaction
2. Trading Signals
3. Why do we need predictive models?

KIM^L Target Price Prediction Modeling Methodology

We consider Kimco Realty Corporation Class L Depositary Shares each of which represents a one-one thousandth fractional interest in a share of 5.125% Class L Cumulative Redeemable Preferred Stock liquidation preference $25000.00 per share Decision Process with Reinforcement Machine Learning (ML) where A is the set of discrete actions of KIM^L 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(Sign Test)^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(Reinforcement Machine Learning (ML)) X S(n):→ (n+3 month) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of KIM^L stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

 

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?

KIM^L Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: KIM^L Kimco Realty Corporation Class L Depositary Shares each of which represents a one-one thousandth fractional interest in a share of 5.125% Class L Cumulative Redeemable Preferred Stock liquidation preference $25000.00 per share
Time series to forecast n: 09 Mar 2023 for (n+3 month)

According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Hold

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%

IFRS Reconciliation Adjustments for Kimco Realty Corporation Class L Depositary Shares each of which represents a one-one thousandth fractional interest in a share of 5.125% Class L Cumulative Redeemable Preferred Stock liquidation preference $25000.00 per share

1. A single hedging instrument may be designated as a hedging instrument of more than one type of risk, provided that there is a specific designation of the hedging instrument and of the different risk positions as hedged items. Those hedged items can be in different hedging relationships.
2. Because the hedge accounting model is based on a general notion of offset between gains and losses on the hedging instrument and the hedged item, hedge effectiveness is determined not only by the economic relationship between those items (ie the changes in their underlyings) but also by the effect of credit risk on the value of both the hedging instrument and the hedged item. The effect of credit risk means that even if there is an economic relationship between the hedging instrument and the hedged item, the level of offset might become erratic. This can result from a change in the credit risk of either the hedging instrument or the hedged item that is of such a magnitude that the credit risk dominates the value changes that result from the economic relationship (ie the effect of the changes in the underlyings). A level of magnitude that gives rise to dominance is one that would result in the loss (or gain) from credit risk frustrating the effect of changes in the underlyings on the value of the hedging instrument or the hedged item, even if those changes were significant.
3. At the date of initial application, an entity shall determine whether the treatment in paragraph 5.7.7 would create or enlarge an accounting mismatch in profit or loss on the basis of the facts and circumstances that exist at the date of initial application. This Standard shall be applied retrospectively on the basis of that determination.
4. 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.

*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.

Conclusions

Kimco Realty Corporation Class L Depositary Shares each of which represents a one-one thousandth fractional interest in a share of 5.125% Class L Cumulative Redeemable Preferred Stock liquidation preference $25000.00 per share is assigned short-term Ba1 & long-term Ba1 estimated rating. Kimco Realty Corporation Class L Depositary Shares each of which represents a one-one thousandth fractional interest in a share of 5.125% Class L Cumulative Redeemable Preferred Stock liquidation preference $25000.00 per share prediction model is evaluated with Reinforcement Machine Learning (ML) and Sign Test^1,2,3,4 and it is concluded that the KIM^L stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Hold

KIM^L Kimco Realty Corporation Class L Depositary Shares each of which represents a one-one thousandth fractional interest in a share of 5.125% Class L Cumulative Redeemable Preferred Stock liquidation preference $25000.00 per share Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	C	Baa2
Balance Sheet	C	B3
Leverage Ratios	Caa2	Ba1
Cash Flow	C	B1
Rates of Return and Profitability	Baa2	Ba3

*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?

Prediction Confidence Score

Trust metric by Neural Network: 76 out of 100 with 582 signals.

References

1. Mnih A, Kavukcuoglu K. 2013. Learning word embeddings efficiently with noise-contrastive estimation. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 2265–73. San Diego, CA: Neural Inf. Process. Syst. Found.
2. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, Newey W. 2017. Double/debiased/ Neyman machine learning of treatment effects. Am. Econ. Rev. 107:261–65
3. Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
4. 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.
5. Athey S, Imbens GW. 2017a. The econometrics of randomized experiments. In Handbook of Economic Field Experiments, Vol. 1, ed. E Duflo, A Banerjee, pp. 73–140. Amsterdam: Elsevier
6. N. B ̈auerle and A. Mundt. Dynamic mean-risk optimization in a binomial model. Mathematical Methods of Operations Research, 70(2):219–239, 2009.
7. Swaminathan A, Joachims T. 2015. Batch learning from logged bandit feedback through counterfactual risk minimization. J. Mach. Learn. Res. 16:1731–55

Frequently Asked Questions

Q: What is the prediction methodology for KIM^L stock?
A: KIM^L stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Sign Test
Q: Is KIM^L stock a buy or sell?
A: The dominant strategy among neural network is to Hold KIM^L Stock.
Q: Is Kimco Realty Corporation Class L Depositary Shares each of which represents a one-one thousandth fractional interest in a share of 5.125% Class L Cumulative Redeemable Preferred Stock liquidation preference $25000.00 per share stock a good investment?
A: The consensus rating for Kimco Realty Corporation Class L Depositary Shares each of which represents a one-one thousandth fractional interest in a share of 5.125% Class L Cumulative Redeemable Preferred Stock liquidation preference $25000.00 per share is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of KIM^L stock?
A: The consensus rating for KIM^L is Hold.
Q: What is the prediction period for KIM^L stock?
A: The prediction period for KIM^L is (n+3 month)

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