Outlook: NextEra Energy Inc. Series N Junior Subordinated Debentures due March 1 2079 is assigned short-term Ba3 & long-term Ba3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Hold
Time series to forecast n: for Weeks2
Methodology : Multi-Instance Learning (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.

## Summary

NextEra Energy Inc. Series N Junior Subordinated Debentures due March 1 2079 prediction model is evaluated with Multi-Instance Learning (ML) and Spearman Correlation1,2,3,4 and it is concluded that the NEE^N stock is predictable in the short/long term. Multi-instance learning (MIL) is a machine learning (ML) problem where a dataset consists of multiple instances, and each instance is associated with a single label. The goal of MIL is to learn a model that can predict the label of a new instance based on the labels of the instances that it is similar to. MIL is a challenging problem because the instances in a dataset are not labeled individually. This means that the model cannot simply learn a mapping from the features of an instance to its label. Instead, the model must learn a way to combine the features of multiple instances to predict the label of a new instance. According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Hold

## Key Points

1. How useful are statistical predictions?
2. Stock Forecast Based On a Predictive Algorithm
3. Is it better to buy and sell or hold?

## NEE^N Target Price Prediction Modeling Methodology

We consider NextEra Energy Inc. Series N Junior Subordinated Debentures due March 1 2079 Decision Process with Multi-Instance Learning (ML) where A is the set of discrete actions of NEE^N 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(Spearman Correlation)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Multi-Instance Learning (ML)) X S(n):→ 8 Weeks $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of NEE^N stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

### Multi-Instance Learning (ML)

Multi-instance learning (MIL) is a machine learning (ML) problem where a dataset consists of multiple instances, and each instance is associated with a single label. The goal of MIL is to learn a model that can predict the label of a new instance based on the labels of the instances that it is similar to. MIL is a challenging problem because the instances in a dataset are not labeled individually. This means that the model cannot simply learn a mapping from the features of an instance to its label. Instead, the model must learn a way to combine the features of multiple instances to predict the label of a new instance.

### Spearman Correlation

Spearman correlation is a nonparametric measure of the strength and direction of association between two variables. It is a rank-based correlation, which means that it does not assume that the data is normally distributed. Spearman correlation is calculated by first ranking the data for each variable, and then calculating the Pearson correlation between the ranks.

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?

## NEE^N Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: NEE^N NextEra Energy Inc. Series N Junior Subordinated Debentures due March 1 2079
Time series to forecast: 8 Weeks

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

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 Multi-Instance Learning (ML) based NEE^N Stock Prediction Model

1. If the holder cannot assess the conditions in paragraph B4.1.21 at initial recognition, the tranche must be measured at fair value through profit or loss. If the underlying pool of instruments can change after initial recognition in such a way that the pool may not meet the conditions in paragraphs B4.1.23–B4.1.24, the tranche does not meet the conditions in paragraph B4.1.21 and must be measured at fair value through profit or loss. However, if the underlying pool includes instruments that are collateralised by assets that do not meet the conditions in paragraphs B4.1.23–B4.1.24, the ability to take possession of such assets shall be disregarded for the purposes of applying this paragraph unless the entity acquired the tranche with the intention of controlling the collateral.
2. When defining default for the purposes of determining the risk of a default occurring, an entity shall apply a default definition that is consistent with the definition used for internal credit risk management purposes for the relevant financial instrument and consider qualitative indicators (for example, financial covenants) when appropriate. However, there is a rebuttable presumption that default does not occur later than when a financial asset is 90 days past due unless an entity has reasonable and supportable information to demonstrate that a more lagging default criterion is more appropriate. The definition of default used for these purposes shall be applied consistently to all financial instruments unless information becomes available that demonstrates that another default definition is more appropriate for a particular financial instrument.
3. A layer component that includes a prepayment option is not eligible to be designated as a hedged item in a fair value hedge if the prepayment option's fair value is affected by changes in the hedged risk, unless the designated layer includes the effect of the related prepayment option when determining the change in the fair value of the hedged item.
4. The risk of a default occurring on financial instruments that have comparable credit risk is higher the longer the expected life of the instrument; for example, the risk of a default occurring on an AAA-rated bond with an expected life of 10 years is higher than that on an AAA-rated bond with an expected life of five years.

*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.

### NEE^N NextEra Energy Inc. Series N Junior Subordinated Debentures due March 1 2079 Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba3Ba3
Income StatementBaa2Baa2
Balance SheetB1Caa2
Leverage RatiosBaa2B1
Cash FlowCaa2Baa2
Rates of Return and ProfitabilityBaa2Baa2

*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

NextEra Energy Inc. Series N Junior Subordinated Debentures due March 1 2079 is assigned short-term Ba3 & long-term Ba3 estimated rating. NextEra Energy Inc. Series N Junior Subordinated Debentures due March 1 2079 prediction model is evaluated with Multi-Instance Learning (ML) and Spearman Correlation1,2,3,4 and it is concluded that the NEE^N stock is predictable in the short/long term. According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Hold

### Prediction Confidence Score

Trust metric by Neural Network: 91 out of 100 with 608 signals.

## References

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2. Harris ZS. 1954. Distributional structure. Word 10:146–62
3. Bell RM, Koren Y. 2007. Lessons from the Netflix prize challenge. ACM SIGKDD Explor. Newsl. 9:75–79
4. A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016
5. Athey S, Mobius MM, Pál J. 2017c. The impact of aggregators on internet news consumption. Unpublished manuscript, Grad. School Bus., Stanford Univ., Stanford, CA
6. Athey S, Bayati M, Doudchenko N, Imbens G, Khosravi K. 2017a. Matrix completion methods for causal panel data models. arXiv:1710.10251 [math.ST]
7. Wager S, Athey S. 2017. Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113:1228–42
Frequently Asked QuestionsQ: What is the prediction methodology for NEE^N stock?
A: NEE^N stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Spearman Correlation
Q: Is NEE^N stock a buy or sell?
A: The dominant strategy among neural network is to Hold NEE^N Stock.
Q: Is NextEra Energy Inc. Series N Junior Subordinated Debentures due March 1 2079 stock a good investment?
A: The consensus rating for NextEra Energy Inc. Series N Junior Subordinated Debentures due March 1 2079 is Hold and is assigned short-term Ba3 & long-term Ba3 estimated rating.
Q: What is the consensus rating of NEE^N stock?
A: The consensus rating for NEE^N is Hold.
Q: What is the prediction period for NEE^N stock?
A: The prediction period for NEE^N is 8 Weeks

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