NEE^Q NextEra Energy Inc. 6.219% Corporate Units

Outlook: NextEra Energy Inc. 6.219% Corporate Units assigned short-term Baa2 & long-term Baa2 forecasted stock rating.
Dominant Strategy : Hold
Time series to forecast n: 17 Dec 2022 for (n+3 month)
Methodology : Modular Neural Network (DNN Layer)

Abstract

In modern financial market, the most crucial problem is to find essential approach to outline and visualizing the predictions in stock-markets to be made by individuals in order to attain maximum profit by investments. The stock market is a transformative, non-straight dynamical and complex system. Long term investment is one of the major investment decisions. Though, evaluating shares and calculating elementary values for companies for long term investment is difficult. In this paper we are going to present comparison of machine learning aided algorithms to evaluate the stock prices in the future to analyze market behaviour.(Qiu, M. and Song, Y., 2016. Predicting the direction of stock market index movement using an optimized artificial neural network model. PloS one, 11(5), p.e0155133.) We evaluate NextEra Energy Inc. 6.219% Corporate Units prediction models with Modular Neural Network (DNN Layer) and Pearson Correlation^1,2,3,4 and conclude that the NEE^Q 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. Game Theory
2. What is statistical models in machine learning?
3. What is prediction in deep learning?

NEE^Q Target Price Prediction Modeling Methodology

We consider NextEra Energy Inc. 6.219% Corporate Units Decision Process with Modular Neural Network (DNN Layer) where A is the set of discrete actions of NEE^Q 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 (DNN Layer)) X S(n):→ (n+3 month) $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of NEE^Q 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?

NEE^Q Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: NEE^Q NextEra Energy Inc. 6.219% Corporate Units
Time series to forecast n: 17 Dec 2022 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%

Adjusted IFRS* Prediction Methods for NextEra Energy Inc. 6.219% Corporate Units

1. It would not be acceptable to designate only some of the financial assets and financial liabilities giving rise to the inconsistency as at fair value through profit or loss if to do so would not eliminate or significantly reduce the inconsistency and would therefore not result in more relevant information. However, it would be acceptable to designate only some of a number of similar financial assets or similar financial liabilities if doing so achieves a significant reduction (and possibly a greater reduction than other allowable designations) in the inconsistency. For example, assume an entity has a number of similar financial liabilities that sum to CU100 and a number of similar financial assets that sum to CU50 but are measured on a different basis. The entity may significantly reduce the measurement inconsistency by designating at initial recognition all of the assets but only some of the liabilities (for example, individual liabilities with a combined total of CU45) as at fair value through profit or loss. However, because designation as at fair value through profit or loss can be applied only to the whole of a financial instrument, the entity in this example must designate one or more liabilities in their entirety. It could not designate either a component of a liability (eg changes in value attributable to only one risk, such as changes in a benchmark interest rate) or a proportion (ie percentage) of a liability.
2. One of the defining characteristics of a derivative is that it has an initial net investment that is smaller than would be required for other types of contracts that would be expected to have a similar response to changes in market factors. An option contract meets that definition because the premium is less than the investment that would be required to obtain the underlying financial instrument to which the option is linked. A currency swap that requires an initial exchange of different currencies of equal fair values meets the definition because it has a zero initial net investment.
3. For example, an entity may use this condition to designate financial liabilities as at fair value through profit or loss if it meets the principle in paragraph 4.2.2(b) and the entity has financial assets and financial liabilities that share one or more risks and those risks are managed and evaluated on a fair value basis in accordance with a documented policy of asset and liability management. An example could be an entity that has issued 'structured products' containing multiple embedded derivatives and manages the resulting risks on a fair value basis using a mix of derivative and non-derivative financial instruments
4. Amounts presented in other comprehensive income shall not be subsequently transferred to profit or loss. However, the entity may transfer the cumulative gain or loss within equity.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

Conclusions

NextEra Energy Inc. 6.219% Corporate Units assigned short-term Baa2 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (DNN Layer) with Pearson Correlation^1,2,3,4 and conclude that the NEE^Q 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

Financial State Forecast for NEE^Q NextEra Energy Inc. 6.219% Corporate Units Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Baa2	Baa2
Operational Risk	78	58
Market Risk	60	82
Technical Analysis	73	88
Fundamental Analysis	89	85
Risk Unsystematic	78	85

Prediction Confidence Score

Trust metric by Neural Network: 77 out of 100 with 673 signals.

References

1. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Is DOW Stock Expected to Go Up?(Stock Forecast). AC Investment Research Journal, 101(3).
2. V. Borkar. Q-learning for risk-sensitive control. Mathematics of Operations Research, 27:294–311, 2002.
3. M. Ono, M. Pavone, Y. Kuwata, and J. Balaram. Chance-constrained dynamic programming with application to risk-aware robotic space exploration. Autonomous Robots, 39(4):555–571, 2015
4. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., How do you decide buy or sell a stock?(SAIC Stock Forecast). AC Investment Research Journal, 101(3).
5. M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Ma- chine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994, pages 157–163, 1994
6. Bell RM, Koren Y. 2007. Lessons from the Netflix prize challenge. ACM SIGKDD Explor. Newsl. 9:75–79
7. Ruiz FJ, Athey S, Blei DM. 2017. SHOPPER: a probabilistic model of consumer choice with substitutes and complements. arXiv:1711.03560 [stat.ML]

Frequently Asked Questions

Q: What is the prediction methodology for NEE^Q stock?
A: NEE^Q stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Pearson Correlation
Q: Is NEE^Q stock a buy or sell?
A: The dominant strategy among neural network is to Hold NEE^Q Stock.
Q: Is NextEra Energy Inc. 6.219% Corporate Units stock a good investment?
A: The consensus rating for NextEra Energy Inc. 6.219% Corporate Units is Hold and assigned short-term Baa2 & long-term Baa2 forecasted stock rating.
Q: What is the consensus rating of NEE^Q stock?
A: The consensus rating for NEE^Q is Hold.
Q: What is the prediction period for NEE^Q stock?
A: The prediction period for NEE^Q is (n+3 month)

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