Outlook: NRG Energy Inc. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 04 Feb 2023 for (n+1 year)
Methodology : Modular Neural Network (Financial Sentiment Analysis)

## Abstract

NRG Energy Inc. Common Stock prediction model is evaluated with Modular Neural Network (Financial Sentiment Analysis) and Multiple Regression1,2,3,4 and it is concluded that the NRG stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Hold

## Key Points

1. What is the use of Markov decision process?
2. Can stock prices be predicted?
3. What is the best way to predict stock prices?

## NRG Target Price Prediction Modeling Methodology

We consider NRG Energy Inc. Common Stock Decision Process with Modular Neural Network (Financial Sentiment Analysis) where A is the set of discrete actions of NRG 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(Multiple Regression)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(Modular Neural Network (Financial Sentiment Analysis)) X S(n):→ (n+1 year) $\stackrel{\to }{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of NRG 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?

## NRG Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: NRG NRG Energy Inc. Common Stock
Time series to forecast n: 04 Feb 2023 for (n+1 year)

According to price forecasts for (n+1 year) 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 NRG Energy Inc. Common Stock

1. In addition to those hedging relationships specified in paragraph 6.9.1, an entity shall apply the requirements in paragraphs 6.9.11 and 6.9.12 to new hedging relationships in which an alternative benchmark rate is designated as a non-contractually specified risk component (see paragraphs 6.3.7(a) and B6.3.8) when, because of interest rate benchmark reform, that risk component is not separately identifiable at the date it is designated.
2. Interest Rate Benchmark Reform, which amended IFRS 9, IAS 39 and IFRS 7, issued in September 2019, added Section 6.8 and amended paragraph 7.2.26. An entity shall apply these amendments for annual periods beginning on or after 1 January 2020. Earlier application is permitted. If an entity applies these amendments for an earlier period, it shall disclose that fact.
3. When designating risk components as hedged items, an entity considers whether the risk components are explicitly specified in a contract (contractually specified risk components) or whether they are implicit in the fair value or the cash flows of an item of which they are a part (noncontractually specified risk components). Non-contractually specified risk components can relate to items that are not a contract (for example, forecast transactions) or contracts that do not explicitly specify the component (for example, a firm commitment that includes only one single price instead of a pricing formula that references different underlyings)
4. For loan commitments, an entity considers changes in the risk of a default occurring on the loan to which a loan commitment relates. For financial guarantee contracts, an entity considers the changes in the risk that the specified debtor will default on the 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

NRG Energy Inc. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating. NRG Energy Inc. Common Stock prediction model is evaluated with Modular Neural Network (Financial Sentiment Analysis) and Multiple Regression1,2,3,4 and it is concluded that the NRG stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Hold

### NRG NRG Energy Inc. Common Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementCaa2Ba1
Balance SheetB3Caa2
Leverage RatiosB2C
Cash FlowB3Baa2
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?

### Prediction Confidence Score

Trust metric by Neural Network: 75 out of 100 with 564 signals.

## References

1. S. J. Russell and A. Zimdars. Q-decomposition for reinforcement learning agents. In Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), August 21-24, 2003, Washington, DC, USA, pages 656–663, 2003.
2. Bell RM, Koren Y. 2007. Lessons from the Netflix prize challenge. ACM SIGKDD Explor. Newsl. 9:75–79
3. Zeileis A, Hothorn T, Hornik K. 2008. Model-based recursive partitioning. J. Comput. Graph. Stat. 17:492–514 Zhou Z, Athey S, Wager S. 2018. Offline multi-action policy learning: generalization and optimization. arXiv:1810.04778 [stat.ML]
4. M. J. Hausknecht and P. Stone. Deep recurrent Q-learning for partially observable MDPs. CoRR, abs/1507.06527, 2015
5. Arora S, Li Y, Liang Y, Ma T. 2016. RAND-WALK: a latent variable model approach to word embeddings. Trans. Assoc. Comput. Linguist. 4:385–99
6. A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015
7. Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
Frequently Asked QuestionsQ: What is the prediction methodology for NRG stock?
A: NRG stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Multiple Regression
Q: Is NRG stock a buy or sell?
A: The dominant strategy among neural network is to Hold NRG Stock.
Q: Is NRG Energy Inc. Common Stock stock a good investment?
A: The consensus rating for NRG Energy Inc. Common Stock is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of NRG stock?
A: The consensus rating for NRG is Hold.
Q: What is the prediction period for NRG stock?
A: The prediction period for NRG is (n+1 year)