Recently, there has been a surge of interest in the use of machine learning to help aid in the accurate predictions of financial markets. Despite the exciting advances in this cross-section of finance and AI, many of the current approaches are limited to using technical analysis to capture historical trends of each stock price and thus limited to certain experimental setups to obtain good prediction results. On the other hand, professional investors additionally use their rich knowledge of inter-market and inter-company relations to map the connectivity of companies and events, and use this map to make better market predictions. For instance, they would predict the movement of a certain company's stock price based not only on its former stock price trends but also on the performance of its suppliers or customers, the overall industry, macroeconomic factors and trade policies. This paper investigates the effectiveness of work at the intersection of market predictions and graph neural networks, which hold the potential to mimic the ways in which investors make decisions by incorporating company knowledge graphs directly into the predictive model.** We evaluate NRG Energy prediction models with Multi-Task Learning (ML) and Linear Regression ^{1,2,3,4} and conclude that the NRG stock is predictable in the short/long term. **

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold NRG stock.**

**NRG, NRG Energy, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Can statistics predict the future?
- Can statistics predict the future?
- Operational Risk

## NRG Target Price Prediction Modeling Methodology

Stock market forecasting is considered to be a challenging topic among time series forecasting. This study proposes a novel two-stage ensemble machine learning model named SVR-ENANFIS for stock price prediction by combining features of support vector regression (SVR) and ensemble adaptive neuro fuzzy inference system (ENANFIS). We consider NRG Energy Stock Decision Process with Linear Regression 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(Linear Regression)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Multi-Task Learning (ML)) X S(n):→ (n+6 month) $\sum _{i=1}^{n}\left({s}_{i}\right)$

n:Time series to forecast

p:Price signals of NRG stock

j:Nash equilibria

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+6 month)

**Sample Set:**Neural Network

**Stock/Index:**NRG NRG Energy

**Time series to forecast n: 19 Oct 2022**for (n+6 month)

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold NRG stock.**

**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 (Yellow to Green): *Technical Analysis%**

## Conclusions

NRG Energy assigned short-term Ba3 & long-term B1 forecasted stock rating.** We evaluate the prediction models Multi-Task Learning (ML) with Linear Regression ^{1,2,3,4} and conclude that the NRG stock is predictable in the short/long term.**

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold NRG stock.**

### Financial State Forecast for NRG Stock Options & Futures

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | Ba3 | B1 |

Operational Risk | 77 | 59 |

Market Risk | 76 | 53 |

Technical Analysis | 38 | 39 |

Fundamental Analysis | 70 | 86 |

Risk Unsystematic | 74 | 61 |

### Prediction Confidence Score

## References

- Meinshausen N. 2007. Relaxed lasso. Comput. Stat. Data Anal. 52:374–93
- V. Borkar. Stochastic approximation: a dynamical systems viewpoint. Cambridge University Press, 2008
- Bessler, D. A. S. W. Fuller (1993), "Cointegration between U.S. wheat markets," Journal of Regional Science, 33, 481–501.
- Friedman JH. 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38:367–78
- Scott SL. 2010. A modern Bayesian look at the multi-armed bandit. Appl. Stoch. Models Bus. Ind. 26:639–58
- Bessler, D. A. R. A. Babula, (1987), "Forecasting wheat exports: Do exchange rates matter?" Journal of Business and Economic Statistics, 5, 397–406.
- Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]

## Frequently Asked Questions

Q: What is the prediction methodology for NRG stock?A: NRG stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Linear 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 stock a good investment?

A: The consensus rating for NRG Energy is Hold and assigned short-term Ba3 & long-term B1 forecasted stock 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+6 month)

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