## Abstract

**We evaluate American Electric Power prediction models with Statistical Inference (ML) and Polynomial Regression ^{1,2,3,4} and conclude that the AEP stock is predictable in the short/long term. **

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold AEP stock.**

**AEP, American Electric Power, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- How do you know when a stock will go up or down?
- Trust metric by Neural Network
- What is neural prediction?

## AEP Target Price Prediction Modeling Methodology

We consider American Electric Power Stock Decision Process with Polynomial Regression where A is the set of discrete actions of AEP 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(Polynomial 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(Statistical Inference (ML)) X S(n):→ (n+4 weeks) $\sum _{i=1}^{n}\left({s}_{i}\right)$

n:Time series to forecast

p:Price signals of AEP 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?

## AEP Stock Forecast (Buy or Sell) for (n+4 weeks)

**Sample Set:**Neural Network

**Stock/Index:**AEP American Electric Power

**Time series to forecast n: 02 Sep 2022**for (n+4 weeks)

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold AEP 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

American Electric Power assigned short-term B2 & long-term Baa2 forecasted stock rating.** We evaluate the prediction models Statistical Inference (ML) with Polynomial Regression ^{1,2,3,4} and conclude that the AEP stock is predictable in the short/long term.**

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold AEP stock.**

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

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

Outlook* | B2 | Baa2 |

Operational Risk | 40 | 68 |

Market Risk | 49 | 84 |

Technical Analysis | 64 | 87 |

Fundamental Analysis | 72 | 63 |

Risk Unsystematic | 54 | 65 |

### Prediction Confidence Score

## References

- 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
- J. Baxter and P. Bartlett. Infinite-horizon policy-gradient estimation. Journal of Artificial Intelligence Re- search, 15:319–350, 2001.
- Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, Vol. 25, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 1097–105. San Diego, CA: Neural Inf. Process. Syst. Found.
- V. Mnih, K. Kavukcuoglu, D. Silver, A. Rusu, J. Veness, M. Bellemare, A. Graves, M. Riedmiller, A. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 02 2015.
- Z. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot, and N. de Freitas. Dueling network architectures for deep reinforcement learning. In Proceedings of the International Conference on Machine Learning (ICML), pages 1995–2003, 2016.
- Harris ZS. 1954. Distributional structure. Word 10:146–62
- R. Rockafellar and S. Uryasev. Conditional value-at-risk for general loss distributions. Journal of Banking and Finance, 26(7):1443 – 1471, 2002

## Frequently Asked Questions

Q: What is the prediction methodology for AEP stock?A: AEP stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Polynomial Regression

Q: Is AEP stock a buy or sell?

A: The dominant strategy among neural network is to Hold AEP Stock.

Q: Is American Electric Power stock a good investment?

A: The consensus rating for American Electric Power is Hold and assigned short-term B2 & long-term Baa2 forecasted stock rating.

Q: What is the consensus rating of AEP stock?

A: The consensus rating for AEP is Hold.

Q: What is the prediction period for AEP stock?

A: The prediction period for AEP is (n+4 weeks)

- Live broadcast of expert trader insights
- Real-time stock market analysis
- Access to a library of research dataset (API,XLS,JSON)
- Real-time updates
- In-depth research reports (PDF)