The aim of this study is to evaluate the effectiveness of using external indicators, such as commodity prices and currency exchange rates, in predicting movements. The performance of each technique is evaluated using different domain specific metrics. A comprehensive evaluation procedure is described, involving the use of trading simulations to assess the practical value of predictive models, and comparison with simple benchmarks that respond to underlying market growth.** We evaluate American Electric Power prediction models with Modular Neural Network (Market Direction Analysis) and Sign Test ^{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

- Buy, Sell and Hold Signals
- What are main components of Markov decision process?
- What are main components of Markov decision process?

## AEP Target Price Prediction Modeling Methodology

Complex networks in stock market and stock price volatility pattern prediction are the important issues in stock price research. Previous studies have used historical information regarding a single stock to predict the future trend of the stock's price, seldom considering comovement among stocks in the same market. In this study, in order to extract the information about relation stocks for prediction, we try to combine the complex network method with machine learning to predict stock price patterns. We consider American Electric Power Stock Decision Process with Sign Test 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(Sign Test)

^{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(Modular Neural Network (Market Direction Analysis)) X S(n):→ (n+4 weeks) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

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: 08 Oct 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 Ba2 & long-term B3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Sign Test ^{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* | Ba2 | B3 |

Operational Risk | 74 | 42 |

Market Risk | 66 | 43 |

Technical Analysis | 76 | 60 |

Fundamental Analysis | 44 | 41 |

Risk Unsystematic | 83 | 31 |

### Prediction Confidence Score

## References

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- Abadie A, Diamond A, Hainmueller J. 2010. Synthetic control methods for comparative case studies: estimat- ing the effect of California's tobacco control program. J. Am. Stat. Assoc. 105:493–505
- E. van der Pol and F. A. Oliehoek. Coordinated deep reinforcement learners for traffic light control. NIPS Workshop on Learning, Inference and Control of Multi-Agent Systems, 2016.
- J. Filar, D. Krass, and K. Ross. Percentile performance criteria for limiting average Markov decision pro- cesses. IEEE Transaction of Automatic Control, 40(1):2–10, 1995.
- Efron B, Hastie T, Johnstone I, Tibshirani R. 2004. Least angle regression. Ann. Stat. 32:407–99
- Cortes C, Vapnik V. 1995. Support-vector networks. Mach. Learn. 20:273–97
- Wager S, Athey S. 2017. Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113:1228–42

## Frequently Asked Questions

Q: What is the prediction methodology for AEP stock?A: AEP stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Sign Test

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 Ba2 & long-term B3 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)

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