Prediction of the trend of the stock market is very crucial. If someone has robust forecasting tools, then he/she will increase the return on investment and can get rich easily and quickly. Because there are a lot of factors that can influence the stock market, the stock forecasting problem has always been very complicated. Support Vector Regression is a tool from machine learning that can build a regression model on the historical time series data in the purpose of predicting the future trend of the stock price. We evaluate ACTIVE ENERGY GROUP PLC prediction models with Modular Neural Network (CNN Layer) and Stepwise Regression1,2,3,4 and conclude that the LON:AEG stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Sell LON:AEG stock.

Keywords: LON:AEG, ACTIVE ENERGY GROUP PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Decision Making
2. How do you pick a stock?
3. What is a prediction confidence? ## LON:AEG Target Price Prediction Modeling Methodology

Fuzzy rough theory can describe real-world situations in a mathematically effective and interpretable way, while evolutionary neural networks can be utilized to solve complex problems. Combining them with these complementary capabilities may lead to evolutionary fuzzy rough neural network with the interpretability and prediction capability. In this article, we propose modifications to the existing models of fuzzy rough neural network and then develop a powerful evolutionary framework for fuzzy rough neural networks by inheriting the merits of both the aforementioned systems. We consider ACTIVE ENERGY GROUP PLC Stock Decision Process with Stepwise Regression where A is the set of discrete actions of LON:AEG 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(Stepwise 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 (CNN Layer)) X S(n):→ (n+16 weeks) $∑ i = 1 n a i$

n:Time series to forecast

p:Price signals of LON:AEG 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?

## LON:AEG Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: LON:AEG ACTIVE ENERGY GROUP PLC
Time series to forecast n: 19 Sep 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Sell LON:AEG 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

ACTIVE ENERGY GROUP PLC assigned short-term B3 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (CNN Layer) with Stepwise Regression1,2,3,4 and conclude that the LON:AEG stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Sell LON:AEG stock.

### Financial State Forecast for LON:AEG Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B3Ba1
Operational Risk 6280
Market Risk3390
Technical Analysis5850
Fundamental Analysis4659
Risk Unsystematic3168

### Prediction Confidence Score

Trust metric by Neural Network: 90 out of 100 with 617 signals.

## References

1. Hill JL. 2011. Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20:217–40
2. Andrews, D. W. K. W. Ploberger (1994), "Optimal tests when a nuisance parameter is present only under the alternative," Econometrica, 62, 1383–1414.
3. S. J. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs, NJ, 3nd edition, 2010
4. White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
5. G. Konidaris, S. Osentoski, and P. Thomas. Value function approximation in reinforcement learning using the Fourier basis. In AAAI, 2011
6. Canova, F. B. E. Hansen (1995), "Are seasonal patterns constant over time? A test for seasonal stability," Journal of Business and Economic Statistics, 13, 237–252.
7. D. Bertsekas. Nonlinear programming. Athena Scientific, 1999.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:AEG stock?
A: LON:AEG stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Stepwise Regression
Q: Is LON:AEG stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:AEG Stock.
Q: Is ACTIVE ENERGY GROUP PLC stock a good investment?
A: The consensus rating for ACTIVE ENERGY GROUP PLC is Sell and assigned short-term B3 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of LON:AEG stock?
A: The consensus rating for LON:AEG is Sell.
Q: What is the prediction period for LON:AEG stock?
A: The prediction period for LON:AEG is (n+16 weeks)

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