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 evaluate FirstEnergy prediction models with Modular Neural Network (Emotional Trigger/Responses Analysis) and Sign Test1,2,3,4 and conclude that the FE stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold FE stock.

Keywords: FE, FirstEnergy, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Why do we need predictive models?
2. Is now good time to invest?
3. Market Outlook

## FE Target Price Prediction Modeling Methodology

The presented paper modeled and predicted stock returns using LSTM. The historical data of stock market were transformed into 30-days-long sequences with 10 learning features and 7-day earning rate labeling. The model was fitted by training on 1200000 sequences and tested using the other 350000 sequences. We consider FirstEnergy Stock Decision Process with Sign Test where A is the set of discrete actions of FE 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= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Emotional Trigger/Responses Analysis)) X S(n):→ (n+3 month) $∑ i = 1 n a i$

n:Time series to forecast

p:Price signals of FE 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?

## FE Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: FE FirstEnergy
Time series to forecast n: 24 Oct 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold FE 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

FirstEnergy assigned short-term B1 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) with Sign Test1,2,3,4 and conclude that the FE stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold FE stock.

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

Rating Short-Term Long-Term Senior
Outlook*B1Ba2
Operational Risk 5952
Market Risk6276
Technical Analysis6873
Fundamental Analysis7574
Risk Unsystematic4663

### Prediction Confidence Score

Trust metric by Neural Network: 84 out of 100 with 552 signals.

## References

1. Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating errors. Nature 323:533–36
2. Chen X. 2007. Large sample sieve estimation of semi-nonparametric models. In Handbook of Econometrics, Vol. 6B, ed. JJ Heckman, EE Learner, pp. 5549–632. Amsterdam: Elsevier
3. Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
4. Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60
5. Harris ZS. 1954. Distributional structure. Word 10:146–62
6. S. Bhatnagar. An actor-critic algorithm with function approximation for discounted cost constrained Markov decision processes. Systems & Control Letters, 59(12):760–766, 2010
7. Clements, M. P. D. F. Hendry (1997), "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, 13, 341–355.
Frequently Asked QuestionsQ: What is the prediction methodology for FE stock?
A: FE stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Sign Test
Q: Is FE stock a buy or sell?
A: The dominant strategy among neural network is to Hold FE Stock.
Q: Is FirstEnergy stock a good investment?
A: The consensus rating for FirstEnergy is Hold and assigned short-term B1 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of FE stock?
A: The consensus rating for FE is Hold.
Q: What is the prediction period for FE stock?
A: The prediction period for FE is (n+3 month)