AES Corporation assigned short-term B2 & long-term Baa2 forecasted stock rating.

Accurate prediction of stock market returns is a very challenging task due to volatile and non-linear nature of the financial stock markets. With the introduction of artificial intelligence and increased computational capabilities, programmed methods of prediction have proved to be more efficient in predicting stock prices. We evaluate AES Corporation prediction models with Modular Neural Network (Market Volatility Analysis) and Beta^1,2,3,4 and conclude that the AES 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 AES stock.

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

Key Points

1. What is a prediction confidence?
2. Dominated Move
3. How accurate is machine learning in stock market?

AES Target Price Prediction Modeling Methodology

Stock market is considered chaotic, complex, volatile and dynamic. Undoubtedly, its prediction is one of the most challenging tasks in time series forecasting. Moreover existing Artificial Neural Network (ANN) approaches fail to provide encouraging results. Meanwhile advances in machine learning have presented favourable results for speech recognition, image classification and language processing. We consider AES Corporation Stock Decision Process with Beta where A is the set of discrete actions of AES 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(Beta)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (Market Volatility Analysis)) X S(n):→ (n+3 month) $\begin{array}{l}\int e^x\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of AES 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?

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

Sample Set: Neural Network
Stock/Index: AES AES Corporation
Time series to forecast n: 25 Oct 2022 for (n+3 month)

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

AES Corporation assigned short-term B2 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Beta^1,2,3,4 and conclude that the AES 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 AES stock.

Financial State Forecast for AES Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B2	Baa2
Operational Risk	47	82
Market Risk	40	84
Technical Analysis	34	69
Fundamental Analysis	57	55
Risk Unsystematic	84	81

Prediction Confidence Score

Trust metric by Neural Network: 79 out of 100 with 532 signals.

References

1. V. Borkar and R. Jain. Risk-constrained Markov decision processes. IEEE Transaction on Automatic Control, 2014
2. Bengio Y, Ducharme R, Vincent P, Janvin C. 2003. A neural probabilistic language model. J. Mach. Learn. Res. 3:1137–55
3. Akgiray, V. (1989), "Conditional heteroscedasticity in time series of stock returns: Evidence and forecasts," Journal of Business, 62, 55–80.
4. Wan M, Wang D, Goldman M, Taddy M, Rao J, et al. 2017. Modeling consumer preferences and price sensitiv- ities from large-scale grocery shopping transaction logs. In Proceedings of the 26th International Conference on the World Wide Web, pp. 1103–12. New York: ACM
5. M. J. Hausknecht and P. Stone. Deep recurrent Q-learning for partially observable MDPs. CoRR, abs/1507.06527, 2015
6. 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.
7. V. Borkar and R. Jain. Risk-constrained Markov decision processes. IEEE Transaction on Automatic Control, 2014

Frequently Asked Questions

Q: What is the prediction methodology for AES stock?
A: AES stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Beta
Q: Is AES stock a buy or sell?
A: The dominant strategy among neural network is to Hold AES Stock.
Q: Is AES Corporation stock a good investment?
A: The consensus rating for AES Corporation is Hold and assigned short-term B2 & long-term Baa2 forecasted stock rating.
Q: What is the consensus rating of AES stock?
A: The consensus rating for AES is Hold.
Q: What is the prediction period for AES stock?
A: The prediction period for AES is (n+3 month)

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