The classical linear multi-factor stock selection model is widely used for long-term stock price trend prediction. However, the stock market is chaotic, complex, and dynamic, for which reasons the linear model assumption may be unreasonable, and it is more meaningful to construct a better-integrated stock selection model based on different feature selection and nonlinear stock price trend prediction methods. We evaluate Schneider Electric prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Multiple Regression1,2,3,4 and conclude that the SU.PA 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 Buy SU.PA stock.

Keywords: SU.PA, Schneider Electric, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

2. Can we predict stock market using machine learning?
3. How useful are statistical predictions?

## SU.PA Target Price Prediction Modeling Methodology

The prediction of stock price performance is a difficult and complex problem. Multivariate analytical techniques using both quantitative and qualitative variables have repeatedly been used to help form the basis of investor stock price expectations and, hence, influence investment decision making. However, the performance of multivariate analytical techniques is often less than conclusive and needs to be improved to more accurately forecast stock price performance. A neural network method has demonstrated its capability of addressing complex problems. We consider Schneider Electric Stock Decision Process with Multiple Regression where A is the set of discrete actions of SU.PA 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(Multiple 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 (News Feed Sentiment Analysis)) X S(n):→ (n+3 month) $∑ i = 1 n a i$

n:Time series to forecast

p:Price signals of SU.PA 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?

## SU.PA Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: SU.PA Schneider Electric
Time series to forecast n: 19 Sep 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Buy SU.PA 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

Schneider Electric assigned short-term Ba2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Multiple Regression1,2,3,4 and conclude that the SU.PA 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 Buy SU.PA stock.

### Financial State Forecast for SU.PA Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba2Ba3
Operational Risk 8160
Market Risk8763
Technical Analysis3553
Fundamental Analysis5574
Risk Unsystematic8359

### Prediction Confidence Score

Trust metric by Neural Network: 74 out of 100 with 825 signals.

## References

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2. White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
3. V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, pages 1928–1937, 2016
4. Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
5. Breiman L, Friedman J, Stone CJ, Olshen RA. 1984. Classification and Regression Trees. Boca Raton, FL: CRC Press
6. 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
7. L. Busoniu, R. Babuska, and B. D. Schutter. A comprehensive survey of multiagent reinforcement learning. IEEE Transactions of Systems, Man, and Cybernetics Part C: Applications and Reviews, 38(2), 2008.
Frequently Asked QuestionsQ: What is the prediction methodology for SU.PA stock?
A: SU.PA stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Multiple Regression
Q: Is SU.PA stock a buy or sell?
A: The dominant strategy among neural network is to Buy SU.PA Stock.
Q: Is Schneider Electric stock a good investment?
A: The consensus rating for Schneider Electric is Buy and assigned short-term Ba2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of SU.PA stock?
A: The consensus rating for SU.PA is Buy.
Q: What is the prediction period for SU.PA stock?
A: The prediction period for SU.PA is (n+3 month)