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 evaluate Euro Stoxx 50 Index prediction models with Modular Neural Network (CNN Layer) and Beta1,2,3,4 and conclude that the Euro Stoxx 50 Index stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold Euro Stoxx 50 Index stock.

Keywords: Euro Stoxx 50 Index, Euro Stoxx 50 Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Market Outlook
2. Reaction Function
3. How do predictive algorithms actually work?

## Euro Stoxx 50 Index Target Price Prediction Modeling Methodology

Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators. We consider Euro Stoxx 50 Index Stock Decision Process with Beta where A is the set of discrete actions of Euro Stoxx 50 Index 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= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (CNN Layer)) X S(n):→ (n+6 month) $∑ i = 1 n r i$

n:Time series to forecast

p:Price signals of Euro Stoxx 50 Index 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?

## Euro Stoxx 50 Index Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: Euro Stoxx 50 Index Euro Stoxx 50 Index
Time series to forecast n: 23 Oct 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold Euro Stoxx 50 Index 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

Euro Stoxx 50 Index assigned short-term Ba2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (CNN Layer) with Beta1,2,3,4 and conclude that the Euro Stoxx 50 Index stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold Euro Stoxx 50 Index stock.

### Financial State Forecast for Euro Stoxx 50 Index Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba2Ba3
Operational Risk 7975
Market Risk7261
Technical Analysis5469
Fundamental Analysis7378
Risk Unsystematic6747

### Prediction Confidence Score

Trust metric by Neural Network: 84 out of 100 with 594 signals.

## References

1. Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
2. Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]
3. Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
4. Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press
5. J. Peters, S. Vijayakumar, and S. Schaal. Natural actor-critic. In Proceedings of the Sixteenth European Conference on Machine Learning, pages 280–291, 2005.
6. S. Proper and K. Tumer. Modeling difference rewards for multiagent learning (extended abstract). In Proceedings of the Eleventh International Joint Conference on Autonomous Agents and Multiagent Systems, Valencia, Spain, June 2012
7. G. Shani, R. Brafman, and D. Heckerman. An MDP-based recommender system. In Proceedings of the Eigh- teenth conference on Uncertainty in artificial intelligence, pages 453–460. Morgan Kaufmann Publishers Inc., 2002
Frequently Asked QuestionsQ: What is the prediction methodology for Euro Stoxx 50 Index stock?
A: Euro Stoxx 50 Index stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Beta
Q: Is Euro Stoxx 50 Index stock a buy or sell?
A: The dominant strategy among neural network is to Hold Euro Stoxx 50 Index Stock.
Q: Is Euro Stoxx 50 Index stock a good investment?
A: The consensus rating for Euro Stoxx 50 Index is Hold and assigned short-term Ba2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of Euro Stoxx 50 Index stock?
A: The consensus rating for Euro Stoxx 50 Index is Hold.
Q: What is the prediction period for Euro Stoxx 50 Index stock?
A: The prediction period for Euro Stoxx 50 Index is (n+6 month)