Development of linguistic technologies and penetration of social media provide powerful possibilities to investigate users' moods and psychological states of people. In this paper we discussed possibility to improve accuracy of stock market indicators predictions by using data about psychological states of Twitter users. For analysis of psychological states we used lexicon-based approach. We evaluate Euro Stoxx 50 Index prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Polynomial Regression1,2,3,4 and conclude that the Euro Stoxx 50 Index stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Sell 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. What is the use of Markov decision process?
2. How accurate is machine learning in stock market?
3. What are main components of Markov decision process?

## Euro Stoxx 50 Index Target Price Prediction Modeling Methodology

Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on "Big Data," it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. We consider Euro Stoxx 50 Index Stock Decision Process with Polynomial Regression 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(Polynomial 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+4 weeks) $∑ i = 1 n a i$

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+4 weeks)

Sample Set: Neural Network
Stock/Index: Euro Stoxx 50 Index Euro Stoxx 50 Index
Time series to forecast n: 23 Sep 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Sell 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 B2 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Polynomial Regression1,2,3,4 and conclude that the Euro Stoxx 50 Index stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Sell Euro Stoxx 50 Index stock.

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

Rating Short-Term Long-Term Senior
Outlook*B2Ba1
Operational Risk 7638
Market Risk4378
Technical Analysis3376
Fundamental Analysis5479
Risk Unsystematic5787

### Prediction Confidence Score

Trust metric by Neural Network: 84 out of 100 with 806 signals.

## References

1. Robins J, Rotnitzky A. 1995. Semiparametric efficiency in multivariate regression models with missing data. J. Am. Stat. Assoc. 90:122–29
2. A. Tamar and S. Mannor. Variance adjusted actor critic algorithms. arXiv preprint arXiv:1310.3697, 2013.
3. Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
4. Robins J, Rotnitzky A. 1995. Semiparametric efficiency in multivariate regression models with missing data. J. Am. Stat. Assoc. 90:122–29
5. Clements, M. P. D. F. Hendry (1996), "Intercept corrections and structural change," Journal of Applied Econometrics, 11, 475–494.
6. M. Benaim, J. Hofbauer, and S. Sorin. Stochastic approximations and differential inclusions, Part II: Appli- cations. Mathematics of Operations Research, 31(4):673–695, 2006
7. Dudik M, Erhan D, Langford J, Li L. 2014. Doubly robust policy evaluation and optimization. Stat. Sci. 29:485–511
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 (News Feed Sentiment Analysis) and Polynomial Regression
Q: Is Euro Stoxx 50 Index stock a buy or sell?
A: The dominant strategy among neural network is to Sell 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 Sell and assigned short-term B2 & long-term Ba1 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 Sell.
Q: What is the prediction period for Euro Stoxx 50 Index stock?
A: The prediction period for Euro Stoxx 50 Index is (n+4 weeks)