As part of this research, different techniques have been studied for data extraction and analysis. After having reviewed the work related to the initial idea of the research, it is shown the development carried out, together with the data extraction and the machine learning algorithms for prediction used. The calculation of technical analysis metrics is also included. The development of a visualization platform has been proposed for high-level interaction between the user and the recommendation system. We evaluate WIG20 Index prediction models with Modular Neural Network (Social Media Sentiment Analysis) and ElasticNet Regression1,2,3,4 and conclude that the WIG20 Index stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold WIG20 Index stock.

Keywords: WIG20 Index, WIG20 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 prediction in deep learning?
2. What is a prediction confidence?

## WIG20 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 WIG20 Index Stock Decision Process with ElasticNet Regression where A is the set of discrete actions of WIG20 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(ElasticNet 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 (Social Media Sentiment Analysis)) X S(n):→ (n+8 weeks) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of WIG20 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?

## WIG20 Index Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: WIG20 Index WIG20 Index
Time series to forecast n: 18 Sep 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold WIG20 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

WIG20 Index assigned short-term Ba2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with ElasticNet Regression1,2,3,4 and conclude that the WIG20 Index stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold WIG20 Index stock.

### Financial State Forecast for WIG20 Index Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba2B1
Operational Risk 6639
Market Risk7667
Technical Analysis8953
Fundamental Analysis4978
Risk Unsystematic6652

### Prediction Confidence Score

Trust metric by Neural Network: 85 out of 100 with 782 signals.

## References

1. Mikolov T, Chen K, Corrado GS, Dean J. 2013a. Efficient estimation of word representations in vector space. arXiv:1301.3781 [cs.CL]
2. Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:267–88
3. Keane MP. 2013. Panel data discrete choice models of consumer demand. In The Oxford Handbook of Panel Data, ed. BH Baltagi, pp. 54–102. Oxford, UK: Oxford Univ. Press
4. A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015
5. Tibshirani R, Hastie T. 1987. Local likelihood estimation. J. Am. Stat. Assoc. 82:559–67
6. R. Rockafellar and S. Uryasev. Optimization of conditional value-at-risk. Journal of Risk, 2:21–42, 2000.
7. Breiman L. 1996. Bagging predictors. Mach. Learn. 24:123–40
Frequently Asked QuestionsQ: What is the prediction methodology for WIG20 Index stock?
A: WIG20 Index stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and ElasticNet Regression
Q: Is WIG20 Index stock a buy or sell?
A: The dominant strategy among neural network is to Hold WIG20 Index Stock.
Q: Is WIG20 Index stock a good investment?
A: The consensus rating for WIG20 Index is Hold and assigned short-term Ba2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of WIG20 Index stock?
A: The consensus rating for WIG20 Index is Hold.
Q: What is the prediction period for WIG20 Index stock?
A: The prediction period for WIG20 Index is (n+8 weeks)