WIG20 Index Stock Forecast & Analysis

Short - term price movements, contribute a considerable measure to the unpredictability of the securities exchanges. Accurately predicting the price fluctuations in stock market is a huge economical advantage. The aforementioned task is generally achieved by analyzing the company, this is called as fundamental analysis. Another method, which is undergoing a lot of research work recently, is to create a predictive algorithmic model using machine learning. To train machines to take trading decisions in such short - period of time, the latter method needs to be adopted. Deep Neural Networks, being the most exceptional innovation in Machine Learning, have been utilized to develop a short-term prediction model. We evaluate WIG20 Index prediction models with Transfer Learning (ML) and Ridge Regression^1,2,3,4 and conclude that the WIG20 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 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 neural prediction?
2. Can we predict stock market using machine learning?
3. What are buy sell or hold recommendations?

WIG20 Index Target Price Prediction Modeling Methodology

Three networks are compared for low false alarm stock trend predictions. Short-term trends, particularly attractive for neural network analysis, can be used profitably in scenarios such as option trading, but only with significant risk. Therefore, we focus on limiting false alarms, which improves the risk/reward ratio by preventing losses. To predict stock trends, we exploit time delay, recurrent, and probabilistic neural networks (TDNN, RNN, and PNN, respectively), utilizing conjugate gradient and multistream extended Kalman filter training for TDNN and RNN. We consider WIG20 Index Stock Decision Process with Ridge 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(Ridge Regression)^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(Transfer Learning (ML)) X S(n):→ (n+6 month) $\sum _{i=1}^n\left(r_i\right)$

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+6 month)

Sample Set: Neural Network
Stock/Index: WIG20 Index WIG20 Index
Time series to forecast n: 24 Oct 2022 for (n+6 month)

According to price forecasts for (n+6 month) 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 Ba3 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Transfer Learning (ML) with Ridge Regression^1,2,3,4 and conclude that the WIG20 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 WIG20 Index stock.

Financial State Forecast for WIG20 Index Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba3	Ba3
Operational Risk	81	43
Market Risk	72	48
Technical Analysis	44	84
Fundamental Analysis	80	73
Risk Unsystematic	57	70

Prediction Confidence Score

Trust metric by Neural Network: 75 out of 100 with 454 signals.

References

1. Li L, Chen S, Kleban J, Gupta A. 2014. Counterfactual estimation and optimization of click metrics for search engines: a case study. In Proceedings of the 24th International Conference on the World Wide Web, pp. 929–34. New York: ACM
2. Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
3. Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press
4. Semenova V, Goldman M, Chernozhukov V, Taddy M. 2018. Orthogonal ML for demand estimation: high dimensional causal inference in dynamic panels. arXiv:1712.09988 [stat.ML]
5. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, Newey W. 2017. Double/debiased/ Neyman machine learning of treatment effects. Am. Econ. Rev. 107:261–65
6. Burgess, D. F. (1975), "Duality theory and pitfalls in the specification of technologies," Journal of Econometrics, 3, 105–121.
7. Ashley, R. (1983), "On the usefulness of macroeconomic forecasts as inputs to forecasting models," Journal of Forecasting, 2, 211–223.

Frequently Asked Questions

Q: What is the prediction methodology for WIG20 Index stock?
A: WIG20 Index stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Ridge 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 Ba3 & long-term Ba3 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+6 month)

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