How do you know when a stock will go up or down? (BEL 20 Index Stock Forecast)

This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies. The stock market prediction can be modeled based on two principal analyses called technical and fundamental. In the technical analysis approach, the regression machine learning (ML) algorithms are employed to predict the stock price trend at the end of a business day based on the historical price data. In contrast, in the fundamental analysis, the classification ML algorithms are applied to classify the public sentiment based on news and social media. We evaluate BEL 20 Index prediction models with Reinforcement Machine Learning (ML) and Polynomial Regression^1,2,3,4 and conclude that the BEL 20 Index stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold BEL 20 Index stock.

Keywords: BEL 20 Index, BEL 20 Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. Is it better to buy and sell or hold?
2. Market Risk
3. Nash Equilibria

BEL 20 Index Target Price Prediction Modeling Methodology

In today's economy, there is a profound impact of the stock market or equity market. Prediction of stock prices is extremely complex, chaotic, and the presence of a dynamic environment makes it a great challenge. Behavioural finance suggests that decision-making process of investors is to a very great extent influenced by the emotions and sentiments in response to a particular news. Thus, to support the decisions of the investors, we have presented an approach combining two distinct fields for analysis of stock exchange. We consider BEL 20 Index Stock Decision Process with Polynomial Regression where A is the set of discrete actions of BEL 20 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= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Reinforcement Machine Learning (ML)) X S(n):→ (n+16 weeks) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of BEL 20 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?

BEL 20 Index Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: BEL 20 Index BEL 20 Index
Time series to forecast n: 13 Sep 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold BEL 20 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

BEL 20 Index assigned short-term B2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Reinforcement Machine Learning (ML) with Polynomial Regression^1,2,3,4 and conclude that the BEL 20 Index stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold BEL 20 Index stock.

Financial State Forecast for BEL 20 Index Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B2	Ba3
Operational Risk	41	67
Market Risk	88	90
Technical Analysis	41	57
Fundamental Analysis	43	72
Risk Unsystematic	61	35

Prediction Confidence Score

Trust metric by Neural Network: 92 out of 100 with 453 signals.

References

1. Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
2. R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972
3. Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
4. Athey S, Mobius MM, Pál J. 2017c. The impact of aggregators on internet news consumption. Unpublished manuscript, Grad. School Bus., Stanford Univ., Stanford, CA
5. Kallus N. 2017. Balanced policy evaluation and learning. arXiv:1705.07384 [stat.ML]
6. Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
7. S. J. Russell and A. Zimdars. Q-decomposition for reinforcement learning agents. In Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), August 21-24, 2003, Washington, DC, USA, pages 656–663, 2003.

Frequently Asked Questions

Q: What is the prediction methodology for BEL 20 Index stock?
A: BEL 20 Index stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Polynomial Regression
Q: Is BEL 20 Index stock a buy or sell?
A: The dominant strategy among neural network is to Hold BEL 20 Index Stock.
Q: Is BEL 20 Index stock a good investment?
A: The consensus rating for BEL 20 Index is Hold and assigned short-term B2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of BEL 20 Index stock?
A: The consensus rating for BEL 20 Index is Hold.
Q: What is the prediction period for BEL 20 Index stock?
A: The prediction period for BEL 20 Index is (n+16 weeks)

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