Stock market prediction is a crucial and challenging task due to its nonlinear, evolutionary, complex, and dynamic nature. Research on the stock market has been an important issue for researchers in recent years. Companies invest in trading the stock market. Predicting the stock market trend accurately will minimize the risk and bring a maximum amount of profit for all the stakeholders. During the last several years, a lot of studies have been done to predict stock market trends using Traditional, Machine learning and deep learning techniques. We evaluate BEL 20 Index prediction models with Inductive Learning (ML) and Spearman Correlation1,2,3,4 and conclude that the BEL 20 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 Sell 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. Technical Analysis with Algorithmic Trading
2. Why do we need predictive models?
3. Investment Risk

## BEL 20 Index Target Price Prediction Modeling Methodology

Security indices are the main tools for evaluation of the status of financial markets. Moreover, a main part of the economy of any country is constituted of investment in stock markets. Therefore, investors could maximize the return of investment if it becomes possible to predict the future trend of stock market with appropriate methods. The nonlinearity and nonstationarity of financial series make their prediction complicated. This study seeks to evaluate the prediction power of machine-learning models in a stock market. We consider BEL 20 Index Stock Decision Process with Spearman Correlation 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(Spearman Correlation)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(Inductive Learning (ML)) X S(n):→ (n+6 month) $\stackrel{\to }{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

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+6 month)

Sample Set: Neural Network
Stock/Index: BEL 20 Index BEL 20 Index
Time series to forecast n: 17 Sep 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Sell 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 Ba1 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Inductive Learning (ML) with Spearman Correlation1,2,3,4 and conclude that the BEL 20 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 Sell BEL 20 Index stock.

### Financial State Forecast for BEL 20 Index Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Operational Risk 8987
Market Risk4130
Technical Analysis7180
Fundamental Analysis7986
Risk Unsystematic7866

### Prediction Confidence Score

Trust metric by Neural Network: 84 out of 100 with 494 signals.

## References

1. Dudik M, Erhan D, Langford J, Li L. 2014. Doubly robust policy evaluation and optimization. Stat. Sci. 29:485–511
2. A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016
3. Barkan O. 2016. Bayesian neural word embedding. arXiv:1603.06571 [math.ST]
4. S. J. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs, NJ, 3nd edition, 2010
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. Athey S, Wager S. 2017. Efficient policy learning. arXiv:1702.02896 [math.ST]
7. Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221
Frequently Asked QuestionsQ: What is the prediction methodology for BEL 20 Index stock?
A: BEL 20 Index stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Spearman Correlation
Q: Is BEL 20 Index stock a buy or sell?
A: The dominant strategy among neural network is to Sell BEL 20 Index Stock.
Q: Is BEL 20 Index stock a good investment?
A: The consensus rating for BEL 20 Index is Sell and assigned short-term Ba1 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of BEL 20 Index stock?
A: The consensus rating for BEL 20 Index is Sell.
Q: What is the prediction period for BEL 20 Index stock?
A: The prediction period for BEL 20 Index is (n+6 month)