Nowadays, people show more and more enthusiasm for applying machine learning methods to finance domain. Many scholars and investors are trying to discover the mystery behind the stock market by applying deep learning. This thesis compares four machine learning methods: long short-term memory (LSTM), gated recurrent units (GRU), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) to test which one performs the best in predicting the stock trend.** We evaluate BEL 20 Index prediction models with Modular Neural Network (Market News Sentiment Analysis) and Beta ^{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+8 weeks) period: The dominant strategy among neural network is to Hold BEL 20 Index stock.**

**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.**

*Keywords:*## Key Points

- How accurate is machine learning in stock market?
- Nash Equilibria
- How accurate is machine learning in stock market?

## BEL 20 Index Target Price Prediction Modeling Methodology

Stock prediction is a very hot topic in our life. However, in the early time, because of some reasons and the limitation of the device, only a few people had the access to the study. Thanks to the rapid development of science and technology, in recent years more and more people are devoted to the study of the prediction and it becomes easier and easier for us to make stock prediction by using different ways now, including machine learning, deep learning and so on. We consider BEL 20 Index Stock Decision Process with Beta 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(Beta)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Market News Sentiment Analysis)) X S(n):→ (n+8 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+8 weeks)

**Sample Set:**Neural Network

**Stock/Index:**BEL 20 Index BEL 20 Index

**Time series to forecast n: 05 Oct 2022**for (n+8 weeks)

**According to price forecasts for (n+8 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 Ba2 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) with Beta ^{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+8 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* | Ba2 | Ba3 |

Operational Risk | 86 | 48 |

Market Risk | 31 | 52 |

Technical Analysis | 81 | 84 |

Fundamental Analysis | 64 | 35 |

Risk Unsystematic | 76 | 86 |

### Prediction Confidence Score

## References

- Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
- Chen, C. L. Liu (1993), "Joint estimation of model parameters and outlier effects in time series," Journal of the American Statistical Association, 88, 284–297.
- Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
- Chamberlain G. 2000. Econometrics and decision theory. J. Econom. 95:255–83
- Canova, F. B. E. Hansen (1995), "Are seasonal patterns constant over time? A test for seasonal stability," Journal of Business and Economic Statistics, 13, 237–252.
- Imbens G, Wooldridge J. 2009. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47:5–86
- Vilnis L, McCallum A. 2015. Word representations via Gaussian embedding. arXiv:1412.6623 [cs.CL]

## 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 Modular Neural Network (Market News Sentiment Analysis) and Beta

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 Ba2 & 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+8 weeks)