In this paper a Bayesian regularized artificial neural network is proposed as a novel method to forecast financial market behavior. Daily market prices and financial technical indicators are utilized as inputs to predict the one day future closing price of individual stocks. The prediction of stock price movement is generally considered to be a challenging and important task for financial time series analysis. ** We evaluate IBEX 35 Index prediction models with Modular Neural Network (CNN Layer) and Polynomial Regression ^{1,2,3,4} and conclude that the IBEX 35 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 IBEX 35 Index stock.**

**IBEX 35 Index, IBEX 35 Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- How can neural networks improve predictions?
- Stock Forecast Based On a Predictive Algorithm
- Can stock prices be predicted?

## IBEX 35 Index Target Price Prediction Modeling Methodology

Neural networks, as an intelligent data mining method, have been used in many different challenging pattern recognition problems such as stock market prediction. However, there is no formal method to determine the optimal neural network for prediction purpose in the literature. In this paper, two kinds of neural networks, a feed forward multi layer Perceptron (MLP) and an Elman recurrent network, are used to predict a company's stock value based on its stock share value history. We consider IBEX 35 Index Stock Decision Process with Polynomial Regression where A is the set of discrete actions of IBEX 35 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 Î³ ∈ [0, 1] is a move factor for expectation.^{1,2,3,4}

F(Polynomial Regression)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (CNN Layer)) X S(n):→ (n+8 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of IBEX 35 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?

## IBEX 35 Index Stock Forecast (Buy or Sell) for (n+8 weeks)

**Sample Set:**Neural Network

**Stock/Index:**IBEX 35 Index IBEX 35 Index

**Time series to forecast n: 14 Sep 2022**for (n+8 weeks)

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold IBEX 35 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

IBEX 35 Index assigned short-term B1 & long-term Ba2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (CNN Layer) with Polynomial Regression ^{1,2,3,4} and conclude that the IBEX 35 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 IBEX 35 Index stock.**

### Financial State Forecast for IBEX 35 Index Stock Options & Futures

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | B1 | Ba2 |

Operational Risk | 55 | 47 |

Market Risk | 55 | 62 |

Technical Analysis | 65 | 85 |

Fundamental Analysis | 57 | 77 |

Risk Unsystematic | 76 | 65 |

### Prediction Confidence Score

## References

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- Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60
- Matzkin RL. 1994. Restrictions of economic theory in nonparametric methods. In Handbook of Econometrics, Vol. 4, ed. R Engle, D McFadden, pp. 2523–58. Amsterdam: Elsevier
- E. Collins. Using Markov decision processes to optimize a nonlinear functional of the final distribution, with manufacturing applications. In Stochastic Modelling in Innovative Manufacturing, pages 30–45. Springer, 1997
- Matzkin RL. 2007. Nonparametric identification. In Handbook of Econometrics, Vol. 6B, ed. J Heckman, E Learner, pp. 5307–68. Amsterdam: Elsevier
- Burkov A. 2019. The Hundred-Page Machine Learning Book. Quebec City, Can.: Andriy Burkov
- M. Babes, E. M. de Cote, and M. L. Littman. Social reward shaping in the prisoner's dilemma. In 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), Estoril, Portugal, May 12-16, 2008, Volume 3, pages 1389–1392, 2008.

## Frequently Asked Questions

Q: What is the prediction methodology for IBEX 35 Index stock?A: IBEX 35 Index stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Polynomial Regression

Q: Is IBEX 35 Index stock a buy or sell?

A: The dominant strategy among neural network is to Hold IBEX 35 Index Stock.

Q: Is IBEX 35 Index stock a good investment?

A: The consensus rating for IBEX 35 Index is Hold and assigned short-term B1 & long-term Ba2 forecasted stock rating.

Q: What is the consensus rating of IBEX 35 Index stock?

A: The consensus rating for IBEX 35 Index is Hold.

Q: What is the prediction period for IBEX 35 Index stock?

A: The prediction period for IBEX 35 Index is (n+8 weeks)