In this paper we investigate ways to use prior knowledge and neural networks to improve multivariate prediction ability. Daily stock prices are predicted as a complicated real-world problem, taking non-numerical factors such as political and international events are into account. We have studied types of prior knowledge which are difficult to insert into initial network structures or to represent in the form of error measurements. ** We evaluate S&P/BMV IPC Index prediction models with Modular Neural Network (Speculative Sentiment Analysis) and Beta ^{1,2,3,4} and conclude that the S&P/BMV IPC 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 S&P/BMV IPC Index stock.**

**S&P/BMV IPC Index, S&P/BMV IPC Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Short/Long Term Stocks
- Is Target price a good indicator?
- Stock Rating

## S&P/BMV IPC Index Target Price Prediction Modeling Methodology

The stock market prediction has attracted much attention from academia as well as business. Due to the non-linear, volatile and complex nature of the market, it is quite difficult to predict. As the stock markets grow bigger, more investors pay attention to develop a systematic approach to predict the stock market. We consider S&P/BMV IPC Index Stock Decision Process with Beta where A is the set of discrete actions of S&P/BMV IPC 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 (Speculative Sentiment Analysis)) X S(n):→ (n+6 month) $\overrightarrow{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of S&P/BMV IPC 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?

## S&P/BMV IPC Index Stock Forecast (Buy or Sell) for (n+6 month)

**Sample Set:**Neural Network

**Stock/Index:**S&P/BMV IPC Index S&P/BMV IPC Index

**Time series to forecast n: 09 Oct 2022**for (n+6 month)

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Sell S&P/BMV IPC 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

S&P/BMV IPC Index assigned short-term Ba3 & long-term B2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) with Beta ^{1,2,3,4} and conclude that the S&P/BMV IPC 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 S&P/BMV IPC Index stock.**

### Financial State Forecast for S&P/BMV IPC Index Stock Options & Futures

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

Outlook* | Ba3 | B2 |

Operational Risk | 57 | 58 |

Market Risk | 75 | 33 |

Technical Analysis | 36 | 70 |

Fundamental Analysis | 81 | 46 |

Risk Unsystematic | 66 | 52 |

### Prediction Confidence Score

## References

- S. Bhatnagar, H. Prasad, and L. Prashanth. Stochastic recursive algorithms for optimization, volume 434. Springer, 2013
- J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.
- Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221
- White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
- C. Szepesvári. Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2010
- Bessler, D. A. R. A. Babula, (1987), "Forecasting wheat exports: Do exchange rates matter?" Journal of Business and Economic Statistics, 5, 397–406.
- M. J. Hausknecht and P. Stone. Deep recurrent Q-learning for partially observable MDPs. CoRR, abs/1507.06527, 2015

## Frequently Asked Questions

Q: What is the prediction methodology for S&P/BMV IPC Index stock?A: S&P/BMV IPC Index stock prediction methodology: We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) and Beta

Q: Is S&P/BMV IPC Index stock a buy or sell?

A: The dominant strategy among neural network is to Sell S&P/BMV IPC Index Stock.

Q: Is S&P/BMV IPC Index stock a good investment?

A: The consensus rating for S&P/BMV IPC Index is Sell and assigned short-term Ba3 & long-term B2 forecasted stock rating.

Q: What is the consensus rating of S&P/BMV IPC Index stock?

A: The consensus rating for S&P/BMV IPC Index is Sell.

Q: What is the prediction period for S&P/BMV IPC Index stock?

A: The prediction period for S&P/BMV IPC Index is (n+6 month)

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