Understanding the pattern of financial activities and predicting their development and changes are research hotspots in academic and financial circles. Because financial data contain complex, incomplete and fuzzy information, predicting their development trends is an extremely difficult challenge. Fluctuations in financial data depend on a myriad of correlated constantly changing factors. Therefore, predicting and analysing financial data are a nonlinear, time-dependent problem. Deep neural networks (DNNs) combine the advantages of deep learning (DL) and neural networks and can be used to solve nonlinear problems more satisfactorily compared to conventional machine learning algorithms.** We evaluate Popular prediction models with Modular Neural Network (Market Volatility Analysis) and Pearson Correlation ^{1,2,3,4} and conclude that the BPOP stock is predictable in the short/long term. **

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Sell BPOP stock.**

**BPOP, Popular, 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
- How do you decide buy or sell a stock?
- Technical Analysis with Algorithmic Trading

## BPOP Target Price Prediction Modeling Methodology

One decision in Stock Market can make huge impact on an investor's life. The stock market is a complex system and often covered in mystery, it is therefore, very difficult to analyze all the impacting factors before making a decision. In this research, we have tried to design a stock market prediction model which is based on different factors. We consider Popular Stock Decision Process with Pearson Correlation where A is the set of discrete actions of BPOP 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(Pearson Correlation)

^{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 Volatility Analysis)) X S(n):→ (n+4 weeks) $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of BPOP 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?

## BPOP Stock Forecast (Buy or Sell) for (n+4 weeks)

**Sample Set:**Neural Network

**Stock/Index:**BPOP Popular

**Time series to forecast n: 22 Oct 2022**for (n+4 weeks)

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Sell BPOP 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

Popular assigned short-term B2 & long-term B1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Pearson Correlation ^{1,2,3,4} and conclude that the BPOP stock is predictable in the short/long term.**

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Sell BPOP stock.**

### Financial State Forecast for BPOP Stock Options & Futures

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

Outlook* | B2 | B1 |

Operational Risk | 34 | 38 |

Market Risk | 61 | 33 |

Technical Analysis | 80 | 87 |

Fundamental Analysis | 53 | 77 |

Risk Unsystematic | 36 | 69 |

### Prediction Confidence Score

## References

- Cheung, Y. M.D. Chinn (1997), "Further investigation of the uncertain unit root in GNP," Journal of Business and Economic Statistics, 15, 68–73.
- H. Khalil and J. Grizzle. Nonlinear systems, volume 3. Prentice hall Upper Saddle River, 2002.
- Scott SL. 2010. A modern Bayesian look at the multi-armed bandit. Appl. Stoch. Models Bus. Ind. 26:639–58
- M. Sobel. The variance of discounted Markov decision processes. Applied Probability, pages 794–802, 1982
- Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
- J. Hu and M. P. Wellman. Nash q-learning for general-sum stochastic games. Journal of Machine Learning Research, 4:1039–1069, 2003.
- Scott SL. 2010. A modern Bayesian look at the multi-armed bandit. Appl. Stoch. Models Bus. Ind. 26:639–58

## Frequently Asked Questions

Q: What is the prediction methodology for BPOP stock?A: BPOP stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Pearson Correlation

Q: Is BPOP stock a buy or sell?

A: The dominant strategy among neural network is to Sell BPOP Stock.

Q: Is Popular stock a good investment?

A: The consensus rating for Popular is Sell and assigned short-term B2 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of BPOP stock?

A: The consensus rating for BPOP is Sell.

Q: What is the prediction period for BPOP stock?

A: The prediction period for BPOP is (n+4 weeks)