The categorization of high dimensional data present a fascinating challenge to machine learning models as frequent number of highly correlated dimensions or attributes can affect the accuracy of classification model. In this paper, the problem of high dimensionality of stock exchange is investigated to predict the market trends by applying the principal component analysis (PCA) with linear regression. PCA can help to improve the predictive performance of machine learning methods while reducing the redundancy among the data.** We evaluate PepsiCo prediction models with Modular Neural Network (Market Volatility Analysis) and Wilcoxon Rank-Sum Test ^{1,2,3,4} and conclude that the PEP stock is predictable in the short/long term. **

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell PEP stock.**

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

*Keywords:*## Key Points

- How do predictive algorithms actually work?
- Short/Long Term Stocks
- Game Theory

## PEP Target Price Prediction Modeling Methodology

The main perfect of this composition is to discover the stylish version to prognosticate the cost of the inventory request. During the procedure of analyzing the colorful ways and variables to remember, we plant that approaches similar as Random woodland, machine help Vector were not absolutely exploited. We consider PepsiCo Stock Decision Process with Wilcoxon Rank-Sum Test where A is the set of discrete actions of PEP 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(Wilcoxon Rank-Sum Test)

^{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+1 year) $\sum _{i=1}^{n}\left({s}_{i}\right)$

n:Time series to forecast

p:Price signals of PEP 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?

## PEP Stock Forecast (Buy or Sell) for (n+1 year)

**Sample Set:**Neural Network

**Stock/Index:**PEP PepsiCo

**Time series to forecast n: 21 Sep 2022**for (n+1 year)

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell PEP 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

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

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell PEP stock.**

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

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

Outlook* | B1 | B2 |

Operational Risk | 45 | 45 |

Market Risk | 87 | 81 |

Technical Analysis | 59 | 39 |

Fundamental Analysis | 69 | 66 |

Risk Unsystematic | 30 | 38 |

### Prediction Confidence Score

## References

- Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
- Kitagawa T, Tetenov A. 2015. Who should be treated? Empirical welfare maximization methods for treatment choice. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
- P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998
- R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972
- V. Borkar. A sensitivity formula for the risk-sensitive cost and the actor-critic algorithm. Systems & Control Letters, 44:339–346, 2001
- Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
- H. Khalil and J. Grizzle. Nonlinear systems, volume 3. Prentice hall Upper Saddle River, 2002.

## Frequently Asked Questions

Q: What is the prediction methodology for PEP stock?A: PEP stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Wilcoxon Rank-Sum Test

Q: Is PEP stock a buy or sell?

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

Q: Is PepsiCo stock a good investment?

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

Q: What is the consensus rating of PEP stock?

A: The consensus rating for PEP is Sell.

Q: What is the prediction period for PEP stock?

A: The prediction period for PEP is (n+1 year)

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