Predicting stock index with traditional time series analysis has proven to be difficult an Artificial Neural network may be suitable for the task. A Neural Network has the ability to extract useful information from large set of data. This paper presents a review of literature application of Artificial Neural Network for stock market predictions and from this literature found that Artificial Neural Network is very useful for predicting world stock markets.** We evaluate Pfizer prediction models with Modular Neural Network (Financial Sentiment Analysis) and Chi-Square ^{1,2,3,4} and conclude that the PFE 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 Wait until speculative trend diminishes PFE stock.**

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

*Keywords:*## Key Points

- Can neural networks predict stock market?
- Trading Signals
- Should I buy stocks now or wait amid such uncertainty?

## PFE Target Price Prediction Modeling Methodology

The main objective of this research is to predict the market performance on day closing using different machine learning techniques. The prediction model uses different attributes as an input and predicts market as Positive & Negative. We consider Pfizer Stock Decision Process with Chi-Square where A is the set of discrete actions of PFE 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(Chi-Square)

^{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 (Financial Sentiment Analysis)) X S(n):→ (n+6 month) $\sum _{i=1}^{n}\left({r}_{i}\right)$

n:Time series to forecast

p:Price signals of PFE 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?

## PFE Stock Forecast (Buy or Sell) for (n+6 month)

**Sample Set:**Neural Network

**Stock/Index:**PFE Pfizer

**Time series to forecast n: 25 Sep 2022**for (n+6 month)

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Wait until speculative trend diminishes PFE 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

Pfizer assigned short-term B3 & long-term B1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with Chi-Square ^{1,2,3,4} and conclude that the PFE 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 Wait until speculative trend diminishes PFE stock.**

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

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

Outlook* | B3 | B1 |

Operational Risk | 41 | 31 |

Market Risk | 61 | 87 |

Technical Analysis | 35 | 51 |

Fundamental Analysis | 58 | 64 |

Risk Unsystematic | 51 | 61 |

### Prediction Confidence Score

## References

- Bamler R, Mandt S. 2017. Dynamic word embeddings via skip-gram filtering. In Proceedings of the 34th Inter- national Conference on Machine Learning, pp. 380–89. La Jolla, CA: Int. Mach. Learn. Soc.
- A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008
- Burkov A. 2019. The Hundred-Page Machine Learning Book. Quebec City, Can.: Andriy Burkov
- J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.
- Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
- Challen, D. W. A. J. Hagger (1983), Macroeconomic Systems: Construction, Validation and Applications. New York: St. Martin's Press.
- Hill JL. 2011. Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20:217–40

## Frequently Asked Questions

Q: What is the prediction methodology for PFE stock?A: PFE stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Chi-Square

Q: Is PFE stock a buy or sell?

A: The dominant strategy among neural network is to Wait until speculative trend diminishes PFE Stock.

Q: Is Pfizer stock a good investment?

A: The consensus rating for Pfizer is Wait until speculative trend diminishes and assigned short-term B3 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of PFE stock?

A: The consensus rating for PFE is Wait until speculative trend diminishes.

Q: What is the prediction period for PFE stock?

A: The prediction period for PFE is (n+6 month)