## Abstract

**We evaluate Meta Platforms prediction models with Modular Neural Network (CNN Layer) and Multiple Regression ^{1,2,3,4} and conclude that the META stock is predictable in the short/long term. **

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold META stock.**

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

*Keywords:*## Key Points

- How useful are statistical predictions?
- Can we predict stock market using machine learning?
- How do you decide buy or sell a stock?

## META Target Price Prediction Modeling Methodology

We consider Meta Platforms Stock Decision Process with Multiple Regression where A is the set of discrete actions of META 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(Multiple 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+3 month) $\overrightarrow{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of META 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?

## META Stock Forecast (Buy or Sell) for (n+3 month)

**Sample Set:**Neural Network

**Stock/Index:**META Meta Platforms

**Time series to forecast n: 01 Sep 2022**for (n+3 month)

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold META 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

Meta Platforms assigned short-term Ba2 & long-term Ba1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (CNN Layer) with Multiple Regression ^{1,2,3,4} and conclude that the META stock is predictable in the short/long term.**

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold META stock.**

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

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

Outlook* | Ba2 | Ba1 |

Operational Risk | 70 | 60 |

Market Risk | 86 | 77 |

Technical Analysis | 37 | 74 |

Fundamental Analysis | 76 | 77 |

Risk Unsystematic | 76 | 71 |

### Prediction Confidence Score

## References

- D. S. Bernstein, S. Zilberstein, and N. Immerman. The complexity of decentralized control of Markov Decision Processes. In UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30 - July 3, 2000, pages 32–37, 2000.
- G. Theocharous and A. Hallak. Lifetime value marketing using reinforcement learning. RLDM 2013, page 19, 2013
- 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
- Nie X, Wager S. 2019. Quasi-oracle estimation of heterogeneous treatment effects. arXiv:1712.04912 [stat.ML]
- Li L, Chen S, Kleban J, Gupta A. 2014. Counterfactual estimation and optimization of click metrics for search engines: a case study. In Proceedings of the 24th International Conference on the World Wide Web, pp. 929–34. New York: ACM
- F. A. Oliehoek, M. T. J. Spaan, and N. A. Vlassis. Optimal and approximate q-value functions for decentralized pomdps. J. Artif. Intell. Res. (JAIR), 32:289–353, 2008
- Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28

## Frequently Asked Questions

Q: What is the prediction methodology for META stock?A: META stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Multiple Regression

Q: Is META stock a buy or sell?

A: The dominant strategy among neural network is to Hold META Stock.

Q: Is Meta Platforms stock a good investment?

A: The consensus rating for Meta Platforms is Hold and assigned short-term Ba2 & long-term Ba1 forecasted stock rating.

Q: What is the consensus rating of META stock?

A: The consensus rating for META is Hold.

Q: What is the prediction period for META stock?

A: The prediction period for META is (n+3 month)

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