With the up-gradation of technology and exploration of new machine learning models, the stock market data analysis has gained attention as these models provide a platform for businessman and traders to choose more profitable stocks. As these data are in large volumes and highly complex so a need of more efficient machine learning model for daily predictions is always looked upon.** We evaluate Zoom Video Communications prediction models with Modular Neural Network (DNN Layer) and Logistic Regression ^{1,2,3,4} and conclude that the ZM 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 Hold ZM stock.**

**ZM, Zoom Video Communications, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- What is the best way to predict stock prices?
- Reaction Function
- Stock Forecast Based On a Predictive Algorithm

## ZM Target Price Prediction Modeling Methodology

Stock market is considered chaotic, complex, volatile and dynamic. Undoubtedly, its prediction is one of the most challenging tasks in time series forecasting. Moreover existing Artificial Neural Network (ANN) approaches fail to provide encouraging results. Meanwhile advances in machine learning have presented favourable results for speech recognition, image classification and language processing. We consider Zoom Video Communications Stock Decision Process with Logistic Regression where A is the set of discrete actions of ZM 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(Logistic 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 (DNN Layer)) X S(n):→ (n+6 month) $\sum _{i=1}^{n}\left({a}_{i}\right)$

n:Time series to forecast

p:Price signals of ZM 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?

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

**Sample Set:**Neural Network

**Stock/Index:**ZM Zoom Video Communications

**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 Hold ZM 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

Zoom Video Communications assigned short-term Ba3 & long-term B3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (DNN Layer) with Logistic Regression ^{1,2,3,4} and conclude that the ZM 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 Hold ZM stock.**

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

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

Outlook* | Ba3 | B3 |

Operational Risk | 40 | 77 |

Market Risk | 55 | 32 |

Technical Analysis | 87 | 43 |

Fundamental Analysis | 62 | 44 |

Risk Unsystematic | 71 | 44 |

### Prediction Confidence Score

## References

- O. Bardou, N. Frikha, and G. Pag`es. Computing VaR and CVaR using stochastic approximation and adaptive unconstrained importance sampling. Monte Carlo Methods and Applications, 15(3):173–210, 2009.
- Armstrong, J. S. M. C. Grohman (1972), "A comparative study of methods for long-range market forecasting," Management Science, 19, 211–221.
- Miller A. 2002. Subset Selection in Regression. New York: CRC Press
- Abadie A, Diamond A, Hainmueller J. 2015. Comparative politics and the synthetic control method. Am. J. Political Sci. 59:495–510
- H. Khalil and J. Grizzle. Nonlinear systems, volume 3. Prentice hall Upper Saddle River, 2002.
- Nie X, Wager S. 2019. Quasi-oracle estimation of heterogeneous treatment effects. arXiv:1712.04912 [stat.ML]
- S. J. Russell and A. Zimdars. Q-decomposition for reinforcement learning agents. In Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), August 21-24, 2003, Washington, DC, USA, pages 656–663, 2003.

## Frequently Asked Questions

Q: What is the prediction methodology for ZM stock?A: ZM stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Logistic Regression

Q: Is ZM stock a buy or sell?

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

Q: Is Zoom Video Communications stock a good investment?

A: The consensus rating for Zoom Video Communications is Hold and assigned short-term Ba3 & long-term B3 forecasted stock rating.

Q: What is the consensus rating of ZM stock?

A: The consensus rating for ZM is Hold.

Q: What is the prediction period for ZM stock?

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

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