Stock market trading is an activity in which investors need fast and accurate information to make effective decisions. Since many stocks are traded on a stock exchange, numerous factors influence the decision-making process. Moreover, the behaviour of stock prices is uncertain and hard to predict. For these reasons, stock price prediction is an important process and a challenging one.** We evaluate Viasat prediction models with Reinforcement Machine Learning (ML) and Independent T-Test ^{1,2,3,4} and conclude that the VSAT stock is predictable in the short/long term. **

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to SellBuy VSAT stock.**

**VSAT, Viasat, 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?
- Is Target price a good indicator?
- How can neural networks improve predictions?

## VSAT Target Price Prediction Modeling Methodology

Nowadays, people show more and more enthusiasm for applying machine learning methods to finance domain. Many scholars and investors are trying to discover the mystery behind the stock market by applying deep learning. This thesis compares four machine learning methods: long short-term memory (LSTM), gated recurrent units (GRU), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) to test which one performs the best in predicting the stock trend. We consider Viasat Stock Decision Process with Independent T-Test where A is the set of discrete actions of VSAT 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(Independent T-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(Reinforcement Machine Learning (ML)) X S(n):→ (n+8 weeks) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of VSAT 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?

## VSAT Stock Forecast (Buy or Sell) for (n+8 weeks)

**Sample Set:**Neural Network

**Stock/Index:**VSAT Viasat

**Time series to forecast n: 17 Sep 2022**for (n+8 weeks)

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to SellBuy VSAT 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

Viasat assigned short-term B2 & long-term B2 forecasted stock rating.** We evaluate the prediction models Reinforcement Machine Learning (ML) with Independent T-Test ^{1,2,3,4} and conclude that the VSAT stock is predictable in the short/long term.**

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to SellBuy VSAT stock.**

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

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

Outlook* | B2 | B2 |

Operational Risk | 57 | 84 |

Market Risk | 47 | 51 |

Technical Analysis | 34 | 34 |

Fundamental Analysis | 62 | 32 |

Risk Unsystematic | 60 | 63 |

### Prediction Confidence Score

## References

- Doudchenko N, Imbens GW. 2016. Balancing, regression, difference-in-differences and synthetic control methods: a synthesis. NBER Work. Pap. 22791
- Athey S, Wager S. 2017. Efficient policy learning. arXiv:1702.02896 [math.ST]
- Hastie T, Tibshirani R, Tibshirani RJ. 2017. Extended comparisons of best subset selection, forward stepwise selection, and the lasso. arXiv:1707.08692 [stat.ME]
- L. Panait and S. Luke. Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems, 11(3):387–434, 2005.
- Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
- Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
- R. Sutton and A. Barto. Introduction to reinforcement learning. MIT Press, 1998

## Frequently Asked Questions

Q: What is the prediction methodology for VSAT stock?A: VSAT stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Independent T-Test

Q: Is VSAT stock a buy or sell?

A: The dominant strategy among neural network is to SellBuy VSAT Stock.

Q: Is Viasat stock a good investment?

A: The consensus rating for Viasat is SellBuy and assigned short-term B2 & long-term B2 forecasted stock rating.

Q: What is the consensus rating of VSAT stock?

A: The consensus rating for VSAT is SellBuy.

Q: What is the prediction period for VSAT stock?

A: The prediction period for VSAT is (n+8 weeks)

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