Development of linguistic technologies and penetration of social media provide powerful possibilities to investigate users' moods and psychological states of people. In this paper we discussed possibility to improve accuracy of stock market indicators predictions by using data about psychological states of Twitter users. For analysis of psychological states we used lexicon-based approach.** We evaluate Qualys prediction models with Modular Neural Network (Emotional Trigger/Responses Analysis) and Wilcoxon Rank-Sum Test ^{1,2,3,4} and conclude that the QLYS 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 Buy QLYS stock.**

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

*Keywords:*## Key Points

- Can machine learning predict?
- Stock Forecast Based On a Predictive Algorithm
- Can statistics predict the future?

## QLYS Target Price Prediction Modeling Methodology

In today's economy, there is a profound impact of the stock market or equity market. Prediction of stock prices is extremely complex, chaotic, and the presence of a dynamic environment makes it a great challenge. Behavioural finance suggests that decision-making process of investors is to a very great extent influenced by the emotions and sentiments in response to a particular news. Thus, to support the decisions of the investors, we have presented an approach combining two distinct fields for analysis of stock exchange. We consider Qualys Stock Decision Process with Wilcoxon Rank-Sum Test where A is the set of discrete actions of QLYS 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 (Emotional Trigger/Responses Analysis)) X S(n):→ (n+1 year) $\sum _{i=1}^{n}\left({a}_{i}\right)$

n:Time series to forecast

p:Price signals of QLYS 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?

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

**Sample Set:**Neural Network

**Stock/Index:**QLYS Qualys

**Time series to forecast n: 09 Sep 2022**for (n+1 year)

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

Qualys assigned short-term Ba3 & long-term Baa2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) with Wilcoxon Rank-Sum Test ^{1,2,3,4} and conclude that the QLYS 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 Buy QLYS stock.**

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

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

Outlook* | Ba3 | Baa2 |

Operational Risk | 78 | 75 |

Market Risk | 38 | 88 |

Technical Analysis | 69 | 58 |

Fundamental Analysis | 62 | 75 |

Risk Unsystematic | 78 | 86 |

### Prediction Confidence Score

## References

- Athey S, Wager S. 2017. Efficient policy learning. arXiv:1702.02896 [math.ST]
- Angrist JD, Pischke JS. 2008. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton, NJ: Princeton Univ. Press
- Tibshirani R, Hastie T. 1987. Local likelihood estimation. J. Am. Stat. Assoc. 82:559–67
- Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
- J. Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki, and T. Graepel. Multi-agent Reinforcement Learning in Sequential Social Dilemmas. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), Sao Paulo, Brazil, 2017
- Bertsimas D, King A, Mazumder R. 2016. Best subset selection via a modern optimization lens. Ann. Stat. 44:813–52
- 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.

## Frequently Asked Questions

Q: What is the prediction methodology for QLYS stock?A: QLYS stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Wilcoxon Rank-Sum Test

Q: Is QLYS stock a buy or sell?

A: The dominant strategy among neural network is to Buy QLYS Stock.

Q: Is Qualys stock a good investment?

A: The consensus rating for Qualys is Buy and assigned short-term Ba3 & long-term Baa2 forecasted stock rating.

Q: What is the consensus rating of QLYS stock?

A: The consensus rating for QLYS is Buy.

Q: What is the prediction period for QLYS stock?

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

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