The research reported in the paper focuses on the stock market prediction problem, the main aim being the development of a methodology to forecast the stock closing price. The methodology is based on some novel variable selection methods and an analysis of neural network and support vector machines based prediction models. Also, a hybrid approach which combines the use of the variables derived from technical and fundamental analysis of stock market indicators in order to improve prediction results of the proposed approaches is reported in this paper. ** We evaluate Wynn Resorts prediction models with Transductive Learning (ML) and Chi-Square ^{1,2,3,4} and conclude that the WYNN stock is predictable in the short/long term. **

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold WYNN stock.**

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

*Keywords:*## Key Points

- Market Signals
- Market Risk
- Can we predict stock market using machine learning?

## WYNN Target Price Prediction Modeling Methodology

Stock markets are affected by many uncertainties and interrelated economic and political factors at both local and global levels. The key to successful stock market forecasting is achieving best results with minimum required input data. To determine the set of relevant factors for making accurate predictions is a complicated task and so regular stock market analysis is very essential. More specifically, the stock market's movements are analyzed and predicted in order to retrieve knowledge that could guide investors on when to buy and sell. We consider Wynn Resorts Stock Decision Process with Chi-Square where A is the set of discrete actions of WYNN 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(Transductive Learning (ML)) X S(n):→ (n+16 weeks) $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of WYNN 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?

## WYNN Stock Forecast (Buy or Sell) for (n+16 weeks)

**Sample Set:**Neural Network

**Stock/Index:**WYNN Wynn Resorts

**Time series to forecast n: 17 Oct 2022**for (n+16 weeks)

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold WYNN 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

Wynn Resorts assigned short-term Ba2 & long-term B1 forecasted stock rating.** We evaluate the prediction models Transductive Learning (ML) with Chi-Square ^{1,2,3,4} and conclude that the WYNN stock is predictable in the short/long term.**

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold WYNN stock.**

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

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

Outlook* | Ba2 | B1 |

Operational Risk | 44 | 36 |

Market Risk | 63 | 90 |

Technical Analysis | 74 | 30 |

Fundamental Analysis | 85 | 79 |

Risk Unsystematic | 75 | 51 |

### Prediction Confidence Score

## References

- H. Khalil and J. Grizzle. Nonlinear systems, volume 3. Prentice hall Upper Saddle River, 2002.
- Byron, R. P. O. Ashenfelter (1995), "Predicting the quality of an unborn grange," Economic Record, 71, 40–53.
- Imbens GW, Rubin DB. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge, UK: Cambridge Univ. Press
- M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Ma- chine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994, pages 157–163, 1994
- D. Bertsekas. Min common/max crossing duality: A geometric view of conjugacy in convex optimization. Lab. for Information and Decision Systems, MIT, Tech. Rep. Report LIDS-P-2796, 2009
- Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
- Canova, F. B. E. Hansen (1995), "Are seasonal patterns constant over time? A test for seasonal stability," Journal of Business and Economic Statistics, 13, 237–252.

## Frequently Asked Questions

Q: What is the prediction methodology for WYNN stock?A: WYNN stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Chi-Square

Q: Is WYNN stock a buy or sell?

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

Q: Is Wynn Resorts stock a good investment?

A: The consensus rating for Wynn Resorts is Hold and assigned short-term Ba2 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of WYNN stock?

A: The consensus rating for WYNN is Hold.

Q: What is the prediction period for WYNN stock?

A: The prediction period for WYNN is (n+16 weeks)

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