Prediction of the Stock Market is a challenging task in predicting the stock prices in the future. Due to the fluctuating nature of the stock, the stock market is too difficult to predict. Stock prices are constantly changing every day. Estimating of the stock market has a high demand for stock customers. Applying all extracted rules at any time is a major challenge to estimate the future stock price with high accuracy. The latest prediction techniques adopted for the stock market such as Artificial Neural Network, Neuro-Fuzzy System, Time Series Linear Models (TSLM), Recurrent Neural Network (RNN). We evaluate Nexstar prediction models with Active Learning (ML) and Pearson Correlation1,2,3,4 and conclude that the NXST 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 NXST stock.

Keywords: NXST, Nexstar, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. What are the most successful trading algorithms?
2. What statistical methods are used to analyze data?
3. Can neural networks predict stock market?

## NXST 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 Nexstar Stock Decision Process with Pearson Correlation where A is the set of discrete actions of NXST 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(Pearson Correlation)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Active Learning (ML)) X S(n):→ (n+16 weeks) $\stackrel{\to }{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of NXST 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?

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

Sample Set: Neural Network
Stock/Index: NXST Nexstar
Time series to forecast n: 15 Sep 2022 for (n+16 weeks)

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

Nexstar assigned short-term B2 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Active Learning (ML) with Pearson Correlation1,2,3,4 and conclude that the NXST 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 NXST stock.

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

Rating Short-Term Long-Term Senior
Outlook*B2Ba2
Operational Risk 8762
Market Risk4469
Technical Analysis3358
Fundamental Analysis3684
Risk Unsystematic7071

### Prediction Confidence Score

Trust metric by Neural Network: 79 out of 100 with 690 signals.

## References

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3. Robins J, Rotnitzky A. 1995. Semiparametric efficiency in multivariate regression models with missing data. J. Am. Stat. Assoc. 90:122–29
4. M. L. Littman. Friend-or-foe q-learning in general-sum games. In Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 2001, pages 322–328, 2001
5. G. Shani, R. Brafman, and D. Heckerman. An MDP-based recommender system. In Proceedings of the Eigh- teenth conference on Uncertainty in artificial intelligence, pages 453–460. Morgan Kaufmann Publishers Inc., 2002
6. Andrews, D. W. K. W. Ploberger (1994), "Optimal tests when a nuisance parameter is present only under the alternative," Econometrica, 62, 1383–1414.
7. Mikolov T, Chen K, Corrado GS, Dean J. 2013a. Efficient estimation of word representations in vector space. arXiv:1301.3781 [cs.CL]
Frequently Asked QuestionsQ: What is the prediction methodology for NXST stock?
A: NXST stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Pearson Correlation
Q: Is NXST stock a buy or sell?
A: The dominant strategy among neural network is to Hold NXST Stock.
Q: Is Nexstar stock a good investment?
A: The consensus rating for Nexstar is Hold and assigned short-term B2 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of NXST stock?
A: The consensus rating for NXST is Hold.
Q: What is the prediction period for NXST stock?
A: The prediction period for NXST is (n+16 weeks)