Can we predict stock market using machine learning? (AR Stock Forecast)

The aim of this study is to evaluate the effectiveness of using external indicators, such as commodity prices and currency exchange rates, in predicting movements. The performance of each technique is evaluated using different domain specific metrics. A comprehensive evaluation procedure is described, involving the use of trading simulations to assess the practical value of predictive models, and comparison with simple benchmarks that respond to underlying market growth. We evaluate Antero Resources prediction models with Modular Neural Network (Emotional Trigger/Responses Analysis) and ElasticNet Regression^1,2,3,4 and conclude that the AR 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 Hold AR stock.

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

Key Points

1. Is it better to buy and sell or hold?
2. Is it better to buy and sell or hold?
3. How do you decide buy or sell a stock?

AR Target Price Prediction Modeling Methodology

Accurate stock market prediction is of great interest to investors; however, stock markets are driven by volatile factors such as microblogs and news that make it hard to predict stock market index based on merely the historical data. The enormous stock market volatility emphasizes the need to effectively assess the role of external factors in stock prediction. Stock markets can be predicted using machine learning algorithms on information contained in social media and financial news, as this data can change investors' behavior. We consider Antero Resources Stock Decision Process with ElasticNet Regression where A is the set of discrete actions of AR 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(ElasticNet Regression)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (Emotional Trigger/Responses Analysis)) X S(n):→ (n+8 weeks) $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of AR 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?

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

Sample Set: Neural Network
Stock/Index: AR Antero Resources
Time series to forecast n: 11 Sep 2022 for (n+8 weeks)

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

Antero Resources assigned short-term B3 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) with ElasticNet Regression^1,2,3,4 and conclude that the AR 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 Hold AR stock.

Financial State Forecast for AR Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B3	Ba2
Operational Risk	30	88
Market Risk	55	87
Technical Analysis	72	87
Fundamental Analysis	34	46
Risk Unsystematic	43	30

Prediction Confidence Score

Trust metric by Neural Network: 91 out of 100 with 751 signals.

References

1. Pennington J, Socher R, Manning CD. 2014. GloVe: global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods on Natural Language Processing, pp. 1532–43. New York: Assoc. Comput. Linguist.
2. Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94
3. Breiman L. 2001a. Random forests. Mach. Learn. 45:5–32
4. N. B ̈auerle and J. Ott. Markov decision processes with average-value-at-risk criteria. Mathematical Methods of Operations Research, 74(3):361–379, 2011
5. Clements, M. P. D. F. Hendry (1995), "Forecasting in cointegrated systems," Journal of Applied Econometrics, 10, 127–146.
6. Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94
7. F. A. Oliehoek and C. Amato. A Concise Introduction to Decentralized POMDPs. SpringerBriefs in Intelligent Systems. Springer, 2016

Frequently Asked Questions

Q: What is the prediction methodology for AR stock?
A: AR stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and ElasticNet Regression
Q: Is AR stock a buy or sell?
A: The dominant strategy among neural network is to Hold AR Stock.
Q: Is Antero Resources stock a good investment?
A: The consensus rating for Antero Resources is Hold and assigned short-term B3 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of AR stock?
A: The consensus rating for AR is Hold.
Q: What is the prediction period for AR stock?
A: The prediction period for AR is (n+8 weeks)

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