HII Target Price Forecast

Stock market forecasting is considered to be a challenging topic among time series forecasting. This study proposes a novel two-stage ensemble machine learning model named SVR-ENANFIS for stock price prediction by combining features of support vector regression (SVR) and ensemble adaptive neuro fuzzy inference system (ENANFIS). We evaluate Huntington Ingalls Industries prediction models with Supervised Machine Learning (ML) and Independent T-Test^1,2,3,4 and conclude that the HII 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 Sell HII stock.

Keywords: HII, Huntington Ingalls Industries, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. Market Signals
2. Probability Distribution
3. Which neural network is best for prediction?

HII Target Price Prediction Modeling Methodology

Recurrent Neural Networks (RNNs) is a sub type of neural networks that use feedback connections. Several types of RNN models are used in predicting financial time series. This study was conducted to develop models to predict daily stock prices based on Recurrent Neural Network (RNN) Approach and to measure the accuracy of the models developed and identify the shortcomings of the models if present. We consider Huntington Ingalls Industries Stock Decision Process with Independent T-Test where A is the set of discrete actions of HII 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= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Supervised Machine Learning (ML)) X S(n):→ (n+16 weeks) $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of HII 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?

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

Sample Set: Neural Network
Stock/Index: HII Huntington Ingalls Industries
Time series to forecast n: 16 Oct 2022 for (n+16 weeks)

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

Huntington Ingalls Industries assigned short-term B3 & long-term B1 forecasted stock rating. We evaluate the prediction models Supervised Machine Learning (ML) with Independent T-Test^1,2,3,4 and conclude that the HII 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 Sell HII stock.

Financial State Forecast for HII Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B3	B1
Operational Risk	39	41
Market Risk	73	47
Technical Analysis	54	68
Fundamental Analysis	43	84
Risk Unsystematic	46	60

Prediction Confidence Score

Trust metric by Neural Network: 74 out of 100 with 747 signals.

References

1. Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
2. S. Proper and K. Tumer. Modeling difference rewards for multiagent learning (extended abstract). In Proceedings of the Eleventh International Joint Conference on Autonomous Agents and Multiagent Systems, Valencia, Spain, June 2012
3. Wager S, Athey S. 2017. Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113:1228–42
4. Bera, A. M. L. Higgins (1997), "ARCH and bilinearity as competing models for nonlinear dependence," Journal of Business Economic Statistics, 15, 43–50.
5. R. Rockafellar and S. Uryasev. Conditional value-at-risk for general loss distributions. Journal of Banking and Finance, 26(7):1443 – 1471, 2002
6. J. Filar, D. Krass, and K. Ross. Percentile performance criteria for limiting average Markov decision pro- cesses. IEEE Transaction of Automatic Control, 40(1):2–10, 1995.
7. P. Artzner, F. Delbaen, J. Eber, and D. Heath. Coherent measures of risk. Journal of Mathematical Finance, 9(3):203–228, 1999

Frequently Asked Questions

Q: What is the prediction methodology for HII stock?
A: HII stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Independent T-Test
Q: Is HII stock a buy or sell?
A: The dominant strategy among neural network is to Sell HII Stock.
Q: Is Huntington Ingalls Industries stock a good investment?
A: The consensus rating for Huntington Ingalls Industries is Sell and assigned short-term B3 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of HII stock?
A: The consensus rating for HII is Sell.
Q: What is the prediction period for HII stock?
A: The prediction period for HII is (n+16 weeks)

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