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 evaluate United Airlines Holdings prediction models with Modular Neural Network (CNN Layer) and Independent T-Test ^{1,2,3,4} and conclude that the UAL 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 Hold UAL stock.**

**UAL, United Airlines Holdings, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Can stock prices be predicted?
- How useful are statistical predictions?
- Can stock prices be predicted?

## UAL Target Price Prediction Modeling Methodology

The stock market is very volatile and non-stationary and generates huge volumes of data in every second. In this article, the existing machine learning algorithms are analyzed for stock market forecasting and also a new pattern-finding algorithm for forecasting stock trend is developed. Three approaches can be used to solve the problem: fundamental analysis, technical analysis, and the machine learning. Experimental analysis done in this article shows that the machine learning could be useful for investors to make profitable decisions. We consider United Airlines Holdings Stock Decision Process with Independent T-Test where A is the set of discrete actions of UAL 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}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (CNN Layer)) X S(n):→ (n+1 year) $\overrightarrow{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of UAL 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?

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

**Sample Set:**Neural Network

**Stock/Index:**UAL United Airlines Holdings

**Time series to forecast n: 18 Oct 2022**for (n+1 year)

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

United Airlines Holdings assigned short-term B1 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (CNN Layer) with Independent T-Test ^{1,2,3,4} and conclude that the UAL 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 Hold UAL stock.**

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

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

Outlook* | B1 | Ba3 |

Operational Risk | 61 | 41 |

Market Risk | 36 | 60 |

Technical Analysis | 48 | 86 |

Fundamental Analysis | 75 | 84 |

Risk Unsystematic | 81 | 60 |

### Prediction Confidence Score

## References

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- Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. 2013b. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 3111–19. San Diego, CA: Neural Inf. Process. Syst. Found.
- P. Milgrom and I. Segal. Envelope theorems for arbitrary choice sets. Econometrica, 70(2):583–601, 2002
- Breusch, T. S. A. R. Pagan (1979), "A simple test for heteroskedasticity and random coefficient variation," Econometrica, 47, 1287–1294.
- Keane MP. 2013. Panel data discrete choice models of consumer demand. In The Oxford Handbook of Panel Data, ed. BH Baltagi, pp. 54–102. Oxford, UK: Oxford Univ. Press
- Mnih A, Kavukcuoglu K. 2013. Learning word embeddings efficiently with noise-contrastive estimation. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 2265–73. San Diego, CA: Neural Inf. Process. Syst. Found.
- S. Devlin, L. Yliniemi, D. Kudenko, and K. Tumer. Potential-based difference rewards for multiagent reinforcement learning. In Proceedings of the Thirteenth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2014

## Frequently Asked Questions

Q: What is the prediction methodology for UAL stock?A: UAL stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Independent T-Test

Q: Is UAL stock a buy or sell?

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

Q: Is United Airlines Holdings stock a good investment?

A: The consensus rating for United Airlines Holdings is Hold and assigned short-term B1 & long-term Ba3 forecasted stock rating.

Q: What is the consensus rating of UAL stock?

A: The consensus rating for UAL is Hold.

Q: What is the prediction period for UAL stock?

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

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