The stock market is one of the key sectors of a country's economy. It provides investors with an opportunity to invest and gain returns on their investment. Predicting the stock market is a very challenging task and has attracted serious interest from researchers from many fields such as statistics, artificial intelligence, economics, and finance. An accurate prediction of the stock market reduces investment risk in the market. Different approaches have been used to predict the stock market. The performances of Machine learning (ML) models are typically superior to those of statistical and econometric models. ** We evaluate Humana prediction models with Modular Neural Network (Emotional Trigger/Responses Analysis) and Beta ^{1,2,3,4} and conclude that the HUM 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 HUM stock.**

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

*Keywords:*## Key Points

- What is the best way to predict stock prices?
- Buy, Sell and Hold Signals
- Decision Making

## HUM Target Price Prediction Modeling Methodology

Prediction of stock market is a long-time attractive topic to researchers from different fields. In particular, numerous studies have been conducted to predict the movement of stock market using machine learning algorithms such as support vector machine (SVM) and reinforcement learning. In this project, we propose a new prediction algorithm that exploits the temporal correlation among global stock markets and various financial products to predict the next-day stock trend. We consider Humana Stock Decision Process with Beta where A is the set of discrete actions of HUM 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(Beta)

^{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 (Emotional Trigger/Responses Analysis)) X S(n):→ (n+8 weeks) $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of HUM 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?

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

**Sample Set:**Neural Network

**Stock/Index:**HUM Humana

**Time series to forecast n: 22 Oct 2022**for (n+8 weeks)

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

Humana assigned short-term B1 & long-term B1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) with Beta ^{1,2,3,4} and conclude that the HUM 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 HUM stock.**

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

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

Outlook* | B1 | B1 |

Operational Risk | 50 | 62 |

Market Risk | 80 | 45 |

Technical Analysis | 90 | 69 |

Fundamental Analysis | 50 | 76 |

Risk Unsystematic | 41 | 30 |

### Prediction Confidence Score

## References

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- Barkan O. 2016. Bayesian neural word embedding. arXiv:1603.06571 [math.ST]
- Allen, P. G. (1994), "Economic forecasting in agriculture," International Journal of Forecasting, 10, 81–135.
- Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
- Mikolov T, Chen K, Corrado GS, Dean J. 2013a. Efficient estimation of word representations in vector space. arXiv:1301.3781 [cs.CL]
- Abadie A, Cattaneo MD. 2018. Econometric methods for program evaluation. Annu. Rev. Econ. 10:465–503
- Bottou L. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade, ed. G Montavon, G Orr, K-R Müller, pp. 421–36. Berlin: Springer

## Frequently Asked Questions

Q: What is the prediction methodology for HUM stock?A: HUM stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Beta

Q: Is HUM stock a buy or sell?

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

Q: Is Humana stock a good investment?

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

Q: What is the consensus rating of HUM stock?

A: The consensus rating for HUM is Hold.

Q: What is the prediction period for HUM stock?

A: The prediction period for HUM is (n+8 weeks)

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