This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies. The stock market prediction can be modeled based on two principal analyses called technical and fundamental. In the technical analysis approach, the regression machine learning (ML) algorithms are employed to predict the stock price trend at the end of a business day based on the historical price data. In contrast, in the fundamental analysis, the classification ML algorithms are applied to classify the public sentiment based on news and social media.** We evaluate AbbVie prediction models with Modular Neural Network (DNN Layer) and Spearman Correlation ^{1,2,3,4} and conclude that the ABBV stock is predictable in the short/long term. **

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold ABBV stock.**

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

*Keywords:*## Key Points

- Operational Risk
- Technical Analysis with Algorithmic Trading
- Can stock prices be predicted?

## ABBV Target Price Prediction Modeling Methodology

Time series forecasting has been widely used to determine the future prices of stock, and the analysis and modeling of finance time series importantly guide investors' decisions and trades. In addition, in a dynamic environment such as the stock market, the nonlinearity of the time series is pronounced, immediately affecting the efficacy of stock price forecasts. Thus, this paper proposes an intelligent time series prediction system that uses sliding-window metaheuristic optimization for the purpose of predicting the stock prices. We consider AbbVie Stock Decision Process with Spearman Correlation where A is the set of discrete actions of ABBV 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(Spearman Correlation)

^{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 (DNN Layer)) X S(n):→ (n+4 weeks) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of ABBV 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?

## ABBV Stock Forecast (Buy or Sell) for (n+4 weeks)

**Sample Set:**Neural Network

**Stock/Index:**ABBV AbbVie

**Time series to forecast n: 13 Oct 2022**for (n+4 weeks)

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold ABBV 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

AbbVie assigned short-term Ba3 & long-term Ba1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (DNN Layer) with Spearman Correlation ^{1,2,3,4} and conclude that the ABBV stock is predictable in the short/long term.**

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold ABBV stock.**

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

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

Outlook* | Ba3 | Ba1 |

Operational Risk | 56 | 57 |

Market Risk | 88 | 81 |

Technical Analysis | 76 | 71 |

Fundamental Analysis | 66 | 86 |

Risk Unsystematic | 36 | 56 |

### Prediction Confidence Score

## References

- Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
- Alexander, J. C. Jr. (1995), "Refining the degree of earnings surprise: A comparison of statistical and analysts' forecasts," Financial Review, 30, 469–506.
- L. Busoniu, R. Babuska, and B. D. Schutter. A comprehensive survey of multiagent reinforcement learning. IEEE Transactions of Systems, Man, and Cybernetics Part C: Applications and Reviews, 38(2), 2008.
- Imbens G, Wooldridge J. 2009. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47:5–86
- Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]
- Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:267–88
- M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Ma- chine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994, pages 157–163, 1994

## Frequently Asked Questions

Q: What is the prediction methodology for ABBV stock?A: ABBV stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Spearman Correlation

Q: Is ABBV stock a buy or sell?

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

Q: Is AbbVie stock a good investment?

A: The consensus rating for AbbVie is Hold and assigned short-term Ba3 & long-term Ba1 forecasted stock rating.

Q: What is the consensus rating of ABBV stock?

A: The consensus rating for ABBV is Hold.

Q: What is the prediction period for ABBV stock?

A: The prediction period for ABBV is (n+4 weeks)

## People also ask

⚐ What are the top stocks to invest in right now?☵ What happens to stocks when they're delisted?