Complex networks in stock market and stock price volatility pattern prediction are the important issues in stock price research. Previous studies have used historical information regarding a single stock to predict the future trend of the stock's price, seldom considering comovement among stocks in the same market. In this study, in order to extract the information about relation stocks for prediction, we try to combine the complex network method with machine learning to predict stock price patterns.** We evaluate Abbott prediction models with Inductive Learning (ML) and Lasso Regression ^{1,2,3,4} and conclude that the ABT stock is predictable in the short/long term. **

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold ABT stock.**

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

*Keywords:*## Key Points

- Investment Risk
- Is now good time to invest?
- What is statistical models in machine learning?

## ABT Target Price Prediction Modeling Methodology

Stock index price prediction is prevalent in both academic and economic fields. The index price is hard to forecast due to its uncertain noise. With the development of computer science, neural networks are applied in kinds of industrial fields. In this paper, we introduce four different methods in machine learning including three typical machine learning models: Multilayer Perceptron (MLP), Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) and one attention-based neural network. We consider Abbott Stock Decision Process with Lasso Regression where A is the set of discrete actions of ABT 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(Lasso Regression)

^{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(Inductive Learning (ML)) X S(n):→ (n+6 month) $\sum _{i=1}^{n}\left({r}_{i}\right)$

n:Time series to forecast

p:Price signals of ABT 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?

## ABT Stock Forecast (Buy or Sell) for (n+6 month)

**Sample Set:**Neural Network

**Stock/Index:**ABT Abbott

**Time series to forecast n: 25 Sep 2022**for (n+6 month)

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold ABT 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

Abbott assigned short-term B3 & long-term B1 forecasted stock rating.** We evaluate the prediction models Inductive Learning (ML) with Lasso Regression ^{1,2,3,4} and conclude that the ABT stock is predictable in the short/long term.**

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold ABT stock.**

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

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

Outlook* | B3 | B1 |

Operational Risk | 30 | 40 |

Market Risk | 62 | 77 |

Technical Analysis | 50 | 53 |

Fundamental Analysis | 70 | 43 |

Risk Unsystematic | 38 | 70 |

### Prediction Confidence Score

## References

- Wooldridge JM. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press
- Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
- A. Tamar, D. Di Castro, and S. Mannor. Policy gradients with variance related risk criteria. In Proceedings of the Twenty-Ninth International Conference on Machine Learning, pages 387–396, 2012.
- D. Bertsekas. Dynamic programming and optimal control. Athena Scientific, 1995.
- Greene WH. 2000. Econometric Analysis. Upper Saddle River, N J: Prentice Hall. 4th ed.
- P. Milgrom and I. Segal. Envelope theorems for arbitrary choice sets. Econometrica, 70(2):583–601, 2002
- Artis, M. J. W. Zhang (1990), "BVAR forecasts for the G-7," International Journal of Forecasting, 6, 349–362.

## Frequently Asked Questions

Q: What is the prediction methodology for ABT stock?A: ABT stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Lasso Regression

Q: Is ABT stock a buy or sell?

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

Q: Is Abbott stock a good investment?

A: The consensus rating for Abbott is Hold and assigned short-term B3 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of ABT stock?

A: The consensus rating for ABT is Hold.

Q: What is the prediction period for ABT stock?

A: The prediction period for ABT is (n+6 month)

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