The main objective of this research is to predict the market performance on day closing using different machine learning techniques. The prediction model uses different attributes as an input and predicts market as Positive & Negative. ** We evaluate Hologic prediction models with Statistical Inference (ML) and Multiple Regression ^{1,2,3,4} and conclude that the HOLX 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 Buy HOLX stock.**

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

*Keywords:*## Key Points

- What are buy sell or hold recommendations?
- Can neural networks predict stock market?
- Investment Risk

## HOLX Target Price Prediction Modeling Methodology

Prediction of the Stock Market is a challenging task in predicting the stock prices in the future. Due to the fluctuating nature of the stock, the stock market is too difficult to predict. Stock prices are constantly changing every day. Estimating of the stock market has a high demand for stock customers. Applying all extracted rules at any time is a major challenge to estimate the future stock price with high accuracy. The latest prediction techniques adopted for the stock market such as Artificial Neural Network, Neuro-Fuzzy System, Time Series Linear Models (TSLM), Recurrent Neural Network (RNN). We consider Hologic Stock Decision Process with Multiple Regression where A is the set of discrete actions of HOLX 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 Î³ ∈ [0, 1] is a move factor for expectation.^{1,2,3,4}

F(Multiple 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(Statistical Inference (ML)) X S(n):→ (n+1 year) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of HOLX 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?

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

**Sample Set:**Neural Network

**Stock/Index:**HOLX Hologic

**Time series to forecast n: 22 Sep 2022**for (n+1 year)

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

Hologic assigned short-term B1 & long-term Caa1 forecasted stock rating.** We evaluate the prediction models Statistical Inference (ML) with Multiple Regression ^{1,2,3,4} and conclude that the HOLX 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 Buy HOLX stock.**

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

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

Outlook* | B1 | Caa1 |

Operational Risk | 72 | 36 |

Market Risk | 90 | 52 |

Technical Analysis | 39 | 37 |

Fundamental Analysis | 39 | 41 |

Risk Unsystematic | 68 | 39 |

### Prediction Confidence Score

## References

- Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
- V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, pages 1928–1937, 2016
- CandÃ¨s EJ, Recht B. 2009. Exact matrix completion via convex optimization. Found. Comput. Math. 9:717
- KÃ¼nzel S, Sekhon J, Bickel P, Yu B. 2017. Meta-learners for estimating heterogeneous treatment effects using machine learning. arXiv:1706.03461 [math.ST]
- Miller A. 2002. Subset Selection in Regression. New York: CRC Press
- Hastie T, Tibshirani R, Wainwright M. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. New York: CRC Press
- A. Tamar and S. Mannor. Variance adjusted actor critic algorithms. arXiv preprint arXiv:1310.3697, 2013.

## Frequently Asked Questions

Q: What is the prediction methodology for HOLX stock?A: HOLX stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Multiple Regression

Q: Is HOLX stock a buy or sell?

A: The dominant strategy among neural network is to Buy HOLX Stock.

Q: Is Hologic stock a good investment?

A: The consensus rating for Hologic is Buy and assigned short-term B1 & long-term Caa1 forecasted stock rating.

Q: What is the consensus rating of HOLX stock?

A: The consensus rating for HOLX is Buy.

Q: What is the prediction period for HOLX stock?

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