Recently, numerous investigations for stock price prediction and portfolio management using machine learning have been trying to develop efficient mechanical trading systems. But these systems have a limitation in that they are mainly based on the supervised learning which is not so adequate for learning problems with long-term goals and delayed rewards. This paper proposes a method of applying reinforcement learning, suitable for modeling and learning various kinds of interactions in real situations, to the problem of stock price prediction.** We evaluate Gildan Activewear Inc. prediction models with Modular Neural Network (Financial Sentiment Analysis) and Pearson Correlation ^{1,2,3,4} and conclude that the GIL 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 GIL stock.**

**GIL, Gildan Activewear Inc., stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Market Outlook
- Can neural networks predict stock market?
- How do you pick a stock?

## GIL Target Price Prediction Modeling Methodology

Sentiment Analysis is new way of machine learning to extract opinion orientation (positive, negative, neutral) from a text segment written for any product, organization, person or any other entity. Sentiment Analysis can be used to predict the mood of people that have impact on stock prices, therefore it can help in prediction of actual stock movement. We consider Gildan Activewear Inc. Stock Decision Process with Pearson Correlation where A is the set of discrete actions of GIL 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(Pearson 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 (Financial Sentiment Analysis)) X S(n):→ (n+8 weeks) $\sum _{i=1}^{n}\left({s}_{i}\right)$

n:Time series to forecast

p:Price signals of GIL 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?

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

**Sample Set:**Neural Network

**Stock/Index:**GIL Gildan Activewear Inc.

**Time series to forecast n: 10 Sep 2022**for (n+8 weeks)

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

Gildan Activewear Inc. assigned short-term Ba3 & long-term B2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with Pearson Correlation ^{1,2,3,4} and conclude that the GIL 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 GIL stock.**

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

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

Outlook* | Ba3 | B2 |

Operational Risk | 64 | 51 |

Market Risk | 89 | 84 |

Technical Analysis | 54 | 34 |

Fundamental Analysis | 75 | 36 |

Risk Unsystematic | 38 | 71 |

### Prediction Confidence Score

## References

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- V. Borkar. A sensitivity formula for the risk-sensitive cost and the actor-critic algorithm. Systems & Control Letters, 44:339–346, 2001
- 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
- Allen, P. G. (1994), "Economic forecasting in agriculture," International Journal of Forecasting, 10, 81–135.
- Hastie T, Tibshirani R, Tibshirani RJ. 2017. Extended comparisons of best subset selection, forward stepwise selection, and the lasso. arXiv:1707.08692 [stat.ME]
- Bastani H, Bayati M. 2015. Online decision-making with high-dimensional covariates. Work. Pap., Univ. Penn./ Stanford Grad. School Bus., Philadelphia/Stanford, CA

## Frequently Asked Questions

Q: What is the prediction methodology for GIL stock?A: GIL stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Pearson Correlation

Q: Is GIL stock a buy or sell?

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

Q: Is Gildan Activewear Inc. stock a good investment?

A: The consensus rating for Gildan Activewear Inc. is Hold and assigned short-term Ba3 & long-term B2 forecasted stock rating.

Q: What is the consensus rating of GIL stock?

A: The consensus rating for GIL is Hold.

Q: What is the prediction period for GIL stock?

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