Accurate prediction of stock price movements is highly challenging and significant topic for investors. Investors need to understand that stock price data is the most essential information which is highly volatile, non-linear, and non-parametric and are affected by many uncertainties and interrelated economic and political factors across the globe. Artificial Neural Networks (ANN) have been found to be an efficient tool in modeling stock prices and quite a large number of studies have been done on it. ** We evaluate Silgan Holdings prediction models with Modular Neural Network (Market Direction Analysis) and Factor ^{1,2,3,4} and conclude that the SLGN stock is predictable in the short/long term. **

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell SLGN stock.**

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

*Keywords:*## Key Points

- Nash Equilibria
- Trading Interaction
- Can neural networks predict stock market?

## SLGN Target Price Prediction Modeling Methodology

Stock market forecasting is considered to be a challenging topic among time series forecasting. This study proposes a novel two-stage ensemble machine learning model named SVR-ENANFIS for stock price prediction by combining features of support vector regression (SVR) and ensemble adaptive neuro fuzzy inference system (ENANFIS). We consider Silgan Holdings Stock Decision Process with Factor where A is the set of discrete actions of SLGN 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(Factor)

^{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 (Market Direction Analysis)) X S(n):→ (n+3 month) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of SLGN 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?

## SLGN Stock Forecast (Buy or Sell) for (n+3 month)

**Sample Set:**Neural Network

**Stock/Index:**SLGN Silgan Holdings

**Time series to forecast n: 24 Oct 2022**for (n+3 month)

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell SLGN 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

Silgan Holdings assigned short-term B3 & long-term Ba1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Factor ^{1,2,3,4} and conclude that the SLGN stock is predictable in the short/long term.**

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell SLGN stock.**

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

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

Outlook* | B3 | Ba1 |

Operational Risk | 67 | 73 |

Market Risk | 49 | 85 |

Technical Analysis | 64 | 75 |

Fundamental Analysis | 41 | 57 |

Risk Unsystematic | 34 | 59 |

### Prediction Confidence Score

## References

- L. Panait and S. Luke. Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems, 11(3):387–434, 2005.
- C. Wu and Y. Lin. Minimizing risk models in Markov decision processes with policies depending on target values. Journal of Mathematical Analysis and Applications, 231(1):47–67, 1999
- Gentzkow M, Kelly BT, Taddy M. 2017. Text as data. NBER Work. Pap. 23276
- Burkov A. 2019. The Hundred-Page Machine Learning Book. Quebec City, Can.: Andriy Burkov
- Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
- Hastie T, Tibshirani R, Friedman J. 2009. The Elements of Statistical Learning. Berlin: Springer
- Zubizarreta JR. 2015. Stable weights that balance covariates for estimation with incomplete outcome data. J. Am. Stat. Assoc. 110:910–22

## Frequently Asked Questions

Q: What is the prediction methodology for SLGN stock?A: SLGN stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Factor

Q: Is SLGN stock a buy or sell?

A: The dominant strategy among neural network is to Sell SLGN Stock.

Q: Is Silgan Holdings stock a good investment?

A: The consensus rating for Silgan Holdings is Sell and assigned short-term B3 & long-term Ba1 forecasted stock rating.

Q: What is the consensus rating of SLGN stock?

A: The consensus rating for SLGN is Sell.

Q: What is the prediction period for SLGN stock?

A: The prediction period for SLGN is (n+3 month)

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