In this paper, we propose a hybrid machine learning system based on Genetic Algor ithm (GA) and Support Vector Machines (SVM) for stock market prediction. A variety of indicators from the technical analysis field of study are used as input features. We also make use of the correlation between stock prices of different companies to forecast the price of a stock, making use of technical indicators of highly correlated stocks, not only the stock to be predicted. The genetic algorithm is used to select the set of most informative input features from among all the technical indicators.** We evaluate The J.M. Smucker Company prediction models with Modular Neural Network (DNN Layer) and Wilcoxon Rank-Sum Test ^{1,2,3,4} and conclude that the SJM stock is predictable in the short/long term. **

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Buy SJM stock.**

**SJM, The J.M. Smucker Company, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- What are the most successful trading algorithms?
- Fundemental Analysis with Algorithmic Trading
- How useful are statistical predictions?

## SJM Target Price Prediction Modeling Methodology

This paper studies the possibilities of making prediction of stock market prices using historical data and machine learning algorithms. We consider The J.M. Smucker Company Stock Decision Process with Wilcoxon Rank-Sum Test where A is the set of discrete actions of SJM 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(Wilcoxon Rank-Sum Test)

^{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+16 weeks) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of SJM 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?

## SJM Stock Forecast (Buy or Sell) for (n+16 weeks)

**Sample Set:**Neural Network

**Stock/Index:**SJM The J.M. Smucker Company

**Time series to forecast n: 13 Oct 2022**for (n+16 weeks)

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Buy SJM 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

The J.M. Smucker Company assigned short-term B3 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (DNN Layer) with Wilcoxon Rank-Sum Test ^{1,2,3,4} and conclude that the SJM stock is predictable in the short/long term.**

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Buy SJM stock.**

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

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

Outlook* | B3 | Ba3 |

Operational Risk | 80 | 36 |

Market Risk | 46 | 89 |

Technical Analysis | 38 | 51 |

Fundamental Analysis | 32 | 89 |

Risk Unsystematic | 58 | 50 |

### Prediction Confidence Score

## References

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- Chipman HA, George EI, McCulloch RE. 2010. Bart: Bayesian additive regression trees. Ann. Appl. Stat. 4:266–98
- Z. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot, and N. de Freitas. Dueling network architectures for deep reinforcement learning. In Proceedings of the International Conference on Machine Learning (ICML), pages 1995–2003, 2016.
- Dietterich TG. 2000. Ensemble methods in machine learning. In Multiple Classifier Systems: First International Workshop, Cagliari, Italy, June 21–23, pp. 1–15. Berlin: Springer
- Bottomley, P. R. Fildes (1998), "The role of prices in models of innovation diffusion," Journal of Forecasting, 17, 539–555.
- Morris CN. 1983. Parametric empirical Bayes inference: theory and applications. J. Am. Stat. Assoc. 78:47–55
- Harris ZS. 1954. Distributional structure. Word 10:146–62

## Frequently Asked Questions

Q: What is the prediction methodology for SJM stock?A: SJM stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Wilcoxon Rank-Sum Test

Q: Is SJM stock a buy or sell?

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

Q: Is The J.M. Smucker Company stock a good investment?

A: The consensus rating for The J.M. Smucker Company is Buy and assigned short-term B3 & long-term Ba3 forecasted stock rating.

Q: What is the consensus rating of SJM stock?

A: The consensus rating for SJM is Buy.

Q: What is the prediction period for SJM stock?

A: The prediction period for SJM is (n+16 weeks)

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