This paper studies the possibilities of making prediction of stock market prices using historical data and machine learning algorithms.** We evaluate Rockwell Automation prediction models with Multi-Task Learning (ML) and Lasso Regression ^{1,2,3,4} and conclude that the ROK 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 Sell ROK stock.**

**ROK, Rockwell Automation, 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?
- What is prediction model?
- Nash Equilibria

## ROK Target Price Prediction Modeling Methodology

Prediction of future movement of stock prices has always been a challenging task for the researchers. While the advocates of the efficient market hypothesis (EMH) believe that it is impossible to design any predictive framework that can accurately predict the movement of stock prices, there are seminal work in the literature that have clearly demonstrated that the seemingly random movement patterns in the time series of a stock price can be predicted with a high level of accuracy. We consider Rockwell Automation Stock Decision Process with Lasso Regression where A is the set of discrete actions of ROK 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(Multi-Task Learning (ML)) X S(n):→ (n+16 weeks) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of ROK 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?

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

**Sample Set:**Neural Network

**Stock/Index:**ROK Rockwell Automation

**Time series to forecast n: 25 Sep 2022**for (n+16 weeks)

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

Rockwell Automation assigned short-term B3 & long-term B3 forecasted stock rating.** We evaluate the prediction models Multi-Task Learning (ML) with Lasso Regression ^{1,2,3,4} and conclude that the ROK 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 Sell ROK stock.**

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

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

Outlook* | B3 | B3 |

Operational Risk | 48 | 38 |

Market Risk | 60 | 57 |

Technical Analysis | 59 | 46 |

Fundamental Analysis | 38 | 59 |

Risk Unsystematic | 34 | 36 |

### Prediction Confidence Score

## References

- L. Prashanth and M. Ghavamzadeh. Actor-critic algorithms for risk-sensitive MDPs. In Proceedings of Advances in Neural Information Processing Systems 26, pages 252–260, 2013.
- Friedman JH. 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38:367–78
- Bell RM, Koren Y. 2007. Lessons from the Netflix prize challenge. ACM SIGKDD Explor. Newsl. 9:75–79
- Hill JL. 2011. Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20:217–40
- Tibshirani R, Hastie T. 1987. Local likelihood estimation. J. Am. Stat. Assoc. 82:559–67
- Blei DM, Lafferty JD. 2009. Topic models. In Text Mining: Classification, Clustering, and Applications, ed. A Srivastava, M Sahami, pp. 101–24. Boca Raton, FL: CRC Press
- M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Ma- chine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994, pages 157–163, 1994

## Frequently Asked Questions

Q: What is the prediction methodology for ROK stock?A: ROK stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Lasso Regression

Q: Is ROK stock a buy or sell?

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

Q: Is Rockwell Automation stock a good investment?

A: The consensus rating for Rockwell Automation is Sell and assigned short-term B3 & long-term B3 forecasted stock rating.

Q: What is the consensus rating of ROK stock?

A: The consensus rating for ROK is Sell.

Q: What is the prediction period for ROK stock?

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