## Abstract

**We evaluate Parker Hannifin prediction models with Modular Neural Network (DNN Layer) and Linear Regression ^{1,2,3,4} and conclude that the PH 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 Hold PH stock.**

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

*Keywords:*## Key Points

- Is Target price a good indicator?
- Market Risk
- What are the most successful trading algorithms?

## PH Target Price Prediction Modeling Methodology

We consider Parker Hannifin Stock Decision Process with Linear Regression where A is the set of discrete actions of PH 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(Linear 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(Modular Neural Network (DNN Layer)) X S(n):→ (n+3 month) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of PH 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?

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

**Sample Set:**Neural Network

**Stock/Index:**PH Parker Hannifin

**Time series to forecast n: 08 Sep 2022**for (n+3 month)

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

Parker Hannifin assigned short-term B2 & long-term B2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (DNN Layer) with Linear Regression ^{1,2,3,4} and conclude that the PH 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 Hold PH stock.**

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

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

Outlook* | B2 | B2 |

Operational Risk | 66 | 49 |

Market Risk | 37 | 90 |

Technical Analysis | 42 | 57 |

Fundamental Analysis | 48 | 39 |

Risk Unsystematic | 85 | 34 |

### Prediction Confidence Score

## References

- Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94
- Clements, M. P. D. F. Hendry (1995), "Forecasting in cointegrated systems," Journal of Applied Econometrics, 10, 127–146.
- Hornik K, Stinchcombe M, White H. 1989. Multilayer feedforward networks are universal approximators. Neural Netw. 2:359–66
- Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2016a. Double machine learning for treatment and causal parameters. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
- J. G. Schneider, W. Wong, A. W. Moore, and M. A. Riedmiller. Distributed value functions. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 371–378, 1999.
- G. J. Laurent, L. Matignon, and N. L. Fort-Piat. The world of independent learners is not Markovian. Int. J. Know.-Based Intell. Eng. Syst., 15(1):55–64, 2011
- Athey S, Imbens GW. 2017b. The state of applied econometrics: causality and policy evaluation. J. Econ. Perspect. 31:3–32

## Frequently Asked Questions

Q: What is the prediction methodology for PH stock?A: PH stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Linear Regression

Q: Is PH stock a buy or sell?

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

Q: Is Parker Hannifin stock a good investment?

A: The consensus rating for Parker Hannifin is Hold and assigned short-term B2 & long-term B2 forecasted stock rating.

Q: What is the consensus rating of PH stock?

A: The consensus rating for PH is Hold.

Q: What is the prediction period for PH stock?

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

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