It has never been easy to invest in a set of assets, the abnormally of financial market does not allow simple models to predict future asset values with higher accuracy. Machine learning, which consist of making computers perform tasks that normally requiring human intelligence is currently the dominant trend in scientific research. This article aims to build a model using Recurrent Neural Networks (RNN) and especially Long-Short Term Memory model (LSTM) to predict future stock market values.** We evaluate Harley-Davidson prediction models with Multi-Task Learning (ML) and Stepwise Regression ^{1,2,3,4} and conclude that the HOG stock is predictable in the short/long term. **

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold HOG stock.**

**HOG, Harley-Davidson, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Prediction Modeling
- Trading Interaction
- Reaction Function

## HOG Target Price Prediction Modeling Methodology

Nowadays, the stock market's prediction is a topic that attracted researchers in the world. Stock market prediction is a process that requires a comprehensive understanding of the data stock movement and analysis it accurately. Therefore, it needs intelligent methods to deal with this task to ensure that the prediction is as correct as possible, which will return profitable benefits to investors. The main goal of this article is the employment of effective machine learning techniques to build a strong model for stock market prediction. We consider Harley-Davidson Stock Decision Process with Stepwise Regression where A is the set of discrete actions of HOG 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(Stepwise 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+1 year) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of HOG 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?

## HOG Stock Forecast (Buy or Sell) for (n+1 year)

**Sample Set:**Neural Network

**Stock/Index:**HOG Harley-Davidson

**Time series to forecast n: 08 Oct 2022**for (n+1 year)

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold HOG 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

Harley-Davidson assigned short-term B3 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Multi-Task Learning (ML) with Stepwise Regression ^{1,2,3,4} and conclude that the HOG stock is predictable in the short/long term.**

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold HOG stock.**

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

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

Outlook* | B3 | Ba3 |

Operational Risk | 34 | 41 |

Market Risk | 47 | 87 |

Technical Analysis | 40 | 70 |

Fundamental Analysis | 45 | 56 |

Risk Unsystematic | 84 | 68 |

### Prediction Confidence Score

## References

- H. Khalil and J. Grizzle. Nonlinear systems, volume 3. Prentice hall Upper Saddle River, 2002.
- Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
- Cheung, Y. M.D. Chinn (1997), "Further investigation of the uncertain unit root in GNP," Journal of Business and Economic Statistics, 15, 68–73.
- Bessler, D. A. T. Covey (1991), "Cointegration: Some results on U.S. cattle prices," Journal of Futures Markets, 11, 461–474.
- Cheung, Y. M.D. Chinn (1997), "Further investigation of the uncertain unit root in GNP," Journal of Business and Economic Statistics, 15, 68–73.
- Lai TL, Robbins H. 1985. Asymptotically efficient adaptive allocation rules. Adv. Appl. Math. 6:4–22
- Imbens GW, Rubin DB. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge, UK: Cambridge Univ. Press

## Frequently Asked Questions

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

Q: Is HOG stock a buy or sell?

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

Q: Is Harley-Davidson stock a good investment?

A: The consensus rating for Harley-Davidson is Hold and assigned short-term B3 & long-term Ba3 forecasted stock rating.

Q: What is the consensus rating of HOG stock?

A: The consensus rating for HOG is Hold.

Q: What is the prediction period for HOG stock?

A: The prediction period for HOG is (n+1 year)

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