The prediction of stock price performance is a difficult and complex problem. Multivariate analytical techniques using both quantitative and qualitative variables have repeatedly been used to help form the basis of investor stock price expectations and, hence, influence investment decision making. However, the performance of multivariate analytical techniques is often less than conclusive and needs to be improved to more accurately forecast stock price performance. A neural network method has demonstrated its capability of addressing complex problems.** We evaluate Unilever prediction models with Ensemble Learning (ML) and Lasso Regression ^{1,2,3,4} and conclude that the ULVR stock is predictable in the short/long term. **

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Sell ULVR stock.**

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

*Keywords:*## Key Points

- Should I buy stocks now or wait amid such uncertainty?
- What is prediction in deep learning?
- Can we predict stock market using machine learning?

## ULVR Target Price Prediction Modeling Methodology

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 consider Unilever Stock Decision Process with Lasso Regression where A is the set of discrete actions of ULVR 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(Ensemble Learning (ML)) X S(n):→ (n+6 month) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of ULVR 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?

## ULVR Stock Forecast (Buy or Sell) for (n+6 month)

**Sample Set:**Neural Network

**Stock/Index:**ULVR Unilever

**Time series to forecast n: 25 Sep 2022**for (n+6 month)

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Sell ULVR 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

Unilever assigned short-term B3 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Ensemble Learning (ML) with Lasso Regression ^{1,2,3,4} and conclude that the ULVR stock is predictable in the short/long term.**

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Sell ULVR stock.**

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

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

Outlook* | B3 | Ba3 |

Operational Risk | 35 | 64 |

Market Risk | 61 | 86 |

Technical Analysis | 41 | 34 |

Fundamental Analysis | 54 | 76 |

Risk Unsystematic | 66 | 46 |

### Prediction Confidence Score

## References

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- Dudik M, Langford J, Li L. 2011. Doubly robust policy evaluation and learning. In Proceedings of the 28th International Conference on Machine Learning, pp. 1097–104. La Jolla, CA: Int. Mach. Learn. Soc.
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- Wager S, Athey S. 2017. Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113:1228–42
- Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
- Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
- Ashley, R. (1988), "On the relative worth of recent macroeconomic forecasts," International Journal of Forecasting, 4, 363–376.

## Frequently Asked Questions

Q: What is the prediction methodology for ULVR stock?A: ULVR stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Lasso Regression

Q: Is ULVR stock a buy or sell?

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

Q: Is Unilever stock a good investment?

A: The consensus rating for Unilever is Sell and assigned short-term B3 & long-term Ba3 forecasted stock rating.

Q: What is the consensus rating of ULVR stock?

A: The consensus rating for ULVR is Sell.

Q: What is the prediction period for ULVR stock?

A: The prediction period for ULVR is (n+6 month)