Prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Many studies predict stock price movements using deep learning models. Although the attention mechanism has gained popularity recently in neural machine translation, little focus has been devoted to attention-based deep learning models for stock prediction. ** We evaluate Lamar Advertising Company prediction models with Modular Neural Network (Market News Sentiment Analysis) and Wilcoxon Rank-Sum Test ^{1,2,3,4} and conclude that the LAMR 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 Sell LAMR stock.**

**LAMR, Lamar Advertising Company, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Investment Risk
- Understanding Buy, Sell, and Hold Ratings
- How do predictive algorithms actually work?

## LAMR Target Price Prediction Modeling Methodology

The stock market prediction has attracted much attention from academia as well as business. Due to the non-linear, volatile and complex nature of the market, it is quite difficult to predict. As the stock markets grow bigger, more investors pay attention to develop a systematic approach to predict the stock market. We consider Lamar Advertising Company Stock Decision Process with Wilcoxon Rank-Sum Test where A is the set of discrete actions of LAMR 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 (Market News Sentiment Analysis)) X S(n):→ (n+3 month) $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of LAMR 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?

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

**Sample Set:**Neural Network

**Stock/Index:**LAMR Lamar Advertising Company

**Time series to forecast n: 10 Oct 2022**for (n+3 month)

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

Lamar Advertising Company assigned short-term B2 & long-term Baa2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) with Wilcoxon Rank-Sum Test ^{1,2,3,4} and conclude that the LAMR 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 Sell LAMR stock.**

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

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

Outlook* | B2 | Baa2 |

Operational Risk | 46 | 81 |

Market Risk | 72 | 45 |

Technical Analysis | 73 | 73 |

Fundamental Analysis | 49 | 87 |

Risk Unsystematic | 45 | 85 |

### Prediction Confidence Score

## References

- Bertsimas D, King A, Mazumder R. 2016. Best subset selection via a modern optimization lens. Ann. Stat. 44:813–52
- E. van der Pol and F. A. Oliehoek. Coordinated deep reinforcement learners for traffic light control. NIPS Workshop on Learning, Inference and Control of Multi-Agent Systems, 2016.
- Athey S, Imbens G, Wager S. 2016a. Efficient inference of average treatment effects in high dimensions via approximate residual balancing. arXiv:1604.07125 [math.ST]
- Chipman HA, George EI, McCulloch RE. 2010. Bart: Bayesian additive regression trees. Ann. Appl. Stat. 4:266–98
- Chen X. 2007. Large sample sieve estimation of semi-nonparametric models. In Handbook of Econometrics, Vol. 6B, ed. JJ Heckman, EE Learner, pp. 5549–632. Amsterdam: Elsevier
- Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
- Babula, R. A. (1988), "Contemporaneous correlation and modeling Canada's imports of U.S. crops," Journal of Agricultural Economics Research, 41, 33–38.

## Frequently Asked Questions

Q: What is the prediction methodology for LAMR stock?A: LAMR stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and Wilcoxon Rank-Sum Test

Q: Is LAMR stock a buy or sell?

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

Q: Is Lamar Advertising Company stock a good investment?

A: The consensus rating for Lamar Advertising Company is Sell and assigned short-term B2 & long-term Baa2 forecasted stock rating.

Q: What is the consensus rating of LAMR stock?

A: The consensus rating for LAMR is Sell.

Q: What is the prediction period for LAMR stock?

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