**Outlook:**Laramide Resources Ltd. is assigned short-term B3 & long-term B1 estimated rating.

**AUC Score :**

**Short-Term Revised**

^{1}:**Dominant Strategy :**Hold

**Time series to forecast n:** for

^{2}

**Methodology :**Modular Neural Network (CNN Layer)

**Hypothesis Testing :**Polynomial Regression

**Surveillance :**Major exchange and OTC

^{1}The accuracy of the model is being monitored on a regular basis.(15-minute period)

^{2}Time series is updated based on short-term trends.

## Abstract

Laramide Resources Ltd. prediction model is evaluated with Modular Neural Network (CNN Layer) and Polynomial Regression^{1,2,3,4}and it is concluded that the LAM:TSX stock is predictable in the short/long term. CNN layers are a powerful tool for extracting features from images. They are able to learn to detect patterns in images that are not easily detected by humans. This makes them well-suited for a variety of MNN applications.

**According to price forecasts for 1 Year period, the dominant strategy among neural network is: Hold**

## Key Points

- Can we predict stock market using machine learning?
- Can we predict stock market using machine learning?
- Game Theory

## LAM:TSX Target Price Prediction Modeling Methodology

We consider Laramide Resources Ltd. Decision Process with Modular Neural Network (CNN Layer) where A is the set of discrete actions of LAM:TSX 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(Polynomial 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 (CNN Layer)) X S(n):→ 1 Year $\overrightarrow{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of LAM:TSX stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

### Modular Neural Network (CNN Layer)

CNN layers are a powerful tool for extracting features from images. They are able to learn to detect patterns in images that are not easily detected by humans. This makes them well-suited for a variety of MNN applications.### Polynomial Regression

Polynomial regression is a type of regression analysis that uses a polynomial function to model the relationship between a dependent variable and one or more independent variables. Polynomial functions are mathematical functions that have a polynomial term, which is a term that is raised to a power greater than 1. In polynomial regression, the dependent variable is modeled as a polynomial function of the independent variables. The degree of the polynomial function is determined by the researcher. The higher the degree of the polynomial function, the more complex the model will be.

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?

## LAM:TSX Stock Forecast (Buy or Sell)

**Sample Set:**Neural Network

**Stock/Index:**LAM:TSX Laramide Resources Ltd.

**Time series to forecast:**1 Year

**According to price forecasts, the dominant strategy among neural network is: Hold**

Strategic Interaction Table Legend:

**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 (Grey to Black): *Technical Analysis%**

### Financial Data Adjustments for Modular Neural Network (CNN Layer) based LAM:TSX Stock Prediction Model

- If an entity prepares interim financial reports in accordance with IAS 34 Interim Financial Reporting the entity need not apply the requirements in this Standard to interim periods prior to the date of initial application if it is impracticable (as defined in IAS 8).
- If, at the date of initial application, determining whether there has been a significant increase in credit risk since initial recognition would require undue cost or effort, an entity shall recognise a loss allowance at an amount equal to lifetime expected credit losses at each reporting date until that financial instrument is derecognised (unless that financial instrument is low credit risk at a reporting date, in which case paragraph 7.2.19(a) applies).
- The assessment of whether lifetime expected credit losses should be recognised is based on significant increases in the likelihood or risk of a default occurring since initial recognition (irrespective of whether a financial instrument has been repriced to reflect an increase in credit risk) instead of on evidence of a financial asset being credit-impaired at the reporting date or an actual default occurring. Generally, there will be a significant increase in credit risk before a financial asset becomes credit-impaired or an actual default occurs.
- If such a mismatch would be created or enlarged, the entity is required to present all changes in fair value (including the effects of changes in the credit risk of the liability) in profit or loss. If such a mismatch would not be created or enlarged, the entity is required to present the effects of changes in the liability's credit risk in other comprehensive income.

*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.

### LAM:TSX Laramide Resources Ltd. Financial Analysis*

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

Outlook* | B3 | B1 |

Income Statement | Caa2 | B1 |

Balance Sheet | C | Ba2 |

Leverage Ratios | B3 | Ba2 |

Cash Flow | B2 | C |

Rates of Return and Profitability | B1 | Ba1 |

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.

How does neural network examine financial reports and understand financial state of the company?

## Conclusions

Laramide Resources Ltd. is assigned short-term B3 & long-term B1 estimated rating. Laramide Resources Ltd. prediction model is evaluated with Modular Neural Network (CNN Layer) and Polynomial Regression^{1,2,3,4} and it is concluded that the LAM:TSX stock is predictable in the short/long term. ** According to price forecasts for 1 Year period, the dominant strategy among neural network is: Hold**

### Prediction Confidence Score

## References

- Athey S, Blei D, Donnelly R, Ruiz F. 2017b. Counterfactual inference for consumer choice across many prod- uct categories. AEA Pap. Proc. 108:64–67
- Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]
- Meinshausen N. 2007. Relaxed lasso. Comput. Stat. Data Anal. 52:374–93
- D. S. Bernstein, S. Zilberstein, and N. Immerman. The complexity of decentralized control of Markov Decision Processes. In UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30 - July 3, 2000, pages 32–37, 2000.
- M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
- Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
- R. Rockafellar and S. Uryasev. Optimization of conditional value-at-risk. Journal of Risk, 2:21–42, 2000.

## Frequently Asked Questions

Q: What is the prediction methodology for LAM:TSX stock?A: LAM:TSX stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Polynomial Regression

Q: Is LAM:TSX stock a buy or sell?

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

Q: Is Laramide Resources Ltd. stock a good investment?

A: The consensus rating for Laramide Resources Ltd. is Hold and is assigned short-term B3 & long-term B1 estimated rating.

Q: What is the consensus rating of LAM:TSX stock?

A: The consensus rating for LAM:TSX is Hold.

Q: What is the prediction period for LAM:TSX stock?

A: The prediction period for LAM:TSX is 1 Year

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