**Outlook:**NexGen Energy Ltd. assigned short-term Caa2 & long-term B3 forecasted stock rating.

**Dominant Strategy :**Sell

**Time series to forecast n: 08 Dec 2022**for (n+4 weeks)

**Methodology :**Active Learning (ML)

## Abstract

Stock index price prediction is prevalent in both academic and economic fields. The index price is hard to forecast due to its uncertain noise. With the development of computer science, neural networks are applied in kinds of industrial fields. In this paper, we introduce four different methods in machine learning including three typical machine learning models: Multilayer Perceptron (MLP), Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) and one attention-based neural network.(Sable, R., Goel, S. and Chatterjee, P., 2019, December. Empirical study on stock market prediction using machine learning. In 2019 International conference on advances in computing, communication and control (ICAC3) (pp. 1-5). IEEE.)** We evaluate NexGen Energy Ltd. prediction models with Active Learning (ML) and Paired T-Test ^{1,2,3,4} and conclude that the NXE:TSX stock is predictable in the short/long term. **

**According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Sell**

## Key Points

- Dominated Move
- How accurate is machine learning in stock market?
- Fundemental Analysis with Algorithmic Trading

## NXE:TSX Target Price Prediction Modeling Methodology

We consider NexGen Energy Ltd. Decision Process with Active Learning (ML) where A is the set of discrete actions of NXE: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(Paired T-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(Active Learning (ML)) X S(n):→ (n+4 weeks) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of NXE:TSX stock

j:Nash equilibria (Neural Network)

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?

## NXE:TSX Stock Forecast (Buy or Sell) for (n+4 weeks)

**Sample Set:**Neural Network

**Stock/Index:**NXE:TSX NexGen Energy Ltd.

**Time series to forecast n: 08 Dec 2022**for (n+4 weeks)

**According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Sell**

**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%**

## Adjusted IFRS* Prediction Methods for NexGen Energy Ltd.

- Annual Improvements to IFRS Standards 2018–2020, issued in May 2020, added paragraphs 7.2.35 and B3.3.6A and amended paragraph B3.3.6. An entity shall apply that amendment for annual reporting periods beginning on or after 1 January 2022. Earlier application is permitted. If an entity applies the amendment for an earlier period, it shall disclose that fact.
- For purchased or originated credit-impaired financial assets, expected credit losses shall be discounted using the credit-adjusted effective interest rate determined at initial recognition.
- For the purposes of the transition provisions in paragraphs 7.2.1, 7.2.3–7.2.28 and 7.3.2, the date of initial application is the date when an entity first applies those requirements of this Standard and must be the beginning of a reporting period after the issue of this Standard. Depending on the entity's chosen approach to applying IFRS 9, the transition can involve one or more than one date of initial application for different requirements.
- When designating risk components as hedged items, an entity considers whether the risk components are explicitly specified in a contract (contractually specified risk components) or whether they are implicit in the fair value or the cash flows of an item of which they are a part (noncontractually specified risk components). Non-contractually specified risk components can relate to items that are not a contract (for example, forecast transactions) or contracts that do not explicitly specify the component (for example, a firm commitment that includes only one single price instead of a pricing formula that references different underlyings)

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

## Conclusions

NexGen Energy Ltd. assigned short-term Caa2 & long-term B3 forecasted stock rating.** We evaluate the prediction models Active Learning (ML) with Paired T-Test ^{1,2,3,4} and conclude that the NXE:TSX stock is predictable in the short/long term.**

**According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Sell**

### Financial State Forecast for NXE:TSX NexGen Energy Ltd. Options & Futures

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

Outlook* | Caa2 | B3 |

Operational Risk | 33 | 30 |

Market Risk | 38 | 54 |

Technical Analysis | 41 | 32 |

Fundamental Analysis | 37 | 72 |

Risk Unsystematic | 79 | 39 |

### Prediction Confidence Score

## References

- Mnih A, Kavukcuoglu K. 2013. Learning word embeddings efficiently with noise-contrastive estimation. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 2265–73. San Diego, CA: Neural Inf. Process. Syst. Found.
- Athey S, Mobius MM, Pál J. 2017c. The impact of aggregators on internet news consumption. Unpublished manuscript, Grad. School Bus., Stanford Univ., Stanford, CA
- M. Ono, M. Pavone, Y. Kuwata, and J. Balaram. Chance-constrained dynamic programming with application to risk-aware robotic space exploration. Autonomous Robots, 39(4):555–571, 2015
- Hastie T, Tibshirani R, Friedman J. 2009. The Elements of Statistical Learning. Berlin: Springer
- Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
- Clements, M. P. D. F. Hendry (1997), "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, 13, 341–355.
- Y. Chow and M. Ghavamzadeh. Algorithms for CVaR optimization in MDPs. In Advances in Neural Infor- mation Processing Systems, pages 3509–3517, 2014.

## Frequently Asked Questions

Q: What is the prediction methodology for NXE:TSX stock?A: NXE:TSX stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Paired T-Test

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

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

Q: Is NexGen Energy Ltd. stock a good investment?

A: The consensus rating for NexGen Energy Ltd. is Sell and assigned short-term Caa2 & long-term B3 forecasted stock rating.

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

A: The consensus rating for NXE:TSX is Sell.

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

A: The prediction period for NXE:TSX is (n+4 weeks)

## People also ask

⚐ What are the top stocks to invest in right now?☵ What happens to stocks when they're delisted?