Modelling A.I. in Economics

NXE:TSX NexGen Energy Ltd.

Outlook: NexGen Energy Ltd. is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 02 May 2023 for (n+3 month)
Methodology : Modular Neural Network (Emotional Trigger/Responses Analysis)

Abstract

NexGen Energy Ltd. prediction model is evaluated with Modular Neural Network (Emotional Trigger/Responses Analysis) and Wilcoxon Rank-Sum Test1,2,3,4 and it is concluded that the NXE:TSX stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy

Key Points

  1. Market Risk
  2. How accurate is machine learning in stock market?
  3. How do you decide buy or sell a stock?

NXE:TSX Target Price Prediction Modeling Methodology

We consider NexGen Energy Ltd. Decision Process with Modular Neural Network (Emotional Trigger/Responses Analysis) 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(Wilcoxon Rank-Sum Test)5,6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Modular Neural Network (Emotional Trigger/Responses Analysis)) X S(n):→ (n+3 month) i = 1 n r i

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+3 month)

Sample Set: Neural Network
Stock/Index: NXE:TSX NexGen Energy Ltd.
Time series to forecast n: 02 May 2023 for (n+3 month)

According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy

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%

IFRS Reconciliation Adjustments for NexGen Energy Ltd.

  1. The expected credit losses on a loan commitment shall be discounted using the effective interest rate, or an approximation thereof, that will be applied when recognising the financial asset resulting from the loan commitment. This is because for the purpose of applying the impairment requirements, a financial asset that is recognised following a draw down on a loan commitment shall be treated as a continuation of that commitment instead of as a new financial instrument. The expected credit losses on the financial asset shall therefore be measured considering the initial credit risk of the loan commitment from the date that the entity became a party to the irrevocable commitment.
  2. 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)
  3. At the date of initial application, an entity shall determine whether the treatment in paragraph 5.7.7 would create or enlarge an accounting mismatch in profit or loss on the basis of the facts and circumstances that exist at the date of initial application. This Standard shall be applied retrospectively on the basis of that determination.
  4. The purpose of estimating expected credit losses is neither to estimate a worstcase scenario nor to estimate the best-case scenario. Instead, an estimate of expected credit losses shall always reflect the possibility that a credit loss occurs and the possibility that no credit loss occurs even if the most likely outcome is no credit loss.

*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.

Conclusions

NexGen Energy Ltd. is assigned short-term Ba1 & long-term Ba1 estimated rating. NexGen Energy Ltd. prediction model is evaluated with Modular Neural Network (Emotional Trigger/Responses Analysis) and Wilcoxon Rank-Sum Test1,2,3,4 and it is concluded that the NXE:TSX stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy

NXE:TSX NexGen Energy Ltd. Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementBa2Baa2
Balance SheetCaa2B3
Leverage RatiosCC
Cash FlowCB1
Rates of Return and ProfitabilityBaa2B3

*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?

Prediction Confidence Score

Trust metric by Neural Network: 79 out of 100 with 780 signals.

References

  1. K. Tuyls and G. Weiss. Multiagent learning: Basics, challenges, and prospects. AI Magazine, 33(3): 41–52, 2012
  2. A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008
  3. Athey S, Wager S. 2017. Efficient policy learning. arXiv:1702.02896 [math.ST]
  4. Hoerl AE, Kennard RW. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67
  5. A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016
  6. P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998
  7. Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
Frequently Asked QuestionsQ: What is the prediction methodology for NXE:TSX stock?
A: NXE:TSX stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Wilcoxon Rank-Sum Test
Q: Is NXE:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Buy NXE:TSX Stock.
Q: Is NexGen Energy Ltd. stock a good investment?
A: The consensus rating for NexGen Energy Ltd. is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of NXE:TSX stock?
A: The consensus rating for NXE:TSX is Buy.
Q: What is the prediction period for NXE:TSX stock?
A: The prediction period for NXE:TSX is (n+3 month)

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