IE:TSX Stock: Are We Headed for a Recession?

Outlook: Ivanhoe Electric Inc. is assigned short-term Ba2 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : Speculative Trend
Time series to forecast n: for Weeks²
Methodology : Multi-Instance Learning (ML)
Hypothesis Testing : Beta
Surveillance : Major exchange and OTC

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

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

Abstract

Ivanhoe Electric Inc. prediction model is evaluated with Multi-Instance Learning (ML) and Beta^1,2,3,4 and it is concluded that the IE:TSX stock is predictable in the short/long term. Multi-instance learning (MIL) is a machine learning (ML) problem where a dataset consists of multiple instances, and each instance is associated with a single label. The goal of MIL is to learn a model that can predict the label of a new instance based on the labels of the instances that it is similar to. MIL is a challenging problem because the instances in a dataset are not labeled individually. This means that the model cannot simply learn a mapping from the features of an instance to its label. Instead, the model must learn a way to combine the features of multiple instances to predict the label of a new instance. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Speculative Trend

Key Points

1. Can we predict stock market using machine learning?
2. What are the most successful trading algorithms?
3. Short/Long Term Stocks

IE:TSX Target Price Prediction Modeling Methodology

We consider Ivanhoe Electric Inc. Decision Process with Multi-Instance Learning (ML) where A is the set of discrete actions of IE: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(Beta)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Multi-Instance Learning (ML)) X S(n):→ 6 Month $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of IE:TSX stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Multi-Instance Learning (ML)

Multi-instance learning (MIL) is a machine learning (ML) problem where a dataset consists of multiple instances, and each instance is associated with a single label. The goal of MIL is to learn a model that can predict the label of a new instance based on the labels of the instances that it is similar to. MIL is a challenging problem because the instances in a dataset are not labeled individually. This means that the model cannot simply learn a mapping from the features of an instance to its label. Instead, the model must learn a way to combine the features of multiple instances to predict the label of a new instance.

Beta

In statistics, beta (β) is a measure of the strength of the relationship between two variables. It is calculated as the slope of the line of best fit in a regression analysis. Beta can range from -1 to 1, with a value of 0 indicating no relationship between the two variables. A positive beta indicates that as one variable increases, the other variable also increases. A negative beta indicates that as one variable increases, the other variable decreases. For example, a study might find that there is a positive relationship between height and weight. This means that taller people tend to weigh more. The beta coefficient for this relationship would be positive.

 

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?

IE:TSX Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: IE:TSX Ivanhoe Electric Inc.
Time series to forecast: 6 Month

According to price forecasts, the dominant strategy among neural network is: Speculative Trend

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 Multi-Instance Learning (ML) based IE:TSX Stock Prediction Model

1. Lifetime expected credit losses are not recognised on a financial instrument simply because it was considered to have low credit risk in the previous reporting period and is not considered to have low credit risk at the reporting date. In such a case, an entity shall determine whether there has been a significant increase in credit risk since initial recognition and thus whether lifetime expected credit losses are required to be recognised in accordance with paragraph 5.5.3.
2. When rebalancing a hedging relationship, an entity shall update its analysis of the sources of hedge ineffectiveness that are expected to affect the hedging relationship during its (remaining) term (see paragraph B6.4.2). The documentation of the hedging relationship shall be updated accordingly.
3. Adjusting the hedge ratio by decreasing the volume of the hedging instrument does not affect how the changes in the value of the hedged item are measured. The measurement of the changes in the fair value of the hedging instrument related to the volume that continues to be designated also remains unaffected. However, from the date of rebalancing, the volume by which the hedging instrument was decreased is no longer part of the hedging relationship. For example, if an entity originally hedged the price risk of a commodity using a derivative volume of 100 tonnes as the hedging instrument and reduces that volume by 10 tonnes on rebalancing, a nominal amount of 90 tonnes of the hedging instrument volume would remain (see paragraph B6.5.16 for the consequences for the derivative volume (ie the 10 tonnes) that is no longer a part of the hedging relationship).
4. The business model may be to hold assets to collect contractual cash flows even if the entity sells financial assets when there is an increase in the assets' credit risk. To determine whether there has been an increase in the assets' credit risk, the entity considers reasonable and supportable information, including forward looking information. Irrespective of their frequency and value, sales due to an increase in the assets' credit risk are not inconsistent with a business model whose objective is to hold financial assets to collect contractual cash flows because the credit quality of financial assets is relevant to the entity's ability to collect contractual cash flows. Credit risk management activities that are aimed at minimising potential credit losses due to credit deterioration are integral to such a business model. Selling a financial asset because it no longer meets the credit criteria specified in the entity's documented investment policy is an example of a sale that has occurred due to an increase in credit risk. However, in the absence of such a policy, the entity may demonstrate in other ways that the sale occurred due to an increase in credit risk.

*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.

IE:TSX Ivanhoe Electric Inc. Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba2	B1
Income Statement	B3	Caa2
Balance Sheet	Baa2	Caa2
Leverage Ratios	Ba1	Caa2
Cash Flow	B2	Ba3
Rates of Return and Profitability	Baa2	Baa2

*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

Ivanhoe Electric Inc. is assigned short-term Ba2 & long-term B1 estimated rating. Ivanhoe Electric Inc. prediction model is evaluated with Multi-Instance Learning (ML) and Beta^1,2,3,4 and it is concluded that the IE:TSX stock is predictable in the short/long term. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Speculative Trend

Prediction Confidence Score

Trust metric by Neural Network: 74 out of 100 with 741 signals.

References

1. Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Can Neural Networks Predict Stock Market?. AC Investment Research Journal, 220(44).
2. Breiman L, Friedman J, Stone CJ, Olshen RA. 1984. Classification and Regression Trees. Boca Raton, FL: CRC Press
3. C. Szepesvári. Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2010
4. 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.
5. A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015
6. Candès EJ, Recht B. 2009. Exact matrix completion via convex optimization. Found. Comput. Math. 9:717
7. R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998

Frequently Asked Questions

Q: What is the prediction methodology for IE:TSX stock?
A: IE:TSX stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Beta
Q: Is IE:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Speculative Trend IE:TSX Stock.
Q: Is Ivanhoe Electric Inc. stock a good investment?
A: The consensus rating for Ivanhoe Electric Inc. is Speculative Trend and is assigned short-term Ba2 & long-term B1 estimated rating.
Q: What is the consensus rating of IE:TSX stock?
A: The consensus rating for IE:TSX is Speculative Trend.
Q: What is the prediction period for IE:TSX stock?
A: The prediction period for IE:TSX is 6 Month

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