SunOpta Inc. is assigned short-term B3 & long-term Baa2 estimated rating.

Outlook: SunOpta Inc. is assigned short-term B3 & long-term Baa2 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : Sell
Time series to forecast n: for Weeks²
Methodology : Multi-Instance Learning (ML)
Hypothesis Testing : Stepwise Regression
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.

Summary

SunOpta Inc. prediction model is evaluated with Multi-Instance Learning (ML) and Stepwise Regression^1,2,3,4 and it is concluded that the SOY: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 4 Weeks period, the dominant strategy among neural network is: Sell

Key Points

1. What are the most successful trading algorithms?
2. What is statistical models in machine learning?
3. Dominated Move

SOY:TSX Target Price Prediction Modeling Methodology

We consider SunOpta Inc. Decision Process with Multi-Instance Learning (ML) where A is the set of discrete actions of SOY: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(Stepwise Regression)^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):→ 4 Weeks $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

p:Price signals of SOY: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.

Stepwise Regression

Stepwise regression is a method of variable selection in which variables are added or removed from a model one at a time, based on their statistical significance. There are two main types of stepwise regression: forward selection and backward elimination. In forward selection, variables are added to the model one at a time, starting with the variable with the highest F-statistic. The F-statistic is a measure of how much improvement in the model is gained by adding the variable. Variables are added to the model until no variable adds a statistically significant improvement to the model.

 

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?

SOY:TSX Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: SOY:TSX SunOpta Inc.
Time series to forecast: 4 Weeks

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

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 SOY:TSX Stock Prediction Model

1. If the holder cannot assess the conditions in paragraph B4.1.21 at initial recognition, the tranche must be measured at fair value through profit or loss. If the underlying pool of instruments can change after initial recognition in such a way that the pool may not meet the conditions in paragraphs B4.1.23–B4.1.24, the tranche does not meet the conditions in paragraph B4.1.21 and must be measured at fair value through profit or loss. However, if the underlying pool includes instruments that are collateralised by assets that do not meet the conditions in paragraphs B4.1.23–B4.1.24, the ability to take possession of such assets shall be disregarded for the purposes of applying this paragraph unless the entity acquired the tranche with the intention of controlling the collateral.
2. Rebalancing does not apply if the risk management objective for a hedging relationship has changed. Instead, hedge accounting for that hedging relationship shall be discontinued (despite that an entity might designate a new hedging relationship that involves the hedging instrument or hedged item of the previous hedging relationship as described in paragraph B6.5.28).
3. If the underlyings are not the same but are economically related, there can be situations in which the values of the hedging instrument and the hedged item move in the same direction, for example, because the price differential between the two related underlyings changes while the underlyings themselves do not move significantly. That is still consistent with an economic relationship between the hedging instrument and the hedged item if the values of the hedging instrument and the hedged item are still expected to typically move in the opposite direction when the underlyings move.
4. If items are hedged together as a group in a cash flow hedge, they might affect different line items in the statement of profit or loss and other comprehensive income. The presentation of hedging gains or losses in that statement depends on the group of items

*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.

SOY:TSX SunOpta Inc. Financial Analysis*

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

SunOpta Inc. is assigned short-term B3 & long-term Baa2 estimated rating. SunOpta Inc. prediction model is evaluated with Multi-Instance Learning (ML) and Stepwise Regression^1,2,3,4 and it is concluded that the SOY:TSX stock is predictable in the short/long term. According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Sell

Prediction Confidence Score

Trust metric by Neural Network: 78 out of 100 with 661 signals.

References

1. Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating errors. Nature 323:533–36
2. Kitagawa T, Tetenov A. 2015. Who should be treated? Empirical welfare maximization methods for treatment choice. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
3. Keane MP. 2013. Panel data discrete choice models of consumer demand. In The Oxford Handbook of Panel Data, ed. BH Baltagi, pp. 54–102. Oxford, UK: Oxford Univ. Press
4. V. Borkar. An actor-critic algorithm for constrained Markov decision processes. Systems & Control Letters, 54(3):207–213, 2005.
5. ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. The Dow Jones Industrial Average (No. Stock Analysis). AC Investment Research.
6. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2018a. Double/debiased machine learning for treatment and structural parameters. Econom. J. 21:C1–68
7. Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106

Frequently Asked Questions

Q: What is the prediction methodology for SOY:TSX stock?
A: SOY:TSX stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Stepwise Regression
Q: Is SOY:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Sell SOY:TSX Stock.
Q: Is SunOpta Inc. stock a good investment?
A: The consensus rating for SunOpta Inc. is Sell and is assigned short-term B3 & long-term Baa2 estimated rating.
Q: What is the consensus rating of SOY:TSX stock?
A: The consensus rating for SOY:TSX is Sell.
Q: What is the prediction period for SOY:TSX stock?
A: The prediction period for SOY:TSX is 4 Weeks

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