...........................

**Outlook:**SunOpta Inc. assigned short-term B2 & long-term Ba1 forecasted stock rating.

**Signal:**Hold

**Time series to forecast n: 06 Dec 2022**for (n+8 weeks)

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## Abstract

Neural networks (NNs), as artificial intelligence (AI) methods, have become very important in making stock market predictions. Much research on the applications of NNs for solving business problems have proven their advantages over statistical and other methods that do not include AI, although there is no optimal methodology for a certain problem. (Cocianu, C.L. and Grigoryan, H., 2016. MACHINE LEARNING TECHNIQUES FOR STOCK MARKET PREDICTION. A CASE STUDY OF OMV PETROM. Economic Computation & Economic Cybernetics Studies & Research, 50(3).)** We evaluate SunOpta Inc. prediction models with Modular Neural Network (CNN Layer) and Spearman Correlation ^{1,2,3,4} and conclude that the SOY:TSX stock is predictable in the short/long term. **

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold SOY:TSX stock.**

## Key Points

- Decision Making
- What is the use of Markov decision process?
- What is Markov decision process in reinforcement learning?

## SOY:TSX Target Price Prediction Modeling Methodology

We consider SunOpta Inc. Decision Process with Modular Neural Network (CNN Layer) 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(Spearman Correlation)

^{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):→ (n+8 weeks) $\sum _{i=1}^{n}\left({r}_{i}\right)$

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

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) for (n+8 weeks)

**Sample Set:**Neural Network

**Stock/Index:**SOY:TSX SunOpta Inc.

**Time series to forecast n: 06 Dec 2022**for (n+8 weeks)

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold SOY:TSX stock.**

**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 SunOpta Inc.

- At the date of initial application, an entity shall use reasonable and supportable information that is available without undue cost or effort to determine the credit risk at the date that a financial instrument was initially recognised (or for loan commitments and financial guarantee contracts at the date that the entity became a party to the irrevocable commitment in accordance with paragraph 5.5.6) and compare that to the credit risk at the date of initial application of this Standard.
- For the purpose of applying paragraph 6.5.11, at the point when an entity amends the description of a hedged item as required in paragraph 6.9.1(b), the amount accumulated in the cash flow hedge reserve shall be deemed to be based on the alternative benchmark rate on which the hedged future cash flows are determined.
- An entity need not undertake an exhaustive search for information but shall consider all reasonable and supportable information that is available without undue cost or effort and that is relevant to the estimate of expected credit losses, including the effect of expected prepayments. The information used shall include factors that are specific to the borrower, general economic conditions and an assessment of both the current as well as the forecast direction of conditions at the reporting date. An entity may use various sources of data, that may be both internal (entity-specific) and external. Possible data sources include internal historical credit loss experience, internal ratings, credit loss experience of other entities and external ratings, reports and statistics. Entities that have no, or insufficient, sources of entityspecific data may use peer group experience for the comparable financial instrument (or groups of financial instruments).
- An entity shall apply Prepayment Features with Negative Compensation (Amendments to IFRS 9) retrospectively in accordance with IAS 8, except as specified in paragraphs 7.2.30–7.2.34

*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

SunOpta Inc. assigned short-term B2 & long-term Ba1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (CNN Layer) with Spearman Correlation ^{1,2,3,4} and conclude that the SOY:TSX stock is predictable in the short/long term.**

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold SOY:TSX stock.**

### Financial State Forecast for SOY:TSX SunOpta Inc. Options & Futures

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

Outlook* | B2 | Ba1 |

Operational Risk | 58 | 90 |

Market Risk | 30 | 78 |

Technical Analysis | 80 | 65 |

Fundamental Analysis | 49 | 44 |

Risk Unsystematic | 57 | 73 |

### Prediction Confidence Score

## References

- 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
- N. B ̈auerle and J. Ott. Markov decision processes with average-value-at-risk criteria. Mathematical Methods of Operations Research, 74(3):361–379, 2011
- Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
- Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.
- Chow, G. C. (1960), "Tests of equality between sets of coefficients in two linear regressions," Econometrica, 28, 591–605.
- Breiman L. 2001a. Random forests. Mach. Learn. 45:5–32
- Athey S, Imbens G, Wager S. 2016a. Efficient inference of average treatment effects in high dimensions via approximate residual balancing. arXiv:1604.07125 [math.ST]

## Frequently Asked Questions

Q: What is the prediction methodology for SOY:TSX stock?A: SOY:TSX stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Spearman Correlation

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

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

Q: Is SunOpta Inc. stock a good investment?

A: The consensus rating for SunOpta Inc. is Hold and assigned short-term B2 & long-term Ba1 forecasted stock rating.

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

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

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

A: The prediction period for SOY:TSX is (n+8 weeks)