Outlook: Neovasc Inc. is assigned short-term Ba1 & long-term Ba1 estimated rating.
Time series to forecast n: 06 Jan 2023 for (n+1 year)
Methodology : Modular Neural Network (Market Direction Analysis)

## Abstract

Neovasc Inc. prediction model is evaluated with Modular Neural Network (Market Direction Analysis) and Pearson Correlation1,2,3,4 and it is concluded that the NVCN:TSX stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy

## Key Points

1. Can machine learning predict?
2. Fundemental Analysis with Algorithmic Trading
3. Fundemental Analysis with Algorithmic Trading

## NVCN:TSX Target Price Prediction Modeling Methodology

We consider Neovasc Inc. Decision Process with Modular Neural Network (Market Direction Analysis) where A is the set of discrete actions of NVCN: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(Pearson Correlation)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Market Direction Analysis)) X S(n):→ (n+1 year) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of NVCN: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?

## NVCN:TSX Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: NVCN:TSX Neovasc Inc.
Time series to forecast n: 06 Jan 2023 for (n+1 year)

According to price forecasts for (n+1 year) 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 Neovasc Inc.

1. However, the fact that a financial asset is non-recourse does not in itself necessarily preclude the financial asset from meeting the condition in paragraphs 4.1.2(b) and 4.1.2A(b). In such situations, the creditor is required to assess ('look through to') the particular underlying assets or cash flows to determine whether the contractual cash flows of the financial asset being classified are payments of principal and interest on the principal amount outstanding. If the terms of the financial asset give rise to any other cash flows or limit the cash flows in a manner inconsistent with payments representing principal and interest, the financial asset does not meet the condition in paragraphs 4.1.2(b) and 4.1.2A(b). Whether the underlying assets are financial assets or non-financial assets does not in itself affect this assessment.
2. A net position is eligible for hedge accounting only if an entity hedges on a net basis for risk management purposes. Whether an entity hedges in this way is a matter of fact (not merely of assertion or documentation). Hence, an entity cannot apply hedge accounting on a net basis solely to achieve a particular accounting outcome if that would not reflect its risk management approach. Net position hedging must form part of an established risk management strategy. Normally this would be approved by key management personnel as defined in IAS 24.
3. Annual Improvements to IFRSs 2010–2012 Cycle, issued in December 2013, amended paragraphs 4.2.1 and 5.7.5 as a consequential amendment derived from the amendment to IFRS 3. An entity shall apply that amendment prospectively to business combinations to which the amendment to IFRS 3 applies.
4. An entity shall assess at the inception of the hedging relationship, and on an ongoing basis, whether a hedging relationship meets the hedge effectiveness requirements. At a minimum, an entity shall perform the ongoing assessment at each reporting date or upon a significant change in the circumstances affecting the hedge effectiveness requirements, whichever comes first. The assessment relates to expectations about hedge effectiveness and is therefore only forward-looking.

*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

Neovasc Inc. is assigned short-term Ba1 & long-term Ba1 estimated rating. Neovasc Inc. prediction model is evaluated with Modular Neural Network (Market Direction Analysis) and Pearson Correlation1,2,3,4 and it is concluded that the NVCN:TSX stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy

### NVCN:TSX Neovasc Inc. Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementCB1
Balance SheetBaa2B1
Leverage RatiosBa3B1
Cash FlowBaa2Baa2
Rates of Return and ProfitabilityBaa2Ba2

*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: 80 out of 100 with 740 signals.

## References

1. Dimakopoulou M, Zhou Z, Athey S, Imbens G. 2018. Balanced linear contextual bandits. arXiv:1812.06227 [cs.LG]
2. D. S. Bernstein, S. Zilberstein, and N. Immerman. The complexity of decentralized control of Markov Decision Processes. In UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30 - July 3, 2000, pages 32–37, 2000.
3. Babula, R. A. (1988), "Contemporaneous correlation and modeling Canada's imports of U.S. crops," Journal of Agricultural Economics Research, 41, 33–38.
4. V. Borkar. Stochastic approximation: a dynamical systems viewpoint. Cambridge University Press, 2008
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. 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
7. Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
Frequently Asked QuestionsQ: What is the prediction methodology for NVCN:TSX stock?
A: NVCN:TSX stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Pearson Correlation
Q: Is NVCN:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Buy NVCN:TSX Stock.
Q: Is Neovasc Inc. stock a good investment?
A: The consensus rating for Neovasc Inc. is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of NVCN:TSX stock?
A: The consensus rating for NVCN:TSX is Buy.
Q: What is the prediction period for NVCN:TSX stock?
A: The prediction period for NVCN:TSX is (n+1 year)