Outlook: Helius Medical Technologies Inc. Class A Common Stock (DE) is assigned short-term Ba3 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Time series to forecast n: for Weeks2
Methodology : Multi-Instance Learning (ML)
Hypothesis Testing : Polynomial 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

Helius Medical Technologies Inc. Class A Common Stock (DE) prediction model is evaluated with Multi-Instance Learning (ML) and Polynomial Regression1,2,3,4 and it is concluded that the HSDT 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.5 According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Buy

## Key Points

1. Multi-Instance Learning (ML) for HSDT stock price prediction process.
2. Polynomial Regression
3. Market Outlook
4. Investment Risk
5. Can neural networks predict stock market?

## HSDT Stock Price Forecast

We consider Helius Medical Technologies Inc. Class A Common Stock (DE) Decision Process with Multi-Instance Learning (ML) where A is the set of discrete actions of HSDT 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

Sample Set: Neural Network
Stock/Index: HSDT Helius Medical Technologies Inc. Class A Common Stock (DE)
Time series to forecast: 4 Weeks

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

F(Polynomial Regression)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(Multi-Instance Learning (ML)) X S(n):→ 4 Weeks $∑ i = 1 n a i$

n:Time series to forecast

p:Price signals of HSDT stock

j:Nash equilibria (Neural Network)

k:Dominated move of HSDT stock holders

a:Best response for HSDT target price

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.5 Polynomial regression is a type of regression analysis that uses a polynomial function to model the relationship between a dependent variable and one or more independent variables. Polynomial functions are mathematical functions that have a polynomial term, which is a term that is raised to a power greater than 1. In polynomial regression, the dependent variable is modeled as a polynomial function of the independent variables. The degree of the polynomial function is determined by the researcher. The higher the degree of the polynomial function, the more complex the model will be.6,7

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### HSDT Stock Forecast (Buy or Sell) Strategic Interaction Table

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 HSDT Stock Prediction Model

1. An alternative benchmark rate designated as a non-contractually specified risk component that is not separately identifiable (see paragraphs 6.3.7(a) and B6.3.8) at the date it is designated shall be deemed to have met that requirement at that date, if, and only if, the entity reasonably expects the alternative benchmark rate will be separately identifiable within 24 months. The 24-month period applies to each alternative benchmark rate separately and starts from the date the entity designates the alternative benchmark rate as a non-contractually specified risk component for the first time (ie the 24- month period applies on a rate-by-rate basis).
2. 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.
3. An entity must look through until it can identify the underlying pool of instruments that are creating (instead of passing through) the cash flows. This is the underlying pool of financial instruments.
4. Accordingly the date of the modification shall be treated as the date of initial recognition of that financial asset when applying the impairment requirements to the modified financial asset. This typically means measuring the loss allowance at an amount equal to 12-month expected credit losses until the requirements for the recognition of lifetime expected credit losses in paragraph 5.5.3 are met. However, in some unusual circumstances following a modification that results in derecognition of the original financial asset, there may be evidence that the modified financial asset is credit-impaired at initial recognition, and thus, the financial asset should be recognised as an originated credit-impaired financial asset. This might occur, for example, in a situation in which there was a substantial modification of a distressed asset that resulted in the derecognition of the original financial asset. In such a case, it may be possible for the modification to result in a new financial asset which is credit-impaired at initial recognition.

*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.

### HSDT Helius Medical Technologies Inc. Class A Common Stock (DE) Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba3B1
Income StatementBaa2B1
Balance SheetBa1Baa2
Leverage RatiosCaa2Baa2
Cash FlowBaa2Caa2
Rates of Return and ProfitabilityB3C

*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?

## References

1. Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.
2. K. Tuyls and G. Weiss. Multiagent learning: Basics, challenges, and prospects. AI Magazine, 33(3): 41–52, 2012
3. Imbens G, Wooldridge J. 2009. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47:5–86
4. Chamberlain G. 2000. Econometrics and decision theory. J. Econom. 95:255–83
5. LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
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. Athey S, Imbens GW. 2017b. The state of applied econometrics: causality and policy evaluation. J. Econ. Perspect. 31:3–32
Frequently Asked QuestionsQ: Is HSDT stock expected to rise?
A: HSDT stock prediction model is evaluated with Multi-Instance Learning (ML) and Polynomial Regression and it is concluded that dominant strategy for HSDT stock is Buy
Q: Is HSDT stock a buy or sell?
A: The dominant strategy among neural network is to Buy HSDT Stock.
Q: Is Helius Medical Technologies Inc. Class A Common Stock (DE) stock a good investment?
A: The consensus rating for Helius Medical Technologies Inc. Class A Common Stock (DE) is Buy and is assigned short-term Ba3 & long-term B1 estimated rating.
Q: What is the consensus rating of HSDT stock?
A: The consensus rating for HSDT is Buy.
Q: What is the forecast for HSDT stock?
A: HSDT target price forecast: Buy
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