OGI:TSX Stock: Future is Bright, but The Road Ahead is Bumpy

Outlook: Organigram Holdings Inc. is assigned short-term B2 & long-term Ba2 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : Buy
Time series to forecast n: for Weeks²
Methodology : Reinforcement Machine Learning (ML)
Hypothesis Testing : Sign Test
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

Organigram Holdings Inc. prediction model is evaluated with Reinforcement Machine Learning (ML) and Sign Test^1,2,3,4 and it is concluded that the OGI:TSX stock is predictable in the short/long term. Reinforcement machine learning (RL) is a type of machine learning where an agent learns to take actions in an environment in order to maximize a reward. The agent does this by trial and error, and is able to learn from its mistakes. RL is a powerful tool that can be used for a variety of tasks, including game playing, robotics, and finance. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Buy

Key Points

1. Probability Distribution
2. Probability Distribution
3. Stock Forecast Based On a Predictive Algorithm

OGI:TSX Target Price Prediction Modeling Methodology

We consider Organigram Holdings Inc. Decision Process with Reinforcement Machine Learning (ML) where A is the set of discrete actions of OGI: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(Sign Test)^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(Reinforcement Machine Learning (ML)) X S(n):→ 16 Weeks $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of OGI:TSX stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Reinforcement Machine Learning (ML)

Reinforcement machine learning (RL) is a type of machine learning where an agent learns to take actions in an environment in order to maximize a reward. The agent does this by trial and error, and is able to learn from its mistakes. RL is a powerful tool that can be used for a variety of tasks, including game playing, robotics, and finance.

Sign Test

The sign test is a non-parametric hypothesis test that is used to compare two paired samples. In a paired sample, each data point in one sample is paired with a data point in the other sample. The pairs are typically related in some way, such as before and after measurements, or measurements from the same subject under different conditions. The sign test is a non-parametric test, which means that it does not assume that the data is normally distributed. The sign test is also a dependent samples test, which means that the data points in each pair are correlated.

 

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?

OGI:TSX Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: OGI:TSX Organigram Holdings Inc.
Time series to forecast: 16 Weeks

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

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 Reinforcement Machine Learning (ML) based OGI:TSX Stock Prediction Model

1. The underlying pool must contain one or more instruments that have contractual cash flows that are solely payments of principal and interest on the principal amount outstanding
2. If a collar, in the form of a purchased call and written put, prevents a transferred asset from being derecognised and the entity measures the asset at fair value, it continues to measure the asset at fair value. The associated liability is measured at (i) the sum of the call exercise price and fair value of the put option less the time value of the call option, if the call option is in or at the money, or (ii) the sum of the fair value of the asset and the fair value of the put option less the time value of the call option if the call option is out of the money. The adjustment to the associated liability ensures that the net carrying amount of the asset and the associated liability is the fair value of the options held and written by the entity. For example, assume an entity transfers a financial asset that is measured at fair value while simultaneously purchasing a call with an exercise price of CU120 and writing a put with an exercise price of CU80. Assume also that the fair value of the asset is CU100 at the date of the transfer. The time value of the put and call are CU1 and CU5 respectively. In this case, the entity recognises an asset of CU100 (the fair value of the asset) and a liability of CU96 [(CU100 + CU1) – CU5]. This gives a net asset value of CU4, which is the fair value of the options held and written by the entity.
3. When an entity discontinues measuring the financial instrument that gives rise to the credit risk, or a proportion of that financial instrument, at fair value through profit or loss, that financial instrument's fair value at the date of discontinuation becomes its new carrying amount. Subsequently, the same measurement that was used before designating the financial instrument at fair value through profit or loss shall be applied (including amortisation that results from the new carrying amount). For example, a financial asset that had originally been classified as measured at amortised cost would revert to that measurement and its effective interest rate would be recalculated based on its new gross carrying amount on the date of discontinuing measurement at fair value through profit or loss.
4. The definition of a derivative in this Standard includes contracts that are settled gross by delivery of the underlying item (eg a forward contract to purchase a fixed rate debt instrument). An entity may have a contract to buy or sell a non-financial item that can be settled net in cash or another financial instrument or by exchanging financial instruments (eg a contract to buy or sell a commodity at a fixed price at a future date). Such a contract is within the scope of this Standard unless it was entered into and continues to be held for the purpose of delivery of a non-financial item in accordance with the entity's expected purchase, sale or usage requirements. However, this Standard applies to such contracts for an entity's expected purchase, sale or usage requirements if the entity makes a designation in accordance with paragraph 2.5 (see paragraphs 2.4–2.7).

*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.

OGI:TSX Organigram Holdings Inc. Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	B2	Ba2
Income Statement	C	Ba1
Balance Sheet	Baa2	Baa2
Leverage Ratios	B1	Baa2
Cash Flow	C	C
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

Organigram Holdings Inc. is assigned short-term B2 & long-term Ba2 estimated rating. Organigram Holdings Inc. prediction model is evaluated with Reinforcement Machine Learning (ML) and Sign Test^1,2,3,4 and it is concluded that the OGI:TSX stock is predictable in the short/long term. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Buy

Prediction Confidence Score

Trust metric by Neural Network: 82 out of 100 with 491 signals.

References

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4. Greene WH. 2000. Econometric Analysis. Upper Saddle River, N J: Prentice Hall. 4th ed.
5. Barrett, C. B. (1997), "Heteroscedastic price forecasting for food security management in developing countries," Oxford Development Studies, 25, 225–236.
6. Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Google's Stock Price Set to Soar in the Next 3 Months. AC Investment Research Journal, 220(44).
7. Wan M, Wang D, Goldman M, Taddy M, Rao J, et al. 2017. Modeling consumer preferences and price sensitiv- ities from large-scale grocery shopping transaction logs. In Proceedings of the 26th International Conference on the World Wide Web, pp. 1103–12. New York: ACM

Frequently Asked Questions

Q: What is the prediction methodology for OGI:TSX stock?
A: OGI:TSX stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Sign Test
Q: Is OGI:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Buy OGI:TSX Stock.
Q: Is Organigram Holdings Inc. stock a good investment?
A: The consensus rating for Organigram Holdings Inc. is Buy and is assigned short-term B2 & long-term Ba2 estimated rating.
Q: What is the consensus rating of OGI:TSX stock?
A: The consensus rating for OGI:TSX is Buy.
Q: What is the prediction period for OGI:TSX stock?
A: The prediction period for OGI:TSX is 16 Weeks