ANALYZING OGI STOCK: A COMPREHENSIVE EVALUATION OF GROWTH POTENTIAL AND INVESTMENT OPPORTUNITIES

Outlook: Organigram Holdings Inc. Common Shares is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 08 Jun 2023 for 6 Month
Methodology : Statistical Inference (ML)

Abstract

Organigram Holdings Inc. Common Shares prediction model is evaluated with Statistical Inference (ML) and Wilcoxon Rank-Sum Test^1,2,3,4 and it is concluded that the OGI stock is predictable in the short/long term. Statistical inference is a process of drawing conclusions about a population based on data from a sample of that population. In machine learning (ML), statistical inference is used to make predictions about new data based on data that has already been seen. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Sell

Key Points

1. What are the most successful trading algorithms?
2. Technical Analysis with Algorithmic Trading
3. Why do we need predictive models?

OGI Target Price Prediction Modeling Methodology

We consider Organigram Holdings Inc. Common Shares Decision Process with Statistical Inference (ML) where A is the set of discrete actions of OGI 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(Wilcoxon Rank-Sum 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(Statistical Inference (ML)) X S(n):→ 6 Month $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of OGI stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Statistical inference is a process of drawing conclusions about a population based on data from a sample of that population. In machine learning (ML), statistical inference is used to make predictions about new data based on data that has already been seen.

The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a non-parametric test that is used to compare the medians of two independent samples. It is a rank-based test, which means that it does not assume that the data is normally distributed. The Wilcoxon rank-sum test is calculated by first ranking the data from both samples, and then finding the sum of the ranks for one of the samples. The Wilcoxon rank-sum test statistic is then calculated by subtracting the sum of the ranks for one sample from the sum of the ranks for the other sample. The p-value for the Wilcoxon rank-sum test is calculated using a table of critical values. The p-value is the probability of obtaining a test statistic at least as extreme as the one observed, assuming that the null hypothesis is true.

 

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 Stock Forecast (Buy or Sell) for 6 Month

Sample Set: Neural Network
Stock/Index: OGI Organigram Holdings Inc. Common Shares
Time series to forecast n: 08 Jun 2023 for 6 Month

According to price forecasts for 6 Month period, the dominant strategy among neural network is: Sell

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 Organigram Holdings Inc. Common Shares

1. 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.
2. The significance of a change in the credit risk since initial recognition depends on the risk of a default occurring as at initial recognition. Thus, a given change, in absolute terms, in the risk of a default occurring will be more significant for a financial instrument with a lower initial risk of a default occurring compared to a financial instrument with a higher initial risk of a default occurring.
3. That the transferee is unlikely to sell the transferred asset does not, of itself, mean that the transferor has retained control of the transferred asset. However, if a put option or guarantee constrains the transferee from selling the transferred asset, then the transferor has retained control of the transferred asset. For example, if a put option or guarantee is sufficiently valuable it constrains the transferee from selling the transferred asset because the transferee would, in practice, not sell the transferred asset to a third party without attaching a similar option or other restrictive conditions. Instead, the transferee would hold the transferred asset so as to obtain payments under the guarantee or put option. Under these circumstances the transferor has retained control of the transferred asset.
4. When applying the effective interest method, an entity generally amortises any fees, points paid or received, transaction costs and other premiums or discounts that are included in the calculation of the effective interest rate over the expected life of the financial instrument. However, a shorter period is used if this is the period to which the fees, points paid or received, transaction costs, premiums or discounts relate. This will be the case when the variable to which the fees, points paid or received, transaction costs, premiums or discounts relate is repriced to market rates before the expected maturity of the financial instrument. In such a case, the appropriate amortisation period is the period to the next such repricing date. For example, if a premium or discount on a floating-rate financial instrument reflects the interest that has accrued on that financial instrument since the interest was last paid, or changes in the market rates since the floating interest rate was reset to the market rates, it will be amortised to the next date when the floating interest is reset to market rates. This is because the premium or discount relates to the period to the next interest reset date because, at that date, the variable to which the premium or discount relates (ie interest rates) is reset to the market rates. If, however, the premium or discount results from a change in the credit spread over the floating rate specified in the financial instrument, or other variables that are not reset to the market rates, it is amortised over the expected life of the financial instrument.

*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

Organigram Holdings Inc. Common Shares is assigned short-term Ba1 & long-term Ba1 estimated rating. Organigram Holdings Inc. Common Shares prediction model is evaluated with Statistical Inference (ML) and Wilcoxon Rank-Sum Test^1,2,3,4 and it is concluded that the OGI stock is predictable in the short/long term. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Sell

OGI Organigram Holdings Inc. Common Shares Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	B3	Baa2
Balance Sheet	B3	C
Leverage Ratios	B3	B1
Cash Flow	Baa2	Caa2
Rates of Return and Profitability	B3	B2

*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: 81 out of 100 with 494 signals.

References

1. M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
2. Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
3. R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972
4. Belsley, D. A. (1988), "Modelling and forecast reliability," International Journal of Forecasting, 4, 427–447.
5. Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:267–88
6. D. Bertsekas. Min common/max crossing duality: A geometric view of conjugacy in convex optimization. Lab. for Information and Decision Systems, MIT, Tech. Rep. Report LIDS-P-2796, 2009
7. A. Tamar and S. Mannor. Variance adjusted actor critic algorithms. arXiv preprint arXiv:1310.3697, 2013.

Frequently Asked Questions

Q: What is the prediction methodology for OGI stock?
A: OGI stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Wilcoxon Rank-Sum Test
Q: Is OGI stock a buy or sell?
A: The dominant strategy among neural network is to Sell OGI Stock.
Q: Is Organigram Holdings Inc. Common Shares stock a good investment?
A: The consensus rating for Organigram Holdings Inc. Common Shares is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of OGI stock?
A: The consensus rating for OGI is Sell.
Q: What is the prediction period for OGI stock?
A: The prediction period for OGI is 6 Month

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