AGRI Stock: A Cautionary Tale

Outlook: AgriFORCE Growing Systems Ltd. Common Shares is assigned short-term B2 & long-term B2 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 22 Jun 2023 for 8 Weeks
Methodology : Multi-Instance Learning (ML)

Summary

AgriFORCE Growing Systems Ltd. Common Shares prediction model is evaluated with Multi-Instance Learning (ML) and Lasso Regression^1,2,3,4 and it is concluded that the AGRI 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. According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Sell

Key Points

1. Why do we need predictive models?
2. Market Risk
3. Should I buy stocks now or wait amid such uncertainty?

AGRI Target Price Prediction Modeling Methodology

We consider AgriFORCE Growing Systems Ltd. Common Shares Decision Process with Multi-Instance Learning (ML) where A is the set of discrete actions of AGRI 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(Lasso Regression)^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(Multi-Instance Learning (ML)) X S(n):→ 8 Weeks $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of AGRI stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Multi-Instance Learning (ML)

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.

Lasso Regression

Lasso regression, also known as L1 regularization, is a type of regression analysis that adds a penalty to the least squares objective function in order to reduce the variance of the estimates and to induce sparsity in the model. This is done by adding a term to the objective function that is proportional to the sum of the absolute values of the coefficients. The penalty term is called the "lasso" penalty, and it is controlled by a parameter called the "lasso constant". Lasso regression can be used to address the problem of multicollinearity in linear regression, as well as the problem of overfitting. Multicollinearity occurs when two or more independent variables are highly correlated. This can cause the standard errors of the coefficients to be large, and it can also cause the coefficients to be unstable. Overfitting occurs when a model is too closely fit to the training data, and as a result, it does not generalize well to new data.

 

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?

AGRI Stock Forecast (Buy or Sell) for 8 Weeks

Sample Set: Neural Network
Stock/Index: AGRI AgriFORCE Growing Systems Ltd. Common Shares
Time series to forecast n: 22 Jun 2023 for 8 Weeks

According to price forecasts for 8 Weeks 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 AgriFORCE Growing Systems Ltd. Common Shares

1. A hedge of a firm commitment (for example, a hedge of the change in fuel price relating to an unrecognised contractual commitment by an electric utility to purchase fuel at a fixed price) is a hedge of an exposure to a change in fair value. Accordingly, such a hedge is a fair value hedge. However, in accordance with paragraph 6.5.4, a hedge of the foreign currency risk of a firm commitment could alternatively be accounted for as a cash flow hedge.
2. When measuring hedge ineffectiveness, an entity shall consider the time value of money. Consequently, the entity determines the value of the hedged item on a present value basis and therefore the change in the value of the hedged item also includes the effect of the time value of money.
3. If items are hedged together as a group in a cash flow hedge, they might affect different line items in the statement of profit or loss and other comprehensive income. The presentation of hedging gains or losses in that statement depends on the group of items
4. To the extent that a transfer of a financial asset does not qualify for derecognition, the transferor's contractual rights or obligations related to the transfer are not accounted for separately as derivatives if recognising both the derivative and either the transferred asset or the liability arising from the transfer would result in recognising the same rights or obligations twice. For example, a call option retained by the transferor may prevent a transfer of financial assets from being accounted for as a sale. In that case, the call option is not separately recognised as a derivative asset.

*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

AgriFORCE Growing Systems Ltd. Common Shares is assigned short-term B2 & long-term B2 estimated rating. AgriFORCE Growing Systems Ltd. Common Shares prediction model is evaluated with Multi-Instance Learning (ML) and Lasso Regression^1,2,3,4 and it is concluded that the AGRI stock is predictable in the short/long term. According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Sell

AGRI AgriFORCE Growing Systems Ltd. Common Shares Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	B2	B2
Income Statement	B3	B2
Balance Sheet	Ba1	Ba3
Leverage Ratios	Caa2	B3
Cash Flow	C	Caa2
Rates of Return and Profitability	Ba3	Caa2

*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: 77 out of 100 with 837 signals.

References

1. Bamler R, Mandt S. 2017. Dynamic word embeddings via skip-gram filtering. In Proceedings of the 34th Inter- national Conference on Machine Learning, pp. 380–89. La Jolla, CA: Int. Mach. Learn. Soc.
2. P. Milgrom and I. Segal. Envelope theorems for arbitrary choice sets. Econometrica, 70(2):583–601, 2002
3. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., GXO Options & Futures Prediction. AC Investment Research Journal, 101(3).
4. Byron, R. P. O. Ashenfelter (1995), "Predicting the quality of an unborn grange," Economic Record, 71, 40–53.
5. Friedman JH. 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38:367–78
6. Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94
7. Arora S, Li Y, Liang Y, Ma T. 2016. RAND-WALK: a latent variable model approach to word embeddings. Trans. Assoc. Comput. Linguist. 4:385–99

Frequently Asked Questions

Q: What is the prediction methodology for AGRI stock?
A: AGRI stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Lasso Regression
Q: Is AGRI stock a buy or sell?
A: The dominant strategy among neural network is to Sell AGRI Stock.
Q: Is AgriFORCE Growing Systems Ltd. Common Shares stock a good investment?
A: The consensus rating for AgriFORCE Growing Systems Ltd. Common Shares is Sell and is assigned short-term B2 & long-term B2 estimated rating.
Q: What is the consensus rating of AGRI stock?
A: The consensus rating for AGRI is Sell.
Q: What is the prediction period for AGRI stock?
A: The prediction period for AGRI is 8 Weeks