InterCure Ltd. Research Report

## Summary

A speculator on a Stock Market, aside from having money to spare, needs at least one other thing — a means of producing accurate and understandable predictions ahead of others in the Market, so that a tactical and price advantage can be gained. This work demonstrates that it is possible to predict one such Market to a high degree of accuracy. We evaluate InterCure Ltd. prediction models with Reinforcement Machine Learning (ML) and Chi-Square1,2,3,4 and conclude that the INCR.U:TSX stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell INCR.U:TSX stock.

## Key Points

2. What is neural prediction?
3. How useful are statistical predictions?

## INCR.U:TSX Target Price Prediction Modeling Methodology

We consider InterCure Ltd. Stock Decision Process with Reinforcement Machine Learning (ML) where A is the set of discrete actions of INCR.U: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(Chi-Square)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(Reinforcement Machine Learning (ML)) X S(n):→ (n+3 month) $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of INCR.U: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?

## INCR.U:TSX Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: INCR.U:TSX InterCure Ltd.
Time series to forecast n: 22 Nov 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell INCR.U:TSX stock.

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 (Yellow to Green): *Technical Analysis%

## Adjusted IFRS* Prediction Methods for InterCure Ltd.

1. IFRS 7 defines credit risk as 'the risk that one party to a financial instrument will cause a financial loss for the other party by failing to discharge an obligation'. The requirement in paragraph 5.7.7(a) relates to the risk that the issuer will fail to perform on that particular liability. It does not necessarily relate to the creditworthiness of the issuer. For example, if an entity issues a collateralised liability and a non-collateralised liability that are otherwise identical, the credit risk of those two liabilities will be different, even though they are issued by the same entity. The credit risk on the collateralised liability will be less than the credit risk of the non-collateralised liability. The credit risk for a collateralised liability may be close to zero.
2. Hedging relationships that qualified for hedge accounting in accordance with IAS 39 that also qualify for hedge accounting in accordance with the criteria of this Standard (see paragraph 6.4.1), after taking into account any rebalancing of the hedging relationship on transition (see paragraph 7.2.25(b)), shall be regarded as continuing hedging relationships.
3. When designating a risk component as a hedged item, the hedge accounting requirements apply to that risk component in the same way as they apply to other hedged items that are not risk components. For example, the qualifying criteria apply, including that the hedging relationship must meet the hedge effectiveness requirements, and any hedge ineffectiveness must be measured and recognised.
4. In some cases, the qualitative and non-statistical quantitative information available may be sufficient to determine that a financial instrument has met the criterion for the recognition of a loss allowance at an amount equal to lifetime expected credit losses. That is, the information does not need to flow through a statistical model or credit ratings process in order to determine whether there has been a significant increase in the credit risk of the financial instrument. In other cases, an entity may need to consider other information, including information from its statistical models or credit ratings processes.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

## Conclusions

InterCure Ltd. assigned short-term Ba2 & long-term B1 forecasted stock rating. We evaluate the prediction models Reinforcement Machine Learning (ML) with Chi-Square1,2,3,4 and conclude that the INCR.U:TSX stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell INCR.U:TSX stock.

### Financial State Forecast for INCR.U:TSX InterCure Ltd. Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba2B1
Operational Risk 5539
Market Risk8144
Technical Analysis6676
Fundamental Analysis7489
Risk Unsystematic6740

### Prediction Confidence Score

Trust metric by Neural Network: 87 out of 100 with 846 signals.

## References

1. Hastie T, Tibshirani R, Friedman J. 2009. The Elements of Statistical Learning. Berlin: Springer
2. Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
3. Schapire RE, Freund Y. 2012. Boosting: Foundations and Algorithms. Cambridge, MA: MIT Press
4. J. Ott. A Markov decision model for a surveillance application and risk-sensitive Markov decision processes. PhD thesis, Karlsruhe Institute of Technology, 2010.
5. Dudik M, Langford J, Li L. 2011. Doubly robust policy evaluation and learning. In Proceedings of the 28th International Conference on Machine Learning, pp. 1097–104. La Jolla, CA: Int. Mach. Learn. Soc.
6. Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
7. E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999
Frequently Asked QuestionsQ: What is the prediction methodology for INCR.U:TSX stock?
A: INCR.U:TSX stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Chi-Square
Q: Is INCR.U:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Sell INCR.U:TSX Stock.
Q: Is InterCure Ltd. stock a good investment?
A: The consensus rating for InterCure Ltd. is Sell and assigned short-term Ba2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of INCR.U:TSX stock?
A: The consensus rating for INCR.U:TSX is Sell.
Q: What is the prediction period for INCR.U:TSX stock?
A: The prediction period for INCR.U:TSX is (n+3 month)