SIQ SMARTGROUP CORPORATION LTD

Outlook: SMARTGROUP CORPORATION LTD is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 07 Feb 2023 for (n+16 weeks)
Methodology : Modular Neural Network (CNN Layer)

Abstract

SMARTGROUP CORPORATION LTD prediction model is evaluated with Modular Neural Network (CNN Layer) and Linear Regression^1,2,3,4 and it is concluded that the SIQ stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Buy

Key Points

1. Prediction Modeling
2. Nash Equilibria
3. Buy, Sell and Hold Signals

SIQ Target Price Prediction Modeling Methodology

We consider SMARTGROUP CORPORATION LTD Decision Process with Modular Neural Network (CNN Layer) where A is the set of discrete actions of SIQ 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(Linear 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(Modular Neural Network (CNN Layer)) X S(n):→ (n+16 weeks) $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of SIQ 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?

SIQ Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: SIQ SMARTGROUP CORPORATION LTD
Time series to forecast n: 07 Feb 2023 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Buy

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 SMARTGROUP CORPORATION LTD

1. Adjusting the hedge ratio by decreasing the volume of the hedged item does not affect how the changes in the fair value of the hedging instrument are measured. The measurement of the changes in the value of the hedged item related to the volume that continues to be designated also remains unaffected. However, from the date of rebalancing, the volume by which the hedged item was decreased is no longer part of the hedging relationship. For example, if an entity originally hedged a volume of 100 tonnes of a commodity at a forward price of CU80 and reduces that volume by 10 tonnes on rebalancing, the hedged item after rebalancing would be 90 tonnes hedged at CU80. The 10 tonnes of the hedged item that are no longer part of the hedging relationship would be accounted for in accordance with the requirements for the discontinuation of hedge accounting (see paragraphs 6.5.6–6.5.7 and B6.5.22–B6.5.28).
2. Rebalancing does not apply if the risk management objective for a hedging relationship has changed. Instead, hedge accounting for that hedging relationship shall be discontinued (despite that an entity might designate a new hedging relationship that involves the hedging instrument or hedged item of the previous hedging relationship as described in paragraph B6.5.28).
3. 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.
4. For example, an entity may use this condition to designate financial liabilities as at fair value through profit or loss if it meets the principle in paragraph 4.2.2(b) and the entity has financial assets and financial liabilities that share one or more risks and those risks are managed and evaluated on a fair value basis in accordance with a documented policy of asset and liability management. An example could be an entity that has issued 'structured products' containing multiple embedded derivatives and manages the resulting risks on a fair value basis using a mix of derivative and non-derivative financial instruments

*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

SMARTGROUP CORPORATION LTD is assigned short-term Ba1 & long-term Ba1 estimated rating. SMARTGROUP CORPORATION LTD prediction model is evaluated with Modular Neural Network (CNN Layer) and Linear Regression^1,2,3,4 and it is concluded that the SIQ stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Buy

SIQ SMARTGROUP CORPORATION LTD Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Ba3	C
Balance Sheet	Baa2	Baa2
Leverage Ratios	C	Baa2
Cash Flow	Baa2	B2
Rates of Return and Profitability	B2	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?

Prediction Confidence Score

Trust metric by Neural Network: 93 out of 100 with 818 signals.

References

1. Christou, C., P. A. V. B. Swamy G. S. Tavlas (1996), "Modelling optimal strategies for the allocation of wealth in multicurrency investments," International Journal of Forecasting, 12, 483–493.
2. J. G. Schneider, W. Wong, A. W. Moore, and M. A. Riedmiller. Distributed value functions. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 371–378, 1999.
3. Robins J, Rotnitzky A. 1995. Semiparametric efficiency in multivariate regression models with missing data. J. Am. Stat. Assoc. 90:122–29
4. A. Shapiro, W. Tekaya, J. da Costa, and M. Soares. Risk neutral and risk averse stochastic dual dynamic programming method. European journal of operational research, 224(2):375–391, 2013
5. Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
6. Candès E, Tao T. 2007. The Dantzig selector: statistical estimation when p is much larger than n. Ann. Stat. 35:2313–51
7. Chen, C. L. Liu (1993), "Joint estimation of model parameters and outlier effects in time series," Journal of the American Statistical Association, 88, 284–297.

Frequently Asked Questions

Q: What is the prediction methodology for SIQ stock?
A: SIQ stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Linear Regression
Q: Is SIQ stock a buy or sell?
A: The dominant strategy among neural network is to Buy SIQ Stock.
Q: Is SMARTGROUP CORPORATION LTD stock a good investment?
A: The consensus rating for SMARTGROUP CORPORATION LTD is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of SIQ stock?
A: The consensus rating for SIQ is Buy.
Q: What is the prediction period for SIQ stock?
A: The prediction period for SIQ is (n+16 weeks)

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