X2M X2M CONNECT LIMITED

Outlook: X2M CONNECT LIMITED is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 05 Feb 2023 for (n+8 weeks)
Methodology : Inductive Learning (ML)

Abstract

X2M CONNECT LIMITED prediction model is evaluated with Inductive Learning (ML) and ElasticNet Regression^1,2,3,4 and it is concluded that the X2M stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Hold

Key Points

1. What is a prediction confidence?
2. Prediction Modeling
3. Understanding Buy, Sell, and Hold Ratings

X2M Target Price Prediction Modeling Methodology

We consider X2M CONNECT LIMITED Decision Process with Inductive Learning (ML) where A is the set of discrete actions of X2M 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(ElasticNet 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(Inductive Learning (ML)) X S(n):→ (n+8 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of X2M 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?

X2M Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: X2M X2M CONNECT LIMITED
Time series to forecast n: 05 Feb 2023 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Hold

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 X2M CONNECT LIMITED

1. Financial assets that are held within a business model whose objective is to hold assets in order to collect contractual cash flows are managed to realise cash flows by collecting contractual payments over the life of the instrument. That is, the entity manages the assets held within the portfolio to collect those particular contractual cash flows (instead of managing the overall return on the portfolio by both holding and selling assets). In determining whether cash flows are going to be realised by collecting the financial assets' contractual cash flows, it is necessary to consider the frequency, value and timing of sales in prior periods, the reasons for those sales and expectations about future sales activity. However sales in themselves do not determine the business model and therefore cannot be considered in isolation. Instead, information about past sales and expectations about future sales provide evidence related to how the entity's stated objective for managing the financial assets is achieved and, specifically, how cash flows are realised. An entity must consider information about past sales within the context of the reasons for those sales and the conditions that existed at that time as compared to current conditions.
2. However, the fact that a financial asset is non-recourse does not in itself necessarily preclude the financial asset from meeting the condition in paragraphs 4.1.2(b) and 4.1.2A(b). In such situations, the creditor is required to assess ('look through to') the particular underlying assets or cash flows to determine whether the contractual cash flows of the financial asset being classified are payments of principal and interest on the principal amount outstanding. If the terms of the financial asset give rise to any other cash flows or limit the cash flows in a manner inconsistent with payments representing principal and interest, the financial asset does not meet the condition in paragraphs 4.1.2(b) and 4.1.2A(b). Whether the underlying assets are financial assets or non-financial assets does not in itself affect this assessment.
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. At the date of initial application, an entity shall determine whether the treatment in paragraph 5.7.7 would create or enlarge an accounting mismatch in profit or loss on the basis of the facts and circumstances that exist at the date of initial application. This Standard shall be applied retrospectively on the basis of that determination.

*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

X2M CONNECT LIMITED is assigned short-term Ba1 & long-term Ba1 estimated rating. X2M CONNECT LIMITED prediction model is evaluated with Inductive Learning (ML) and ElasticNet Regression^1,2,3,4 and it is concluded that the X2M stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Hold

X2M X2M CONNECT LIMITED Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Baa2	B1
Balance Sheet	Baa2	Baa2
Leverage Ratios	Ba3	Baa2
Cash Flow	C	C
Rates of Return and Profitability	Ba2	C

*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: 79 out of 100 with 632 signals.

References

1. Zeileis A, Hothorn T, Hornik K. 2008. Model-based recursive partitioning. J. Comput. Graph. Stat. 17:492–514 Zhou Z, Athey S, Wager S. 2018. Offline multi-action policy learning: generalization and optimization. arXiv:1810.04778 [stat.ML]
2. Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
3. Breiman L. 1993. Better subset selection using the non-negative garotte. Tech. Rep., Univ. Calif., Berkeley
4. Ruiz FJ, Athey S, Blei DM. 2017. SHOPPER: a probabilistic model of consumer choice with substitutes and complements. arXiv:1711.03560 [stat.ML]
5. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2018a. Double/debiased machine learning for treatment and structural parameters. Econom. J. 21:C1–68
6. Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.
7. Burgess, D. F. (1975), "Duality theory and pitfalls in the specification of technologies," Journal of Econometrics, 3, 105–121.

Frequently Asked Questions

Q: What is the prediction methodology for X2M stock?
A: X2M stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and ElasticNet Regression
Q: Is X2M stock a buy or sell?
A: The dominant strategy among neural network is to Hold X2M Stock.
Q: Is X2M CONNECT LIMITED stock a good investment?
A: The consensus rating for X2M CONNECT LIMITED is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of X2M stock?
A: The consensus rating for X2M is Hold.
Q: What is the prediction period for X2M stock?
A: The prediction period for X2M is (n+8 weeks)

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