**Outlook:**CONNEXION TELEMATICS LTD is assigned short-term Ba1 & long-term Ba1 estimated rating.

**Dominant Strategy :**Wait until speculative trend diminishes

**Time series to forecast n: 20 Feb 2023**for (n+4 weeks)

**Methodology :**Supervised Machine Learning (ML)

## Abstract

CONNEXION TELEMATICS LTD prediction model is evaluated with Supervised Machine Learning (ML) and Beta^{1,2,3,4}and it is concluded that the CXZ stock is predictable in the short/long term.

**According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Wait until speculative trend diminishes**

## Key Points

- Can statistics predict the future?
- What is statistical models in machine learning?
- What is the use of Markov decision process?

## CXZ Target Price Prediction Modeling Methodology

We consider CONNEXION TELEMATICS LTD Decision Process with Supervised Machine Learning (ML) where A is the set of discrete actions of CXZ 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(Beta)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Supervised Machine Learning (ML)) X S(n):→ (n+4 weeks) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of CXZ 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?

## CXZ Stock Forecast (Buy or Sell) for (n+4 weeks)

**Sample Set:**Neural Network

**Stock/Index:**CXZ CONNEXION TELEMATICS LTD

**Time series to forecast n: 20 Feb 2023**for (n+4 weeks)

**According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Wait until speculative trend diminishes**

**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 CONNEXION TELEMATICS LTD

- As with all fair value measurements, an entity's measurement method for determining the portion of the change in the liability's fair value that is attributable to changes in its credit risk must make maximum use of relevant observable inputs and minimum use of unobservable inputs.
- To calculate the change in the value of the hedged item for the purpose of measuring hedge ineffectiveness, an entity may use a derivative that would have terms that match the critical terms of the hedged item (this is commonly referred to as a 'hypothetical derivative'), and, for example for a hedge of a forecast transaction, would be calibrated using the hedged price (or rate) level. For example, if the hedge was for a two-sided risk at the current market level, the hypothetical derivative would represent a hypothetical forward contract that is calibrated to a value of nil at the time of designation of the hedging relationship. If the hedge was for example for a one-sided risk, the hypothetical derivative would represent the intrinsic value of a hypothetical option that at the time of designation of the hedging relationship is at the money if the hedged price level is the current market level, or out of the money if the hedged price level is above (or, for a hedge of a long position, below) the current market level. Using a hypothetical derivative is one possible way of calculating the change in the value of the hedged item. The hypothetical derivative replicates the hedged item and hence results in the same outcome as if that change in value was determined by a different approach. Hence, using a 'hypothetical derivative' is not a method in its own right but a mathematical expedient that can only be used to calculate the value of the hedged item. Consequently, a 'hypothetical derivative' cannot be used to include features in the value of the hedged item that only exist in the hedging instrument (but not in the hedged item). An example is debt denominated in a foreign currency (irrespective of whether it is fixed-rate or variable-rate debt). When using a hypothetical derivative to calculate the change in the value of such debt or the present value of the cumulative change in its cash flows, the hypothetical derivative cannot simply impute a charge for exchanging different currencies even though actual derivatives under which different currencies are exchanged might include such a charge (for example, cross-currency interest rate swaps).
- However, an entity is not required to separately recognise interest revenue or impairment gains or losses for a financial asset measured at fair value through profit or loss. Consequently, when an entity reclassifies a financial asset out of the fair value through profit or loss measurement category, the effective interest rate is determined on the basis of the fair value of the asset at the reclassification date. In addition, for the purposes of applying Section 5.5 to the financial asset from the reclassification date, the date of the reclassification is treated as the date of initial recognition.
- An entity may manage and evaluate the performance of a group of financial liabilities or financial assets and financial liabilities in such a way that measuring that group at fair value through profit or loss results in more relevant information. The focus in this instance is on the way the entity manages and evaluates performance, instead of on the nature of its 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

CONNEXION TELEMATICS LTD is assigned short-term Ba1 & long-term Ba1 estimated rating. CONNEXION TELEMATICS LTD prediction model is evaluated with Supervised Machine Learning (ML) and Beta^{1,2,3,4} and it is concluded that the CXZ stock is predictable in the short/long term. ** According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Wait until speculative trend diminishes**

### CXZ CONNEXION TELEMATICS LTD Financial Analysis*

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | Ba1 | Ba1 |

Income Statement | Baa2 | Ba1 |

Balance Sheet | C | B2 |

Leverage Ratios | Caa2 | Caa2 |

Cash Flow | Ba3 | Ba1 |

Rates of Return and Profitability | C | Ba3 |

*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

## References

- Chow, G. C. (1960), "Tests of equality between sets of coefficients in two linear regressions," Econometrica, 28, 591–605.
- M. J. Hausknecht and P. Stone. Deep recurrent Q-learning for partially observable MDPs. CoRR, abs/1507.06527, 2015
- Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
- A. Tamar, D. Di Castro, and S. Mannor. Policy gradients with variance related risk criteria. In Proceedings of the Twenty-Ninth International Conference on Machine Learning, pages 387–396, 2012.
- Hoerl AE, Kennard RW. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67
- N. B ̈auerle and J. Ott. Markov decision processes with average-value-at-risk criteria. Mathematical Methods of Operations Research, 74(3):361–379, 2011
- Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.

## Frequently Asked Questions

Q: What is the prediction methodology for CXZ stock?A: CXZ stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Beta

Q: Is CXZ stock a buy or sell?

A: The dominant strategy among neural network is to Wait until speculative trend diminishes CXZ Stock.

Q: Is CONNEXION TELEMATICS LTD stock a good investment?

A: The consensus rating for CONNEXION TELEMATICS LTD is Wait until speculative trend diminishes and is assigned short-term Ba1 & long-term Ba1 estimated rating.

Q: What is the consensus rating of CXZ stock?

A: The consensus rating for CXZ is Wait until speculative trend diminishes.

Q: What is the prediction period for CXZ stock?

A: The prediction period for CXZ is (n+4 weeks)