LON:KZG KAZERA GLOBAL PLC

Outlook: KAZERA GLOBAL PLC is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 06 Feb 2023 for (n+6 month)
Methodology : Modular Neural Network (CNN Layer)

Abstract

KAZERA GLOBAL PLC prediction model is evaluated with Modular Neural Network (CNN Layer) and Polynomial Regression^1,2,3,4 and it is concluded that the LON:KZG stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell

Key Points

1. How do you pick a stock?
2. Which neural network is best for prediction?
3. What are main components of Markov decision process?

LON:KZG Target Price Prediction Modeling Methodology

We consider KAZERA GLOBAL PLC Decision Process with Modular Neural Network (CNN Layer) where A is the set of discrete actions of LON:KZG 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(Polynomial 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+6 month) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of LON:KZG 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?

LON:KZG Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: LON:KZG KAZERA GLOBAL PLC
Time series to forecast n: 06 Feb 2023 for (n+6 month)

According to price forecasts for (n+6 month) 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 KAZERA GLOBAL PLC

1. 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.
2. For purchased or originated credit-impaired financial assets, expected credit losses shall be discounted using the credit-adjusted effective interest rate determined at initial recognition.
3. An entity has not retained control of a transferred asset if the transferee has the practical ability to sell the transferred asset. An entity has retained control of a transferred asset if the transferee does not have the practical ability to sell the transferred asset. A transferee has the practical ability to sell the transferred asset if it is traded in an active market because the transferee could repurchase the transferred asset in the market if it needs to return the asset to the entity. For example, a transferee may have the practical ability to sell a transferred asset if the transferred asset is subject to an option that allows the entity to repurchase it, but the transferee can readily obtain the transferred asset in the market if the option is exercised. A transferee does not have the practical ability to sell the transferred asset if the entity retains such an option and the transferee cannot readily obtain the transferred asset in the market if the entity exercises its option
4. An entity that first applies IFRS 17 as amended in June 2020 at the same time it first applies this Standard shall apply paragraphs 7.2.1–7.2.28 instead of paragraphs 7.2.38–7.2.42.

*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

KAZERA GLOBAL PLC is assigned short-term Ba1 & long-term Ba1 estimated rating. KAZERA GLOBAL PLC prediction model is evaluated with Modular Neural Network (CNN Layer) and Polynomial Regression^1,2,3,4 and it is concluded that the LON:KZG stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell

LON:KZG KAZERA GLOBAL PLC Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Caa2	Ba1
Balance Sheet	Baa2	Baa2
Leverage Ratios	B1	Baa2
Cash Flow	Caa2	B3
Rates of Return and Profitability	Caa2	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: 87 out of 100 with 635 signals.

References

1. M. J. Hausknecht and P. Stone. Deep recurrent Q-learning for partially observable MDPs. CoRR, abs/1507.06527, 2015
2. Greene WH. 2000. Econometric Analysis. Upper Saddle River, N J: Prentice Hall. 4th ed.
3. Bessler, D. A. S. W. Fuller (1993), "Cointegration between U.S. wheat markets," Journal of Regional Science, 33, 481–501.
4. Clements, M. P. D. F. Hendry (1995), "Forecasting in cointegrated systems," Journal of Applied Econometrics, 10, 127–146.
5. Semenova V, Goldman M, Chernozhukov V, Taddy M. 2018. Orthogonal ML for demand estimation: high dimensional causal inference in dynamic panels. arXiv:1712.09988 [stat.ML]
6. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., MO Stock Price Prediction. AC Investment Research Journal, 101(3).
7. A. Tamar and S. Mannor. Variance adjusted actor critic algorithms. arXiv preprint arXiv:1310.3697, 2013.

Frequently Asked Questions

Q: What is the prediction methodology for LON:KZG stock?
A: LON:KZG stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Polynomial Regression
Q: Is LON:KZG stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:KZG Stock.
Q: Is KAZERA GLOBAL PLC stock a good investment?
A: The consensus rating for KAZERA GLOBAL PLC is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:KZG stock?
A: The consensus rating for LON:KZG is Sell.
Q: What is the prediction period for LON:KZG stock?
A: The prediction period for LON:KZG is (n+6 month)

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