KXIN Kaixin Auto Holdings Ordinary Shares

Outlook: Kaixin Auto Holdings Ordinary Shares is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 02 Jun 2023 for (n+16 weeks)
Methodology : Statistical Inference (ML)

Abstract

Kaixin Auto Holdings Ordinary Shares prediction model is evaluated with Statistical Inference (ML) and Multiple Regression^1,2,3,4 and it is concluded that the KXIN stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell

Key Points

1. Trading Signals
2. Stock Rating
3. Technical Analysis with Algorithmic Trading

KXIN Target Price Prediction Modeling Methodology

We consider Kaixin Auto Holdings Ordinary Shares Decision Process with Statistical Inference (ML) where A is the set of discrete actions of KXIN 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(Multiple 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(Statistical Inference (ML)) X S(n):→ (n+16 weeks) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of KXIN 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?

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

Sample Set: Neural Network
Stock/Index: KXIN Kaixin Auto Holdings Ordinary Shares
Time series to forecast n: 02 Jun 2023 for (n+16 weeks)

According to price forecasts for (n+16 weeks) 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 Kaixin Auto Holdings Ordinary Shares

1. For example, an entity hedges an exposure to Foreign Currency A using a currency derivative that references Foreign Currency B and Foreign Currencies A and B are pegged (ie their exchange rate is maintained within a band or at an exchange rate set by a central bank or other authority). If the exchange rate between Foreign Currency A and Foreign Currency B were changed (ie a new band or rate was set), rebalancing the hedging relationship to reflect the new exchange rate would ensure that the hedging relationship would continue to meet the hedge effectiveness requirement for the hedge ratio in the new circumstances. In contrast, if there was a default on the currency derivative, changing the hedge ratio could not ensure that the hedging relationship would continue to meet that hedge effectiveness requirement. Hence, rebalancing does not facilitate the continuation of a hedging relationship in situations in which the relationship between the hedging instrument and the hedged item changes in a way that cannot be compensated for by adjusting the hedge ratio
2. At the date of initial application, an entity shall use reasonable and supportable information that is available without undue cost or effort to determine the credit risk at the date that a financial instrument was initially recognised (or for loan commitments and financial guarantee contracts at the date that the entity became a party to the irrevocable commitment in accordance with paragraph 5.5.6) and compare that to the credit risk at the date of initial application of this Standard.
3. An entity shall apply the impairment requirements in Section 5.5 retrospectively in accordance with IAS 8 subject to paragraphs 7.2.15 and 7.2.18–7.2.20.
4. Unless paragraph 6.8.8 applies, for a hedge of a non-contractually specified benchmark component of interest rate risk, an entity shall apply the requirement in paragraphs 6.3.7(a) and B6.3.8—that the risk component shall be separately identifiable—only at the inception of the hedging relationship.

*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

Kaixin Auto Holdings Ordinary Shares is assigned short-term Ba1 & long-term Ba1 estimated rating. Kaixin Auto Holdings Ordinary Shares prediction model is evaluated with Statistical Inference (ML) and Multiple Regression^1,2,3,4 and it is concluded that the KXIN stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell

KXIN Kaixin Auto Holdings Ordinary Shares Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Baa2	Baa2
Balance Sheet	Caa2	Caa2
Leverage Ratios	B2	Caa2
Cash Flow	Baa2	Caa2
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: 82 out of 100 with 737 signals.

References

1. Andrews, D. W. K. W. Ploberger (1994), "Optimal tests when a nuisance parameter is present only under the alternative," Econometrica, 62, 1383–1414.
2. V. Borkar. An actor-critic algorithm for constrained Markov decision processes. Systems & Control Letters, 54(3):207–213, 2005.
3. Morris CN. 1983. Parametric empirical Bayes inference: theory and applications. J. Am. Stat. Assoc. 78:47–55
4. Efron B, Hastie T, Johnstone I, Tibshirani R. 2004. Least angle regression. Ann. Stat. 32:407–99
5. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. 2013b. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 3111–19. San Diego, CA: Neural Inf. Process. Syst. Found.
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. Batchelor, R. P. Dua (1993), "Survey vs ARCH measures of inflation uncertainty," Oxford Bulletin of Economics Statistics, 55, 341–353.

Frequently Asked Questions

Q: What is the prediction methodology for KXIN stock?
A: KXIN stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Multiple Regression
Q: Is KXIN stock a buy or sell?
A: The dominant strategy among neural network is to Sell KXIN Stock.
Q: Is Kaixin Auto Holdings Ordinary Shares stock a good investment?
A: The consensus rating for Kaixin Auto Holdings Ordinary Shares is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of KXIN stock?
A: The consensus rating for KXIN is Sell.
Q: What is the prediction period for KXIN stock?
A: The prediction period for KXIN is (n+16 weeks)

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