LON:CAR CARCLO PLC

Outlook: CARCLO PLC is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 12 May 2023 for (n+6 month)
Methodology : Modular Neural Network (Market Volatility Analysis)

Abstract

CARCLO PLC prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and Statistical Hypothesis Testing^1,2,3,4 and it is concluded that the LON:CAR 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. Should I buy stocks now or wait amid such uncertainty?
2. Operational Risk
3. Should I buy stocks now or wait amid such uncertainty?

LON:CAR Target Price Prediction Modeling Methodology

We consider CARCLO PLC Decision Process with Modular Neural Network (Market Volatility Analysis) where A is the set of discrete actions of LON:CAR 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(Statistical Hypothesis Testing)^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 (Market Volatility Analysis)) X S(n):→ (n+6 month) $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of LON:CAR 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:CAR Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: LON:CAR CARCLO PLC
Time series to forecast n: 12 May 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 CARCLO PLC

1. If, at the date of initial application, it is impracticable (as defined in IAS 8) for an entity to assess a modified time value of money element in accordance with paragraphs B4.1.9B–B4.1.9D on the basis of the facts and circumstances that existed at the initial recognition of the financial asset, an entity shall assess the contractual cash flow characteristics of that financial asset on the basis of the facts and circumstances that existed at the initial recognition of the financial asset without taking into account the requirements related to the modification of the time value of money element in paragraphs B4.1.9B–B4.1.9D. (See also paragraph 42R of IFRS 7.)
2. Paragraphs 6.9.7–6.9.13 provide exceptions to the requirements specified in those paragraphs only. An entity shall apply all other hedge accounting requirements in this Standard, including the qualifying criteria in paragraph 6.4.1, to hedging relationships that were directly affected by interest rate benchmark reform.
3. An embedded prepayment option in an interest-only or principal-only strip is closely related to the host contract provided the host contract (i) initially resulted from separating the right to receive contractual cash flows of a financial instrument that, in and of itself, did not contain an embedded derivative, and (ii) does not contain any terms not present in the original host debt contract.
4. If an entity previously accounted at cost (in accordance with IAS 39), for an investment in an equity instrument that does not have a quoted price in an active market for an identical instrument (ie a Level 1 input) (or for a derivative asset that is linked to and must be settled by delivery of such an equity instrument) it shall measure that instrument at fair value at the date of initial application. Any difference between the previous carrying amount and the fair value shall be recognised in the opening retained earnings (or other component of equity, as appropriate) of the reporting period that includes the date of initial application.

*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

CARCLO PLC is assigned short-term Ba1 & long-term Ba1 estimated rating. CARCLO PLC prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and Statistical Hypothesis Testing^1,2,3,4 and it is concluded that the LON:CAR 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:CAR CARCLO PLC Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Caa2	B3
Balance Sheet	Baa2	C
Leverage Ratios	Ba2	B3
Cash Flow	B3	Baa2
Rates of Return and Profitability	Baa2	Caa2

*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: 78 out of 100 with 871 signals.

References

1. Efron B, Hastie T. 2016. Computer Age Statistical Inference, Vol. 5. Cambridge, UK: Cambridge Univ. Press
2. A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016
3. Abadie A, Diamond A, Hainmueller J. 2010. Synthetic control methods for comparative case studies: estimat- ing the effect of California's tobacco control program. J. Am. Stat. Assoc. 105:493–505
4. Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
5. Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
6. Breusch, T. S. A. R. Pagan (1979), "A simple test for heteroskedasticity and random coefficient variation," Econometrica, 47, 1287–1294.
7. J. Filar, D. Krass, and K. Ross. Percentile performance criteria for limiting average Markov decision pro- cesses. IEEE Transaction of Automatic Control, 40(1):2–10, 1995.

Frequently Asked Questions

Q: What is the prediction methodology for LON:CAR stock?
A: LON:CAR stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Statistical Hypothesis Testing
Q: Is LON:CAR stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:CAR Stock.
Q: Is CARCLO PLC stock a good investment?
A: The consensus rating for CARCLO PLC is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:CAR stock?
A: The consensus rating for LON:CAR is Sell.
Q: What is the prediction period for LON:CAR stock?
A: The prediction period for LON:CAR is (n+6 month)

Premium

• Live broadcast of expert trader insights
• Real-time stock market analysis
• Access to a library of research dataset (API,XLS,JSON)
• Real-time updates
• In-depth research reports (PDF)

Login