CCP Stock: Future is Bright, but The Road Ahead is Bumpy

Outlook: CREDIT CORP GROUP LIMITED is assigned short-term Ba3 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised* :
Dominant Strategy : Buy
Time series to forecast n: for 3 Month
Methodology : Transfer Learning (ML)
Hypothesis Testing : Chi-Square
Surveillance : Major exchange and OTC

Abstract

CREDIT CORP GROUP LIMITED prediction model is evaluated with Transfer Learning (ML) and Chi-Square1,2,3,4 and it is concluded that the CCP stock is predictable in the short/long term. Transfer learning is a machine learning (ML) method where a model developed for one task is reused as the starting point for a model on a second task. This can be useful when the second task is similar to the first task, or when there is limited data available for the second task. According to price forecasts for 3 Month period, the dominant strategy among neural network is: Buy


*Revision

We revised our short-term strategy to .(Based on stock rating surveillance.) We also affirmed our outlook and CREDIT CORP GROUP LIMITED is assigned short-term Ba3 & long-term B1 estimated rating.

Graph 43

Key Points

  1. Market Signals
  2. Trust metric by Neural Network
  3. What statistical methods are used to analyze data?

CCP Target Price Prediction Modeling Methodology

We consider CREDIT CORP GROUP LIMITED Decision Process with Transfer Learning (ML) where A is the set of discrete actions of CCP 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(Chi-Square)5,6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Transfer Learning (ML)) X S(n):→ 3 Month i = 1 n a i

n:Time series to forecast

p:Price signals of CCP stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Transfer Learning (ML)

Transfer learning is a machine learning (ML) method where a model developed for one task is reused as the starting point for a model on a second task. This can be useful when the second task is similar to the first task, or when there is limited data available for the second task.

Chi-Square

A chi-squared test is a statistical hypothesis test that assesses whether observed frequencies in a sample differ significantly from expected frequencies. It is one of the most widely used statistical tests in the social sciences and in many areas of observational research. The chi-squared test is a non-parametric test, meaning that it does not assume that the data is normally distributed. This makes it a versatile tool that can be used to analyze a wide variety of data. There are two main types of chi-squared tests: the chi-squared goodness of fit test and the chi-squared test of independence.

 

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?

CCP Stock Forecast (Buy or Sell) for 3 Month

Sample Set: Neural Network
Stock/Index: CCP CREDIT CORP GROUP LIMITED
Time series to forecast: 3 Month

According to price forecasts for 3 Month period, the dominant strategy among neural network is: Buy

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 CREDIT CORP GROUP LIMITED

  1. If the holder cannot assess the conditions in paragraph B4.1.21 at initial recognition, the tranche must be measured at fair value through profit or loss. If the underlying pool of instruments can change after initial recognition in such a way that the pool may not meet the conditions in paragraphs B4.1.23–B4.1.24, the tranche does not meet the conditions in paragraph B4.1.21 and must be measured at fair value through profit or loss. However, if the underlying pool includes instruments that are collateralised by assets that do not meet the conditions in paragraphs B4.1.23–B4.1.24, the ability to take possession of such assets shall be disregarded for the purposes of applying this paragraph unless the entity acquired the tranche with the intention of controlling the collateral.
  2. An entity is not required to restate prior periods to reflect the application of these amendments. The entity may restate prior periods only if it is possible to do so without the use of hindsight. If an entity restates prior periods, the restated financial statements must reflect all the requirements in this Standard for the affected financial instruments. If an entity does not restate prior periods, the entity shall recognise any difference between the previous carrying amount and the carrying amount at the beginning of the annual reporting period that includes the date of initial application of these amendments in the opening retained earnings (or other component of equity, as appropriate) of the annual reporting period that includes the date of initial application of these amendments.
  3. If such a mismatch would be created or enlarged, the entity is required to present all changes in fair value (including the effects of changes in the credit risk of the liability) in profit or loss. If such a mismatch would not be created or enlarged, the entity is required to present the effects of changes in the liability's credit risk in other comprehensive income.
  4. An entity shall apply this Standard retrospectively, in accordance with IAS 8 Accounting Policies, Changes in Accounting Estimates and Errors, except as specified in paragraphs 7.2.4–7.2.26 and 7.2.28. This Standard shall not be applied to items that have already been derecognised at 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

CREDIT CORP GROUP LIMITED is assigned short-term Ba3 & long-term B1 estimated rating. CREDIT CORP GROUP LIMITED prediction model is evaluated with Transfer Learning (ML) and Chi-Square1,2,3,4 and it is concluded that the CCP stock is predictable in the short/long term. According to price forecasts for 3 Month period, the dominant strategy among neural network is: Buy

CCP CREDIT CORP GROUP LIMITED Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba3B1
Income StatementBaa2Baa2
Balance SheetBaa2C
Leverage RatiosB2B3
Cash FlowCaa2Baa2
Rates of Return and ProfitabilityBa2Ba3

*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: 77 out of 100 with 619 signals.

References

  1. Athey S, Bayati M, Doudchenko N, Imbens G, Khosravi K. 2017a. Matrix completion methods for causal panel data models. arXiv:1710.10251 [math.ST]
  2. Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94
  3. M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
  4. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Can stock prices be predicted?(SMI Index Stock Forecast). AC Investment Research Journal, 101(3).
  5. V. Borkar. Stochastic approximation: a dynamical systems viewpoint. Cambridge University Press, 2008
  6. Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
  7. R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972
Frequently Asked QuestionsQ: What is the prediction methodology for CCP stock?
A: CCP stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Chi-Square
Q: Is CCP stock a buy or sell?
A: The dominant strategy among neural network is to Buy CCP Stock.
Q: Is CREDIT CORP GROUP LIMITED stock a good investment?
A: The consensus rating for CREDIT CORP GROUP LIMITED is Buy and is assigned short-term Ba3 & long-term B1 estimated rating.
Q: What is the consensus rating of CCP stock?
A: The consensus rating for CCP is Buy.
Q: What is the prediction period for CCP stock?
A: The prediction period for CCP is 3 Month

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