Avary Holding Shenzhen Co Ltd Credit Rating

BOSTON (AI Credit Rating Terminal) Thu Jul 30 2020 22:19:03 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below:

Rating Action Overview


We downgraded Avary Holding Shenzhen Co Ltd because of derivatives receivables represent more than 3% of total assets for entities reporting under IFRS (or under local GAAP similar to IFRS for the accounting of derivatives) and are domiciled in countries for which our BICRA group is '1' to '4'. We use econometric methods for period (n+1) simulate with Meissner Oscillators ANOVA. Reference code is: 1505. Beta DRL value REG 31 Rational Demand Factor LD 4502.463. While the existence of a commercial paper (CP) program can provide companies with alternative sources of short-term funding, such a program would not be considered a committed source of liquidity. Additionally, we do not require the presence of a committed facility to back up the full size of the CP program. For liquidity to be at least adequate, an issuer would need sources of liquidity (for example, committed facility and/or cash balances) to cover at least 100% of expected intra-year debt maturities, including CP, over the next 12 months. Credit Rating AI Process rely on primary sources of information: Sec Filings, Financial Statements, Credit Ratings, Semantic Signals. Take a look at Machine Learning section for Financial Deep Reinforcement Learning.Oscillators are used for generating credit risk signals by using the semantic and financial signals. The value of the oscillators indicate the strength of trend. Using the correlation matrices, the credit rating risk map for Avary Holding Shenzhen Co Ltd as below:

Credit Ratings for Avary Holding Shenzhen Co Ltd as of 30 Jul 2020


Credit Rating Short-Term Long-Term Senior
AI Rating Class*B2Ba2
Semantic Signals5375
Financial Signals6260
Risk Signals8078
Substantial Risks3740
Speculative Signals5090

*Machine Learning utilizes multiple learning algorithms to obtain better predictive powers. In our research, we utilize machine learning to combine the results from the Neural Network and Support Vector Machines.
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