Qwest Corporation 6.875% Notes due 2054 Credit Rating

BOSTON (AI Credit Rating Terminal) Sat Aug 01 2020 22:49:02 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below:

Rating Action Overview


We downgraded Qwest Corporation 6.875% Notes due 2054 bacause the bail-in of that type of liability would not provide any economic benefit to the resolution or would even destroy value. We use econometric methods for period (n+7) simulate with Anomaly ANOVA. Reference code is: 3630. Beta DRL value REG 22 Rational Demand Factor LD 4359.9024. Under times of stress, such actions could include dividend cuts, suspension of share repurchases, or maintenance of minimum cash balances. This is particularly relevant for exceptional and strong assessments, where issuers are required to carry higher levels of excess liquidity even during times of stress. For example, when assessing liquidity, we would generally expect companies to be able to cover the full amount of dividends and share repurchases included in our base-case forecast, while still maintaining excess liquidity and achieving the required A/B and A-B measures under a stress case. 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 Qwest Corporation 6.875% Notes due 2054 as below:

Credit Ratings for Qwest Corporation 6.875% Notes due 2054 as of 01 Aug 2020


Credit Rating Short-Term Long-Term Senior
AI Rating Class*B2Ba3
Semantic Signals9061
Financial Signals3572
Risk Signals4440
Substantial Risks7789
Speculative Signals3357

*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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