RYANAIR HOLDINGS PLC Credit Rating & Financial Statements Analysis

BOSTON (AI Credit Rating Terminal) Sat Jun 05 2021 14:00:02 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below:

Financial Statements Overview & Credit Rating Rationales


We rerated RYANAIR HOLDINGS PLC because the steps to carry out a bail-in of that type of liability would involve such high operational complexities that would make its bail-in unlikely in a reasonable time. (We use econometric methods for period (n+30) simulate with RC Phase Shift Oscillator Pearson Correlation). Given that it can be difficult to identify outstanding CP at any point in time, when considering coverage, we may include our expectations for peak outstanding CP during the year as opposed to CP balances as of the last filing date, especially if we believe reported balances are not reflective of typical borrowing patterns. 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.

Risk Heat Map for RYANAIR HOLDINGS PLC as of 05 Jun 2021


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 RYANAIR HOLDINGS PLC as below:

Credit Ratings for RYANAIR HOLDINGS PLC as of 05 Jun 2021


Credit Rating Short-Term Long-Term Senior
AI Rating Class*B1Ba1
Semantic Signals5085
Financial Signals6639
Risk Signals8164
Substantial Risks4886
Speculative Signals5980

*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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Disclaimers: AC Investment Inc. currently does not act as an equities executing broker, credit rating agency or route orders containing equities securities. In our Machine Learning experiment, we focus on an approach known as Decision making using game theory. We apply principles from game theory to model the relationships between rating actions, news, market signals and decision making.The rating information provided is for informational, non-commercial purposes only, does not constitute investment advice and is subject to conditions available in our Legal Disclaimer. Usage as a credit rating or as a benchmark is not permitted.

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