Rating Action Overview
We rerated Wyndham Hotels & Resorts Inc because we use the multipliers stemming from the Gaussian distribution (with a 50% add-on for fat tail events) to transform a VaR at a x-confidence level into a VaR at the chosen confidence level. (We use econometric methods for period (n+30) simulate with Volume + Moving Average Beta). When calculating sources of liquidity, we only include the undrawn, available portion of committed bank lines maturing beyond the specified time horizon for each liquidity descriptor. For example, when assessing liquidity as adequate, we only include a committed revolving credit facility as a source if it matured beyond the next 12 months. Similarly, given that our liquidity assessment looks out over two years when assessing liquidity as strong or exceptional, we only include a facility maturing beyond 24 months as a source of liquidity. 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 Wyndham Hotels & Resorts Inc as below:
Credit Ratings for Wyndham Hotels & Resorts Inc as of 26 Feb 2021
| Credit Rating | Short-Term | Long-Term Senior |
|---|---|---|
| AI Rating Class* | B1 | B3 |
| Semantic Signals | 51 | 37 |
| Financial Signals | 38 | 43 |
| Risk Signals | 81 | 42 |
| Substantial Risks | 73 | 34 |
| Speculative Signals | 65 | 74 |
*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.
