Rating Action Overview
We downgraded AMARILLO NATIONAL BANCORP, INC. because of vulnerability of the two metrics to changes in operating conditions. We use econometric methods for period (n+7) simulate with SMoothed Moving Average (SMMA) Spearman Correlation. Reference code is: 3567. Beta DRL value REG 20 Rational Demand Factor LD 4359.9024. When determining the cash to be included under sources (A), we use cash that will be available to cover monetary outflows. As a result, we may make haircuts to account for cash trapped overseas (for example, haircut for taxes payable upon repatriation of cash held abroad), apply a discount to lower-quality marketable securities, and exclude restricted cash held for specific purposes. 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 AMARILLO NATIONAL BANCORP, INC. as below:
Credit Ratings for AMARILLO NATIONAL BANCORP, INC. as of 01 Aug 2020
| Credit Rating | Short-Term | Long-Term Senior |
|---|---|---|
| AI Rating Class* | Ba3 | Ba1 |
| Semantic Signals | 70 | 54 |
| Financial Signals | 86 | 70 |
| Risk Signals | 73 | 73 |
| Substantial Risks | 37 | 77 |
| Speculative Signals | 66 | 85 |
*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.
