BANNER COUNTY BAN CORPORATION Credit Rating

BOSTON (AI Credit Rating Terminal) Sat Aug 01 2020 20:19:20 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below:

Rating Action Overview


We downgraded BANNER COUNTY BAN CORPORATION because of covenant, legal, tax, regulatory, or other characteristics of the group structure (for example, minority interests) are not a significant constraint on the flow of loss-absorbing capital among group members. We use econometric methods for period (n+7) simulate with Money Flow Index (MFI) Logistic Regression. Reference code is: 4563. Beta DRL value REG 45 Rational Demand Factor LD 4359.9024. For companies in more volatile sectors, we assess the resiliency of liquidity through a cycle. If we do not believe the resulting descriptor reflects sustainable liquidity characteristics, we could adjust our liquidity assessment downward. For example, we could lower our liquidity assessment on a volatile company to strong from exceptional if we believe key quantitative measures typical of exceptional liquidity are not sustainable over the forecast period. This could especially be true if we believe there is a higher prospect of ratios weakening from the peak of an economic cycle. 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 BANNER COUNTY BAN CORPORATION as below:

Credit Ratings for BANNER COUNTY BAN CORPORATION as of 01 Aug 2020


Credit Rating Short-Term Long-Term Senior
AI Rating Class*B1Ba1
Semantic Signals6570
Financial Signals7047
Risk Signals4665
Substantial Risks5788
Speculative Signals6377

*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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