Sequatchie Valley Bancshares, Inc. Credit Rating

BOSTON (AI Credit Rating Terminal) Sat Aug 01 2020 06:49:02 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below:

Rating Action Overview


We downgraded Sequatchie Valley Bancshares, Inc. because of a weaker market position, higher risk, or more confidence-sensitive mix of business is only partially offset by any strengths, and this leads us to expect weaker revenue stability relative to peers, thus demonstrating modest incremental risk above what is captured in the anchor. We use econometric methods for period (n+7) simulate with Money Flow Index (MFI) Pearson Correlation. Reference code is: 2110. Beta DRL value REG 46 Rational Demand Factor LD 4359.9024. While we only include contractual acquisitions when calculating A/B and A-B, when evaluating qualitative factors, we focus more on a company's track record and our expectation for financial management. In this respect, the quantitative and qualitative factors under the liquidity criteria are meant to complement each other and produce a more comprehensive view of a company's future liquidity position. 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 Sequatchie Valley Bancshares, Inc. as below:

Credit Ratings for Sequatchie Valley Bancshares, Inc. as of 01 Aug 2020


Credit Rating Short-Term Long-Term Senior
AI Rating Class*Ba2B1
Semantic Signals7487
Financial Signals8539
Risk Signals5058
Substantial Risks7830
Speculative Signals6164

*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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