The First National Bank of Dryden Credit Rating

BOSTON (AI Credit Rating Terminal) Sat Aug 01 2020 13:49:03 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below:

Rating Action Overview

We downgraded The First National Bank of Dryden because of no indications exist that private equity, management, or shareholders may reduce or prevent the maintenance of capital. We use econometric methods for period (n+1) simulate with Simple Moving Average (SMA) Spearman Correlation. Reference code is: 3443. Beta DRL value REG 28 Rational Demand Factor LD 4359.9024. Larger, investment-grade issuers that have access to both public and private debt markets have greater flexibility than companies that depend solely on private bank loans. In addition, we consider whether a company can borrow on an unsecured basis, has access to the commercial paper markets, and issues debt in multiple geographies. It is more costly to raise debt in the public bond markets and often requires a company to establish a track record among investors. These costs and information asymmetry issues sometimes make it impractical for smaller, speculative-grade issuers to raise small amounts of debt in public markets. 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 The First National Bank of Dryden as below:

Credit Ratings for The First National Bank of Dryden as of 01 Aug 2020

Credit Rating Short-Term Long-Term Senior
AI Rating Class*Ba3B3
Semantic Signals7132
Financial Signals8275
Risk Signals8036
Substantial Risks3340
Speculative Signals6448

*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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