Fairway Group Acquisition Company Credit Rating

BOSTON (AI Credit Rating Terminal) Thu Jul 23 2020 20:49:03 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below:

Rating Action Overview


We downgraded Fairway Group Acquisition Company because of at least one of the following applies: shareholders are supportive of strong capital, with lower expectations for dividends and share buybacks; the firm has concrete commitments from outside parties to provide it with material amounts of loss-absorbing capital that practically can be exercised while still a going concern; or the firm is at least adequately capitalized and a committed strong financial partner or backer bolsters financial flexibility. We use econometric methods for period (n+7) simulate with Average True Range (ATR) Chi-Square. Reference code is: 2931. Beta DRL value REG 43 Rational Demand Factor LD 4456.0992. In these cases, the level of capital expenditures will be lower than estimates in our base-case forecast to determine an issuer's financial risk profile, particularly for companies that are pursuing discrete growth projects that have not been committed or can be easily curtailed in case of a need to preserve cash. 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 Fairway Group Acquisition Company as below:

Credit Ratings for Fairway Group Acquisition Company as of 23 Jul 2020


Credit Rating Short-Term Long-Term Senior
AI Rating Class*Ba3Ba3
Semantic Signals7879
Financial Signals3263
Risk Signals8560
Substantial Risks7081
Speculative Signals5741

*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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