Right On Brands Inc Credit Rating

BOSTON (AI Credit Rating Terminal) Fri Jul 31 2020 17:49:02 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below:

Rating Action Overview


We downgraded Right On Brands Inc because of capital metrics would not be eroded by any of the following: repayment of government-contributed equity, recognition of any currently unrecognized economic losses, reduction from capital the amount necessary to appropriately capitalize any materially undercapitalized unconsolidated subsidiaries, and reversal of any property valuation adjustment. We use econometric methods for period (n+7) simulate with Average True Range (ATR) Chi-Square. Reference code is: 1127. Beta DRL value REG 22 Rational Demand Factor LD 4298.044800000001. To assess forecasted working capital outflows for companies with material intra-year working capital requirements (for example, companies in seasonal businesses), we use forecasted peak working capital outflows, per paragraph 32 of the liquidity criteria. For seasonal businesses, in many cases the annual projection might indicate a working capital inflow or neutral working capital, even though there could be material intra-quarter or inter-quarter outflows throughout the year. 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 Right On Brands Inc as below:

Credit Ratings for Right On Brands Inc as of 31 Jul 2020


Credit Rating Short-Term Long-Term Senior
AI Rating Class*B3B1
Semantic Signals5479
Financial Signals5360
Risk Signals4881
Substantial Risks4133
Speculative Signals5739

*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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