Western Asset Mdl Mrkt Dbt Fnd Inc Credit Rating

BOSTON (AI Credit Rating Terminal) Thu Jul 23 2020 18:19:03 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below:

Rating Action Overview


We downgraded Western Asset Mdl Mrkt Dbt Fnd 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+1) simulate with EMR ANOVA. Reference code is: 1058. Beta DRL value REG 36 Rational Demand Factor LD 4440.135. For companies that engage in reverse factoring--where accounts payable (AP) days are extended beyond the term customary for the industry and supply chain--we assess the likelihood and potential impact on liquidity of these arrangements ceasing to exist. In such a scenario, a company could be subject to material working capital outflows if AP days with its suppliers revert back to industry norms. Accordingly, we exclude these arrangements from sources of liquidity. 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 Western Asset Mdl Mrkt Dbt Fnd Inc as below:

Credit Ratings for Western Asset Mdl Mrkt Dbt Fnd Inc as of 23 Jul 2020


Credit Rating Short-Term Long-Term Senior
AI Rating Class*B1B3
Semantic Signals6248
Financial Signals5639
Risk Signals3230
Substantial Risks6550
Speculative Signals8147

*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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