Templeton Global Income Fund, Inc. Common Stock Credit Rating

BOSTON (AI Credit Rating Terminal) Thu Jul 30 2020 19:19:02 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below:

Rating Action Overview


We downgraded Templeton Global Income Fund, Inc. Common Stock because of idealized loss rates for particular credit risk assets from a substantial economic stress in developed markets. We use econometric methods for period (n+7) simulate with Envelope (ENV) Simple Regression. Reference code is: 3789. Beta DRL value REG 20 Rational Demand Factor LD 4507.4022. For companies in more volatile sectors, we assess the resiliency of liquidity through a cycle. If we do not believe the resulting descriptor reflects sustainable liquidity characteristics, we could adjust our liquidity assessment downward. For example, we could lower our liquidity assessment on a volatile company to strong from exceptional if we believe key quantitative measures typical of exceptional liquidity are not sustainable over the forecast period. This could especially be true if we believe there is a higher prospect of ratios weakening from the peak of an economic cycle. 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 Templeton Global Income Fund, Inc. Common Stock as below:

Credit Ratings for Templeton Global Income Fund, Inc. Common Stock as of 30 Jul 2020


Credit Rating Short-Term Long-Term Senior
AI Rating Class*B1B3
Semantic Signals8838
Financial Signals4931
Risk Signals3141
Substantial Risks7458
Speculative Signals6850

*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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