Templeton Emerging Markets Income Fund Credit Rating

BOSTON (AI Credit Rating Terminal) Sat Aug 01 2020 15:19:02 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below:

Rating Action Overview


We downgraded Templeton Emerging Markets Income Fund because the steps to carry out a bail-in of that type of liability would involve such high operational complexities that would make its bail-in unlikely in a reasonable time. We use econometric methods for period (n+1) simulate with Hartley Oscillator Ridge Regression. Reference code is: 1923. Beta DRL value REG 46 Rational Demand Factor LD 4359.9024. Our liquidity uses include dividends and share repurchases that we expect under a stress scenario. Unlike other potential uses of liquidity, such as debt maturities or maintenance capital spending, we view dividends and share repurchases as more discretionary, although more so for the latter. For this reason, when evaluating a company's liquidity position, we may use a lower estimate of dividends and shareholder repurchases than in our base-case forecast based on our views of management and the company's track record in terms of shareholder returns and maintaining a certain minimum level 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 Templeton Emerging Markets Income Fund as below:

Credit Ratings for Templeton Emerging Markets Income Fund as of 01 Aug 2020


Credit Rating Short-Term Long-Term Senior
AI Rating Class*Ba2B1
Semantic Signals8641
Financial Signals8482
Risk Signals4331
Substantial Risks4974
Speculative Signals8366

*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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