Atlas Pipeline Partners, L.P. Cumulative Redeemable Perpetual Preferred Units Class E Credit Rating

BOSTON (AI Credit Rating Terminal) Sat Aug 01 2020 06:19:02 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below:

Rating Action Overview


We downgraded Atlas Pipeline Partners, L.P. Cumulative Redeemable Perpetual Preferred Units Class E because of adding the amount of unrecognized gains, after tax, when calculating ACE and TAC. Nevertheless, the adjustment for unrecognized gains would be reduced by the amount of the surplus that we view as unrealizable. We use econometric methods for period (n+1) simulate with SMoothed Moving Average (SMMA) Multiple Regression. Reference code is: 3756. Beta DRL value REG 15 Rational Demand Factor LD 4359.9024. To assess an issuer's standing in the credit markets, we may look at factors such as equity, debt, and credit default swaps (CDS) trading levels, where available, relative to peers and market averages. For example, lower-than-average debt trading levels or widening rating-adjusted spreads relative to market averages may indicate decreasing market confidence about a company's prospects and ability to meet its debt maturities. As a result, the company could have increased difficulty accessing the capital markets. 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 Atlas Pipeline Partners, L.P. Cumulative Redeemable Perpetual Preferred Units Class E as below:

Credit Ratings for Atlas Pipeline Partners, L.P. Cumulative Redeemable Perpetual Preferred Units Class E as of 01 Aug 2020


Credit Rating Short-Term Long-Term Senior
AI Rating Class*Ba3Ba3
Semantic Signals3555
Financial Signals6445
Risk Signals6061
Substantial Risks7672
Speculative Signals8981

*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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