Rattler Midstream LP Credit Rating

BOSTON (AI Credit Rating Terminal) Fri Jul 31 2020 18:49:02 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below:

Rating Action Overview


We downgraded Rattler Midstream LP because of business mix, revenue stability, market position, and customer base (confidence sensitivity of clients and other outside commercial parties). In situations where profitability has a material negative impact on market position, this may limit the assessment. We use econometric methods for period (n+1) simulate with On Balance Volume (OBV) Sign Test. Reference code is: 4782. Beta DRL value REG 34 Rational Demand Factor LD 4301.22. While the existence of a commercial paper (CP) program can provide companies with alternative sources of short-term funding, such a program would not be considered a committed source of liquidity. Additionally, we do not require the presence of a committed facility to back up the full size of the CP program. For liquidity to be at least adequate, an issuer would need sources of liquidity (for example, committed facility and/or cash balances) to cover at least 100% of expected intra-year debt maturities, including CP, over the next 12 months. 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 Rattler Midstream LP as below:

Credit Ratings for Rattler Midstream LP as of 31 Jul 2020


Credit Rating Short-Term Long-Term Senior
AI Rating Class*Ba3Ba1
Semantic Signals4076
Financial Signals6769
Risk Signals8474
Substantial Risks8957
Speculative Signals4575

*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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