JMP Group Inc 7.25% Senior Notes due 2021 Credit Rating

BOSTON (AI Credit Rating Terminal) Tue Jun 30 2020 08:49:02 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below:

Rating Action Overview


We downgraded JMP Group Inc 7.25% Senior Notes due 2021 because In addition to the risk weight based on revenues by business line, we apply a risk weight of 6.25% to cash and money market . We use econometric methods for period (n+30) simulate with Modified (Smoothed) Moving Average Spearman Correlation. Reference code is: 4695. Beta DRL value REG 41 Rational Demand Factor LD 4101.2118. 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 JMP Group Inc 7.25% Senior Notes due 2021 as below:

Credit Ratings for JMP Group Inc 7.25% Senior Notes due 2021 as of 30 Jun 2020


Credit Rating Short-Term Long-Term Senior
AI Rating Class*B1Ba3
Semantic Signals8266
Financial Signals6747
Risk Signals3052
Substantial Risks3787
Speculative Signals8159

*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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