Nicholas Financial, Inc. Credit Rating

BOSTON (AI Credit Rating Terminal) Thu Jul 23 2020 11:19:03 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below:

Rating Action Overview


We downgraded Nicholas Financial, Inc. because of normalized loss rates using default and transition studies for corporate, sovereign, and financial institutions exposures and our assessment of long-term average annualized through-the-cycle expected losses informed by historical losses for retail and personal exposures. This normalized, through-the-cycle loss estimate is more conservative than an expected loss calculation based on a shorter time horizon, which might exclude periods of recession. We use econometric methods for period (n+1) simulate with Wien Bridge Oscillator Paired T-Test. Reference code is: 4621. Beta DRL value REG 46 Rational Demand Factor LD 4611.3606. We do not include potential future debt issuances as a source of liquidity because of the uncertainty of a company's ability to access debt markets in times of financial stress, even for investment-grade issuers. For instance, in the case of a proposed financing, with the intended use of proceeds to repay existing debt, we will assess a company's liquidity excluding the proposed financing until it's obtained or fully underwritten. 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 Nicholas Financial, Inc. as below:

Credit Ratings for Nicholas Financial, Inc. as of 23 Jul 2020


Credit Rating Short-Term Long-Term Senior
AI Rating Class*B1B2
Semantic Signals3731
Financial Signals4630
Risk Signals4481
Substantial Risks7561
Speculative Signals8939

*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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