BOSTON (AI Credit Rating Terminal) Fri Jul 31 2020 01:19:02 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below:

Rating Action Overview

We downgraded NIPPON ACTIVE VALUE FUND PLC because of at least one of the following applies: shareholders are supportive of strong capital, with lower expectations for dividends and share buybacks; the firm has concrete commitments from outside parties to provide it with material amounts of loss-absorbing capital that practically can be exercised while still a going concern; or the firm is at least adequately capitalized and a committed strong financial partner or backer bolsters financial flexibility. We use econometric methods for period (n+7) simulate with Money Flow Index (MFI) ANOVA. Reference code is: 3151. Beta DRL value REG 35 Rational Demand Factor LD 4502.463. For new issuers, while our ratings are prospective, we will not include proposed financing as a source in our liquidity calculations until the financing has been obtained or is fully underwritten. Similarly, we would not include rights issues as a source of liquidity for a company, unless the rights issue is irrevocably guaranteed (for example, an underwriter agrees to buy any securities not taken up by existing holders). 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 NIPPON ACTIVE VALUE FUND PLC as below:

Credit Ratings for NIPPON ACTIVE VALUE FUND PLC as of 31 Jul 2020

Credit Rating Short-Term Long-Term Senior
AI Rating Class*Caa2Ba3
Semantic Signals3847
Financial Signals3262
Risk Signals4577
Substantial Risks3685
Speculative Signals7139

*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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